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Documentation Index

Fetch the complete documentation index at: https://docs.sense-lab.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Framework Integrations

AMFS integrates with popular multi-agent frameworks so you can add persistent memory to your existing workflows.

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Setup

Set your connection env vars — all framework integrations pick them up automatically: 
export AMFS_HTTP_URL="https://amfs-login.sense-lab.ai"
export AMFS_API_KEY="<your-api-key>"

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CrewAI

Add AMFS tools to your CrewAI agents: 
pip install amfs-crewai

from amfs import AgentMemory
from amfs_crewai import AMFSTool

mem = AgentMemory(agent_id="crewai-agent")
tools = AMFSTool(mem).tools()
# Returns [AMFSReadTool, AMFSWriteTool, AMFSListTool]

# Pass to your CrewAI agent
from crewai import Agent

agent = Agent(
    role="Researcher",
    goal="Analyze the codebase",
    tools=tools,
)
The tools give your CrewAI agents the ability to read, write, and list memory entries as part of their task execution.

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LangGraph

Use AMFS as a checkpointer for LangGraph: 
pip install amfs-langgraph

from amfs import AgentMemory
from amfs_langgraph import AMFSCheckpointer

mem = AgentMemory(agent_id="graph-agent")
checkpointer = AMFSCheckpointer(mem)

# Pass to your LangGraph graph builder
The checkpointer persists graph state to AMFS, enabling cross-session graph execution.

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LangChain

Use AMFS as chat memory for LangChain: 
pip install amfs-langchain

from amfs import AgentMemory
from amfs_langchain import AMFSChatMemory

mem = AgentMemory(agent_id="chat-agent")
chat_memory = AMFSChatMemory(mem, session_key="conv-123")

# Save conversation context
chat_memory.save_context(
    {"input": "What's the retry policy?"},
    {"output": "We use exponential backoff with max 3 retries."},
)

# Load conversation history
history = chat_memory.load_memory_variables({})

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AutoGen

Use AMFS as a memory store for AutoGen agents: 
pip install amfs-autogen

from amfs import AgentMemory
from amfs_autogen import AMFSMemoryStore

mem = AgentMemory(agent_id="autogen-agent")
store = AMFSMemoryStore(mem)

# Store and retrieve data
store.add("user_prefs", {"theme": "dark"})
prefs = store.get("user_prefs")

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Building Your Own Integration

All integrations are thin wrappers around the AgentMemory API. To build your own:
  1. Accept an AgentMemory instance in your constructor
  2. Map your framework’s memory/tool API to mem.read(), mem.write(), mem.list(), etc.
  3. Use entity_path to scope data (e.g., per-conversation, per-task, per-agent)
class MyFrameworkMemory:
    def __init__(self, mem: AgentMemory, scope: str = "default"):
        self._mem = mem
        self._scope = scope

    def save(self, key: str, data: dict):
        self._mem.write(self._scope, key, data)

    def load(self, key: str):
        entry = self._mem.read(self._scope, key)
        return entry.value if entry else None