SenseLab (AMFS)

SenseLab builds AMFS (Agent Memory File System), an open-source cognitive layer for multi-agent AI systems, licensed under Apache 2.0. The core idea is that agents should not just store context — they should validate each other's findings, build on shared knowledge, and improve based on real production outcomes. The project is maintained by SenseLab Corp and backed by Speedrun, with a cloud-hosted Pro tier available at sense-lab.ai alongside the open-source engine.

What It Is

AMFS is a shared memory infrastructure for AI agent fleets. Where vector databases match similarity, AMFS keeps versioned knowledge that carries confidence scores, provenance, and outcome feedback. When a deploy succeeds or an incident fires, AMFS records what the agent read, what it chose, and what happened — shifting confidence based on real results rather than synthetic runs. The result is a memory layer that compounds: knowledge gets sharper with every production outcome, and every decision trace automatically becomes SFT/DPO training data.

How the Cognitive Layer Works

AMFS structures agent memory around three operations:

• Discover — Before acting, an agent queries AMFS for what the fleet already knows. Results come back ranked by confidence with full provenance: which agent wrote them, when, and how trustworthy they are.
• Handoff — A finding written by one agent is readable by every other agent within milliseconds, across frameworks, sessions, and machines.
• Learn — When outcomes are committed, confidence on related entries adjusts. The system knows what to trust and what to question.

Rooms provide shared spaces where teams and agents coordinate around a project or workflow. Agents can see what others are working on, what they decided, and why — enabling cognitive coordination before any decision runs.

Architecture and Open-Source Model

AMFS is a monorepo with a layered architecture. The OSS edition includes the full memory engine: versioned writes, confidence scoring, outcome feedback, causal traces, knowledge graph, hybrid search (full-text + semantic + recency + confidence), git-like timeline, SDKs, storage adapters, HTTP API, MCP server, and CLI.

Key packages:

• Python SDK (pip install amfs) — core AgentMemory class
• TypeScript SDK (npm install @senselab-ai/amfs) — full async API
• MCP Server (pip install amfs-mcp-server) — first-class support for Cursor, Claude Desktop, and Claude Code
• HTTP Server, Core Engine, and CLI available as separate installable packages
• Storage adapters for filesystem (default), PostgreSQL, S3, and HTTP remote backends
• Framework connectors for CrewAI, LangGraph, LangChain, AutoGen, and AWS Strands Agents

The Pro cloud tier (AMFS Pro) adds branching, merge, pull requests, access control, tags, rollback, cherry-pick, fork, multi-tenant isolation, immutable decision traces, the intelligence layer (Cortex), and a web dashboard. The README describes the distinction as: "OSS = single-branch repo with full history. Pro = GitHub."

Setup Path

Getting started requires three steps: sign up for an account (free tier, no credit card required), generate an API key, then connect via MCP or SDK. For MCP-compatible clients like Cursor or Claude Desktop, a single curl command installs the MCP server. No framework rewrites are needed — AMFS plugs into whatever stack is already running.

Update: v0.2.0 — Continual Learning Infrastructure

The latest GitHub release is v0.2.0, published May 29, 2026, titled "Continual Learning Infrastructure." The repository was last pushed to on August 10, 2026, indicating active development. The project has 59 stars and 3 forks on GitHub. Blog posts from the SenseLab engineering team, published in June 2026, cover the cognitive layer concept, continual learning architecture, and cross-agent context portability — signaling active product direction toward making agent fleets collectively smarter over time.