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
How to use
Install
pip install mengram-ai # or: npm install mengram-aiTools exposed
X-Quota-Add-UsedX-Quota-Add-LimitX-Quota-Search-UsedX-Quota-Search-Limit
Tested with
Claude Desktop, Claude Code, Cursor, Windsurf, Cline, ChatGPT
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
Other entries in the index that connect to this one. Click through to see the chain.
Get the free Developer’s Field Guide
A 27-page field guide to the AI coding workflow with Claude. Claude Code, MCP servers, the prompt patterns that work, and what to delegate. Free.
Enter your work email. We send it straight over, plus a few short notes worth knowing. Unsubscribe any time.