alibaizhanov/mengram
by Various
Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw i
MCP
alibaizhanov/mengram
Added 1 June 2026
Overview
Mengram is an open-source memory layer for AI agents that implements semantic, episodic, and procedural memory. It learns from failures to refine agent behavior over time and offers a free API with Python and JavaScript SDKs, plus integrations with LangChain, CrewAI, and OpenClaw.
Best for
Best for
Developers building memory-enhanced AI agents who want a free, multi-memory solution with framework integrations
Use cases
- Give AI agents persistent memory across conversations and sessions
- Enable agents to learn from past mistakes and improve procedures automatically
- Augment agentic frameworks like LangChain and CrewAI with structured memory
Notes
Mengram is an open-source memory layer for AI agents that implements semantic, episodic, and procedural memory. It learns from failures to refine agent behavior over time and offers a free API with Python and JavaScript SDKs, plus integrations with LangChain, CrewAI, and OpenClaw.
172 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Give AI agents persistent memory across conversations and sessions
- Enable agents to learn from past mistakes and improve procedures automatically
- Augment agentic frameworks like LangChain and CrewAI with structured memory
Pros
- Free API and open-source code reduce cost and vendor lock-in
- Covers multiple memory types (semantic, episodic, procedural) in one library
- Integrates with popular agent frameworks out of the box
Cons
- Small project with 172 stars may have limited community support
- Dependency on Python and JS SDKs restricts non-Python/JS stacks
- Procedural memory learning from failures is experimental and not battle-tested at scale
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Free API and open-source code reduce cost and vendor lock-in
- Covers multiple memory types (semantic, episodic, procedural) in one library
- Integrates with popular agent frameworks out of the box
Cons
- Small project with 172 stars may have limited community support
- Dependency on Python and JS SDKs restricts non-Python/JS stacks
- Procedural memory learning from failures is experimental and not battle-tested at scale
Pairs with
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