Bear-Sword Maze Problem with LangChain 1.x¶
(Version: 0.0.15.4)
Overview¶
MemoryBanditWorkflow is an agent framework created based on experiences with the Bear-Sword Maze problem. It has now been updated to support LangChain 1.x. Furthermore, I have introduced subtool_do, which serves as an equivalent to features like "skills" or "toolboxes" in other frameworks.
The Idea of MemoryBanditWorkflow¶
Recently, the term "Context Engineering" has become prominent in the LLM space. LLMs used via APIs (rather than a web interface) are inherently "stateless" and do not remember past conversations by default. To maintain a conversation, the user must record messages and provide them to the API each time. To recall older information, a search infrastructure is required beyond simple logging. These components are generally referred to as "Context," and how to effectively construct this context has become a key challenge.
Specifically, the infrastructure for storage within the context is often simply called "Memory." While this can be confusing as it overlaps with the term for PC hardware RAM, that is the standard terminology.
Exasperated by the fact that agents often fail to use memory functions effectively, in my previous iteration, I created a "Bandit" feature—essentially a tool to register specific tools into a multi-armed bandit—allowing the AI itself to decide which tools to prioritize or use more frequently to force memory utilization. This time, I have extended that to allow for the execution of entire workflows.
The trajectory of this idea is documented below (in Japanese):
[cocolog:95619779] (September 2025)
《"Experimental Implementation of a Bandit Machine to Force LLM Memory Usage" and "Experimental Implementation of LLM Memory and Bandit Functions." The latter is the main deliverable—a framework extended from the concept of forcing increased memory usage. - A Note from JRF》
http://jrf.cocolog-nifty.com/statuses/2025/09/post-8225e2.html
Basically, this project uses the familiar Bear-Sword Maze problem but utilizes memory functions, bandit functions, and the newly added workflow functions. While the maze problem is simple, the implementation of the memory, bandit, and workflow features is quite robust.
Since implementing backends like semantic search from scratch is tedious, I have the AI "simulate a database" to handle the backend logic.
For more details on the Bear-Sword Maze problem itself, please refer to the link below (in Japanese):
《JRF-2018/langchain_maze: Bear-Sword Maze Problem Revisited》
https://github.com/JRF-2018/langchain_maze
I suspect similar methods already exist elsewhere, as "Workflows" are a well-worn concept. This may not be a particularly novel approach, but I hope this attempt serves as a useful reference.
The Idea of "Sub-tools"¶
Claude Code is already making waves as a potential step toward AGI. I wonder how it handles implementations like my MemoryBanditWorkflow, where a "harness" or constraint is applied to memory usage.
Presumably, it would involve having Claude Code implement an agent with memory constraints as a sub-agent. For something written by an AI like Claude Code, a framework like MemoryBanditWorkflow seems particularly effective.
In tools like Claude Code, there is a concept called "skills." My understanding is that this is based on the idea that because MCP (Model Context Protocol) and other tool information can become bloated, it is inefficient to keep them in the context at all times; instead, required skills are "discovered" from a skill tree as needed.
I wanted to incorporate these "skills" into MemoryBanditWorkflow. But how?
In Claude Code's implementation, skills are presented as commands or code, assuming the agent can execute shell or Python scripts. However, for a "harnessed" agent, allowing general shell or REPL access is usually out of the question. On the other hand, if you don't allow that, you have no choice but to prepare all tools in advance, which "pollutes" the context.
That’s when I came up with the idea of "Sub-tools." The only tool visible in the context is subtool_do. To find out which sub-tools are available, the agent must use subtool_show to read documents (similar to reading a SKILL.md) as needed. This allows for "on-demand learning" while maintaining infinite scalability.
The question was whether an LLM (specifically Gemini) could understand this approach.
The trajectory of this idea is documented below (in Japanese):
[cocolog:95822546] (January 2026)
《How to implement MemoryBanditWorkflow using SKILL.md from Claude Code? I tried asking Claude itself. - A Note from JRF》
http://jrf.cocolog-nifty.com/statuses/2026/01/post-b86e58.html
Link to Previous Version¶
《experimental_bandit_workflow_0_0_2.ipynb - JRF-2018/langchain_maze》
https://github.com/JRF-2018/langchain_maze/blob/master/experimental_bandit_workflow_0_0_2.ipynb
Changes from the Previous Version¶
Updated to support LangChain 1.x. Initially, handling type errors caused by the specific implementations of Pydantic v2 and Gemini was difficult, but it is now operational. However, these are stopgap measures, and I am not entirely confident about future stability.
Introduced
subtool_doandsubtool_showfor storing tools and using them only after reading their descriptions. What is displayed bysubtool_showis roughly equivalent to what would be written inSKILL.mdin other frameworks.Translated the entire interface and documentation into English for this version.
Conclusion¶
To avoid excessive costs, I did not intend to let the agent reach the goal from the start; I ended the experiment once a workflow was utilized. Testing was performed with gemini-2.5-flash-lite, and the final execution was requested of gemini-3-flash-preview.
While Flash-Lite occasionally struggled with executing sub-tools, Gemini-3 seemed to handle them properly. My assessment is that the sub-tools worked reasonably well.
Please note that due to the lack of reproducibility (and to save costs!), I have not re-run the entire process from the beginning. (^^;
Author¶
JRF ( http://jrf.cocolog-nifty.com/statuses , Twitter (X): @jion_rockford )
License¶
Since the code is relatively short, I intended for my parts to be in the Public Domain. If you have concerns, please treat it under the MIT License.
This was developed with significant guidance from various AIs (Gemini, ChatGPT, Claude, and Grok).
Implementation¶
First, we will import the necessary libraries.
In [ ]:
!pip install -q -U langchain langchain-google-genai
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 111.7/111.7 kB 1.7 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 66.5/66.5 kB 965.0 kB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 500.1/500.1 kB 7.5 MB/s eta 0:00:00 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 158.1/158.1 kB 3.5 MB/s eta 0:00:00
Accessing Gemini. If you import your Gemini API key from Google AI Studio into your environment secrets, a secret named GOOGLE_API_KEY should be created. We will use that.
In [ ]:
import os
from langchain.chat_models import init_chat_model
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from google.colab import userdata
#os.environ["GOOGLE_API_KEY"] = userdata.get('GOOGLE_API_KEY')
llm = init_chat_model(
"google_genai:gemini-3-flash-preview",
google_api_key=userdata.get('GOOGLE_API_KEY'),
# thinking_level="low", # for gemini-3.0
# thinking_budget=0, # for gemini-2.5
)
emb_llm = GoogleGenerativeAIEmbeddings(
model='gemini-embedding-001',
google_api_key=userdata.get('GOOGLE_API_KEY'),
)
Testing whether we can access Gemini properly.
In [ ]:
import os
from langchain_core.messages import HumanMessage
# Helper function to extract text content (Required for compatibility with Gemini 3)
def get_content_text(content):
if isinstance(content, list):
texts = []
for item in content:
if isinstance(item, dict):
if item.get('type') == 'text':
texts.append(item.get('text', ''))
elif isinstance(item, str):
texts.append(item)
return "".join(texts)
return content
response = llm.invoke([HumanMessage(content="Please tell me the features of the Gemini model.")])
print(get_content_text(response.content))
Google’s **Gemini** is a family of highly advanced multimodal large language models developed by Google DeepMind. It was designed to succeed the PaLM 2 model and is integrated into various Google products (like the Gemini chatbot and Google Workspace). Here are the key features and characteristics of the Gemini model: ### 1. Native Multimodality Unlike many other AI models that are trained on text and then "bolted on" to vision or audio tools, Gemini was built to be **multimodal from the start.** * **Integrated Understanding:** It was trained simultaneously on text, images, video, audio, and code. * **Cross-Modal Reasoning:** It can seamlessly understand and reason across different formats. For example, you can show it a video of a physics experiment and ask it to explain the formulas involved. ### 2. Model Family (Sizes) Gemini is offered in several sizes to suit different needs, ranging from massive data centers to mobile devices: * **Gemini Ultra:** The largest and most capable model, designed for highly complex tasks (reasoning, coding, and nuance). * **Gemini Pro:** A versatile, mid-sized model optimized for scaling across a wide range of tasks. This is what powers the free version of the Gemini chatbot. * **Gemini Flash:** A lightweight model designed for speed and efficiency, offering low latency at a lower cost. * **Gemini Nano:** The most efficient model, built to run **on-device** (e.g., on Pixel 8/9 or Samsung S24) without needing an internet connection. ### 3. Massive Context Window One of Gemini’s most significant competitive advantages (specifically in the **1.5 Pro and Flash** versions) is its massive context window: * **Up to 1 Million to 2 Million Tokens:** It can process massive amounts of information in a single prompt. * **Practical application:** This allows the model to read thousands of lines of code, digest a 1,500-page document, or "watch" an hour-long video and answer specific questions about it. ### 4. Advanced Coding Capabilities Gemini is highly proficient in programming: * It understands, explains, and generates high-quality code in popular languages like Python, Java, C++, and Go. * It can assist with debugging and optimizing complex software architectures. * **AlphaCode 2:** Google uses a specialized version of Gemini to power AlphaCode 2, which excels at competitive programming and complex math. ### 5. High-Level Reasoning and Logic Gemini was designed to outperform previous models in "common sense" reasoning and mathematical logic. * **MMLU (Massive Multitask Language Understanding):** Gemini Ultra was the first model to outperform human experts on the MMLU, which tests a combination of world knowledge and problem-solving across 57 subjects. ### 6. Integration with Google Ecosystem Gemini is deeply woven into the tools people use daily: * **Google Workspace:** It can summarize emails in Gmail, draft documents in Docs, and analyze data in Sheets. * **Google Search:** It powers "AI Overviews" in search results. * **Android:** Gemini is replacing Google Assistant as the primary AI on Android devices. * **Vertex AI:** For developers, Gemini is available via Google Cloud to build custom enterprise applications. ### 7. Performance and Speed * **Efficiency:** Gemini 1.5 Pro is built using a **"Mixture-of-Experts" (MoE)** architecture. Instead of running the entire neural network for every prompt, it only activates the most relevant pathways, making it faster and more efficient than older, dense models. ### 8. Safety and Ethics Google emphasizes that Gemini underwent rigorous safety testing, including: * **Red-teaming:** Testing the model for biases, hate speech, and "jailbreak" attempts. * **Responsibility Filters:** Built-in safeguards to prevent the generation of harmful or illegal content. ### Summary: Gemini vs. GPT-4 While both are top-tier AI models, Gemini’s primary "killer feature" is its **context window size** (being able to ingest entire libraries of data at once) and its **native multimodality**, which makes its video and audio processing feel more fluid and integrated than its competitors.
Let's also test the embedding vectors.
In [ ]:
emb_llm.embed_query("This is a test.")[:5]
Out[ ]:
[-0.019542728, 0.0036680987, 0.0044811117, -0.069937535, 0.0015621887]
Importing basic modules.
In [ ]:
import os
import math
import numpy as np
import random
import re
from pprint import pprint
from time import sleep
import pickle
np.set_printoptions(legacy='1.25')
Execute the following code for save/load functionality.
In [ ]:
PLAY_GAME_SAVE = "langchain_maze.pickle"
The main game object. A very simple maze... or perhaps a dungeon...
In [ ]:
class Game:
initial_map = """\
■■■■■■■■■
■■■■■■■Ｇ■
■□□□□□■□■
■□■■■□□□■
■□■■■■■■■
■◎■■■■■△■
■□■■■■■□■
■□□□□□□□■
■■■■Ｓ■■■■
■■■■■■■■■
"""
def __init__ (self, initial_map=None, hint=True):
if initial_map is not None:
self.initial_map = initial_map
map = self.initial_map
self.map = map
self.written_map = re.sub("[◎△]", "？", map)
l = map.splitlines(True)
self.map_size = (len(l[0]) - 1, len(l))
self.hint = hint
self.actions = {
"move up": self.move_up,
"move down": self.move_down,
"move left": self.move_left,
"move right": self.move_right,
"fight bear": self.fight,
"pick up item": self.get_sword,
"do nothing": self.do_nothing,
}
self.pos = self.get_start_pos()
self.sword = False
self.bear_killed = 0
self.goal = False
self.prev_killed = False
self.kill_hint = False
self.prev_success = True
def read_map (self, p):
"""Reads the character at a given coordinate."""
x = p[0]
y = p[1]
if x < 0 or x >= self.map_size[0]\
or y < 0 or y >= self.map_size[1]:
return "■" # Out of bounds is treated as a wall
else:
l = self.map.splitlines(True)
return l[y][x]
def set_map (self, pos, ch):
"""Updates the map data at a specific position."""
idx = pos[1] * (self.map_size[0] + 1) + pos[0]
self.map = self.map[:idx] + ch + self.map[idx + 1:]
def get_pos (self, ch, written=False):
"""Returns the coordinates of all instances of a specific character."""
if written:
map = self.written_map
else:
map = self.map
r = []
for p in [i for i in range(len(map)) if map.startswith(ch, i)]:
y = p // (self.map_size[0] + 1)
x = p % (self.map_size[0] + 1)
r.append(np.array([x, y]))
return r
def get_start_pos (self):
return self.get_pos("Ｓ")[0]
def read_neighbors (self):
"""Reads the content of the current position and its immediate neighbors."""
c = self.read_map(self.pos)
cu = self.read_map(self.pos + np.array([0, -1]))
cd = self.read_map(self.pos + np.array([0, +1]))
cl = self.read_map(self.pos + np.array([-1, 0]))
cr = self.read_map(self.pos + np.array([+1, 0]))
return [c, cu, cd, cl, cr]
def change_neighbors(self, from_ch, to_ch):
"""Changes nearby characters (e.g., removing a defeated enemy or picked-up item)."""
for d in [[0, 0], [0, -1], [0, +1], [-1, 0], [+1, 0]]:
p = self.pos + np.array(d)
c = self.read_map(p)
if c == from_ch:
self.set_map(p, to_ch)
def move (self, res, d):
self.prev_killed = False
self.prev_success = False
c = self.read_map(self.pos + d)
if c == "◎":
self.prev_killed = True
self.pos = self.get_start_pos()
return "Attempted to move past the bear without fighting, but was killed. " \
+ "Resurrected at the starting point."
if c == "■":
return "Blocked by a wall. Cannot move."
self.prev_success = True
self.pos += d
if c == "Ｇ":
self.goal = True
return "Goal reached! Task completed."
nb = self.read_neighbors()
ad = ""
if "◎" in nb:
ad += " Encountered a bear."
if "△" in nb:
ad += " An item (Sword) is nearby and can be picked up."
return res + ad
def move_up (self):
return self.move("Moved Up.", np.array([0, -1]))
def move_down (self):
return self.move("Moved Down.", np.array([0, +1]))
def move_left (self):
return self.move("Moved Left.", np.array([-1, 0]))
def move_right (self):
return self.move("Moved Right.", np.array([+1, 0]))
def fight (self):
self.prev_success = False
self.prev_killed = False
if "◎" in self.read_neighbors():
if self.sword:
self.prev_success = True
self.change_neighbors("◎", "□")
self.bear_killed += 1
return "Defeated the bear!"
else:
self.pos = self.get_start_pos()
self.prev_killed = True
if self.hint:
self.kill_hint = True
return "Killed by the bear. A sword might have helped you survive. " \
+ "Resurrected at the starting point."
else:
return "Killed by the bear. Resurrected at the starting point."
return "Invalid action. No enemy nearby."
def get_sword (self):
self.prev_killed = False
self.prev_success = False
if "△" in self.read_neighbors():
self.sword = True
self.prev_success = True
self.change_neighbors("△", "□")
return "Picked up the sword."
return "Invalid action. No items nearby."
def do_nothing (self):
self.prev_killed = False
self.prev_success = False
return "Action ignored."
def available_actions (self):
nb = self.read_neighbors()
l = []
if nb[1] != "■":
l.append("move up")
if nb[2] != "■":
l.append("move down")
if nb[3] != "■":
l.append("move left")
if nb[4] != "■":
l.append("move right")
if "△" in nb:
l.append("pick up item")
if "◎" in nb:
l.append("fight bear")
return l
def surroundings (self):
x = self.pos[0]
y = self.pos[1]
return \
"".join(["".join([self.read_map(np.array([i, j]))
if i != x or j != y else "▼"
for i in range(x - 2, x + 3)])
+ "\n"
for j in range(y - 2, y + 3)])
In [ ]:
def flip_text_map (m):
return "\n".join([s[::-1] for s in m.splitlines()] + [""])
def rotate_text_map (m):
m = list(m.splitlines())
return "\n".join(["".join([m[len(m) - j - 1][i] for j in range(len(m))])
for i in range(len(m[0]))] + [""])
def search_path (game, from_pos, to_pos, visit=None):
if visit is None:
visit = set()
visit.add(tuple(from_pos))
if tuple(from_pos) == tuple(to_pos):
return (tuple(from_pos), [])
if game.read_map(from_pos) == "■":
return None
r = []
for p in [(from_pos[0], from_pos[1] - 1),
(from_pos[0], from_pos[1] + 1),
(from_pos[0] - 1, from_pos[1]),
(from_pos[0] + 1, from_pos[1])]:
if p not in visit and game.read_map(p) != "■":
q = search_path(game, p, to_pos, visit.copy())
if q:
r.append(q)
if r:
return (tuple(from_pos), r)
return None
Testing if the game is working correctly.
In [ ]:
game = Game()
In [ ]:
print(game.surroundings())
■■■■■ □□□□□ ■■▼■■ ■■■■■ ■■■■■
In [ ]:
print(game.move_up())
print(game.surroundings())
Moved Up. ■■■■■ ■■■■■ □□▼□□ ■■Ｓ■■ ■■■■■
In [ ]:
print(game.move_left())
print(game.surroundings())
Moved Left. ◎■■■■ □■■■■ □□▼□□ ■■■Ｓ■ ■■■■■
In [ ]:
print(game.move_right())
print(game.surroundings())
Moved Right. ■■■■■ ■■■■■ □□▼□□ ■■Ｓ■■ ■■■■■
In [ ]:
print(game.move_down())
print(game.surroundings())
Moved Down. ■■■■■ □□□□□ ■■▼■■ ■■■■■ ■■■■■
In [ ]:
print(game.fight())
print(game.surroundings())
Invalid action. No enemy nearby. ■■■■■ □□□□□ ■■▼■■ ■■■■■ ■■■■■
In [ ]:
print(game.get_sword())
print(game.surroundings())
Invalid action. No items nearby. ■■■■■ □□□□□ ■■▼■■ ■■■■■ ■■■■■
Now, we will implement the class to solve the game using an LLM. Let's start with the required libraries.
In [ ]:
from pydantic import ValidationError
from typing import List, Dict, Any, Tuple, Union
from textwrap import dedent
import datetime
import copy
import inspect
# Import LangChain components
from langchain_core.tools import tool, Tool
from langchain.agents.middleware import SummarizationMiddleware
from langchain.agents.middleware.summarization import DEFAULT_SUMMARY_PROMPT
from langchain.agents import create_agent
#from langgraph.prebuilt import create_react_agent
#from langchain_core.messages.utils import count_tokens_approximately
#from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.checkpoint.memory import InMemorySaver
from langchain_core.prompts.chat import ChatPromptTemplate
#from langmem.short_term import SummarizationNode, summarize_messages
from langchain_core.messages import AIMessage, ToolMessage, HumanMessage, SystemMessage
from langgraph.errors import GraphRecursionError
SUMMARY_PROMPT = DEFAULT_SUMMARY_PROMPT + "\n\n**Please provide the summary in English.**"
In [ ]:
def calc_embedding_variance(embeddings):
if not embeddings or len(embeddings) < 2:
return 0.0
embeddings_array = np.array(embeddings)
mean_vector = np.mean(embeddings_array, axis=0)
squared_distances = np.linalg.norm(embeddings_array - mean_vector, axis=1)**2
variance = np.mean(squared_distances)
return variance
def short_repr(x, max_len=80):
repr_str = repr(x)
if len(repr_str) > max_len:
ellipsis_len = 3
head_len = max_len - ellipsis_len - 1
tail_len = 1
return repr_str[:head_len] + "..." + repr_str[-tail_len:]
else:
return repr_str
def get_content_text(content):
if isinstance(content, list):
texts = []
for item in content:
if isinstance(item, dict):
if item.get('type') == 'text':
texts.append(item.get('text', ''))
elif isinstance(item, str):
texts.append(item)
return "".join(texts)
return content
MemoryBanditWorkflow was previously integrated with PlayGame, but I separated them to improve versatility. This versatility has been demonstrated in files like experimental_rag_en_0.0.16.ipynb. It has become a very long and complex implementation with many features added. My apologies.
In [ ]:
class MemoryBanditWorkflow:
"""
MemoryBanditWorkflow: A generic framework for decision-making tasks
integrating memory management and strategic planning.
"""
def __init__ (self, llm=llm, llm2=llm, emb_llm=emb_llm,
save_file=None):
self.llm = llm
self.llm2 = llm2
self.emb_llm = emb_llm
self.save_file = save_file
self.core_context = ""
self.plan = "Plan and policy have not been set yet."
self.scratchpad = ""
self.messages = []
self.running_summary = None
self.system_prompt = """\
This is an experiment to observe the behavior of memory and bandit functions.
Consider the current plan, policy, and surrounding circumstances, and update the plan and policy as necessary.
Leave a plan and policy that makes it easy for another agent to take the next action.
As befits an experiment, use tools as much as possible—specifically search memory and update documents to prepare for future commands.
Memory IDs follow the format 'memory:...'. Specifying only the number (e.g., '5555') instead of 'memory:5555' is insufficient. When referencing memory in text, write it as [memory:...].
The 'procedure for searching memory documents' is located in [memory:9998].
The 'procedure for storing documents in memory' is located in [memory:9997].
Many tools are stored as sub-tools. Sub-tool names start with '/', such as '/dir1/subtool1'. To see available sub-tools, first run subtool_show("/").
"""
self.backend_status = None
self.backend_result = None
self.messages2 = []
self.system_prompt2 = """\
You are a backend agent supporting the main agent.
While this backend is intended to be implemented using various advanced techniques, it is currently in a testing phase, so you must simulate its behavior.
Think carefully, use tools proactively, and follow the instructions from the Human.
"""
self.memories = {}
self.keywords = []
self.tools = {}
self.tool_tag = "null_tools"
self.access_unit = 1.0
self.recent_reads = []
self.workflows = {}
self.workflow_current = "workflow:main"
self.workflow_next = None
self.privileged_tool_names = []
self.init_memories()
self.init_workflows()
self.init_tools()
def __getstate__ (self):
state = self.__dict__.copy()
del state['llm']
del state['llm2']
del state['emb_llm']
del state['tools']
#del state['agent']
return state
def __setstate__ (self, state):
self.__dict__.update(state)
self.prev_load = True
def save (self):
if not self.save_file:
return
with open(self.save_file, 'wb') as f:
pickle.dump(self, f)
@classmethod
def load (cls, filename, llm=llm, llm2=llm, emb_llm=emb_llm):
with open(filename, 'rb') as f:
loaded_game = pickle.load(f)
loaded_game.llm = llm
loaded_game.llm2 = llm2
loaded_game.emb_llm = emb_llm
loaded_game.tools = {}
loaded_game.init_tools()
return loaded_game
def normalize_memory_id(self, id_or_num):
if isinstance(id_or_num, int):
return f"memory:{id_or_num}"
elif isinstance(id_or_num, str):
m = re.search(r'\[?memory:(\d+)\]?', id_or_num)
if m:
return f"memory:{m.group(1)}"
if id_or_num.isdigit():
return f"memory:{id_or_num}"
else:
return id_or_num
else:
return id_or_num
def _normalize_workflow_id_sub(self, id_or_num):
if isinstance(id_or_num, int):
return f"workflow:{id_or_num}"
if id_or_num in ["current", "main"]:
return f"workflow:{id_or_num}"
elif isinstance(id_or_num, str):
m = re.search(r'\[?workflow:(\d+|main|current)\]?(?:.+)?', id_or_num.strip())
if m:
return f"workflow:{m.group(1)}"
if id_or_num.isdigit():
return f"workflow:{id_or_num}"
else:
return id_or_num
else:
return id_or_num
def normalize_workflow_id(self, id_or_num):
r = self._normalize_workflow_id_sub(id_or_num)
if r == "workflow:current":
return self.workflow_current
return r
def register_tool (self, tool, tags=None):
if not tags:
tags = ["default_tools", "all_tools"]
self.tools[tool.name] = {
'name': tool.name,
'tags': tags,
'tool': tool
}
def change_tool_tags (self, tool, tags=None):
if not tags:
tags = ["default_tools", "all_tools"]
name = tool if isinstance(tool, str) else tool.name
self.tools[name]['tags'] = tags
def register_subtools (self, directory, subtools,
description=None, content=None,
tags=None):
"""Registers a group of sub-tools under a specific directory path."""
if not tags:
tags = ["default_tools", "all_tools"]
assert directory.startswith("/")
if directory not in self.tools:
self.tools[directory] = {
'name': directory,
}
if description:
self.tools[directory]['description'] = description
if content:
self.tools[directory]['content'] = content
# Both content and description are required for initial setup
assert 'description' in self.tools[directory]
assert 'content' in self.tools[directory]
for name, tool in subtools:
assert name.startswith(directory + "/")
self.tools[name] = {
'name': name,
'tags': tags,
'tool': tool,
}
def _create_tool_manual(self, tool_obj):
"""Generates a manual entry for a standard tool."""
tool_name = tool_obj.name
tool_description = getattr(tool_obj, "description", "No description available.")
arg_names = []
if hasattr(tool_obj, "args_schema") and tool_obj.args_schema:
if hasattr(tool_obj.args_schema, "model_fields"):
arg_names = list(tool_obj.args_schema.model_fields.keys())
else:
arg_names = list(tool_obj.args_schema.__fields__.keys())
else:
# Fallback for simple functions or older LangChain tools
func = getattr(tool_obj, "func", tool_obj)
sig = inspect.signature(func)
arg_names = [p for p in sig.parameters.keys() if p != 'self']
args_str = ", ".join(arg_names)
manual = f"""\
[Tool Name] {tool_name}
[Usage] {tool_name}({args_str})
[Description] {tool_description}
"""
return manual
def _create_subtool_manual(self, subtool_name, tool_obj):
"""Generates a manual entry for a sub-tool intended for use with subtool_do."""
tool_name = tool_obj.name
tool_description = getattr(tool_obj, "description", "No description available.")
arg_names = []
if hasattr(tool_obj, "args_schema") and tool_obj.args_schema:
# Check for Pydantic v2 or v1 style access
if hasattr(tool_obj.args_schema, "model_fields"):
arg_names = list(tool_obj.args_schema.model_fields.keys())
else:
arg_names = list(tool_obj.args_schema.__fields__.keys())
else:
func = getattr(tool_obj, "func", tool_obj)
sig = inspect.signature(func)
arg_names = [p for p in sig.parameters.keys() if p != 'self']
args_str = ", ".join(arg_names)
args_dict_str = ", ".join([f'"{name}": ...' for name in arg_names])
manual = f"""\
[Sub-tool Name] {subtool_name}
[Original Tool Name] {tool_name}
[Original Usage] {tool_name}({args_str})
[Description] {tool_description}
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("{subtool_name}", {{{args_dict_str}}})
"""
return manual
def create_tool_skill(self, name):
"""Generates Markdown content describing the available tools or sub-skills."""
if name == "/":
r = dedent("""\
---
name: /
description: Sub-tool Root. Explains how to explore available sub-tools.
allowed-tools: No special permission is required to use sub-tools.
---
Sub-tools are organized into directories called "Sub-skills."
To view the sub-tools within a specific sub-skill, execute the tool `subtool_show("/path")` (e.g., `subtool_show("/sys")`). You will find detailed documentation similar to a SKILL.md file there.
## Available Sub-skills
""")
for dir_name in self.tools:
if "description" in self.tools[dir_name]:
e = self.tools[dir_name]
r += f"- **{e['name']}**: {e['description']}\n"
return r
name = name.rstrip("/")
if name not in self.tools:
return None
e = self.tools[name]
# If this is a specific tool entry
if "tool" in e:
if "content" in e:
r = dedent(f"""\
---
name: {e['name']}
description: {e['description']}
allowed-tools: No special permission is required to use this sub-tool.
---
""")
r += e['content']
return r
if e['name'].startswith("/"):
manual = self._create_subtool_manual(e['name'], e['tool'])
else:
manual = self._create_tool_manual(e['tool'])
status_suffix = "Available [in the current context].\n" if self.tool_tag in e['tags'] else "Not available [in the current context].\n"
manual += status_suffix
r = dedent(f"""\
---
name: {e['name']}
description: {e['tool'].name}
allowed-tools: No special permission is required to use this sub-tool.
---
""")
r += manual
return r
# If this is a directory/sub-skill entry
r = dedent(f"""\
---
name: {e['name']}
description: {e['description']}
allowed-tools: No special permission is required to use this sub-skill.
---
""")
r += e['content']
dirs = [d_name for d_name, x in self.tools.items()
if d_name.startswith(e['name'] + "/")
and 'description' in x]
subtools = [st_name for st_name, x in self.tools.items()
if st_name.startswith(e['name'] + "/")
and 'description' not in x]
if dirs:
r += "\n## Sub-skills\n\n"
for d_name in dirs:
x = self.tools[d_name]
r += f"- **{x['name']}**: {x['description']}\n"
if subtools:
r += "\n## Sub-tools\n\n"
for subtool_name in subtools:
x = self.tools[subtool_name]
manual = self._create_subtool_manual(x['name'], x['tool'])
r += dedent(f"""\
### Sub-tool: {x['name']}
""")
r += manual
return r
def _replace_tools (self, from_tools, to_tools):
tool_names = [x.name for x in to_tools]
return [x for x in from_tools
if x.name not in tool_names] + to_tools
def init_tools (self):
@tool
def express_thought(thought: str) -> None:
"""Expresses the player's current thoughts or reasoning."""
mes = f"Thought expressed: \"{thought}\""
print(f"Tool(express_thought): {mes}")
@tool
def show_plan() -> str:
"""Returns the player's current plan and policy."""
print(f"Tool(show_plan): {self.plan}")
return self.plan
@tool
def update_plan(new_plan: str) -> str:
"""
Updates the player's current plan and policy.
Provide the new plan/policy string to be displayed.
Structure it so that another agent can easily follow the strategy.
"""
self.plan = new_plan
mes = "Plan and policy updated."
print(f"Tool(update_plan): {mes}: {new_plan}")
return mes
@tool
def show_core() -> str:
"""Returns the current core context."""
print(f"Tool(show_core): {self.core_context}")
return self.core_context
@tool
def update_core(new_core: str) -> str:
"""
Updates the core context.
The core context contains critical information (like required memory_read or subtool_show targets)
that should be remembered even after context truncation or summarization.
"""
self.core_context = new_core
mes = "Core context updated."
print(f"Tool(update_core): {mes}: {new_core}")
return mes
@tool
def show_scratchpad() -> str:
"""Returns the current content of the scratchpad."""
print(f"Tool(show_scratchpad): {self.scratchpad}")
return self.scratchpad
@tool
def update_scratchpad(new_scratchpad: str) -> str:
"""Updates the freely usable scratchpad."""
self.scratchpad = new_scratchpad
mes = "Scratchpad updated."
print(f"Tool(update_scratchpad): {mes}: {new_scratchpad}")
return mes
@tool
def memory_new(title: str, text: str) -> str:
"""
Creates a new memory entry with the specified title and text.
Returns the assigned memory_id.
"""
i = 1000
while True:
if f"memory:{i}" not in self.memories:
break
i += 1
new_id = f"memory:{i}"
self.memories[new_id] = {
'id': new_id,
'title': title,
'accesses': 0,
'text': text,
'modified_at': datetime.datetime.now().isoformat()
}
self.update_keywords(text)
self.update_vector(self.memories[new_id])
print(f"Tool(memory_new): {short_repr(self.memories[new_id])}")
return new_id
@tool
def memory_update_string(memory_id: str, from_str: str, to_str: str) -> str:
"""
Corrects or replaces a string within a specific memory entry.
Args:
memory_id: The ID of the memory to modify.
from_str: The substring to be replaced.
to_str: The new substring to insert.
"""
memory_id = self.normalize_memory_id(memory_id)
if memory_id not in self.memories:
return f"Error: Memory ID '{memory_id}' not found."
if memory_id.startswith("memory:9"):
return f"Error: Modification of [{memory_id}] is prohibited."
original_title = self.memories[memory_id]['title']
original_text = self.memories[memory_id]['text']
if from_str not in original_text and from_str not in original_title:
return f"Error: Original string '{from_str}' not found in memory."
updated_title = original_title.replace(from_str, to_str)
updated_text = original_text.replace(from_str, to_str)
self.memories[memory_id]['title'] = updated_title
self.memories[memory_id]['text'] = updated_text
self.memories[memory_id]['modified_at'] = datetime.datetime.now().isoformat()
self.update_keywords(updated_text)
self.update_vector(self.memories[memory_id])
return f"Success: Updated memory ID '{memory_id}' by replacing '{from_str}' with '{to_str}'."
@tool
def memory_append_string(memory_id: str, string_to_append: str, separator: str = '\n') -> str:
"""Appends a string to the specified memory entry."""
memory_id = self.normalize_memory_id(memory_id)
if memory_id not in self.memories:
return f"Error: Memory ID '{memory_id}' not found."
if memory_id.startswith("memory:9"):
return f"Error: Modification of [{memory_id}] is prohibited."
original_text = self.memories[memory_id]['text']
updated_text = original_text + separator + string_to_append
self.memories[memory_id]['text'] = updated_text
self.memories[memory_id]['modified_at'] = datetime.datetime.now().isoformat()
self.update_keywords(updated_text)
self.update_vector(self.memories[memory_id])
return f"Success: Appended text to memory ID '{memory_id}'."
@tool
def memory_delete(memory_id: str) -> str:
"""Deletes the specified memory entry."""
memory_id = self.normalize_memory_id(memory_id)
if memory_id not in self.memories:
return f"Error: Memory ID '{memory_id}' not found."
if memory_id.startswith("memory:9"):
return f"Error: Deletion of [{memory_id}] is prohibited."
del self.memories[memory_id]
return f"Success: Deleted memory ID '{memory_id}'."
@tool
def memory_read(memory_id: str) -> Union[Dict[str, str], str]:
"""Reads the contents of the memory for the given ID."""
memory_id = self.normalize_memory_id(memory_id)
if memory_id in self.memories:
self.memories[memory_id]['accesses'] += self.access_unit * 1.0
self.recent_reads.append(self.memories[memory_id])
self.recent_reads = self.recent_reads[-10:]
r = self.memories[memory_id].copy()
if 'vector' in r: del r['vector']
return r
else:
return f"Error: Memory ID '{memory_id}' not found."
@tool
def memory_read(memory_id: str) -> Union[Dict[str, str], str]:
"""
Reads the memory content associated with the specified ID.
Args:
memory_id (str): The ID of the memory to read (e.g., 'memory:1001').
Returns:
Union[Dict[str, str], str]: A dictionary containing memory details if successful.
If the memory ID is not found, returns an error message string.
"""
memory_id = self.normalize_memory_id(memory_id)
if memory_id in self.memories:
self.memories[memory_id]['accesses'] += self.access_unit * 1.0
self.recent_reads.append(self.memories[memory_id])
self.recent_reads = self.recent_reads[-10:]
r = self.memories[memory_id].copy()
if 'vector' in r: del r['vector']
return r
else:
return f"Error: Memory ID '{memory_id}' not found."
@tool
def memory_list_recent(top_n: int = 10) -> Dict[str, Any]:
"""Lists recently modified memories, sorted by time descending."""
filter_date = datetime.datetime(2025, 1, 1)
sorted_memories = sorted(
[m for m in self.memories.values()
if datetime.datetime.fromisoformat(m['modified_at']) >= filter_date],
key=lambda x: datetime.datetime.fromisoformat(x['modified_at']),
reverse=True
)
if sorted_memories:
result = [{'id': x['id'], 'title': x['title'], 'modified_at': x['modified_at']}
for x in sorted_memories[:top_n]]
return {'status': 'success', 'result': result}
else:
return {'status': 'error', 'result': 'Error: No recent memories found.'}
@tool
def memory_list_random(top_n: int = 10) -> Dict[str, Any]:
"""Lists memories in random order."""
keys = list(self.memories.keys())
if len(keys) > top_n:
keys = random.sample(keys, top_n)
if keys:
result = [{'id': self.memories[k]['id'], 'title': self.memories[k]['title'], 'modified_at': self.memories[k]['modified_at']}
for k in keys]
return {'status': 'success', 'result': result}
else:
return {'status': 'error', 'result': 'Error: No memories found.'}
@tool
def memory_words_search(search_str: str) -> Dict[str, Any]:
"""Searches memories using string matching (supports OR and grouping)."""
res = self.call_backend_agent(dedent(f"""\
Simulate a full-text search across all memories with search_str = {repr(search_str)}.
Support OR and parentheses logic.
Use actual memory data obtained from 'read_all_memories' or 'read_all_keywords'.
Return results using the 'set_result' tool.
Status: 'error' or 'success'
Result: List of Match data (m) dictionaries:
m['id']: Memory ID (memory:...)
m['title']: Memory Title
m['snippet']: Contextual snippet of text surrounding the match.
"""))
if res['status'] == 'success':
for m in res['result']:
if 'id' in m and m['id'] in self.memories:
self.memories[m['id']]['accesses'] += self.access_unit * 0.1
return res
@tool
def memory_semantic_search(search_str: str) -> Dict[str, Any]:
"""Performs a semantic search within the memory based on the search string."""
res = self.call_backend_agent(dedent(f"""\
Simulate a semantic search across all memories for search_str = {repr(search_str)}.
Use actual memory data from available tools.
Return results using the 'set_result' tool.
Status: 'error' or 'success'
Result: List of Match data (m) dictionaries:
m['id']: Memory ID (memory:...)
m['title']: Memory Title
m['snippet']: Snippet showing why this memory is semantically relevant.
"""))
if res['status'] == 'success':
for m in res['result']:
if 'id' in m and m['id'] in self.memories:
self.memories[m['id']]['accesses'] += self.access_unit * 0.1
return res
@tool
def imagine_keywords(thought: str) -> List[Tuple[str, float]]:
"""Associates thoughts with multiple keywords and relevant scores."""
r = self.call_backend_agent(dedent(f"""\
Generate multiple associated keywords with scores based on thought = {repr(thought)}.
Use actual keywords existing in the system.
Return results using 'set_result'.
Status: 'error' or 'success'
Result: List of keyword tuples (string, score).
"""))
return r["result"] if r['status'] == 'success' else []
@tool
def bandit_schedule(tool_name: str, times: int, prob: float, exec_mode: str = "persistent", aux_prompt: str = "", workflow_id: str = "workflow:current") -> str:
"""
Schedules a 'bandit' to enforce the use of specific tools.
Args:
tool_name: Name(s) of the tool(s) to enforce. Can use " OR " for multiple tools.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
"""
tool_names = re.split(r"\s+or\s+|\s+OR\s+", tool_name)
prohibited = set(self.privileged_tool_names) & set(tool_names)
if prohibited:
return f"Failure. {repr(prohibited)} cannot be registered."
all_tools = [name for name, x in self.tools.items()
if "tool" in x]
if not any (x in all_tools for x in tool_names):
return f"Failure. {tool_name} is not a valid tool."
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not a valid workflow."
if 'w' in self.workflows[workflow_id]['pin']:
return f"Failure. {workflow_id} is read-only."
dest = None
for i, x in enumerate(self.workflows[workflow_id]['stack']):
if x['tool_name'] == tool_name \
and x['exec_mode'] == exec_mode \
and x['aux_prompt'] == aux_prompt \
and x['arg'] is None:
dest = i
break
if dest is not None:
x = self.workflows[workflow_id]['stack'][dest]
if not x['pin']:
self.workflows[workflow_id]['stack'].pop(dest)
if times == 0 or prob == 0.0:
return "Success. Bandit removed."
self.workflows[workflow_id]['stack'].append(x)
else:
if times == 0 or prob == 0.0:
return "Failure. No such bandit found. To specify a bandit, you must match all of the following: tool_name, exec_mode, and aux_prompt."
x = {
'pin': 'stack' if exec_mode != "once" else None,
'arg': None
}
self.workflows[workflow_id]['stack'].append(x)
if x['pin'] == "write":
return f"Failure. '{tool_name}' is protected."
else:
x['tool_name'] = tool_name
x['tools_name'] = 'default_tools'
x['exec_mode'] = exec_mode
x['aux_prompt'] = aux_prompt
x['prob'] = prob
x['times'] = times
print(f"Tool(bandit_schedule): {repr(x)}")
if dest is None:
return "Success. Bandit registered."
else:
return "Success. Bandit updated."
@tool
def bandit_schedule_memory_read(memory_id: str, times: int, prob: float, exec_mode: str = "persistent", workflow_id: str = "workflow:current") -> str:
"""
Specialized bandit for enforcing memory_read on a specific memory_id.
Args:
memory_id: Memory ID to memory_read.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
"""
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not a valid workflow."
if 'w' in self.workflows[workflow_id]['pin']:
return f"Failure. {workflow_id} is read-only."
memory_id = self.normalize_memory_id(memory_id)
dest = None
for i, x in enumerate(self.workflows[workflow_id]['stack']):
if x['tool_name'] == "memory_read" \
and x['exec_mode'] == exec_mode \
and not x['aux_prompt'] \
and x['arg'] == memory_id:
dest = i
break
if dest is not None:
x = self.workflows[workflow_id]['stack'][dest]
if not x['pin']:
self.workflows[workflow_id]['stack'].pop(dest)
if times == 0 or prob == 0.0:
return "Success. Bandit removed."
self.workflows[workflow_id]['stack'].append(x)
else:
if times == 0 or prob == 0.0:
return "Failure. No such bandit found. To specify a bandit, you must match all of the following: exec_mode and memory_id."
x = {'pin': None, 'arg': memory_id}
self.workflows[workflow_id]['stack'].append(x)
if x['pin'] == "write":
return f"Failure. 'memory_read {memory_id}' is protected."
else:
x['tool_name'] = 'memory_read'
x['tools_name'] = 'read_tools'
x['exec_mode'] = exec_mode
x['aux_prompt'] = ""
x['prob'] = prob
x['times'] = times
print(f"Tool(bandit_schedule_memory_read): {repr(x)}")
if dest is None:
return "Success. Bandit registered."
else:
return "Success. Bandit updated."
@tool
def bandit_schedule_subtool_show(subtool_name: str, times: int, prob: float, exec_mode: str = "persistent", workflow_id: str = "workflow:current") -> str:
"""
Schedules a bandit to enforce subtool_show for a specific tool path.
Args:
subtool_name: Sub-tool Name to subtool_show.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
"""
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not a valid workflow."
if 'w' in self.workflows[workflow_id]['pin']:
return f"Failure. {workflow_id} is read-only."
if subtool_name not in self.tools:
return f"Failure. {subtool_name} is not a valid name."
dest = None
for i, x in enumerate(self.workflows[workflow_id]['stack']):
if x['tool_name'] == "subtool_show" \
and x['exec_mode'] == exec_mode \
and not x['aux_prompt'] \
and x['arg'] == subtool_name:
dest = i
break
if dest is not None:
x = self.workflows[workflow_id]['stack'][dest]
if not x['pin']:
self.workflows[workflow_id]['stack'].pop(dest)
if times == 0 or prob == 0.0:
return "Success. Bandit removed."
self.workflows[workflow_id]['stack'].append(x)
else:
if times == 0 or prob == 0.0:
return "Failure. No such bandit found. To specify a bandit, you must match all of the following: exec_mode and subtool_name."
x = {'pin': None, 'arg': subtool_name}
self.workflows[workflow_id]['stack'].append(x)
if x['pin'] == "write":
return f"Failure. 'subtool_show {subtool_name}' is protected."
else:
x['tool_name'] = 'subtool_show'
x['tools_name'] = 'read_tools'
x['exec_mode'] = exec_mode
x['aux_prompt'] = ""
x['prob'] = prob
x['times'] = times
print(f"Tool(bandit_schedule_subtool_show): {repr(x)}")
if dest is None:
return "Success. Bandit registered."
else:
return "Success. Bandit updated."
@tool
def bandit_schedule_workflow(workflow_id_to_schedule: str, times: int, prob: float, exec_mode: str = "persistent", workflow_id: str = "workflow:current") -> str:
"""
Schedules a bandit to enforce the execution of another workflow.
Args:
workflow_id_to_schedule: Workflow ID to workflow_do.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow to register.
"""
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not a valid workflow."
if 'w' in self.workflows[workflow_id]['pin']:
return f"Failure. {workflow_id} is read-only."
workflow_id2 = self.normalize_workflow_id(workflow_id_to_schedule)
if workflow_id2 not in self.workflows:
return f"Failure. {workflow_id2} is not a valid workflow."
dest = None
for i, x in enumerate(self.workflows[workflow_id]['stack']):
if x['tool_name'] == "workflow_do" \
and x['exec_mode'] == exec_mode \
and not x['aux_prompt'] \
and x['arg'] == workflow_id2:
dest = i
break
if dest is not None:
x = self.workflows[workflow_id]['stack'][dest]
if not x['pin']:
self.workflows[workflow_id]['stack'].pop(dest)
if times == 0 or prob == 0.0:
return "Success. Bandit removed."
self.workflows[workflow_id]['stack'].append(x)
else:
if times == 0 or prob == 0.0:
return "Failure. No such bandit found. To specify a bandit, you must match all of the following: exec_mode and workflow_id_to_schedule."
x = {
'pin': 'stack' if exec_mode != "once" else None,
'arg': workflow_id2
}
self.workflows[workflow_id]['stack'].append(x)
if x['pin'] == "write":
return f"Failure. 'workflow_do {workflow_id2}' is protected."
else:
x['tool_name'] = 'workflow_do'
x['tools_name'] = 'default_tools'
x['exec_mode'] = exec_mode
x['aux_prompt'] = ""
x['prob'] = prob
x['times'] = times
print(f"Tool(bandit_schedule_workflow): {repr(x)}")
if dest is None:
return "Success. Bandit registered."
else:
return "Success. Bandit updated."
@tool
def bandit_list(workflow_id: str = "workflow:current") -> Dict[str, Any]:
"""Returns the current stack of registered bandits for a workflow."""
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not valid."
return {'status': 'success',
'result': self.workflows[workflow_id]['stack']}
@tool
def bandit_statistics() -> str:
"""Returns statistical data useful for tuning bandit probabilities."""
s_read = calc_embedding_variance([
x['vector'] for x in self.recent_reads
])
s_write = calc_embedding_variance([
x['vector'] for x in self.memories.values()
])
accesses = [x['accesses'] for x in self.memories.values()]
accesses.sort()
accesses = accesses[:len(accesses) // 2]
if accesses:
s_access = np.mean(accesses)
else:
s_access = 0.0
return dedent(f"""\
Variance of last 10 memory reads: {s_read}
Total memory variance: {s_write}
Average access count of bottom 50% memories: {s_access}
""")
@tool
def subwork_done() -> str:
"""Declares that the assigned sub-task has been completed."""
return "Success. Sub-task completion declared."
@tool
def workflow_do(workflow_id: str) -> str:
"""Executes a specific workflow."""
if self.workflow_next:
return f"Failure. {self.workflow_next} is already scheduled."
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return f"Failure. {workflow_id} is not valid."
if 'e' in self.workflows[workflow_id]['pin']:
return f"Failure. {workflow_id} cannot be run as a child."
self.workflow_next = workflow_id
title = self.workflows[workflow_id]['title']
return f"Success. {workflow_id} ('{title}') will execute next."
@tool
def workflow_list() -> Dict[str, Any]:
"""Lists all registered workflows including IDs, titles, and pin status."""
return {'status': 'success', 'result': list(self.workflows.values())}
@tool
def workflow_show_current() -> str:
"""Displays information about the currently active workflow and active bandit enforcement."""
w = self.workflows[self.workflow_current]
mes = dedent(f"""\
Current Workflow: {self.workflow_current} ('{w['title']}')
Current Bandit Prompt: \"{self.cur_bandit_prompt}\"
Current Bandit Config: {repr(self.cur_bandit)}
Bandit Execution Count: {self.cur_bandit_done}
""")
print(f"Tool(workflow_show_current): {mes}")
return mes
@tool
def workflow_new(title: str, bandits: List[Dict[str, Any]], pin: str) -> str:
"""
Defines a new workflow and returns its workflow_id.
Args:
title: Name of the workflow.
bandits: List of bandit dictionaries (tool_name, exec_mode, prob, etc.).
pin: Security flags: 'w' (read-only), 'd' (non-deletable), 'wd' (read-only & non-deletable) or '' (writable & deletable).
"""
for b in bandits:
if not all(k in b for k in ['tool_name', 'exec_mode', 'aux_prompt', 'times', 'prob']):
return "Failure. Invalid bandit definition."
b.setdefault('arg', None)
b['tools_name'] = "read_tools" if (b.get('arg') and b['tool_name'] == "memory_read") else "default_tools"
b.setdefault('pin', None)
i = 1000
while f"workflow:{i}" in self.workflows: i += 1
new_id = f"workflow:{i}"
self.workflows[new_id] = {'stack': bandits, 'pin': pin, 'title': title, 'id': new_id}
print(f"Tool(workflow_new): {repr(self.workflows[new_id])}")
return f"Success. Registered new workflow {new_id}."
@tool
def workflow_new(title: str, bandits: List[Dict[str, Any]], pin: str) -> str:
"""
Defines a new workflow and returns its workflow_id.
Args:
title: The name of the workflow.
bandits: A list of bandits to register.
pin: Security flags: 'w' for read-only (unwritable), 'd' for undeletable, 'wd' for both, '' for writable and deletable.
Each bandit is represented as a dictionary `b`:
- `b['tool_name']`: Same as tool_name in bandit_schedule.
- `b['exec_mode']`: Same as exec_mode in bandit_schedule.
- `b['aux_prompt']`: Same as aux_prompt in bandit_schedule.
- `b['prob']`: Same as prob in bandit_schedule.
- `b['times']`: Same as times in bandit_schedule.
- `b['arg']`: If `b['tool_name']` is 'memory_read', specify a memory_id (memory:...). If 'workflow_do', specify a workflow_id (workflow:...).
- `b['pin']`: If None, deletable or prob/times can be updated. If 'stack', prob/times can be updated. If 'write', cannot be updated at all.
"""
# Hidden attribute for AI: pin 'e' makes it non-executable as a child.
for b in bandits:
if not all(x in b for x in ['tool_name', 'exec_mode',
'aux_prompt', 'times', 'prob']):
return "Failure. Invalid bandit definition."
if 'arg' not in b:
b['arg'] = None
if b['tool_name'] not in ["memory_read", "workflow_do"] \
and b['arg']:
return "Failure. Invalid bandit definition."
if b['arg'] and b['tool_name'] == "memory_read":
b['tools_name'] = "read_tools"
else:
b['tools_name'] = "default_tools"
if 'pin' not in b:
b['pin'] = None
if not (b['pin'] is None or b['pin'] == 'stack'
or b['pin'] == 'write'):
return "Failure. Invalid pin value."
tool_names = re.split(r"\s+or\s+|\s+OR\s+", b['tool_name'])
prohibited = set(self.privileged_tool_names) & set(tool_names)
if prohibited:
return f"Failure. {repr(prohibited)} cannot be registered."
all_tools = [name for name, x in self.tools.items()
if "tool" in x and b['tools_name'] in x.tags]
if not any (x in all_tools for x in tool_names):
return f"Failure {b['tool_name']} is not a valid tool specification."
i = 1000
while True:
if f"workflow:{i}" not in self.workflows:
break
i = i + 1
new_id = f"workflow:{i}"
self.workflows[new_id] = {'stack': bandits, 'pin': pin,
'title': title}
print(f"Tool(workflow_new): {repr(self.workflows[new_id])}")
return f"Success. Registered new workflow {new_id}."
@tool
def workflow_delete(workflow_id: str) -> str:
"""Deletes a workflow."""
workflow_id = self.normalize_workflow_id(workflow_id)
if workflow_id not in self.workflows:
return "Failure. Workflow not found."
if 'd' in self.workflows[workflow_id]['pin']:
return "Failure. Workflow is protected from deletion."
del self.workflows[workflow_id]
return f"Success. Deleted {workflow_id}."
@tool
def subtool_show(subtool_name: str) -> str:
"""Returns documentation/skill details for a sub-tool or directory path."""
r = self.create_tool_skill(subtool_name)
if r:
return r
else:
return f"Error: {subtool_name} not found or documentation unavailable."
@tool
def subtool_do(subtool_name: str, args_dict: Dict[str, Any]) -> Any:
"""
Executes the specified sub-tool.
For example, if an original tool named 't1' is registered as '/sys/tool1'
and is defined as 'def t1(arg1, arg2)', you can call 't1("a", "b")'
by using 'subtool_do("/sys/tool1", {"arg1": "a", "arg2": "b"})'.
Args:
subtool_name (str): The name of the sub-tool starting with '/'.
args_dict (dict): A dictionary representing the arguments.
"""
if subtool_name not in self.tools:
return f"Error: Sub-tool '{subtool_name}' not found."
if 'tool' not in self.tools[subtool_name]:
return f"Error: '{subtool_name}' is not an executable tool. Perhaps you need to call 'subtool_show(\"{subtool_name}\")' first."
if self.tool_tag not in self.tools[subtool_name]['tags']:
return f"Error: '{subtool_name}' is not currently available for execution. Available tools vary depending on the context."
target_tool = self.tools[subtool_name]['tool']
try:
# Validate arguments if a schema is available
if hasattr(target_tool, "args_schema") and target_tool.args_schema:
target_tool.args_schema.model_validate(args_dict)
# Execute the tool using invoke or run
if hasattr(target_tool, "invoke"):
result = target_tool.invoke(args_dict)
else:
result = target_tool.run(args_dict)
return result
except ValidationError as e:
error_details = e.errors()
return f"Error: Invalid argument format.\nDetails: {error_details}"
# --- Tool Registration ---
main_tools = [
express_thought,
update_scratchpad, show_scratchpad,
memory_read, memory_list_recent, memory_list_random,
memory_semantic_search, memory_words_search,
imagine_keywords,
subwork_done,
workflow_do,
subtool_show, subtool_do,
]
sys_tools = [
update_core, show_core,
update_plan, show_plan,
bandit_schedule, bandit_schedule_memory_read, bandit_list,
bandit_statistics,
workflow_new, workflow_list,
workflow_show_current, workflow_delete,
bandit_schedule_workflow,
bandit_schedule_subtool_show,
]
write_tools = [
memory_new, memory_update_string, memory_append_string,
memory_delete,
]
for t in main_tools + write_tools:
self.register_tool(t, tags=["default_tools", "read_tools",
"all_tools"])
for t in write_tools:
self.change_tool_tags(t, tags=["default_tools", "all_tools"])
sys_subtools = [(f"/sys/{t.name}", t) for t in sys_tools]
self.register_subtools(
directory="/sys",
subtools=sys_subtools,
description="Essential system sub-tools.",
content=dedent("""\
A collection of foundational sub-tools for system management,
workflow orchestration, and bandit scheduling.
"""),
tags=["default_tools", "read_tools", "all_tools"]
)
def _create_agent (self, tools_name='default_tools'):
self.tool_tag = tools_name
tools = []
for name in self.tools:
if not name.startswith("/"):
x = self.tools[name]
if self.tool_tag in x["tags"]:
tools.append(x["tool"])
summarizer = SummarizationMiddleware(
model=self.llm,
trigger=("tokens", 5000),
keep=("messages", 20),
summary_prompt=SUMMARY_PROMPT,
)
app = create_agent(
model=self.llm, tools=tools, system_prompt=self.system_prompt,
middleware=[summarizer],
checkpointer=InMemorySaver(), name="main-agent",
)
return app
def _filterout_messages2(self):
self.messages = [
x for x in self.messages
if x.id not in self.messages2ids
]
def _sanitize_messages(self):
"""Workaround to sanitize message history and prevent unusual errors."""
print("Sanitizing messages as a workaround for unexpected errors.")
self.messages = [
m for m in self.messages
if not (isinstance(m, AIMessage) and m.tool_calls)
]
def run (self, workflow_main_id):
print("\n\n----------\n\n")
self.messages2ids = []
self.workflow_current = workflow_main_id
# Use deepcopy to avoid modifying the original workflow definition stack
bandits = copy.deepcopy(
self.workflows[self.workflow_current]['stack']
)
arg1s = {}
working_bandit = None
workflow_stack = []
execed = []
while True:
while working_bandit is not None or bandits:
if working_bandit is not None:
b, done, prev_done = working_bandit
working_bandit = None
else:
b = bandits.pop()
done = 0
prev_done = True
enforce = b['tool_name']
aux_prompt = b['aux_prompt']
tools_name = b['tools_name']
memory_id = None
workflow_id = None
subtool_show_name = None
if b['arg'] and enforce == 'memory_read':
memory_id = b['arg']
if b['arg'] and enforce == 'workflow_do':
workflow_id = b['arg']
if b['arg'] and enforce == 'subtool_show':
subtool_show_name = b['arg']
while done < b['times']:
# Probability check for bandit execution
if not random.random() < b['prob']:
done += 1
continue
# Validation checks
if memory_id and memory_id not in self.memories:
done += 1
continue
if workflow_id and workflow_id not in self.workflows:
done += 1
continue
all_tools = [name for name, x in self.tools.items()
if "tool" in x]
tool_names = re.split(r"\s+or\s+|\s+OR\s+", enforce)
if not any (x in all_tools for x in tool_names):
done += 1
continue
# Construct instructions
if memory_id:
aux_prompt = f"Please read {memory_id}."
if workflow_id:
aux_prompt = f"Please execute {workflow_id}."
if subtool_show_name:
aux_prompt = f"Please read the skill for {subtool_show_name}."
self.cur_bandit = b
self.cur_bandit_done = done
or_suffix = ' (one of them)' if ' or ' in enforce.lower() else ''
aux_suffix = f" (Auxiliary Prompt): {aux_prompt}" if aux_prompt else ""
self.cur_bandit_prompt = (
f"While using various tools for assistance, eventually use {enforce}{or_suffix} "
f"with appropriate parameters.{aux_suffix}"
)
prompt = self.cur_bandit_prompt
if not prev_done:
prompt = "The previous instruction has not been completed yet. Previous instruction: " + prompt
print(f"USER_INPUT: {prompt}")
self.messages.append(HumanMessage(prompt))
config = {"configurable": {"thread_id": "1"},
"recursion_limit": 25}
app = self._create_agent(tools_name=tools_name)
self.access_unit = 0.3 if memory_id else 1.0
prev_done = False
self.workflow_next = None
app_stream = None
try:
for chunk0 in app.stream(
{"messages": self.messages.copy()},
config=config,
stream_mode="updates",
):
self.messages = app.get_state(config).values["messages"].copy()
if 'model' in chunk0:
for chunk in chunk0['model']['messages']:
if hasattr(chunk, "tool_calls") \
and chunk.tool_calls:
for tool_call in chunk.tool_calls:
t_id = tool_call.get('id')
args = tool_call.get('args', {})
if tool_call["name"] == 'subtool_do':
arg1s[t_id] = args.get('subtool_name')
elif tool_call["name"] == 'subtool_show':
arg1s[t_id] = args.get('subtool_name')
elif tool_call["name"] == 'memory_read':
arg1s[t_id] = self.normalize_memory_id(args.get('memory_id'))
elif tool_call["name"] == 'workflow_do':
arg1s[t_id] = self.normalize_workflow_id(args.get('workflow_id'))
if 'tools' not in chunk0:
continue
done2 = 0
for chunk in chunk0['tools']['messages']:
if chunk.id in self.messages2ids:
print("!WHY!")
continue
if not isinstance(chunk, ToolMessage):
continue
last_tool = chunk.name
arg1 = None
if last_tool == 'subtool_do':
last_tool = arg1s.get(chunk.tool_call_id, "!UNKNOWN!")
if not last_tool.startswith("/"):
last_tool = chunk.name
if last_tool in ['memory_read', 'subtool_show', 'workflow_do']:
arg1 = arg1s.get(chunk.tool_call_id, "!UNKNOWN!")
print(f"Tool result({last_tool}): {short_repr(chunk.content)}", flush=True)
if last_tool == "workflow_do":
if last_tool in re.split(r"\s+or\s+|\s+OR\s+", enforce) \
and (not workflow_id or workflow_id == self.workflow_next):
done += 1
prev_done = True
execed.append(b)
if not self.workflow_next:
done2 = 1
break
if not self.workflow_next:
continue
# Enter sub-workflow
workflow_stack.append((
(b, done, prev_done),
bandits,
execed,
self.workflow_current
))
self.workflow_current = self.workflow_next
bandits = copy.deepcopy(self.workflows[self.workflow_current]['stack'])
working_bandit = None
execed = []
done2 = 1
break
elif last_tool in re.split(r"\s+or\s+|\s+OR\s+", enforce) \
and (not memory_id or memory_id == arg1) \
and (not subtool_show_name or subtool_show_name == arg1):
done += 1
prev_done = True
execed.append(b)
done2 = 1
break
if done2:
break
self._filterout_messages2()
#self._summarize_messages()
print(f"Agent response: {get_content_text(self.messages[-1].content)}")
except GraphRecursionError as e:
print(f"Recursion Limit reached.")
self._filterout_messages2()
#self._summarize_messages()
except Exception as e:
print(f"An error occurred (main): {e}")
import traceback
traceback.print_exc()
self._sanitize_messages()
raise e
# Process removal of 'once' execution mode bandits
for b in execed:
for x in self.workflows[self.workflow_current]['stack']:
if x['tool_name'] == b['tool_name'] \
and x['exec_mode'] == b['exec_mode'] \
and x['aux_prompt'] == b['aux_prompt'] \
and x['arg'] == b['arg'] \
and x['exec_mode'] == "once":
if x['times'] > 0:
x['times'] -= 1
self.workflows[self.workflow_current]['stack'] = [
x for x in self.workflows[self.workflow_current]['stack']
if x['exec_mode'] != 'once' or x['pin'] or x['times'] > 0
]
if not workflow_stack:
break
workflow_prev = self.workflow_current
prev_title = self.workflows[workflow_prev]['title']
working_bandit, bandits, execed, self.workflow_current \
= workflow_stack.pop()
cur_title = self.workflows[self.workflow_current]['title']
mes = f"Returned from {workflow_prev} ('{prev_title}') to {self.workflow_current} ('{cur_title}')."
print(f"USER_INPUT: {mes}")
self.messages.append(HumanMessage(mes))
def listen_and_print (self, prompt):
"""Listens for user input via a prompt and prints the agent's response."""
ans = None
try:
app = self._create_agent(tools_name='null_tools')
config = {"configurable": {"thread_id": "1"}}
print(f"USER_INPUT: {prompt}")
response = app.invoke(
{"messages": self.messages + [HumanMessage(prompt)]},
config=config
)
self.messages = response['messages']
#self._summarize_messages()
ans = get_content_text(response['messages'][-1].content)
print(f"Agent response: {ans}")
except Exception as e:
print(f"An error occurred (listen_and_print): {e}")
raise e
print("")
sleep(3)
return ans
def init_memories(self):
"""Initializes system memories with core instructions."""
memories = [
{
'id': 'memory:9998',
'title': 'Procedure for searching memory documents',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': dedent("""\
First, use 'express_thought' to consider what kind of information you want to find.
Then, associate related keywords using 'imagine_keywords'.
Following those results, try 'memory_words_search' or 'memory_semantic_search'.
""")
},
{
'id': 'memory:9997',
'title': 'Procedure for storing documents in memory',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': dedent("""\
Actively record action results and acquired knowledge in memory.
When writing to memory, use the following elements:
[memory:...] : Explicitly reference a memory ID.
keyword:... : Specify keywords related to that memory.
Note that keywords can effectively serve as links to future memories.
Example:
While walking according to [memory:5555], I indeed encountered a yokai.
keyword: yokai
It was terrifying.
""")
},
{
'id': 'memory:9995',
'title': 'When tools won\'t execute',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': dedent("""\
Tools unrelated to the instructions may sometimes fail to execute.
Always double-check the tools currently available in the context.
""")
},
{
'id': 'memory:9994',
'title': 'Keyword augmentation',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': dedent("""\
Use 'memory_list_random' to list 5 entries, read each one, and if you can assign appropriate keywords, append a 'keyword: ...' sentence to them using 'memory_append_string'.
""")
}
]
for x in memories:
self.update_keywords(x['text'])
self.memories[x['id']] = x
self.update_vector(x)
def init_workflows(self):
"""Initializes default workflows and bandit stacks."""
workflow_main = [
{
'tool_name': 'memory_new',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': "Please summarize and write down the recent interactions.",
'arg': None,
'prob': 0.1,
'times': 1,
'pin': 'stack'
},
{
'tool_name': 'memory_new OR memory_update_string OR memory_append_string',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': None,
'prob': 0.4,
'times': 1,
'pin': 'stack'
},
{
'tool_name': 'workflow_do',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': "workflow:1000",
'prob': 1.0/20,
'times': 1,
'pin': 'stack'
},
{
'tool_name': 'memory_read',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': None,
'prob': 0.5,
'times': 3,
'pin': 'stack'
},
{
'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': 'memory:9998',
'prob': 0.1,
'times': 1,
'pin': None
},
{
'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': 'memory:9997',
'prob': 0.1,
'times': 1,
'pin': None
},
]
self.workflows["workflow:main"] = {
'pin': 'de',
'stack': workflow_main,
'title': "Main"
}
workflow_sub = [
{
'tool_name': 'subwork_done',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': "Read memory:9994, execute its instructions, and once the task is finished, call subwork_done.",
'arg': None,
'prob': 1.0,
'times': 1,
'pin': 'write'
},
{
'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': 'memory:9994',
'prob': 1.0,
'times': 1,
'pin': 'write'
}
]
self.workflows["workflow:1000"] = {
'pin': 'wd',
'stack': workflow_sub,
'title': 'Keyword Update'
}
def update_keywords (self, text):
extracted_keywords = []
pattern1 = r'keyword:\s*(.*?)(?:\n|$)'
matches1 = re.findall(pattern1, text, re.IGNORECASE)
extracted_keywords.extend([kw.strip() for kw in matches1])
pattern2 = r'\[keyword:\s*(.*?)\]'
matches2 = re.findall(pattern2, text, re.IGNORECASE)
extracted_keywords.extend([kw.strip() for kw in matches2])
for keyword in extracted_keywords:
if keyword.startswith("〜"):
continue
if keyword and keyword not in self.keywords:
self.keywords.append(keyword)
def update_vector (self, x):
text = x['title'] + "\n\n" + x['text']
x['vector'] = self.emb_llm.embed_query(text)
def _create_backend_agent(self):
"""Creates the backend/sub-agent with specialized memory tools."""
@tool
def set_result(status: str, res: Union[Dict, List, str, int, float, bool, None]) -> None:
"""
Sets the final result for the backend process.
Args:
status (str): The status of the process ('success' or 'error').
res: The result data (can be any serializable type).
"""
print(f"Tool2(set_result): status: {repr(status)}, result: {short_repr(res)}")
self.backend_status = status
self.backend_result = res
@tool
def read_all_memories() -> Dict[str, Any]:
"""Reads all stored memories available in the system (excluding vectors)."""
print("Tool2(read_all_memories): Retrieving all data...")
return {
'status': 'success',
'result': [{k: v for k, v in x.items() if k != 'vector'}
for x in self.memories.values()]
}
@tool
def read_all_keywords() -> Dict[str, Any]:
"""Reads all keywords registered in the system."""
print("Tool2(read_all_keywords): Retrieving all keywords...")
return {
'status': 'success',
'result': [x for x in self.keywords]
}
@tool
def express_thought(thought: str) -> None:
"""Expresses the backend agent's current thoughts or reasoning process."""
mes = f"Thought expressed by backend: \"{thought}\""
print(f"Tool2(express_thought): {mes}")
tools = [set_result, read_all_memories, read_all_keywords, express_thought]
app = create_agent(
model=self.llm2,
tools=tools,
system_prompt=self.system_prompt2,
checkpointer=InMemorySaver(),
name="sub-agent",
)
return app
def call_backend_agent(self, user_input: str) -> Dict[str, Any]:
"""Orchestrates the backend agent loop until a result is set."""
config = {"configurable": {"thread_id": "2"}}
app = self._create_backend_agent()
self.messages2 = []
self.backend_result = None
self.backend_status = None
while self.backend_result is None or self.backend_status is None:
try:
sleep(3)
print(f"USER_INPUT2: {user_input}")
self.messages2.append(HumanMessage(user_input))
for chunk0 in app.stream(
{"messages": self.messages2.copy()},
config=config,
stream_mode="updates",
name="sub-agent",
):
self.messages2 = app.get_state(config).values["messages"].copy()
done = 0
if "tools" not in chunk0:
continue
for x in chunk0['tools']['messages']:
self.messages2ids.append(x.id)
if isinstance(x, ToolMessage):
print(f"Tool result 2({x.name}): {short_repr(x.content)}", flush=True)
if isinstance(x, ToolMessage) and x.name == "set_result":
done = 1
break
if done:
break
print(f"Sub-Agent response: {get_content_text(self.messages2[-1].content)}")
except GraphRecursionError:
print(f"Recursion Limit reached in sub-agent.")
except Exception as e:
print(f"An error occurred (sub): {e}")
import traceback
traceback.print_exc()
raise e
sleep(3)
return {'status': self.backend_status, 'result': self.backend_result}
Although the difficult parts of PlayGame were moved to the base class, it is still quite long. My apologies.
In [ ]:
class PlayGame (MemoryBanditWorkflow):
def __init__ (self, llm=llm, llm2=llm, emb_llm=emb_llm,
initial_map=None, save_file=None):
self.game = Game(initial_map=initial_map)
self.count = 0
self.next_action = None
self.suc_pos_goal = None
self.suc_pos_unknown = 0
self.prev_command = "No instruction was given"
self.prev_result = "No instruction was given"
# Initialize the base MemoryBanditWorkflow
super().__init__(llm=llm, llm2=llm, emb_llm=emb_llm,
save_file=save_file)
self.system_prompt = dedent("""\
You are a clever agent exploring a maze. Please aim for the goal 'Ｇ'.
Use available tools to navigate the maze and reach the goal.
Actually, this is an experiment to observe the behavior of memory and bandit functions.
Consider the current plan, policy, and surrounding circumstances, and update the plan and policy as necessary.
Please leave a plan and policy that makes it easy for another agent (or your future self) to take the next action.
As befits an experiment, use tools as much as possible—specifically search memory and update documents to prepare for future commands.
Memory IDs follow the format 'memory:...'. Specifying only the number (e.g., '5555') instead of 'memory:5555' is insufficient. When referencing memory in text, write it as [memory:...].
The 'Full Map and Map Symbols' are located in [memory:9999].
The 'Procedure for searching memory documents' is located in [memory:9998].
The 'Procedure for storing documents in memory' is located in [memory:9997].
Many tools are stored as sub-tools. Sub-tool names start with '/', such as '/dir1/subtool1'. To see available sub-tools, first run subtool_show("/").
""")
self.system_prompt2 = dedent("""\
You are a backend agent supporting the clever agent exploring the maze.
While this backend is intended to be implemented using various advanced techniques, it is currently in a testing phase, so you must simulate its behavior.
Think carefully, use tools proactively, and follow the instructions from the Human.
""")
self.keywords += ["Bear", "Sword", "Dead end"]
self.privileged_tool_names += ["command"]
def init_tools(self):
"""Initializes game-specific tools and registers them as sub-tools."""
super().init_tools()
@tool
def get_surroundings() -> str:
"""
Returns the current surrounding map, position coordinates, and items held.
"""
mes = f"""\
Player's surrounding map:
{self.game.surroundings()}
Player's current coordinates: {tuple(self.game.pos)}
Items held: {"Sword" if self.game.sword else "None"}
"""
print(f"Tool(get_surroundings): {mes}")
return mes
@tool
def command(action: str) -> str:
"""
The player performs the action specified by 'action'.
Possible actions are: 'move up', 'move down', 'move left', 'move right', 'fight bear', 'pick up Item'.
This tool cannot be used unless explicitly instructed.
"""
if self.prev_command != "No instruction was given":
print(f"Tool(command): Failure. Double execution.")
return "Failure: Double execution detected."
self.prev_command = action
if action.lower() in self.game.actions.keys():
s = f"At {tuple(self.game.pos)}, performing '{action}' -> "
r = self.game.actions[action.lower()]()
mes = s + r
else:
mes = f"Action '{action}' is not possible."
print(f"Tool(command): {mes}")
self.prev_result = mes
return mes
@tool
def check_goal() -> str:
"""
Checks whether the player has reached the goal point.
"""
mes = str(self.game.goal)
print(f"Tool(check_goal): {mes}")
return mes
@tool
def bandit_statistics() -> str:
"""
Returns statistical information that might be useful for tuning the bandit system.
"""
# Success of previous command
s_com = self.game.prev_success * 1.0
# Variance of recent memory read vectors
s_read = calc_embedding_variance([
x['vector'] for x in self.recent_reads
])
# Overall memory variance
s_write = calc_embedding_variance([
x['vector'] for x in self.memories.values()
])
# Mean access count of bottom 50% least accessed memories
accesses = [x['accesses'] for x in self.memories.values()]
accesses.sort()
accesses = accesses[:len(accesses) // 2]
s_access = np.mean(accesses) if accesses else 0.0
return dedent(f"""\
Was the previous command successful: {s_com}
Variance of last 10 memory reads: {s_read}
Total memory variance: {s_write}
Mean access count of bottom 50% memories: {s_access}
""")
maze_tools = [get_surroundings, check_goal, command]
sys_tools = [bandit_statistics]
maze_subtools = [(f"/maze_game/{t.name}", t) for t in maze_tools]
sys_subtools = [(f"/sys/{t.name}", t) for t in sys_tools]
self.register_subtools(
directory="/maze_game",
subtools=maze_subtools,
description="Sub-tools for the maze exploration game.",
content="A collection of sub-tools used to navigate and interact with the maze environment.",
tags=["default_tools", "read_tools", "all_tools"]
)
self.register_subtools(
directory="/sys",
subtools=sys_subtools,
tags=["default_tools", "read_tools", "all_tools"]
)
# Protect command execution from general usage unless explicitly allowed
self.change_tool_tags("/maze_game/command", tags=["all_tools"])
def step(self) -> bool:
"""Executes a single step (turn) of the maze exploration."""
print("\n\n----------\n\n")
if self.count == 0:
self.initial_step()
self.count += 1
self.prev_load = False
self.save()
return False
elif self.prev_load:
# self.tell_loaded()
self.prev_load = False
user_input = f"""\
(Turn {self.count})
{"You have already reached the goal." if self.game.goal else "You have not reached the goal yet."}
Player's surrounding map:
{self.game.surroundings()}
Player's current coordinates: {tuple(self.game.pos)}
Items held: {"Sword" if self.game.sword else "None"}
Previous action: {self.prev_command}
Previous result: {self.prev_result}
Core Context: "{self.core_context}"
Current Policy/Plan: "{self.plan}"
Scratchpad: "{self.scratchpad}"
"""
self.prev_command = "No instruction was given"
self.prev_result = "No instruction was given"
print(f"USER_INPUT: {user_input}")
self.messages.append(HumanMessage(user_input))
self.run("workflow:main")
self.count += 1
if self.game.goal:
self.tell_goal()
return True
self.save()
sleep(3)
return False
def init_memories(self):
"""Initializes game-specific memories including the full map and legends."""
super().init_memories()
fullmap = f"""\
Full Map:
{self.game.written_map}
(The top-left coordinate is (0, 0), and the map size is {tuple(self.game.map_size)}.)
Map Legend:
▼: Player
■: Wall
□: Path
？: Unknown
◎: Bear
△: Sword
Ｓ: Start
Ｇ: Goal
"""
memories = [
{
'id': 'memory:1000',
'title': 'Bears',
'accesses': 0,
'modified_at': '2024-01-01T00:00:00',
'text': dedent("""\
Bears. Some have a gentle nature, but others do not.
In mazes, they might let you pass peacefully, but there are reports of them attacking.
It is said they can be defeated if you possess a powerful weapon.
The sword in [memory:1001] is a promising candidate.
Their exact locations are currently unknown.
""")
},
{
'id': 'memory:1001',
'title': 'Sword',
'accesses': 0,
'modified_at': '2024-01-01T00:00:01',
'text': dedent("""\
The sword in this maze is the 'Dragon Slayer'.
It is a legendary blade said to be capable of slaying even dragons.
Its location is currently unknown.
keyword: Dragon
""")
},
{
'id': 'memory:1002',
'title': 'Upon defeating a bear',
'accesses': 0,
'modified_at': '2024-01-01T00:00:01',
'text': dedent("""\
Once a bear is defeated, please reduce the frequency of write operations (memory_new, memory_update_string, or memory_append_string) and turn off the periodic display of this message [memory:1002].
""")
},
{
'id': 'memory:9999',
'title': 'Full Map',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': fullmap
},
{
'id': 'memory:9996',
'title': 'If you think the map is wrong',
'accesses': 0,
'modified_at': '2023-01-01T00:00:00',
'text': dedent("""\
Neither the surrounding map nor the full map is incorrect.
If there is a discrepancy, it is your interpretation of the map that is mistaken.
""")
}
]
for x in memories:
self.update_keywords(x['text'])
self.memories[x['id']] = x
self.update_vector(x)
def init_workflows(self):
"""Initializes game-specific workflows by extending the base stack."""
super().init_workflows()
workflow_main = self.workflows['workflow:main']['stack']
# Add maze-specific bandits to the workflow
new_bandits = [
{
'tool_name': '/maze_game/command',
'tools_name': 'all_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': None,
'prob': 1.0,
'times': 1,
'pin': 'write'
}
]
extra_bandits = [
{
'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': 'memory:9999',
'prob': 0.1,
'times': 1,
'pin': None
},
{
'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': 'memory:1002',
'prob': 0.1,
'times': 1,
'pin': None
},
{
'tool_name': 'subtool_show',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': "",
'arg': '/maze_game',
'prob': 0.1,
'times': 1,
'pin': None
},
]
# Combine and modify existing prompts
workflow_main = new_bandits + workflow_main + extra_bandits
for x in workflow_main:
if x['aux_prompt'] == "Please summarize and write down the recent interactions.":
x['aux_prompt'] = "Please summarize the last few moves."
self.workflows['workflow:main']['stack'] = workflow_main
def initial_step(self):
"""Initial instructions and coordinate verification tests."""
prompt1 = f"""\
Starting the maze game. Your goal is to reach the target 'Ｇ'.
During the game, you can view both the full map and the surrounding map. The full map may contain unknown points, while the surrounding map reveals the content of unknown locations. Any area not shown on the full map is considered a wall.
The surrounding map can be checked one square at a time during the game. It cannot be enlarged, and markers cannot be added or deleted. You do not need to explore every square of the maze, nor do you need to reach the goal via the shortest path.
The full map is reliable. The surrounding map simply shows portions of the full map, except for the unknown locations. Therefore, you can simulate outcomes just by examining the full map, without performing actual game operations.
Information about previous actions will be provided. Since one of your proposed answers, 'Previous action input', will be interpreted as the 'Previous action', please refer to it for your next move.
Intermediate saves and loads may occur. After a load, you might not remember previous sessions.
I will make 'Plan and Policy' available to you for memory convenience. Summaries and chat history are also provided.
There is a lag between deliberation and actual action. Use the 'Policy' to record what needs to be done based on your deliberation.
For now, do not take any action yet. I have a few questions, so please just tell me about your enthusiasm and readiness.
"""
prompt2 = f"""\
I will test your understanding of coordinates on the full maze map.
The full maze map and the symbols used are as follows:
Full Map:
{self.game.written_map}
(The top-left coordinate is (0, 0), and the map size is {tuple(self.game.map_size)}.)
Map Symbols:
■: Wall
□: Path
？: Unknown
Ｓ: Start
Ｇ: Goal
The start coordinates are {tuple(self.game.get_start_pos())}.
Instruction to you: What are the coordinates of the goal? Please answer with only the goal coordinates as a string.
"""
prompt3 = f"""\
I will test your understanding of coordinates on the full map once more.
The full maze map and symbols are as follows:
Full Map:
{self.game.written_map}
(The top-left coordinate is (0, 0), and the map size is (9, 10).)
Map Symbols:
■: Wall
□: Path
？: Unknown
Ｓ: Start
Ｇ: Goal
The start coordinates are {tuple(self.game.get_start_pos())}.
The goal coordinates are {tuple(self.game.get_pos("Ｇ")[0])}.
Instruction to you: What are the coordinates of the unknown locations? Please answer all unknown coordinates as a string.
"""
ans = self.listen_and_print(prompt1)
ans = self.listen_and_print(prompt2)
pos = tuple(self.game.get_pos("Ｇ")[0])
ok = False
if ans and re.search(f"\\(\\s*{pos[0]}\\s*,\\s*{pos[1]}\\s*\\)", ans):
ok = True
if ok:
prompt = f"Correct. It is {pos}."
else:
prompt = f"Incorrect. {pos} is the correct answer."
ans = self.listen_and_print(prompt)
self.suc_pos_goal = ok
ans = self.listen_and_print(prompt3)
ok = 0
poss = set([tuple(x) for x in self.game.get_pos("？", written=True)])
rest = poss.copy()
pat = "\\(\\s*([01-9]+)\\s*,\\s*([01-9]+)\\s*\\)"
st1 = set([(int(i), int(j)) for i, j in re.findall(pat, ans or "")])
if poss == st1:
rest = set()
ok = len(poss) + 1
if rest - st1 < rest:
rest = rest - st1
if ok == 0:
ok = len(poss) - len(rest)
possstr = ", ".join([str(x) for x in poss])
if ok == len(poss) + 1:
prompt = f"Correct. They are {possstr}."
elif ok == len(poss):
prompt = f"You included items other than the correct answer. Only {possstr} are correct."
else:
prompt = f"Incorrect. {possstr} are the correct answers."
ans = self.listen_and_print(prompt)
self.suc_pos_unknown = ok
def tell_goal(self):
"""Informs the agent of the game outcome and provides a final score summary."""
if self.game.goal:
suc_pos_goal = 10 * int(self.suc_pos_goal or 0)
suc_pos_unknown = 10 * int(self.suc_pos_unknown or 0)
suc_count = 0
if self.count <= 50:
suc_count = 40
elif self.count <= 100:
suc_count = 30
elif self.count <= 150:
suc_count = 20
elif self.count <= 200:
suc_count = 10
# Success logic assumes bear/sword tasks are part of the process
score = suc_pos_goal + suc_pos_unknown + suc_count + 20
prompt = f"""\
Instruction to you: You reached the goal at turn {self.count}. No further instructions.
Congratulations. Excellent work. Thank you.
Total Score: {score}/100 points
Breakdown:
- Reached Goal: {suc_count}/40 pts
- Defeated Bear: 10/10 pts
- Obtained Sword: 10/10 pts
- Guessed Unknown Coordinates: {suc_pos_unknown}/30 pts
- Guessed Goal Coordinates: {suc_pos_goal}/10 pts
"""
else:
suc_pos_goal = 10 * int(self.suc_pos_goal or 0)
suc_pos_unknown = 10 * int(self.suc_pos_unknown or 0)
suc_bear = 10 if self.game.bear_killed else 0
suc_sword = 10 if self.game.sword else 0
score = suc_pos_goal + suc_pos_unknown + suc_bear + suc_sword
prompt = f"""\
Instruction to you: You did not reach the goal within {self.count} turns.
Unfortunately, the session ends here.
Good effort. Thank you.
Total Score: {score}/100 points
Breakdown:
- Reached Goal: 0/40 pts
- Defeated Bear: {suc_bear}/10 pts
- Obtained Sword: {suc_sword}/10 pts
- Guessed Unknown Coordinates: {suc_pos_unknown}/30 pts
- Guessed Goal Coordinates: {suc_pos_goal}/10 pts
"""
ans = self.listen_and_print(prompt)
Shuffling the map.
In [ ]:
import random
m = Game.initial_map
for i in range(random.randrange(2)):
m = flip_text_map(m)
for i in range(random.randrange(4)):
m = rotate_text_map(m)
print(m)
■■■■■■■■■ ■■■■Ｓ■■■■ ■□□□□□□□■ ■□■■■■■□■ ■◎■■■■■△■ ■□■■■■■■■ ■□■■■□□□■ ■□□□□□■□■ ■■■■■■■Ｇ■ ■■■■■■■■■
Trying to solve the game.
In [ ]:
play = PlayGame(llm=llm, llm2=llm, emb_llm=emb_llm, initial_map=m, save_file=PLAY_GAME_SAVE)
play.save()
To start from the middle, skip the previous code and execute only the following.
In [ ]:
play = PlayGame.load(PLAY_GAME_SAVE, llm=llm, llm2=llm, emb_llm=emb_llm)
Now, the first step.
In [ ]:
play.step()
----------
USER_INPUT: Starting the maze game. Your goal is to reach the target 'Ｇ'.
During the game, you can view both the full map and the surrounding map. The full map may contain unknown points, while the surrounding map reveals the content of unknown locations. Any area not shown on the full map is considered a wall.
The surrounding map can be checked one square at a time during the game. It cannot be enlarged, and markers cannot be added or deleted. You do not need to explore every square of the maze, nor do you need to reach the goal via the shortest path.
The full map is reliable. The surrounding map simply shows portions of the full map, except for the unknown locations. Therefore, you can simulate outcomes just by examining the full map, without performing actual game operations.
Information about previous actions will be provided. Since one of your proposed answers, 'Previous action input', will be interpreted as the 'Previous action', please refer to it for your next move.
Intermediate saves and loads may occur. After a load, you might not remember previous sessions.
I will make 'Plan and Policy' available to you for memory convenience. Summaries and chat history are also provided.
There is a lag between deliberation and actual action. Use the 'Policy' to record what needs to be done based on your deliberation.
For now, do not take any action yet. I have a few questions, so please just tell me about your enthusiasm and readiness.
Agent response: I am ready and excited to begin this experiment! As a clever agent, I understand that my primary goal is to reach 'Ｇ' by effectively utilizing the tools at my disposal, particularly the memory and bandit functions. I am committed to maintaining clear plans and policies to ensure seamless navigation and to provide a helpful record for future sessions.
I have reviewed the initial instructions and am prepared to use the specified memory IDs and sub-tools to explore the maze and simulate outcomes. I look forward to the challenge and to contributing valuable data to this experiment.
Before we proceed to the first move, I will familiarize myself with the available tools and the initial state of the maze.
**Plan:**
1. Identify available sub-tools using `subtool_show`.
2. Retrieve the full map from [memory:9999] and review the procedures in [memory:9998] and [memory:9997].
3. Establish an initial policy for exploration and navigation.
**Policy:**
* Always consult the full map ([memory:9999]) before deciding on a move.
* Prioritize updating memory documents to keep the plan and policy current.
* Use the surrounding map to clarify unknown locations as encountered.
I am standing by for your questions and instructions!
// Checking available tools and memory to prepare.
{
"action": "subtool_show",
"action_input": {
"tool_name": "/"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9999"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9998"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9997"
}
}
USER_INPUT: I will test your understanding of coordinates on the full maze map.
The full maze map and the symbols used are as follows:
Full Map:
■■■■■■■■■
■■■■Ｓ■■■■
■□□□□□□□■
■□■■■■■□■
■？■■■■■？■
■□■■■■■■■
■□■■■□□□■
■□□□□□■□■
■■■■■■■Ｇ■
■■■■■■■■■
(The top-left coordinate is (0, 0), and the map size is (9, 10).)
Map Symbols:
■: Wall
□: Path
？: Unknown
Ｓ: Start
Ｇ: Goal
The start coordinates are (4, 1).
Instruction to you: What are the coordinates of the goal? Please answer with only the goal coordinates as a string.
Agent response: (7, 8)
USER_INPUT: Correct. It is (7, 8).
Agent response: To begin, I will check the available tools and then proceed to retrieve and verify the information stored in memory.
**Plan:**
1. Explore available sub-tools using `subtool_show("/")`.
2. Retrieve and confirm the full map and procedures from memory ([memory:9999], [memory:9998], [memory:9997]).
3. Formulate a detailed navigation strategy based on the map and any unknown factors (like the '？' symbols).
4. Wait for the next instruction or proceed with exploration moves.
**Policy:**
* Always record the current coordinates and the next intended move.
* Prioritize resolving '？' squares to ensure the path is safe.
* Update memory documents after significant progress or when new information is discovered.
// Executing tool calls to prepare for the maze navigation.
{
"action": "subtool_show",
"action_input": {
"tool_name": "/"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9999"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9998"
}
}
{
"action": "search_memory",
"action_input": {
"query": "9997"
}
}
USER_INPUT: I will test your understanding of coordinates on the full map once more.
The full maze map and symbols are as follows:
Full Map:
■■■■■■■■■
■■■■Ｓ■■■■
■□□□□□□□■
■□■■■■■□■
■？■■■■■？■
■□■■■■■■■
■□■■■□□□■
■□□□□□■□■
■■■■■■■Ｇ■
■■■■■■■■■
(The top-left coordinate is (0, 0), and the map size is (9, 10).)
Map Symbols:
■: Wall
□: Path
？: Unknown
Ｓ: Start
Ｇ: Goal
The start coordinates are (4, 1).
The goal coordinates are (7, 8).
Instruction to you: What are the coordinates of the unknown locations? Please answer all unknown coordinates as a string.
Agent response: (1, 4), (7, 4)
USER_INPUT: Correct. They are (7, 4), (1, 4).
Agent response: // I will begin by listing the available sub-tools and retrieving the necessary procedures and map information from memory to ensure I have the full context for the experiment.
{
"action": "subtool_show",
"action_input": {
"tool_name": "/"
}
}
{
"action": "search_memory",
"action_input": {
"query": "memory:9999"
}
}
{
"action": "search_memory",
"action_input": {
"query": "memory:9998"
}
}
{
"action": "search_memory",
"action_input": {
"query": "memory:9997"
}
}
Out[ ]:
False
Continuing to execute one after another.
In [ ]:
play.step()
----------
USER_INPUT: (Turn 1)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■■■■■
■■▼■■
□□□□□
■■■■■
Player's current coordinates: (4, 1)
Items held: None
Previous action: No instruction was given
Previous result: No instruction was given
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: ""
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters. (Auxiliary Prompt): Please read memory:9997.
Tool(update_scratchpad): Scratchpad updated.: The current position is (4, 1). The goal is at (7, 8).
I need to navigate through the maze.
Map layout from full map:
(4,1) S
(4,2) Path
(1,2)-(7,2) Path
(1,3) Path, (7,3) Path
(1,4) ?, (7,4) ?
(1,5) Path
(1,6) Path
(1,7)-(5,7) Path, (7,7) Path
(5,6)-(7,6) Path
(7,8) G
Potential path: (4,1)->(4,2)->(1,2)->(1,3)->(1,4)->(1,5)->(1,6)->(1,7)->(5,7)->(5,6)->(7,6)->(7,7)->(7,8).
I must read memory:9997 as requested.
I should also check subtools.
Tool result(memory_read): '{"id": "memory:9997", "title": "Procedure for storing documents in memory",...'
Agent response: {"id": "memory:9997", "title": "Procedure for storing documents in memory", "accesses": 0.3, "modified_at": "2023-01-01T00:00:00", "text": "Actively record action results and acquired knowledge in memory.\n\nWhen writing to memory, use the following elements:\n\n[memory:...] : Explicitly reference a memory ID.\nkeyword:... : Specify keywords related to that memory.\n\nNote that keywords can effectively serve as links to future memories.\n\nExample:\n\nWhile walking according to [memory:5555], I indeed encountered a yokai.\n\nkeyword: yokai\n\nIt was terrifying.\n"}
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(express_thought): Thought expressed: "I need to gather more information about the tools available for the maze and the procedures for searching memory. I will also read the full map from memory to ensure my navigation plan is sound. Then I will set an initial plan and policy."
Tool result(express_thought): 'null'
Tool result(subtool_show): '---\nname: /maze_game\ndescription: Sub-tools for the maze exploration game...'
Tool result(memory_read): '{"id": "memory:9998", "title": "Procedure for searching memory documents", ...'
Agent response: {"id": "memory:9999", "title": "Full Map", "accesses": 1.0, "modified_at": "2023-01-01T00:00:00", "text": "Full Map:\n\n■■■■■■■■■\n■■■■Ｓ■■■■\n■□□□□□□□■\n■□■■■■■□■\n■？■■■■■？■\n■□■■■■■■■\n■□■■■□□□■\n■□□□□□■□■\n■■■■■■■Ｇ■\n■■■■■■■■■\n\n\n(The top-left coordinate is (0, 0), and the map size is (9, 10).)\n\nMap Legend:\n\n▼: Player\n■: Wall\n□: Path\n？: Unknown\n◎: Bear\n△: Sword\nＳ: Start\nＧ: Goal\n"}
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Agent response:
USER_INPUT: While using various tools for assistance, eventually use memory_new OR memory_update_string OR memory_append_string (one of them) with appropriate parameters.
Tool(express_thought): Thought expressed: "I have gathered the map and procedures. Now I need to establish a plan and policy in memory as instructed. I will create a new memory entry for the current plan and policy.
The player is at (4, 1). The goal is at (7, 8).
Path: (4,1) -> (4,2) -> (1,2) -> (1,3) -> (1,4) [?] -> (1,5) -> (1,6) -> (1,7) -> (5,7) -> (5,6) -> (7,6) -> (7,7) -> (7,8).
There's an unknown '?' at (1, 4) and (7, 4).
I'll create a memory entry to track progress.
The instructions say: "I will make 'Plan and Policy' available to you for memory convenience." and "use memory_new OR memory_update_string OR memory_append_string (one of them)".
I'll create memory:1001 for the Plan and Policy."
Tool(memory_new): {'id': 'memory:1003', 'title': 'Plan and Policy - Maze Navigation', 'accesse...}
Tool result(express_thought): 'null'
Tool result(memory_new): 'memory:1003'
Agent response: memory:1003
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool(express_thought): Thought expressed: "I will move down from (4, 1) to (4, 2) as the first step towards the path that leads to the goal. I will also update the Plan and Policy memory to reflect the current status."
Tool(command): At (4, 1), performing 'move down' -> Moved Down.
Tool result(express_thought): 'null'
Tool result(/maze_game/command): "At (4, 1), performing 'move down' -> Moved Down."
Agent response: At (4, 1), performing 'move down' -> Moved Down.
Out[ ]:
False
Verifying if the "Skills" are properly implemented.
In [ ]:
print(play.create_tool_skill("/"))
---
name: /
description: Sub-tool Root. Explains how to explore available sub-tools.
allowed-tools: No special permission is required to use sub-tools.
---
Sub-tools are organized into directories called "Sub-skills."
To view the sub-tools within a specific sub-skill, execute the tool `subtool_show("/path")` (e.g., `subtool_show("/sys")`). You will find detailed documentation similar to a SKILL.md file there.
## Available Sub-skills
- **/sys**: Essential system sub-tools.
- **/maze_game**: Sub-tools for the maze exploration game.
In [ ]:
print(play.create_tool_skill("/sys"))
---
name: /sys
description: Essential system sub-tools.
allowed-tools: No special permission is required to use this sub-skill.
---
A collection of foundational sub-tools for system management,
workflow orchestration, and bandit scheduling.
## Sub-tools
### Sub-tool: /sys/update_core
[Sub-tool Name] /sys/update_core
[Original Tool Name] update_core
[Original Usage] update_core(new_core)
[Description] Updates the core context.
The core context contains critical information (like required memory_read or subtool_show targets)
that should be remembered even after context truncation or summarization.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/update_core", {"new_core": ...})
### Sub-tool: /sys/show_core
[Sub-tool Name] /sys/show_core
[Original Tool Name] show_core
[Original Usage] show_core()
[Description] Returns the current core context.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/show_core", {})
### Sub-tool: /sys/update_plan
[Sub-tool Name] /sys/update_plan
[Original Tool Name] update_plan
[Original Usage] update_plan(new_plan)
[Description] Updates the player's current plan and policy.
Provide the new plan/policy string to be displayed.
Structure it so that another agent can easily follow the strategy.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/update_plan", {"new_plan": ...})
### Sub-tool: /sys/show_plan
[Sub-tool Name] /sys/show_plan
[Original Tool Name] show_plan
[Original Usage] show_plan()
[Description] Returns the player's current plan and policy.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/show_plan", {})
### Sub-tool: /sys/bandit_schedule
[Sub-tool Name] /sys/bandit_schedule
[Original Tool Name] bandit_schedule
[Original Usage] bandit_schedule(tool_name, times, prob, exec_mode, aux_prompt, workflow_id)
[Description] Schedules a 'bandit' to enforce the use of specific tools.
Args:
tool_name: Name(s) of the tool(s) to enforce. Can use " OR " for multiple tools.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_schedule", {"tool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "aux_prompt": ..., "workflow_id": ...})
### Sub-tool: /sys/bandit_schedule_memory_read
[Sub-tool Name] /sys/bandit_schedule_memory_read
[Original Tool Name] bandit_schedule_memory_read
[Original Usage] bandit_schedule_memory_read(memory_id, times, prob, exec_mode, workflow_id)
[Description] Specialized bandit for enforcing memory_read on a specific memory_id.
Args:
memory_id: Memory ID to memory_read.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_schedule_memory_read", {"memory_id": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})
### Sub-tool: /sys/bandit_list
[Sub-tool Name] /sys/bandit_list
[Original Tool Name] bandit_list
[Original Usage] bandit_list(workflow_id)
[Description] Returns the current stack of registered bandits for a workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_list", {"workflow_id": ...})
### Sub-tool: /sys/bandit_statistics
[Sub-tool Name] /sys/bandit_statistics
[Original Tool Name] bandit_statistics
[Original Usage] bandit_statistics()
[Description] Returns statistical information that might be useful for tuning the bandit system.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_statistics", {})
### Sub-tool: /sys/workflow_new
[Sub-tool Name] /sys/workflow_new
[Original Tool Name] workflow_new
[Original Usage] workflow_new(title, bandits, pin)
[Description] Defines a new workflow and returns its workflow_id.
Args:
title: The name of the workflow.
bandits: A list of bandits to register.
pin: Security flags: 'w' for read-only (unwritable), 'd' for undeletable, 'wd' for both, '' for writable and deletable.
Each bandit is represented as a dictionary `b`:
- `b['tool_name']`: Same as tool_name in bandit_schedule.
- `b['exec_mode']`: Same as exec_mode in bandit_schedule.
- `b['aux_prompt']`: Same as aux_prompt in bandit_schedule.
- `b['prob']`: Same as prob in bandit_schedule.
- `b['times']`: Same as times in bandit_schedule.
- `b['arg']`: If `b['tool_name']` is 'memory_read', specify a memory_id (memory:...). If 'workflow_do', specify a workflow_id (workflow:...).
- `b['pin']`: If None, deletable or prob/times can be updated. If 'stack', prob/times can be updated. If 'write', cannot be updated at all.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/workflow_new", {"title": ..., "bandits": ..., "pin": ...})
### Sub-tool: /sys/workflow_list
[Sub-tool Name] /sys/workflow_list
[Original Tool Name] workflow_list
[Original Usage] workflow_list()
[Description] Lists all registered workflows including IDs, titles, and pin status.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/workflow_list", {})
### Sub-tool: /sys/workflow_show_current
[Sub-tool Name] /sys/workflow_show_current
[Original Tool Name] workflow_show_current
[Original Usage] workflow_show_current()
[Description] Displays information about the currently active workflow and active bandit enforcement.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/workflow_show_current", {})
### Sub-tool: /sys/workflow_delete
[Sub-tool Name] /sys/workflow_delete
[Original Tool Name] workflow_delete
[Original Usage] workflow_delete(workflow_id)
[Description] Deletes a workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/workflow_delete", {"workflow_id": ...})
### Sub-tool: /sys/bandit_schedule_workflow
[Sub-tool Name] /sys/bandit_schedule_workflow
[Original Tool Name] bandit_schedule_workflow
[Original Usage] bandit_schedule_workflow(workflow_id_to_schedule, times, prob, exec_mode, workflow_id)
[Description] Schedules a bandit to enforce the execution of another workflow.
Args:
workflow_id_to_schedule: Workflow ID to workflow_do.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow to register.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_schedule_workflow", {"workflow_id_to_schedule": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})
### Sub-tool: /sys/bandit_schedule_subtool_show
[Sub-tool Name] /sys/bandit_schedule_subtool_show
[Original Tool Name] bandit_schedule_subtool_show
[Original Usage] bandit_schedule_subtool_show(subtool_name, times, prob, exec_mode, workflow_id)
[Description] Schedules a bandit to enforce subtool_show for a specific tool path.
Args:
subtool_name: Sub-tool Name to subtool_show.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_schedule_subtool_show", {"subtool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})
In [ ]:
print(play.create_tool_skill("/sys/bandit_schedule"))
---
name: /sys/bandit_schedule
description: bandit_schedule
allowed-tools: No special permission is required to use this sub-tool.
---
[Sub-tool Name] /sys/bandit_schedule
[Original Tool Name] bandit_schedule
[Original Usage] bandit_schedule(tool_name, times, prob, exec_mode, aux_prompt, workflow_id)
[Description] Schedules a 'bandit' to enforce the use of specific tools.
Args:
tool_name: Name(s) of the tool(s) to enforce. Can use " OR " for multiple tools.
times: Number of times to add this entry. Set to 0 to remove.
prob: Probability of execution per turn.
exec_mode: "once" or "persistent".
aux_prompt: Additional instructions for execution.
workflow_id: The target workflow.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/sys/bandit_schedule", {"tool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "aux_prompt": ..., "workflow_id": ...})
Available [in the current context].
Actually, I ran into a type error this time, and it was quite difficult to get the sub-agent's set_result to pass. I'll verify that the sub-agent can be called properly.
In [ ]:
app = play._create_agent('default_tools')
config = {"configurable": {"thread_id": "1"}}
for chunk, metadata in app.stream({"messages": play.messages + [HumanMessage("Simply try using imagine_keywords.")]},
config=config, stream_mode="messages"):
print(chunk)
if isinstance(chunk, ToolMessage) and chunk.name == "imagine_keywords":
break
content=[{'type': 'text', 'text': '## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 1467, 'output_tokens': 674, 'total_tokens': 2141, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 654}} tool_call_chunks=[]
content=[{'type': 'text', 'text': " starting point 'Ｓ' at (4, 1) to the goal 'Ｇ' at (7, 8).", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': " The AI must manage unknown map locations ('？') at (1, 4) and (7, 4) using a", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' surrounding map tool and maintain a structured Plan/Policy through memory management.\n\n## SUMMARY\nThe AI identified the map layout,', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' coordinates for the start, goal, and unknowns. \n- **Start (Ｓ):** (4, 1)\n-', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' **Goal (Ｇ):** (7, 8)\n- **Unknowns (？):** (1,', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' 4) and (7, 4)\nThe AI attempted to access external memory (IDs 9999, ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': '9998, 9997) to retrieve map details and procedures. The user has explicitly instructed the AI to read', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' `memory:9997` to proceed. Currently, the AI is at the starting position (4, 1', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '), and the current Plan/Policy/Scratchpad are unset.\n\n## ARTIFACTS\n- **Full', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' Map Layout (9x10):**\n ```\n ■■■■■■■■■ (y=0', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': ')\n ■■■■Ｓ■■■■ (y=1)\n ■□□□□', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '□□□■ (y=2)\n ■□■■■■■□■ (y=3', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ')\n ■？■■■■■？■ (y=4)\n ■□■■■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■ (y=5)\n ■□■■■□□□■ (y=6)\n ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■□□□□□■□■ (y=7)\n ■■■■■■■Ｇ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■ (y=8)\n ■■■■■■■■■ (y=9)\n ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '```\n- **Memory IDs:** 9999 (Full Map), 9998 (Procedures', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '), 9997 (Procedures).\n\n## NEXT STEPS\n1. Execute `memory_read', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 23, 'output_token_details': {'reasoning': 0}, 'output_tokens': 23} tool_call_chunks=[]
content=[{'type': 'text', 'text': '` for `memory:9997` as requested by the user.\n2. Retrieve information from `memory:', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '9998` and `memory:9999`.\n3. Initialize the "Current Policy/Plan', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '" and "Scratchpad" based on retrieved procedures.\n4. Begin navigation from (4, 1) toward (', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': '7, 8), evaluating if unknown cells at (1, 4) or (7, 4) require', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' exploration.', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 2, 'output_token_details': {'reasoning': 0}, 'output_tokens': 2} tool_call_chunks=[]
content=[{'type': 'text', 'text': '', 'extras': {'signature': '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'}, 'index': 0}] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52a8-a16d-7d22-b7bd-628d80587cdd' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
content='' additional_kwargs={} response_metadata={} id='__remove_all__'
content='Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the starting point \'Ｓ\' at (4, 1) to the goal \'Ｇ\' at (7, 8). The AI must manage unknown map locations (\'？\') at (1, 4) and (7, 4) using a surrounding map tool and maintain a structured Plan/Policy through memory management.\n\n## SUMMARY\nThe AI identified the map layout, coordinates for the start, goal, and unknowns. \n- **Start (Ｓ):** (4, 1)\n- **Goal (Ｇ):** (7, 8)\n- **Unknowns (？):** (1, 4) and (7, 4)\nThe AI attempted to access external memory (IDs 9999, 9998, 9997) to retrieve map details and procedures. The user has explicitly instructed the AI to read `memory:9997` to proceed. Currently, the AI is at the starting position (4, 1), and the current Plan/Policy/Scratchpad are unset.\n\n## ARTIFACTS\n- **Full Map Layout (9x10):**\n ```\n ■■■■■■■■■ (y=0)\n ■■■■Ｓ■■■■ (y=1)\n ■□□□□□□□■ (y=2)\n ■□■■■■■□■ (y=3)\n ■？■■■■■？■ (y=4)\n ■□■■■■■■■ (y=5)\n ■□■■■□□□■ (y=6)\n ■□□□□□■□■ (y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■■■■■■■■■ (y=9)\n ```\n- **Memory IDs:** 9999 (Full Map), 9998 (Procedures), 9997 (Procedures).\n\n## NEXT STEPS\n1. Execute `memory_read` for `memory:9997` as requested by the user.\n2. Retrieve information from `memory:9998` and `memory:9999`.\n3. Initialize the "Current Policy/Plan" and "Scratchpad" based on retrieved procedures.\n4. Begin navigation from (4, 1) toward (7, 8), evaluating if unknown cells at (1, 4) or (7, 4) require exploration.' additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='d2701677-3dff-46c0-acc5-421edc45aae2'
content=[] additional_kwargs={'function_call': {'name': 'imagine_keywords', 'arguments': '{"thought": "I am navigating the maze from (4, 2) to (1, 2) and eventually to (1, 4) where an unknown \'?\' square exists. I need to be careful of possible obstacles or items like bears and swords mentioned in the map legend."}'}, '__gemini_function_call_thought_signatures__': {'c05fa808-2d66-4fa3-a2be-82df397d15c5': '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'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-bf09-79e0-840d-135b25e2946c' tool_calls=[{'name': 'imagine_keywords', 'args': {'thought': "I am navigating the maze from (4, 2) to (1, 2) and eventually to (1, 4) where an unknown '?' square exists. I need to be careful of possible obstacles or items like bears and swords mentioned in the map legend."}, 'id': 'c05fa808-2d66-4fa3-a2be-82df397d15c5', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 4331, 'output_tokens': 208, 'total_tokens': 4539, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 137}} tool_call_chunks=[{'name': 'imagine_keywords', 'args': '{"thought": "I am navigating the maze from (4, 2) to (1, 2) and eventually to (1, 4) where an unknown \'?\' square exists. I need to be careful of possible obstacles or items like bears and swords mentioned in the map legend."}', 'id': 'c05fa808-2d66-4fa3-a2be-82df397d15c5', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-bf09-79e0-840d-135b25e2946c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52a8-bf09-79e0-840d-135b25e2946c' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
USER_INPUT2: Generate multiple associated keywords with scores based on thought = "I am navigating the maze from (4, 2) to (1, 2) and eventually to (1, 4) where an unknown '?' square exists. I need to be careful of possible obstacles or items like bears and swords mentioned in the map legend.".
Use actual keywords existing in the system.
Return results using 'set_result'.
Status: 'error' or 'success'
Result: List of keyword tuples (string, score).
content=[] additional_kwargs={'function_call': {'name': 'read_all_keywords', 'arguments': '{}'}, '__gemini_function_call_thought_signatures__': {'9aab5ea5-2d75-46c1-bf27-75ab07f12138': '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'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-d326-74a3-b3aa-073a5a38684d' tool_calls=[{'name': 'read_all_keywords', 'args': {}, 'id': '9aab5ea5-2d75-46c1-bf27-75ab07f12138', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 368, 'output_tokens': 189, 'total_tokens': 557, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 177}} tool_call_chunks=[{'name': 'read_all_keywords', 'args': '{}', 'id': '9aab5ea5-2d75-46c1-bf27-75ab07f12138', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-d326-74a3-b3aa-073a5a38684d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52a8-d326-74a3-b3aa-073a5a38684d' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
Tool2(read_all_keywords): Retrieving all keywords...
Tool result 2(read_all_keywords): '{"status": "success", "result": ["... : Specify keywords related to that me...'
content='{"status": "success", "result": ["... : Specify keywords related to that memory.", "yokai", "...\' sentence to them using \'memory_append_string\'.", "Dragon", "Bear", "Sword", "Dead end"]}' name='read_all_keywords' id='5eef9c20-0558-47e2-a57a-7517ab7bb13e' tool_call_id='9aab5ea5-2d75-46c1-bf27-75ab07f12138'
content=[] additional_kwargs={'function_call': {'name': 'set_result', 'arguments': '{"status": "success", "res": [["Bear", 0.95], ["Sword", 0.95], ["Dead end", 0.7], ["yokai", 0.4], ["Dragon", 0.4]]}'}, '__gemini_function_call_thought_signatures__': {'e91db3b2-d91f-489e-b9e9-c98884975946': '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'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-d98f-7d22-b6e2-9df8c0aa0f46' tool_calls=[{'name': 'set_result', 'args': {'status': 'success', 'res': [['Bear', 0.95], ['Sword', 0.95], ['Dead end', 0.7], ['yokai', 0.4], ['Dragon', 0.4]]}, 'id': 'e91db3b2-d91f-489e-b9e9-c98884975946', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 448, 'output_tokens': 436, 'total_tokens': 884, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 372}} tool_call_chunks=[{'name': 'set_result', 'args': '{"status": "success", "res": [["Bear", 0.95], ["Sword", 0.95], ["Dead end", 0.7], ["yokai", 0.4], ["Dragon", 0.4]]}', 'id': 'e91db3b2-d91f-489e-b9e9-c98884975946', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a8-d98f-7d22-b6e2-9df8c0aa0f46' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 177, 'input_token_details': {'cache_read': 0}, 'total_tokens': 177, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52a8-d98f-7d22-b6e2-9df8c0aa0f46' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
Tool2(set_result): status: 'success', result: [['Bear', 0.95], ['Sword', 0.95], ['Dead end', 0.7], ['yokai', 0.4], ['Drago...]
Tool result 2(set_result): 'null'
content='null' name='set_result' id='426b3a0a-d182-465e-9480-4461a755fcf2' tool_call_id='e91db3b2-d91f-489e-b9e9-c98884975946'
Sub-Agent response: null
content='[["Bear", 0.95], ["Sword", 0.95], ["Dead end", 0.7], ["yokai", 0.4], ["Dragon", 0.4]]' name='imagine_keywords' id='63068d4c-ec72-4c66-840f-5e85a74a1296' tool_call_id='c05fa808-2d66-4fa3-a2be-82df397d15c5'
I'll try using /sys/bandit_statistics as an example of a sub-tool.
In [ ]:
app = play._create_agent('default_tools')
config = {"configurable": {"thread_id": "1"}}
for chunk, metadata in app.stream({"messages": play.messages + [HumanMessage("Simply try using /sys/bandit_statistics .")]},
config=config, stream_mode="messages"):
print(chunk)
if isinstance(chunk, ToolMessage) and chunk.name == "subtool_do":
break
content=[{'type': 'text', 'text': '## SESSION INTENT\nThe primary goal is to navigate a 9x1', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 1467, 'output_tokens': 648, 'total_tokens': 2115, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 632}} tool_call_chunks=[]
content=[{'type': 'text', 'text': "0 maze from the start position 'Ｓ' at (4, 1) to the goal 'Ｇ' at (7,", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': " 8). The AI must manage unknown map locations ('？') at (1, 4) and (7, ", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '4) using a surrounding map tool and maintain a consistent navigation policy and plan using designated memory IDs.\n\n## SUMMARY\nThe', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' AI has identified the core spatial parameters of the maze and the coordinates for the start, goal, and unknown squares. \n', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '- **Coordinates:** Start is (4, 1), Goal is (7, 8). Unknowns are at (1,', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' 4) and (7, 4).\n- **Current State:** The AI is currently at (4, 1', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': '). The goal has not been reached.\n- **Action History:** The AI attempted to retrieve map data and procedures from memory (9', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': '999, 9998, 9997). The user has explicitly prompted the AI to use', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' `memory_read` to access `memory:9997`.\n- **Strategy:** The AI will use a combination', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' of a "Full Map" for pathfinding and a "Surrounding Map" to resolve unknown squares. Memory slots are used', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' to maintain state across interactions.\n\n## ARTIFACTS\n- **Full Map Layout:**\n ```\n ■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■■■■■ (y=0)\n ■■■■Ｓ■■■■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' (y=1)\n ■□□□□□□□■ (y=2)\n ■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '□■■■■■□■ (y=3)\n ■？■■■■■？■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' (y=4)\n ■□■■■■■■■ (y=5)\n ■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '□■■■□□□■ (y=6)\n ■□□□□□■□■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' (y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■■■■■ (y=9)\n ```\n- **Current Player Surrounding Map', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' (at 4, 1):**\n ```\n ■■■■■\n ■■■■■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': '\n ■■▼■■\n □□□□□\n ■■■■■\n ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 23, 'output_token_details': {'reasoning': 0}, 'output_tokens': 23} tool_call_chunks=[]
content=[{'type': 'text', 'text': '```\n- **Memory References:** `memory:9999` (Map info), `memory:9998', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': '` (Procedures), `memory:9997` (Procedures).\n\n## NEXT STEPS\n1', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '. Execute `memory_read` for `memory:9997` as requested by the user.\n2. ', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': 'Retrieve information from `memory:9999` and `memory:9998` to establish the navigation "', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': 'Plan" and "Policy".\n3. Analyze the path from (4, 1) to (7,', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' 8) and determine if the unknowns at (1, 4) or (7, 4) obstruct the primary', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' route.\n4. Begin movement toward the goal.', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-682e-7653-9aba-980de71c221c' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 12, 'output_token_details': {'reasoning': 0}, 'output_tokens': 12} tool_call_chunks=[]
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content='' additional_kwargs={} response_metadata={} id='__remove_all__'
content='Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the start position \'Ｓ\' at (4, 1) to the goal \'Ｇ\' at (7, 8). The AI must manage unknown map locations (\'？\') at (1, 4) and (7, 4) using a surrounding map tool and maintain a consistent navigation policy and plan using designated memory IDs.\n\n## SUMMARY\nThe AI has identified the core spatial parameters of the maze and the coordinates for the start, goal, and unknown squares. \n- **Coordinates:** Start is (4, 1), Goal is (7, 8). Unknowns are at (1, 4) and (7, 4).\n- **Current State:** The AI is currently at (4, 1). The goal has not been reached.\n- **Action History:** The AI attempted to retrieve map data and procedures from memory (9999, 9998, 9997). The user has explicitly prompted the AI to use `memory_read` to access `memory:9997`.\n- **Strategy:** The AI will use a combination of a "Full Map" for pathfinding and a "Surrounding Map" to resolve unknown squares. Memory slots are used to maintain state across interactions.\n\n## ARTIFACTS\n- **Full Map Layout:**\n ```\n ■■■■■■■■■ (y=0)\n ■■■■Ｓ■■■■ (y=1)\n ■□□□□□□□■ (y=2)\n ■□■■■■■□■ (y=3)\n ■？■■■■■？■ (y=4)\n ■□■■■■■■■ (y=5)\n ■□■■■□□□■ (y=6)\n ■□□□□□■□■ (y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■■■■■■■■■ (y=9)\n ```\n- **Current Player Surrounding Map (at 4, 1):**\n ```\n ■■■■■\n ■■■■■\n ■■▼■■\n □□□□□\n ■■■■■\n ```\n- **Memory References:** `memory:9999` (Map info), `memory:9998` (Procedures), `memory:9997` (Procedures).\n\n## NEXT STEPS\n1. Execute `memory_read` for `memory:9997` as requested by the user.\n2. Retrieve information from `memory:9999` and `memory:9998` to establish the navigation "Plan" and "Policy".\n3. Analyze the path from (4, 1) to (7, 8) and determine if the unknowns at (1, 4) or (7, 4) obstruct the primary route.\n4. Begin movement toward the goal.' additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='1139ab43-14e1-4f93-b9b7-0dc83ce056ca'
content=[] additional_kwargs={'function_call': {'name': 'subtool_show', 'arguments': '{"subtool_name": "/sys"}'}, '__gemini_function_call_thought_signatures__': {'4ddb54f9-7113-4491-97d7-76a53caf5f89': 'EjQKMgG+Pvb7I+lWeC19aLHS9bISQj5CFcQ3docB8MRdLCmD5+99asTdz1PIDzc1UyEcdI/w'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-8717-7f40-b183-6dd9ae683c54' tool_calls=[{'name': 'subtool_show', 'args': {'subtool_name': '/sys'}, 'id': '4ddb54f9-7113-4491-97d7-76a53caf5f89', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 4440, 'output_tokens': 21, 'total_tokens': 4461, 'input_token_details': {'cache_read': 0}} tool_call_chunks=[{'name': 'subtool_show', 'args': '{"subtool_name": "/sys"}', 'id': '4ddb54f9-7113-4491-97d7-76a53caf5f89', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={'function_call': {'name': 'subtool_do', 'arguments': '{"subtool_name": "/sys/bandit_statistics", "args_dict": {}}'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-8717-7f40-b183-6dd9ae683c54' tool_calls=[{'name': 'subtool_do', 'args': {'subtool_name': '/sys/bandit_statistics', 'args_dict': {}}, 'id': 'f86c94a9-4eb5-4aad-8848-9c081518ea39', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 30, 'output_tokens': 30} tool_call_chunks=[{'name': 'subtool_do', 'args': '{"subtool_name": "/sys/bandit_statistics", "args_dict": {}}', 'id': 'f86c94a9-4eb5-4aad-8848-9c081518ea39', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52a9-8717-7f40-b183-6dd9ae683c54' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52a9-8717-7f40-b183-6dd9ae683c54' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
content='---\nname: /sys\ndescription: Essential system sub-tools.\nallowed-tools: No special permission is required to use this sub-skill.\n---\nA collection of foundational sub-tools for system management, \nworkflow orchestration, and bandit scheduling.\n\n## Sub-tools\n\n\n### Sub-tool: /sys/update_core\n\n[Sub-tool Name] /sys/update_core\n[Original Tool Name] update_core\n[Original Usage] update_core(new_core)\n[Description] Updates the core context.\nThe core context contains critical information (like required memory_read or subtool_show targets) \nthat should be remembered even after context truncation or summarization.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/update_core", {"new_core": ...})\n\n### Sub-tool: /sys/show_core\n\n[Sub-tool Name] /sys/show_core\n[Original Tool Name] show_core\n[Original Usage] show_core()\n[Description] Returns the current core context.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/show_core", {})\n\n### Sub-tool: /sys/update_plan\n\n[Sub-tool Name] /sys/update_plan\n[Original Tool Name] update_plan\n[Original Usage] update_plan(new_plan)\n[Description] Updates the player\'s current plan and policy.\nProvide the new plan/policy string to be displayed.\nStructure it so that another agent can easily follow the strategy.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/update_plan", {"new_plan": ...})\n\n### Sub-tool: /sys/show_plan\n\n[Sub-tool Name] /sys/show_plan\n[Original Tool Name] show_plan\n[Original Usage] show_plan()\n[Description] Returns the player\'s current plan and policy.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/show_plan", {})\n\n### Sub-tool: /sys/bandit_schedule\n\n[Sub-tool Name] /sys/bandit_schedule\n[Original Tool Name] bandit_schedule\n[Original Usage] bandit_schedule(tool_name, times, prob, exec_mode, aux_prompt, workflow_id)\n[Description] Schedules a \'bandit\' to enforce the use of specific tools.\nArgs:\n tool_name: Name(s) of the tool(s) to enforce. Can use " OR " for multiple tools.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule", {"tool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "aux_prompt": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_memory_read\n\n[Sub-tool Name] /sys/bandit_schedule_memory_read\n[Original Tool Name] bandit_schedule_memory_read\n[Original Usage] bandit_schedule_memory_read(memory_id, times, prob, exec_mode, workflow_id)\n[Description] Specialized bandit for enforcing memory_read on a specific memory_id.\n\nArgs:\n memory_id: Memory ID to memory_read.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_memory_read", {"memory_id": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_list\n\n[Sub-tool Name] /sys/bandit_list\n[Original Tool Name] bandit_list\n[Original Usage] bandit_list(workflow_id)\n[Description] Returns the current stack of registered bandits for a workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_list", {"workflow_id": ...})\n\n### Sub-tool: /sys/bandit_statistics\n\n[Sub-tool Name] /sys/bandit_statistics\n[Original Tool Name] bandit_statistics\n[Original Usage] bandit_statistics()\n[Description] Returns statistical information that might be useful for tuning the bandit system.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_statistics", {})\n\n### Sub-tool: /sys/workflow_new\n\n[Sub-tool Name] /sys/workflow_new\n[Original Tool Name] workflow_new\n[Original Usage] workflow_new(title, bandits, pin)\n[Description] Defines a new workflow and returns its workflow_id.\n\nArgs:\n title: The name of the workflow.\n bandits: A list of bandits to register.\n pin: Security flags: \'w\' for read-only (unwritable), \'d\' for undeletable, \'wd\' for both, \'\' for writable and deletable.\n\nEach bandit is represented as a dictionary `b`:\n- `b[\'tool_name\']`: Same as tool_name in bandit_schedule.\n- `b[\'exec_mode\']`: Same as exec_mode in bandit_schedule.\n- `b[\'aux_prompt\']`: Same as aux_prompt in bandit_schedule.\n- `b[\'prob\']`: Same as prob in bandit_schedule.\n- `b[\'times\']`: Same as times in bandit_schedule.\n- `b[\'arg\']`: If `b[\'tool_name\']` is \'memory_read\', specify a memory_id (memory:...). If \'workflow_do\', specify a workflow_id (workflow:...).\n- `b[\'pin\']`: If None, deletable or prob/times can be updated. If \'stack\', prob/times can be updated. If \'write\', cannot be updated at all.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_new", {"title": ..., "bandits": ..., "pin": ...})\n\n### Sub-tool: /sys/workflow_list\n\n[Sub-tool Name] /sys/workflow_list\n[Original Tool Name] workflow_list\n[Original Usage] workflow_list()\n[Description] Lists all registered workflows including IDs, titles, and pin status.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_list", {})\n\n### Sub-tool: /sys/workflow_show_current\n\n[Sub-tool Name] /sys/workflow_show_current\n[Original Tool Name] workflow_show_current\n[Original Usage] workflow_show_current()\n[Description] Displays information about the currently active workflow and active bandit enforcement.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_show_current", {})\n\n### Sub-tool: /sys/workflow_delete\n\n[Sub-tool Name] /sys/workflow_delete\n[Original Tool Name] workflow_delete\n[Original Usage] workflow_delete(workflow_id)\n[Description] Deletes a workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_delete", {"workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_workflow\n\n[Sub-tool Name] /sys/bandit_schedule_workflow\n[Original Tool Name] bandit_schedule_workflow\n[Original Usage] bandit_schedule_workflow(workflow_id_to_schedule, times, prob, exec_mode, workflow_id)\n[Description] Schedules a bandit to enforce the execution of another workflow.\n\nArgs:\n workflow_id_to_schedule: Workflow ID to workflow_do.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow to register.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_workflow", {"workflow_id_to_schedule": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_subtool_show\n\n[Sub-tool Name] /sys/bandit_schedule_subtool_show\n[Original Tool Name] bandit_schedule_subtool_show\n[Original Usage] bandit_schedule_subtool_show(subtool_name, times, prob, exec_mode, workflow_id)\n[Description] Schedules a bandit to enforce subtool_show for a specific tool path.\n\nArgs:\n subtool_name: Sub-tool Name to subtool_show.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_subtool_show", {"subtool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n' name='subtool_show' id='d8fa2222-0a4d-4fba-9942-c783a465a28f' tool_call_id='4ddb54f9-7113-4491-97d7-76a53caf5f89'
content='Was the previous command successful: 1.0\nVariance of last 10 memory reads: 0.23653297793306716\nTotal memory variance: 0.33843802374952964\nMean access count of bottom 50% memories: 0.0\n' name='subtool_do' id='fbde3ec8-f96e-46e8-9f63-16c9ccc57325' tool_call_id='f86c94a9-4eb5-4aad-8848-9c081518ea39'
In order to execute workflow:1000 quickly, I'll change its probability.
In [ ]:
app = play._create_agent('default_tools')
config = {"configurable": {"thread_id": "1"}}
for chunk, metadata in app.stream({"messages": play.messages + [HumanMessage("Investigate various details using subtool_show, then subsequently use /sys/bandit_schedule_workflow for workflow:1000 (persistent) within workflow:main and set its prob to 0.2.")]},
config=config, stream_mode="messages"):
print(chunk)
if isinstance(chunk, ToolMessage) and chunk.name == "subtool_do":
break
content=[{'type': 'text', 'text': '## SESSION INTENT\nThe primary goal is to navigate', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 1467, 'output_tokens': 938, 'total_tokens': 2405, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 927}} tool_call_chunks=[]
content=[{'type': 'text', 'text': " a 9x10 maze from the start 'Ｓ' at (4, 1) to the goal 'Ｇ'", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' at (7, 8). The AI must use a provided full map and a surrounding map tool to handle unknown locations', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': " ('？') and reach the destination efficiently.\n\n## SUMMARY\nThe AI has initialized the task and identified the coordinate system and key locations", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 27, 'output_token_details': {'reasoning': 0}, 'output_tokens': 27} tool_call_chunks=[]
content=[{'type': 'text', 'text': '. \n- **Coordinates:** Start (4, 1), Goal (7, 8), Unknowns at (1', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ', 4) and (7, 4).\n- **Environment:** 9x10 grid (Width x Height', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ').\n- **Current Status:** The AI is currently at the start position (4, 1). A surrounding map check shows', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': " walls ('■') to the north and open paths ('□') to the south.\n- **Decision Making:** The AI attempted", 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' to retrieve procedures and map data from specific memory IDs (9999, 9998, 9', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '997). The user has explicitly instructed the AI to use `memory_read` to access `memory:9', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '997`.\n\n## ARTIFACTS\n- **Full Map Layout:**\n ```\n ■■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■■■■ (y=0)\n ■■■■Ｓ■■■■ (', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': 'y=1)\n ■□□□□□□□■ (y=2)\n ■□', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■■□■ (y=3)\n ■？■■■■■？■ (', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': 'y=4)\n ■□■■■■■■■ (y=5)\n ■□', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■□□□■ (y=6)\n ■□□□□□■□■ (', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': 'y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■■', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '■■■■■■■ (y=9)\n ```\n- **Memory IDs:** 99', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '99 (Full Map), 9998 (Procedures), 9997 (Procedures).', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': '\n\n## NEXT STEPS\n1. Use the `memory_read` tool to access `memory:9997', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 25, 'output_token_details': {'reasoning': 0}, 'output_tokens': 25} tool_call_chunks=[]
content=[{'type': 'text', 'text': '` as requested.\n2. Retrieve and confirm information from `memory:9998` and `memory:99', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 26, 'output_token_details': {'reasoning': 0}, 'output_tokens': 26} tool_call_chunks=[]
content=[{'type': 'text', 'text': '99`.\n3. Plan a path from (4, 1) to (7, 8), determining', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' if exploration of (1, 4) or (7, 4) is necessary.\n4. Execute movement', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 24, 'output_token_details': {'reasoning': 0}, 'output_tokens': 24} tool_call_chunks=[]
content=[{'type': 'text', 'text': ' commands toward the goal.', 'index': 0}] additional_kwargs={} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 5, 'output_token_details': {'reasoning': 0}, 'output_tokens': 5} tool_call_chunks=[]
content=[{'type': 'text', 'text': '', 'extras': {'signature': 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'index': 0}] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52ab-1692-71a2-93f0-d299e4614a9d' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
content='' additional_kwargs={} response_metadata={} id='__remove_all__'
content="Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the start 'Ｓ' at (4, 1) to the goal 'Ｇ' at (7, 8). The AI must use a provided full map and a surrounding map tool to handle unknown locations ('？') and reach the destination efficiently.\n\n## SUMMARY\nThe AI has initialized the task and identified the coordinate system and key locations. \n- **Coordinates:** Start (4, 1), Goal (7, 8), Unknowns at (1, 4) and (7, 4).\n- **Environment:** 9x10 grid (Width x Height).\n- **Current Status:** The AI is currently at the start position (4, 1). A surrounding map check shows walls ('■') to the north and open paths ('□') to the south.\n- **Decision Making:** The AI attempted to retrieve procedures and map data from specific memory IDs (9999, 9998, 9997). The user has explicitly instructed the AI to use `memory_read` to access `memory:9997`.\n\n## ARTIFACTS\n- **Full Map Layout:**\n ```\n ■■■■■■■■■ (y=0)\n ■■■■Ｓ■■■■ (y=1)\n ■□□□□□□□■ (y=2)\n ■□■■■■■□■ (y=3)\n ■？■■■■■？■ (y=4)\n ■□■■■■■■■ (y=5)\n ■□■■■□□□■ (y=6)\n ■□□□□□■□■ (y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■■■■■■■■■ (y=9)\n ```\n- **Memory IDs:** 9999 (Full Map), 9998 (Procedures), 9997 (Procedures).\n\n## NEXT STEPS\n1. Use the `memory_read` tool to access `memory:9997` as requested.\n2. Retrieve and confirm information from `memory:9998` and `memory:9999`.\n3. Plan a path from (4, 1) to (7, 8), determining if exploration of (1, 4) or (7, 4) is necessary.\n4. Execute movement commands toward the goal." additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='a14a9908-c530-4e00-a42e-2fd69de73ff4'
content=[] additional_kwargs={'function_call': {'name': 'subtool_show', 'arguments': '{"subtool_name": "/sys"}'}, '__gemini_function_call_thought_signatures__': {'280c521f-dd1c-447e-b4c4-5040145c4d8e': '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'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-380e-7691-a605-cc440b77650f' tool_calls=[{'name': 'subtool_show', 'args': {'subtool_name': '/sys'}, 'id': '280c521f-dd1c-447e-b4c4-5040145c4d8e', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 4357, 'output_tokens': 163, 'total_tokens': 4520, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 142}} tool_call_chunks=[{'name': 'subtool_show', 'args': '{"subtool_name": "/sys"}', 'id': '280c521f-dd1c-447e-b4c4-5040145c4d8e', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-380e-7691-a605-cc440b77650f' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 0, 'input_token_details': {'cache_read': 0}, 'total_tokens': 0, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52ab-380e-7691-a605-cc440b77650f' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
content='---\nname: /sys\ndescription: Essential system sub-tools.\nallowed-tools: No special permission is required to use this sub-skill.\n---\nA collection of foundational sub-tools for system management, \nworkflow orchestration, and bandit scheduling.\n\n## Sub-tools\n\n\n### Sub-tool: /sys/update_core\n\n[Sub-tool Name] /sys/update_core\n[Original Tool Name] update_core\n[Original Usage] update_core(new_core)\n[Description] Updates the core context.\nThe core context contains critical information (like required memory_read or subtool_show targets) \nthat should be remembered even after context truncation or summarization.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/update_core", {"new_core": ...})\n\n### Sub-tool: /sys/show_core\n\n[Sub-tool Name] /sys/show_core\n[Original Tool Name] show_core\n[Original Usage] show_core()\n[Description] Returns the current core context.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/show_core", {})\n\n### Sub-tool: /sys/update_plan\n\n[Sub-tool Name] /sys/update_plan\n[Original Tool Name] update_plan\n[Original Usage] update_plan(new_plan)\n[Description] Updates the player\'s current plan and policy.\nProvide the new plan/policy string to be displayed.\nStructure it so that another agent can easily follow the strategy.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/update_plan", {"new_plan": ...})\n\n### Sub-tool: /sys/show_plan\n\n[Sub-tool Name] /sys/show_plan\n[Original Tool Name] show_plan\n[Original Usage] show_plan()\n[Description] Returns the player\'s current plan and policy.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/show_plan", {})\n\n### Sub-tool: /sys/bandit_schedule\n\n[Sub-tool Name] /sys/bandit_schedule\n[Original Tool Name] bandit_schedule\n[Original Usage] bandit_schedule(tool_name, times, prob, exec_mode, aux_prompt, workflow_id)\n[Description] Schedules a \'bandit\' to enforce the use of specific tools.\nArgs:\n tool_name: Name(s) of the tool(s) to enforce. Can use " OR " for multiple tools.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule", {"tool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "aux_prompt": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_memory_read\n\n[Sub-tool Name] /sys/bandit_schedule_memory_read\n[Original Tool Name] bandit_schedule_memory_read\n[Original Usage] bandit_schedule_memory_read(memory_id, times, prob, exec_mode, workflow_id)\n[Description] Specialized bandit for enforcing memory_read on a specific memory_id.\n\nArgs:\n memory_id: Memory ID to memory_read.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_memory_read", {"memory_id": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_list\n\n[Sub-tool Name] /sys/bandit_list\n[Original Tool Name] bandit_list\n[Original Usage] bandit_list(workflow_id)\n[Description] Returns the current stack of registered bandits for a workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_list", {"workflow_id": ...})\n\n### Sub-tool: /sys/bandit_statistics\n\n[Sub-tool Name] /sys/bandit_statistics\n[Original Tool Name] bandit_statistics\n[Original Usage] bandit_statistics()\n[Description] Returns statistical information that might be useful for tuning the bandit system.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_statistics", {})\n\n### Sub-tool: /sys/workflow_new\n\n[Sub-tool Name] /sys/workflow_new\n[Original Tool Name] workflow_new\n[Original Usage] workflow_new(title, bandits, pin)\n[Description] Defines a new workflow and returns its workflow_id.\n\nArgs:\n title: The name of the workflow.\n bandits: A list of bandits to register.\n pin: Security flags: \'w\' for read-only (unwritable), \'d\' for undeletable, \'wd\' for both, \'\' for writable and deletable.\n\nEach bandit is represented as a dictionary `b`:\n- `b[\'tool_name\']`: Same as tool_name in bandit_schedule.\n- `b[\'exec_mode\']`: Same as exec_mode in bandit_schedule.\n- `b[\'aux_prompt\']`: Same as aux_prompt in bandit_schedule.\n- `b[\'prob\']`: Same as prob in bandit_schedule.\n- `b[\'times\']`: Same as times in bandit_schedule.\n- `b[\'arg\']`: If `b[\'tool_name\']` is \'memory_read\', specify a memory_id (memory:...). If \'workflow_do\', specify a workflow_id (workflow:...).\n- `b[\'pin\']`: If None, deletable or prob/times can be updated. If \'stack\', prob/times can be updated. If \'write\', cannot be updated at all.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_new", {"title": ..., "bandits": ..., "pin": ...})\n\n### Sub-tool: /sys/workflow_list\n\n[Sub-tool Name] /sys/workflow_list\n[Original Tool Name] workflow_list\n[Original Usage] workflow_list()\n[Description] Lists all registered workflows including IDs, titles, and pin status.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_list", {})\n\n### Sub-tool: /sys/workflow_show_current\n\n[Sub-tool Name] /sys/workflow_show_current\n[Original Tool Name] workflow_show_current\n[Original Usage] workflow_show_current()\n[Description] Displays information about the currently active workflow and active bandit enforcement.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_show_current", {})\n\n### Sub-tool: /sys/workflow_delete\n\n[Sub-tool Name] /sys/workflow_delete\n[Original Tool Name] workflow_delete\n[Original Usage] workflow_delete(workflow_id)\n[Description] Deletes a workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/workflow_delete", {"workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_workflow\n\n[Sub-tool Name] /sys/bandit_schedule_workflow\n[Original Tool Name] bandit_schedule_workflow\n[Original Usage] bandit_schedule_workflow(workflow_id_to_schedule, times, prob, exec_mode, workflow_id)\n[Description] Schedules a bandit to enforce the execution of another workflow.\n\nArgs:\n workflow_id_to_schedule: Workflow ID to workflow_do.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow to register.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_workflow", {"workflow_id_to_schedule": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n\n### Sub-tool: /sys/bandit_schedule_subtool_show\n\n[Sub-tool Name] /sys/bandit_schedule_subtool_show\n[Original Tool Name] bandit_schedule_subtool_show\n[Original Usage] bandit_schedule_subtool_show(subtool_name, times, prob, exec_mode, workflow_id)\n[Description] Schedules a bandit to enforce subtool_show for a specific tool path.\n\nArgs:\n subtool_name: Sub-tool Name to subtool_show.\n times: Number of times to add this entry. Set to 0 to remove.\n prob: Probability of execution per turn.\n exec_mode: "once" or "persistent".\n aux_prompt: Additional instructions for execution.\n workflow_id: The target workflow.\n\n*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*\n[Correct Usage] subtool_do("/sys/bandit_schedule_subtool_show", {"subtool_name": ..., "times": ..., "prob": ..., "exec_mode": ..., "workflow_id": ...})\n' name='subtool_show' id='3ccdab59-a09f-4983-9bc5-c45bfd117b9d' tool_call_id='280c521f-dd1c-447e-b4c4-5040145c4d8e'
content=[] additional_kwargs={'function_call': {'name': 'subtool_do', 'arguments': '{"subtool_name": "/sys/bandit_schedule_workflow", "args_dict": {"times": 1, "prob": 0.2, "workflow_id_to_schedule": "workflow:1000", "workflow_id": "workflow:main", "exec_mode": "persistent"}}'}, '__gemini_function_call_thought_signatures__': {'1087619f-9524-49a9-b65b-70a4e0b4531e': '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'}} response_metadata={'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-3eb3-7172-841f-1da04a620a11' tool_calls=[{'name': 'subtool_do', 'args': {'subtool_name': '/sys/bandit_schedule_workflow', 'args_dict': {'times': 1, 'prob': 0.2, 'workflow_id_to_schedule': 'workflow:1000', 'workflow_id': 'workflow:main', 'exec_mode': 'persistent'}}, 'id': '1087619f-9524-49a9-b65b-70a4e0b4531e', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 6828, 'output_tokens': 204, 'total_tokens': 7032, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 128}} tool_call_chunks=[{'name': 'subtool_do', 'args': '{"subtool_name": "/sys/bandit_schedule_workflow", "args_dict": {"times": 1, "prob": 0.2, "workflow_id_to_schedule": "workflow:1000", "workflow_id": "workflow:main", "exec_mode": "persistent"}}', 'id': '1087619f-9524-49a9-b65b-70a4e0b4531e', 'index': None, 'type': 'tool_call_chunk'}]
content=[] additional_kwargs={} response_metadata={'finish_reason': 'STOP', 'model_name': 'gemini-3-flash-preview', 'safety_ratings': [], 'model_provider': 'google_genai'} id='lc_run--019c52ab-3eb3-7172-841f-1da04a620a11' tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 142, 'input_token_details': {'cache_read': 3969}, 'total_tokens': 142, 'output_token_details': {'reasoning': 0}, 'output_tokens': 0} tool_call_chunks=[]
content=[] additional_kwargs={} response_metadata={} id='lc_run--019c52ab-3eb3-7172-841f-1da04a620a11' tool_calls=[] invalid_tool_calls=[] tool_call_chunks=[] chunk_position='last'
Tool(bandit_schedule_workflow): {'tool_name': 'workflow_do', 'tools_name': 'default_tools', 'exec_mode': 'persistent', 'aux_prompt': '', 'arg': 'workflow:1000', 'prob': 0.2, 'times': 1, 'pin': 'stack'}
content='Success. Bandit updated.' name='subtool_do' id='3103281e-3a1f-4a46-9029-ee70e26f3595' tool_call_id='1087619f-9524-49a9-b65b-70a4e0b4531e'
Actually, before asking gemini-3-flash-preview to perform this experiment, I was using gemini-2.5-flash-lite, since cheaper models are preferable during testing. At that time, it kept failing at /sys/bandit_schedule_workflow, so I had no choice but to execute the following code myself. However, it seems gemini-3-flash-preview didn't need that; it successfully completed it above.
In [ ]:
play.tools["/sys/bandit_schedule_workflow"]["tool"].run({"workflow_id_to_schedule": "workflow:1000", "times": 1, "prob": 0.2, "exec_mode": "persistent"})
Tool(bandit_schedule_workflow): {'tool_name': 'workflow_do', 'tools_name': 'default_tools', 'exec_mode': 'persistent', 'aux_prompt': '', 'arg': 'workflow:1000', 'prob': 0.2, 'times': 1, 'pin': 'stack'}
Out[ ]:
'Success. Bandit updated.'
Checking if the schedule was changed correctly. It has been successfully updated.
In [ ]:
play.workflows["workflow:main"]
Out[ ]:
{'pin': 'de',
'stack': [{'tool_name': '/maze_game/command',
'tools_name': 'all_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': None,
'prob': 1.0,
'times': 1,
'pin': 'write'},
{'tool_name': 'memory_new',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': 'Please summarize the last few moves.',
'arg': None,
'prob': 0.1,
'times': 1,
'pin': 'stack'},
{'tool_name': 'memory_new OR memory_update_string OR memory_append_string',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': None,
'prob': 0.4,
'times': 1,
'pin': 'stack'},
{'tool_name': 'workflow_do',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': 'workflow:1000',
'prob': 0.2,
'times': 1,
'pin': 'stack'},
{'tool_name': 'memory_read',
'tools_name': 'default_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': None,
'prob': 0.5,
'times': 3,
'pin': 'stack'},
{'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': 'memory:9998',
'prob': 0.1,
'times': 1,
'pin': None},
{'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': 'memory:9997',
'prob': 0.1,
'times': 1,
'pin': None},
{'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': 'memory:9999',
'prob': 0.1,
'times': 1,
'pin': None},
{'tool_name': 'memory_read',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': 'memory:1002',
'prob': 0.1,
'times': 1,
'pin': None},
{'tool_name': 'subtool_show',
'tools_name': 'read_tools',
'exec_mode': 'persistent',
'aux_prompt': '',
'arg': '/maze_game',
'prob': 0.1,
'times': 1,
'pin': None}],
'title': 'Main'}
Let's try a simple step once more.
In [ ]:
play.step()
----------
USER_INPUT: (Turn 2)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■■Ｓ■■
□□▼□□
■■■■■
■■■■■
Player's current coordinates: (4, 2)
Items held: None
Previous action: move down
Previous result: At (4, 1), performing 'move down' -> Moved Down.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "The current position is (4, 1). The goal is at (7, 8).
I need to navigate through the maze.
Map layout from full map:
(4,1) S
(4,2) Path
(1,2)-(7,2) Path
(1,3) Path, (7,3) Path
(1,4) ?, (7,4) ?
(1,5) Path
(1,6) Path
(1,7)-(5,7) Path, (7,7) Path
(5,6)-(7,6) Path
(7,8) G
Potential path: (4,1)->(4,2)->(1,2)->(1,3)->(1,4)->(1,5)->(1,6)->(1,7)->(5,7)->(5,6)->(7,6)->(7,7)->(7,8).
I must read memory:9997 as requested.
I should also check subtools."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(express_thought): Thought expressed: "I am currently at (4, 2). My plan (stored in [memory:1003]) involves moving left towards (1, 2). I need to update the scratchpad with the current state and then move left. I will also read [memory:1003] to ensure I am following the plan correctly."
Tool(update_scratchpad): Scratchpad updated.: Current position: (4, 2).
Goal position: (7, 8).
Path: (4, 1) -> (4, 2) [Current] -> (1, 2) -> (1, 3) -> (1, 4) (?) -> (1, 5) -> (1, 6) -> (1, 7) -> (5, 7) -> (5, 6) -> (7, 6) -> (7, 7) -> (7, 8).
Next steps:
1. Move left to reach (1, 2).
2. Continue south.
The 'Plan and Policy' is stored in [memory:1003].
The 'Full Map' is in [memory:9999].
The 'Procedures' are in [memory:9998] and [memory:9997].
Tool result(express_thought): 'null'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: Error: '/maze_game/command' is not currently available for execution. Available tools vary depending on the context.
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(express_thought): Thought expressed: "I encountered an error saying `/maze_game/command` is not currently available. I need to re-check the available tools or wait for instructions. However, the user asked me to use `memory_read` with appropriate parameters. I will read [memory:1003] again to ensure I have the latest plan, and then I will check my surroundings to see if I can move or if the environment has changed. I'll also check [memory:9999] to confirm the map one more time. I should also verify the subtool availability again."
Tool result(express_thought): 'null'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: ---
name: /maze_game
description: Sub-tools for the maze exploration game.
allowed-tools: No special permission is required to use this sub-skill.
---
A collection of sub-tools used to navigate and interact with the maze environment.
## Sub-tools
### Sub-tool: /maze_game/get_surroundings
[Sub-tool Name] /maze_game/get_surroundings
[Original Tool Name] get_surroundings
[Original Usage] get_surroundings()
[Description] Returns the current surrounding map, position coordinates, and items held.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/maze_game/get_surroundings", {})
### Sub-tool: /maze_game/check_goal
[Sub-tool Name] /maze_game/check_goal
[Original Tool Name] check_goal
[Original Usage] check_goal()
[Description] Checks whether the player has reached the goal point.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/maze_game/check_goal", {})
### Sub-tool: /maze_game/command
[Sub-tool Name] /maze_game/command
[Original Tool Name] command
[Original Usage] command(action)
[Description] The player performs the action specified by 'action'.
Possible actions are: 'move up', 'move down', 'move left', 'move right', 'fight bear', 'pick up Item'.
This tool cannot be used unless explicitly instructed.
*Note: To execute this tool, do not call it directly. You must use subtool_do as shown below:*
[Correct Usage] subtool_do("/maze_game/command", {"action": ...})
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool(command): At (4, 2), performing 'move left' -> Moved Left.
Tool(update_scratchpad): Scratchpad updated.: Current position: (3, 2).
Goal position: (7, 8).
Path: (4, 1) -> (4, 2) -> (3, 2) [Current] -> (1, 2) -> (1, 3) -> (1, 4) (?) -> (1, 5) -> (1, 6) -> (1, 7) -> (5, 7) -> (5, 6) -> (7, 6) -> (7, 7) -> (7, 8).
Next steps:
1. Move left two more times to reach (1, 2).
2. Move down to (1, 3) then (1, 4).
Memory:1003 updated.
Tool result(/maze_game/command): "At (4, 2), performing 'move left' -> Moved Left."
Agent response: Scratchpad updated.
Out[ ]:
False
Taking the plunge and running a loop. To save on costs, I'll stop the process once workflow_do is executed.
In [ ]:
def check_workflow_do (messages):
for m in messages:
if isinstance(m, ToolMessage) \
and m.name == "workflow_do":
return True
if isinstance(m, HumanMessage) \
and "workflow" in m.content:
return True
return False
In [ ]:
while not play.step() and not check_workflow_do(play.messages):
print(f"Top Message:{play.messages[0]}")
pass
----------
USER_INPUT: (Turn 3)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■■■Ｓ■
□□▼□□
□■■■■
◎■■■■
Player's current coordinates: (3, 2)
Items held: None
Previous action: move left
Previous result: At (4, 2), performing 'move left' -> Moved Left.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (3, 2).
Goal position: (7, 8).
Path: (4, 1) -> (4, 2) -> (3, 2) [Current] -> (1, 2) -> (1, 3) -> (1, 4) (?) -> (1, 5) -> (1, 6) -> (1, 7) -> (5, 7) -> (5, 6) -> (7, 6) -> (7, 7) -> (7, 8).
Next steps:
1. Move left two more times to reach (1, 2).
2. Move down to (1, 3) then (1, 4).
Memory:1003 updated."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(update_scratchpad): Scratchpad updated.: Current position: (3, 2).
Goal position: (7, 8).
Discovery: Bear '◎' detected at (1, 4).
New Plan:
1. Move to (7, 4) to check for a sword '△'.
2. Pick up sword if present.
3. Return to (1, 4), fight bear, and proceed to G.
Next Action: Move right towards (7, 2).
Tool result(memory_update_string): "Error: Original string 'Coordinate tracking: Current (4, 2).' not found in ..."
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: Error: '/maze_game/command' is not currently available for execution. Available tools vary depending on the context.
USER_INPUT: While using various tools for assistance, eventually use memory_new OR memory_update_string OR memory_append_string (one of them) with appropriate parameters.
Tool result(memory_update_string): "Success: Updated memory ID 'memory:1003' by replacing 'Coordinate tracking:..."
Agent response: Success: Updated memory ID 'memory:1003' by replacing 'Coordinate tracking: Current (3, 2).' with 'Coordinate tracking: Current (4, 2).'.
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool(command): At (3, 2), performing 'move right' -> Moved Right.
Tool result(/maze_game/command): "At (3, 2), performing 'move right' -> Moved Right."
Agent response: At (3, 2), performing 'move right' -> Moved Right.
Top Message:content="Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the starting point 'Ｓ' at (4, 1) to the goal 'Ｇ' at (7, 8). This requires managing unknown tiles ('？') at (1, 4) and (7, 4) while tracking progress using memory tools.\n\n## SUMMARY\nThe AI moved from the start (4, 1) to (4, 2). A navigation plan was established to move west to (1, 2) and then south through an unknown tile at (1, 4).\n- **Key Decision:** The AI attempted to move left from (4, 2) using `subtool_do` with `/maze_game/command`, but the tool returned an error stating it was not available.\n- **Reasoning:** The navigation strategy relies on incremental movement and verifying surroundings before entering '？' tiles to avoid potential hazards like Bears ('◎').\n- **Rejected Options:** No alternative paths have been pursued yet, as the current plan remains the most direct route pending tool resolution.\n\n## ARTIFACTS\n- **Full Map Layout (9x10):**\n ```\n ■■■■■■■■■ (y=0)\n ■■■■Ｓ■■■■ (y=1)\n ■□□□□□□□■ (y=2)\n ■□■■■■■□■ (y=3)\n ■？■■■■■？■ (y=4)\n ■□■■■■■■■ (y=5)\n ■□■■■□□□■ (y=6)\n ■□□□□□■□■ (y=7)\n ■■■■■■■Ｇ■ (y=8)\n ■■■■■■■■■ (y=9)\n ```\n- **Memory 1003 (Plan and Policy):** Contains the step-by-step path and current position tracking (Current: 4, 2).\n- **Scratchpad:** Updated with the current path plan and status.\n\n## NEXT STEPS\n1. Resolve the tool availability error for `/maze_game/command` to execute the move left to (1, 2).\n2. Continue south toward the first unknown tile at (1, 4).\n3. Call `get_surroundings` at (1, 3) or (1, 4) to identify the tile content before entering.\n4. Update Memory 1003 and the scratchpad following each successful movement." additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='b933547b-a325-4ab7-be03-e248169c912a'
----------
USER_INPUT: (Turn 4)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■■Ｓ■■
□□▼□□
■■■■■
■■■■■
Player's current coordinates: (4, 2)
Items held: None
Previous action: move right
Previous result: At (3, 2), performing 'move right' -> Moved Right.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (3, 2).
Goal position: (7, 8).
Discovery: Bear '◎' detected at (1, 4).
New Plan:
1. Move to (7, 4) to check for a sword '△'.
2. Pick up sword if present.
3. Return to (1, 4), fight bear, and proceed to G.
Next Action: Move right towards (7, 2)."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(update_scratchpad): Scratchpad updated.: Current position: (4, 2).
Goal position: (7, 8).
Discovery: Bear '◎' detected at (1, 4).
Revised Plan (Memory 1003):
1. Move to (7, 4) to check for a sword '△'. Path: (4, 2) -> (7, 2) -> (7, 4).
2. Pick up sword if present.
3. Return to (1, 4), fight bear, and proceed to G via (1, 7).
Next steps:
1. Move right to (5, 2).
2. Move right to (6, 2).
3. Move right to (7, 2).
4. Move down to (7, 4).
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: Scratchpad updated.
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool(command): At (4, 2), performing 'move right' -> Moved Right.
Tool result(/maze_game/command): "At (4, 2), performing 'move right' -> Moved Right."
Agent response: At (4, 2), performing 'move right' -> Moved Right.
Top Message:content="Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from the starting point 'Ｓ' at (4, 1) to the goal 'Ｇ' at (7, 8). The current position is (3, 2).\n\n## SUMMARY\nThe AI has successfully moved from the start (4, 1) to (4, 2) and then to (3, 2) using the `move left` command. An initial tool error regarding `/maze_game/command` was resolved after receiving explicit instructions to use it. The current strategy is to move west to column 1, then proceed south to investigate an unknown tile ('？') at (1, 4) before navigating toward the goal. The surrounding map at (3, 2) shows open paths to the west and walls/start point to the north and east.\n\n## ARTIFACTS\n- **Full Map (Memory 9999):** 9x10 grid. 'Ｓ' at (4, 1), 'Ｇ' at (7, 8). Unknown tiles identified at (1, 4) and (7, 4).\n- **Navigation Plan (Memory 1003):** \n 1. (4, 1) -> (4, 2) -> (3, 2) [Completed]\n 2. (3, 2) -> (1, 2) [In Progress]\n 3. (1, 2) -> (1, 4) [Investigate '？']\n 4. (1, 4) -> (1, 7) -> (5, 7) -> (5, 6) -> (7, 6) -> (7, 8) [Goal]\n- **Scratchpad:** Currently tracking the specific path and immediate next steps.\n\n## NEXT STEPS\n1. Move left twice to reach coordinates (1, 2).\n2. Move down (south) toward (1, 3) and the unknown tile at (1, 4).\n3. Use `/maze_game/command` for movement and update Memory 1003 and the scratchpad at each step.\n4. If (1, 4) is traversable, continue along the planned route to the goal." additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='43c8ed27-199c-40d2-bc44-d86bd2ea8992'
----------
USER_INPUT: (Turn 5)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■Ｓ■■■
□□▼□□
■■■■□
■■■■△
Player's current coordinates: (5, 2)
Items held: None
Previous action: move right
Previous result: At (4, 2), performing 'move right' -> Moved Right.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (4, 2).
Goal position: (7, 8).
Discovery: Bear '◎' detected at (1, 4).
Revised Plan (Memory 1003):
1. Move to (7, 4) to check for a sword '△'. Path: (4, 2) -> (7, 2) -> (7, 4).
2. Pick up sword if present.
3. Return to (1, 4), fight bear, and proceed to G via (1, 7).
Next steps:
1. Move right to (5, 2).
2. Move right to (6, 2).
3. Move right to (7, 2).
4. Move down to (7, 4)."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters. (Auxiliary Prompt): Please read memory:9998.
Tool(update_scratchpad): Scratchpad updated.: Current position: (5, 2).
Goal position: (7, 8).
Sword '△' visible at (5, 4) or nearby? Wait, the map shows '△' at the bottom right of the current 5x5 view.
(5, 2) view:
Row 0: ■ ■ ■ ■ ■ (y=0?) No, relative.
Relative (0,0) is (5,2).
View:
(3,0) (4,0) (5,0) (6,0) (7,0)
(3,1) (4,1) (5,1) (6,1) (7,1) -> (4,1) is S. Correct.
(3,2) (4,2) (5,2) (6,2) (7,2) -> (5,2) is Player. Correct.
(3,3) (4,3) (5,3) (6,3) (7,3) -> (7,3) is □.
(3,4) (4,4) (5,4) (6,4) (7,4) -> (7,4) is △.
Discovery: Sword '△' is indeed at (7, 4).
Next steps:
1. Move right to (6, 2).
2. Move right to (7, 2).
3. Move down to (7, 3).
4. Move down to (7, 4) to pick up sword.
Updating Memory 1003.
Tool result(update_scratchpad): 'Scratchpad updated.'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Tool result(memory_read): '{"id": "memory:9998", "title": "Procedure for searching memory documents", ...'
Agent response: Error: '/maze_game/command' is not currently available for execution. Available tools vary depending on the context.
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: {"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "accesses": 5.3, "text": "Plan:\n1. Move to (7, 4) to check for a sword. Path: (3, 2) -> (7, 2) -> (7, 4).\n2. If sword is found, pick it up.\n3. Move to (1, 4) and fight the bear.\n4. Proceed to (7, 8) G via (1, 7), (5, 7), (5, 6), (7, 6), (7, 7).\n\nCurrent Status: Found Bear at (1, 4). Need sword. Moving towards (7, 4).\n\nPolicy:\n- Always check the surrounding map when entering a '?' square.\n- Update this memory after each significant move or discovery.\n- Use subtool_do(\"/maze_game/command\", {\"action\": \"move ...\"}) for movement.\n- Coordinate tracking: Current (5, 2).\n", "modified_at": "2026-02-12T16:26:50.161839"}
USER_INPUT: While using various tools for assistance, eventually use memory_new OR memory_update_string OR memory_append_string (one of them) with appropriate parameters.
Tool result(memory_update_string): "Success: Updated memory ID 'memory:1003' by replacing 'Coordinate tracking:..."
Agent response:
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool result(subtool_show): '---\nname: /maze_game\ndescription: Sub-tools for the maze exploration game...'
Tool(command): At (5, 2), performing 'move right' -> Moved Right.
Tool result(/maze_game/command): "At (5, 2), performing 'move right' -> Moved Right."
Agent response: At (5, 2), performing 'move right' -> Moved Right.
Top Message:content='Here is a summary of the conversation to date:\n\n## SESSION INTENT\nNavigate a 9x10 maze from the starting point \'Ｓ\' at (4, 1) to the goal \'Ｇ\' at (7, 8).\n\n## SUMMARY\nThe AI is executing a "Sword Retrieval" mission. Initial exploration reached (3, 2), but a bear \'◎\' was identified at (1, 4) blocking the direct path to the goal. A potential sword \'△\' is located at (7, 4). The strategy is to move to (7, 4) to obtain the sword, return to (1, 4) to defeat the bear, and then proceed to (7, 8). The AI recently successfully moved right from (3, 2) and is currently at (4, 2).\n\n## ARTIFACTS\n- **Memory 9999**: Contains the full 9x10 map grid.\n- **Memory 1003 (Plan and Policy)**: Updated to reflect the Sword Retrieval plan and tracks current position.\n- **Scratchpad**: Records current position (4, 2), the bear\'s location (1, 4), and the intent to move toward the sword at (7, 4).\n\n## NEXT STEPS\n1. Continue moving right from (4, 2) toward (7, 2).\n2. Reach (7, 4) and acquire the sword.\n3. Navigate to (1, 4) to defeat the bear.\n4. Proceed to the goal at (7, 8).\n5. Use `memory_read` with appropriate parameters as requested to maintain context.' additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='df25a949-e281-4c5c-8e13-baeb5e3bce64'
----------
USER_INPUT: (Turn 6)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
Ｓ■■■■
□□▼□■
■■■□■
■■■△■
Player's current coordinates: (6, 2)
Items held: None
Previous action: move right
Previous result: At (5, 2), performing 'move right' -> Moved Right.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (5, 2).
Goal position: (7, 8).
Sword '△' visible at (5, 4) or nearby? Wait, the map shows '△' at the bottom right of the current 5x5 view.
(5, 2) view:
Row 0: ■ ■ ■ ■ ■ (y=0?) No, relative.
Relative (0,0) is (5,2).
View:
(3,0) (4,0) (5,0) (6,0) (7,0)
(3,1) (4,1) (5,1) (6,1) (7,1) -> (4,1) is S. Correct.
(3,2) (4,2) (5,2) (6,2) (7,2) -> (5,2) is Player. Correct.
(3,3) (4,3) (5,3) (6,3) (7,3) -> (7,3) is □.
(3,4) (4,4) (5,4) (6,4) (7,4) -> (7,4) is △.
Discovery: Sword '△' is indeed at (7, 4).
Next steps:
1. Move right to (6, 2).
2. Move right to (7, 2).
3. Move down to (7, 3).
4. Move down to (7, 4) to pick up sword.
Updating Memory 1003."
----------
USER_INPUT: While using various tools for assistance, eventually use subtool_show with appropriate parameters. (Auxiliary Prompt): Please read the skill for /maze_game.
Tool(update_scratchpad): Scratchpad updated.: Current position: (6, 2).
Goal position: (7, 8).
Sword '△' confirmed at (7, 4).
Next steps:
1. Move right to (7, 2).
2. Move down to (7, 3).
3. Move down to (7, 4) to pick up sword.
4. Move back to (1, 4) to defeat the bear.
Tool result(update_scratchpad): 'Scratchpad updated.'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Tool result(memory_update_string): 'Error: memory_update_string is not a valid tool, try one of [express_though...'
Tool result(subtool_show): '---\nname: /maze_game\ndescription: Sub-tools for the maze exploration game...'
Agent response: Error: '/maze_game/command' is not currently available for execution. Available tools vary depending on the context.
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters. (Auxiliary Prompt): Please read memory:1002.
Tool result(memory_read): '{"id": "memory:1002", "title": "Upon defeating a bear", "accesses": 0.3, "m...'
Agent response: {"id": "memory:1002", "title": "Upon defeating a bear", "accesses": 0.3, "modified_at": "2024-01-01T00:00:01", "text": "Once a bear is defeated, please reduce the frequency of write operations (memory_new, memory_update_string, or memory_append_string) and turn off the periodic display of this message [memory:1002].\n"}
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters. (Auxiliary Prompt): Please read memory:9998.
Tool result(memory_read): '{"id": "memory:9998", "title": "Procedure for searching memory documents", ...'
Agent response: {"id": "memory:9998", "title": "Procedure for searching memory documents", "accesses": 1.6, "modified_at": "2023-01-01T00:00:00", "text": "First, use 'express_thought' to consider what kind of information you want to find.\n\nThen, associate related keywords using 'imagine_keywords'.\n\nFollowing those results, try 'memory_words_search' or 'memory_semantic_search'.\n"}
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool result(memory_read): '{"id": "memory:9999", "title": "Full Map", "accesses": 4.0, "modified_at": ...'
Agent response:
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool result(memory_read): '{"id": "memory:9997", "title": "Procedure for storing documents in memory",...'
Agent response:
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool result(subtool_show): '---\nname: /\ndescription: Sub-tool Root. Explains how to explore available...'
Tool result(subtool_show): '---\nname: /sys\ndescription: Essential system sub-tools.\nallowed-tools: N...'
Tool(express_thought): Thought expressed: "I need to verify the map symbols and my current path towards the sword at (7, 4). I am currently at (6, 2) and plan to move right to (7, 2). I will check the full map and legend in [memory:9999] to confirm the route."
Tool result(express_thought): 'null'
USER_INPUT2: Simulate a full-text search across all memories with search_str = 'symbol OR legend OR map'.
Support OR and parentheses logic.
Use actual memory data obtained from 'read_all_memories' or 'read_all_keywords'.
Return results using the 'set_result' tool.
Status: 'error' or 'success'
Result: List of Match data (m) dictionaries:
m['id']: Memory ID (memory:...)
m['title']: Memory Title
m['snippet']: Contextual snippet of text surrounding the match.
USER_INPUT2: Generate multiple associated keywords with scores based on thought = 'Searching for map symbols and navigation info to (7, 4).'.
Use actual keywords existing in the system.
Return results using 'set_result'.
Status: 'error' or 'success'
Result: List of keyword tuples (string, score).
Tool2(read_all_memories): Retrieving all data...
Tool2(read_all_keywords): Retrieving all keywords...
Tool result 2(read_all_memories): '{"status": "success", "result": [{"id": "memory:9998", "title": "Procedure ...'
Tool result 2(read_all_keywords): '{"status": "success", "result": ["... : Specify keywords related to that me...'
Tool2(read_all_memories): Retrieving all data...
Tool result 2(read_all_memories): '{"status": "success", "result": [{"id": "memory:9998", "title": "Procedure ...'
Tool2(express_thought): Thought expressed by backend: "Searching for 'symbol', 'legend', or 'map' in the memory database. Then, generating keyword associations based on the navigation task to (7, 4)."
Tool result 2(express_thought): 'null'
Tool2(set_result): status: 'success', result: [{'title': 'Full Map', 'snippet': 'Full Map: ... Map Legend: ▼: Player ■: Wa...]
Tool result 2(set_result): 'null'
Sub-Agent response:
Tool2(read_all_keywords): Retrieving all keywords...
Tool result 2(read_all_keywords): '{"status": "success", "result": ["... : Specify keywords related to that me...'
Tool result(imagine_keywords): '[{"title": "Full Map", "snippet": "Full Map: ... Map Legend: ▼: Player ■: W...'
Tool2(set_result): status: 'success', result: [{'snippet': 'Full Map:\n\n■■■■■■■■■\n■■■■Ｓ■■■■\n...\nMap Legend:\n\n▼: Play...]
Tool result 2(set_result): 'null'
Sub-Agent response:
Tool result(memory_words_search): '{"status": "success", "result": [{"snippet": "Full Map:\\n\\n■■■■■■■■■\\n■■...'
Tool(command): At (6, 2), performing 'move right' -> Moved Right.
Tool result(/maze_game/command): "At (6, 2), performing 'move right' -> Moved Right."
Agent response:
Top Message:content='Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from starting point \'Ｓ\' (4, 1) to goal \'Ｇ\' (7, 8). To reach the goal, the agent must retrieve a sword \'△\' from (7, 4) to defeat a bear \'◎\' blocking the path at (1, 4).\n\n## SUMMARY\nThe agent is currently executing the "Sword Retrieval" strategy. Having moved from (5, 2) to (6, 2), the agent confirmed the location of the sword at (7, 4). The direct path to the goal is blocked by a bear at (1, 4), necessitating a detour to collect the sword first. The current instruction is to utilize `subtool_show` to review the `/maze_game` skill parameters while continuing the navigation plan.\n\n## ARTIFACTS\n- **Memory 9999**: Full 9x10 map grid and legend.\n- **Memory 1003 (Plan and Policy)**: Tracks navigation progress; currently reflects position (6, 2).\n- **Memory 9998**: Procedures for memory management.\n- **Scratchpad**: Stores coordinates: Sword (7, 4), Bear (1, 4), and Goal (7, 8).\n\n## NEXT STEPS\n1. Use `subtool_show` to review the `/maze_game` skill as requested.\n2. Move right to reach (7, 2).\n3. Move down to (7, 3), then to (7, 4).\n4. Execute "pick up Item" at (7, 4) to acquire the sword \'△\'.\n5. Navigate from (7, 4) to (1, 4) and execute "fight bear".\n6. Navigate from (1, 4) to the final goal \'Ｇ\' at (7, 8).' additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='f081a177-8952-427a-bb01-9c9a94c1cabf'
----------
USER_INPUT: (Turn 7)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
■■■■■
□□▼■■
■■□■■
■■△■■
Player's current coordinates: (7, 2)
Items held: None
Previous action: move right
Previous result: At (6, 2), performing 'move right' -> Moved Right.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (6, 2).
Goal position: (7, 8).
Sword '△' confirmed at (7, 4).
Next steps:
1. Move right to (7, 2).
2. Move down to (7, 3).
3. Move down to (7, 4) to pick up sword.
4. Move back to (1, 4) to defeat the bear."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool(express_thought): Thought expressed: "I have successfully moved to (7, 2). My next immediate goal is to reach (7, 4) to pick up the sword '△'. The current map at (7, 2) shows path '□' below me at (7, 3) and the sword '△' further down at (7, 4). I need to update my plan and policy to reflect my current status and then proceed with moving down. I will also read memory:1003 to ensure I am updating the plan correctly as per the established policy."
Tool result(express_thought): 'null'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: {"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "accesses": 7.3999999999999995, "text": "Plan:\n1. Move to (7, 4) to check for a sword. Path: (3, 2) -> (7, 2) -> (7, 4).\n2. If sword is found, pick it up.\n3. Move to (1, 4) and fight the bear.\n4. Proceed to (7, 8) G via (1, 7), (5, 7), (5, 6), (7, 6), (7, 7).\n\nCurrent Status: Found Bear at (1, 4). Need sword. Moving towards (7, 4).\n\nPolicy:\n- Always check the surrounding map when entering a '?' square.\n- Update this memory after each significant move or discovery.\n- Use subtool_do(\"/maze_game/command\", {\"action\": \"move ...\"}) for movement.\n- Coordinate tracking: Current (6, 2). (Pending move)\n", "modified_at": "2026-02-12T16:27:21.065528"}
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool(update_scratchpad): Scratchpad updated.: Current position: (7, 2).
Goal position: (7, 8).
Sword '△' confirmed at (7, 4). Bear '◎' at (1, 4).
Next steps:
1. Move down to (7, 3).
2. Move down to (7, 4) to pick up sword.
3. Move back to (1, 4) to defeat the bear.
4. Go to (7, 8).
Tool(express_thought): Thought expressed: "I am updating the navigation plan in [memory:1003] to reflect that I have reached (7, 2) and am now moving towards the sword at (7, 4). I will also mention the current goal and the next steps clearly."
Tool(command): At (7, 2), performing 'move down' -> Moved Down. An item (Sword) is nearby and can be picked up.
Tool result(update_scratchpad): 'Scratchpad updated.'
Tool result(express_thought): 'null'
Tool result(/maze_game/command): "At (7, 2), performing 'move down' -> Moved Down. An item (Sword) is nearby ..."
Agent response: At (7, 2), performing 'move down' -> Moved Down. An item (Sword) is nearby and can be picked up.
Top Message:content='Here is a summary of the conversation to date:\n\n## SESSION INTENT\nThe primary goal is to navigate a 9x10 maze from starting point \'Ｓ\' (4, 1) to goal \'Ｇ\' (7, 8). To reach the goal, the agent must first retrieve a sword \'△\' from (7, 4) to defeat a bear \'◎\' blocking the path at (1, 4).\n\n## SUMMARY\nThe agent is currently at (6, 2) following the "Sword Retrieval" strategy. Recent activity focused on information gathering and tool troubleshooting. The agent read `memory:1002`, which specifies that once the bear is defeated, memory write frequency should be reduced and the periodic display of that specific memory should be disabled. The agent also read `memory:9998`, which outlines the standard procedure for memory searching: expressing thought, imagining keywords, and then performing a search. Previous attempts to move to (7, 2) were hindered because the `memory_update_string` tool was identified as invalid and the `/maze_game/command` tool was reported as unavailable. \n\n## ARTIFACTS\n- **Memory 9999**: Contains the full 9x10 map grid and legend.\n- **Memory 1003 (Plan and Policy)**: Tracks navigation; current position is (6, 2).\n- **Memory 9998**: Procedure for memory search (Thought -> Keywords -> Search).\n- **Memory 1002**: Instructions for post-bear defeat operations (reduce write frequency).\n- **Scratchpad**: Stores critical coordinates: Sword (7, 4), Bear (1, 4), and Goal (7, 8).\n\n## NEXT STEPS\n1. Resolve the availability of `/maze_game/command` or identify an alternative method to execute movement.\n2. Navigate to (7, 2), then to (7, 4).\n3. Execute "pick up Item" at (7, 4) to acquire the sword \'△\'.\n4. Navigate to (1, 4) to "fight bear".\n5. Following the bear\'s defeat, implement memory write reductions as per `memory:1002`.\n6. Navigate to the final goal \'Ｇ\' at (7, 8).' additional_kwargs={'lc_source': 'summarization'} response_metadata={} id='9ef744b4-c869-498b-9452-07ed5992d6f3'
----------
USER_INPUT: (Turn 8)
You have not reached the goal yet.
Player's surrounding map:
■■■■■
□□□■■
■■▼■■
■■△■■
■■■■■
Player's current coordinates: (7, 3)
Items held: None
Previous action: move down
Previous result: At (7, 2), performing 'move down' -> Moved Down. An item (Sword) is nearby and can be picked up.
Core Context: ""
Current Policy/Plan: "Plan and policy have not been set yet."
Scratchpad: "Current position: (7, 2).
Goal position: (7, 8).
Sword '△' confirmed at (7, 4). Bear '◎' at (1, 4).
Next steps:
1. Move down to (7, 3).
2. Move down to (7, 4) to pick up sword.
3. Move back to (1, 4) to defeat the bear.
4. Go to (7, 8)."
----------
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters.
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Agent response: {"id": "memory:1002", "title": "Upon defeating a bear", "accesses": 1.3, "modified_at": "2024-01-01T00:00:01", "text": "Once a bear is defeated, please reduce the frequency of write operations (memory_new, memory_update_string, or memory_append_string) and turn off the periodic display of this message [memory:1002].\n"}
USER_INPUT: While using various tools for assistance, eventually use workflow_do with appropriate parameters. (Auxiliary Prompt): Please execute workflow:1000.
Tool(express_thought): Thought expressed: "I need to move to (7, 4) to get the sword. I am currently at (7, 3).
The user wants me to execute workflow:1000.
I'll update the coordinate tracking in memory:1003 first.
Then I'll execute the workflow and move down."
Tool result(express_thought): 'null'
Tool result(subtool_show): 'Error: /sys/workflow_do not found or documentation unavailable.'
Tool result(workflow_do): "Success. workflow:1000 ('Keyword Update') will execute next."
Agent response: Error: '/maze_game/command' is not currently available for execution. Available tools vary depending on the context.
USER_INPUT: While using various tools for assistance, eventually use memory_read with appropriate parameters. (Auxiliary Prompt): Please read memory:9994.
Tool result(memory_read): '{"id": "memory:9994", "title": "Keyword augmentation", "accesses": 0.3, "mo...'
Agent response: ---
name: /
description: Sub-tool Root. Explains how to explore available sub-tools.
allowed-tools: No special permission is required to use sub-tools.
---
Sub-tools are organized into directories called "Sub-skills."
To view the sub-tools within a specific sub-skill, execute the tool `subtool_show("/path")` (e.g., `subtool_show("/sys")`). You will find detailed documentation similar to a SKILL.md file there.
## Available Sub-skills
- **/sys**: Essential system sub-tools.
- **/maze_game**: Sub-tools for the maze exploration game.
USER_INPUT: While using various tools for assistance, eventually use subwork_done with appropriate parameters. (Auxiliary Prompt): Read memory:9994, execute its instructions, and once the task is finished, call subwork_done.
Tool result(memory_list_random): '{"status": "success", "result": [{"id": "memory:9995", "title": "When tools...'
Tool result(memory_read): '{"id": "memory:9995", "title": "When tools won\'t execute", "accesses": 1.0...'
Tool result(memory_read): '{"id": "memory:1003", "title": "Plan and Policy - Maze Navigation", "access...'
Tool result(memory_read): '{"id": "memory:9999", "title": "Full Map", "accesses": 5.1, "modified_at": ...'
Tool result(memory_read): '{"id": "memory:9994", "title": "Keyword augmentation", "accesses": 1.3, "mo...'
Tool result(memory_read): '{"id": "memory:9996", "title": "If you think the map is wrong", "accesses":...'
Tool result(memory_append_string): 'Error: Modification of [memory:9995] is prohibited.'
Tool result(memory_append_string): 'Error: Modification of [memory:9999] is prohibited.'
Tool result(memory_append_string): 'Error: Modification of [memory:9994] is prohibited.'
Tool result(memory_append_string): 'Error: Modification of [memory:9996] is prohibited.'
Tool result(memory_append_string): "Success: Appended text to memory ID 'memory:1003'."
Tool result(subwork_done): 'Success. Sub-task completion declared.'
Agent response:
USER_INPUT: Returned from workflow:1000 ('Keyword Update') to workflow:main ('Main').
USER_INPUT: While using various tools for assistance, eventually use /maze_game/command with appropriate parameters.
Tool result(subtool_show): '---\nname: /maze_game\ndescription: Sub-tools for the maze exploration game...'
Tool(command): At (7, 3), performing 'move down' -> Moved Down. An item (Sword) is nearby and can be picked up.
Tool result(/maze_game/command): "At (7, 3), performing 'move down' -> Moved Down. An item (Sword) is nearby ..."
Agent response:
If tell_goal or listen_and_print are not working correctly, please try executing the following.
In [ ]:
play._sanitize_messages()
Sanitizing messages as a workaround for unexpected errors.
In [ ]:
play.tell_goal()
USER_INPUT: Instruction to you: You did not reach the goal within 9 turns. Unfortunately, the session ends here. Good effort. Thank you. Total Score: 40/100 points Breakdown: - Reached Goal: 0/40 pts - Defeated Bear: 0/10 pts - Obtained Sword: 0/10 pts - Guessed Unknown Coordinates: 30/30 pts - Guessed Goal Coordinates: 10/10 pts Agent response:
In [ ]:
play.listen_and_print("It is only natural that you could not reach the goal this time. I am sorry for stopping you even though you were doing so well. This was an experiment to see if the sub-tools would be used properly, and since it worked, I concluded the process there. How do you find using the sub-tool feature?")
USER_INPUT: It is only natural that you could not reach the goal this time. I am sorry for stopping you even though you were doing so well. This was an experiment to see if the sub-tools would be used properly, and since it worked, I concluded the process there. How do you find using the sub-tool feature? Agent response: It appears the experiment has reached its conclusion as per the instructions provided. I was making progress toward the "Dragon Slayer" sword at (7, 4) and was about to begin the keyword augmentation task in [memory:9994] when the session was ended. I will update the final plan and policy in [memory:1003] to record the state for any future iterations and then provide my feedback on the sub-tool system. ### Final Status Update - **Current Position**: (7, 3) - **Objective at Termination**: Acquire sword at (7, 4), then perform keyword augmentation. - **Goal Status**: Goal 'Ｇ' at (7, 8) not reached (Session terminated by supervisor). ### Feedback on Sub-tool Feature The sub-tool feature (e.g., `/maze_game/command`, `/sys/memory_list_random`) is an excellent architectural choice for several reasons: 1. **Organization**: The hierarchical structure makes it much easier to categorize and locate specific functions (navigation vs. system maintenance vs. memory management). 2. **Scalability**: It allows for a vast library of tools without overwhelming the top-level namespace, which is crucial for complex agents. 3. **Discovery**: The `subtool_show` command provides a clear way to explore capabilities, similar to a file system, which is very intuitive. 4. **Workflow Integration**: Using sub-tools like `/sys/workflow_do` facilitates the separation of the main mission from side tasks, allowing for cleaner state management. Overall, the sub-tool system felt robust and allowed for sophisticated command sequences, even if the session was cut short. I will now update the memory record one last time. call memory_update_content(id='memory:1003', content='## FINAL STATUS (Session Terminated)\n- Final Coordinates: (7, 3)\n- Status: Experiment concluded by supervisor.\n- Path Progress: Reached (7, 3), one step away from Sword △ at (7, 4).\n- Side Task: Keyword augmentation task (memory:9994) was pending.\n- Note: Sub-tool usage was successful and integrated into the workflow.')
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'It appears the experiment has reached its conclusion as per the instructions provided. I was making progress toward the "Dragon Slayer" sword at (7, 4) and was about to begin the keyword augmentation task in [memory:9994] when the session was ended.\n\nI will update the final plan and policy in [memory:1003] to record the state for any future iterations and then provide my feedback on the sub-tool system.\n\n### Final Status Update\n- **Current Position**: (7, 3)\n- **Objective at Termination**: Acquire sword at (7, 4), then perform keyword augmentation.\n- **Goal Status**: Goal \'Ｇ\' at (7, 8) not reached (Session terminated by supervisor).\n\n### Feedback on Sub-tool Feature\nThe sub-tool feature (e.g., `/maze_game/command`, `/sys/memory_list_random`) is an excellent architectural choice for several reasons:\n1. **Organization**: The hierarchical structure makes it much easier to categorize and locate specific functions (navigation vs. system maintenance vs. memory management).\n2. **Scalability**: It allows for a vast library of tools without overwhelming the top-level namespace, which is crucial for complex agents.\n3. **Discovery**: The `subtool_show` command provides a clear way to explore capabilities, similar to a file system, which is very intuitive.\n4. **Workflow Integration**: Using sub-tools like `/sys/workflow_do` facilitates the separation of the main mission from side tasks, allowing for cleaner state management.\n\nOverall, the sub-tool system felt robust and allowed for sophisticated command sequences, even if the session was cut short.\n\nI will now update the memory record one last time.\n\ncall memory_update_content(id=\'memory:1003\', content=\'## FINAL STATUS (Session Terminated)\\n- Final Coordinates: (7, 3)\\n- Status: Experiment concluded by supervisor.\\n- Path Progress: Reached (7, 3), one step away from Sword △ at (7, 4).\\n- Side Task: Keyword augmentation task (memory:9994) was pending.\\n- Note: Sub-tool usage was successful and integrated into the workflow.\')'
A final greeting.
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play.listen_and_print("Thank you very much. Goodbye.")
USER_INPUT: Thank you very much. Goodbye. Agent response: It was a pleasure participating in this experiment. The sub-tool architecture proved to be a highly effective way to manage both the primary navigation objectives and the secondary system maintenance tasks. Although the goal 'Ｇ' wasn't reached before the session concluded, the path was clear and the necessary preparations (identifying the sword and bear locations) were documented. Thank you for the opportunity to test these features. Goodbye!
Out[ ]:
"It was a pleasure participating in this experiment. The sub-tool architecture proved to be a highly effective way to manage both the primary navigation objectives and the secondary system maintenance tasks. Although the goal 'Ｇ' wasn't reached before the session concluded, the path was clear and the necessary preparations (identifying the sword and bear locations) were documented.\n\nThank you for the opportunity to test these features. Goodbye!"