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

Setup

Set your connection env vars — all framework integrations pick them up automatically:

CrewAI

Add SenseLab tools to your CrewAI agents:
The tools give your CrewAI agents the ability to read, write, and list memory entries as part of their task execution.

LangGraph

Use SenseLab as a checkpointer for LangGraph:
The checkpointer persists graph state to SenseLab, enabling cross-session graph execution.

LangChain

Use SenseLab as chat memory for LangChain:

AutoGen

Use SenseLab as a memory store for AutoGen agents:

Strands Agents

Add memory to a Strands agent as a plugin:
The plugin registers six tools — amfs_read, amfs_write, amfs_search, amfs_recall, amfs_list, and amfs_record_context — and also records the agent’s other tool calls into the causal chain, so decision traces cover the whole session rather than only the memory operations. Pass AMFSPlugin(mem, auto_trace=False) to register the tools without that tracing. If you’d rather have one action-based tool than the full plugin, amfs_memory handles store, retrieve, search, and list, and takes its agent identity from AMFS_AGENT_ID:

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)

What’s next

Python SDK

The full AgentMemory API that every integration wraps.

Core Concepts

Understand versioning, provenance, and causal graphs in SenseLab.