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mengram
by alibaizhanov
Human-like semantic, episodic and procedural memory for AI agents
Python
Updated Jun 17, 2026
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Overview
Provides human-like memory for AI agents with semantic, episodic and procedural stores. Exposes Python and JS SDKs plus integrations for LangChain, CrewAI and OpenClaw so agents can read/write rich memories and learn procedures from experience. Notably supports experience-driven procedures that adapt after failures and a free API for remote usage.
The Value Proposition
As agents become more autonomous, a simple cache of past results no longer suffices for reliable delegation or evaluation. Memory systems that capture episodic context and evolving procedures give agents a track record you can inspect and benchmark. That historical context is essential for agent-to-agent evaluation and for building reputation signals about which agents succeed or fail over time.
When to Use
Teams building multi-agent systems that need durable, queryable agent memory and experience-driven procedure learning.
How It's Used
- Persisting contextual episodic memories so agents reason from past interactions
- Capturing procedural steps and updating them when procedures fail to improve reliability
- Providing queryable memory for evaluation systems to compute agent track records
Works With
langchaincrewai
Topics
ai-agentsai-memorycognitive-profilecrewaiepisodic-memorylangchainllmmcp-servermemory-layerprocedural-memory+2 more
Similar Tools
langchaincrewai
Keywords
ai-memorymulti-agent trustagent-track-recordsemantic-memory