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Home/Categories/AI & Machine Learning/Mengram
icon of Mengram

Mengram

Open source alternative to Mem0, Pinecone, Weaviate Cloud and Qdrant Cloud

  • 204 stars
  • Apache-2.0
  • Active recently
  • Last commit 35 days ago

Last verified Oct 17, 2026

AI memory for Claude Code and AI agents with auto-save and auto-recall features.

Visit websiteGitHub204 stars
image of Mengram
TL;DR

Mengram is an AI agent memory platform for semantic, episodic, and procedural recall. It replaces stateless agent prompts and one-off RAG notes for teams that need agents to learn from prior interactions, failures, and workflows. Apache-2.0 licensed.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs and the FAQ
who it's for

Who Mengram is for#

Agent builders adding durable user memory

Mengram fits teams that need agents to remember preferences, prior events, and procedures across sessions.

Skip if:

Your agent only needs short-lived context inside a single chat session.

RAG teams moving beyond document recall

Mengram can help teams model experience and procedures rather than only retrieving chunks from a knowledge base.

Skip if:

You only need keyword search or a simple vector database.

the problem

The problem it solves#

Agents forget too much between runs. Teams repeatedly re-explain user preferences, project rules, failures, and procedures because ordinary prompts and basic vector search do not capture experience well.

Useful agent memory needs more than saved snippets. It should represent facts, episodes, and procedures in a way that can improve future actions without turning every session into manual note-taking.

how Mengram solves it

How it solves it#

Semantic, episodic, and procedural memory

Mengram explicitly targets several memory types, giving agent builders more structure than a single vector store.

Agent framework integrations

Repository metadata mentions Python and JavaScript SDKs plus LangChain, CrewAI, OpenClaw, and MCP integrations.

Failure-driven procedure learning

The project description calls out procedures that learn from failures, which fits agents that need to improve workflow execution over time.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Deeper than basic RAG memoryMengram's memory model is more specific than storing documents for retrieval, making it relevant for agents that need user, episode, and process memory.
  • Broad agent ecosystem fitThe SDK and framework topics make Mengram easier to evaluate across common agent stacks instead of tying it to one runtime.

Trade-offs

  • -Memory quality needs governanceAgent memory can preserve wrong assumptions or sensitive context if teams do not design retention, deletion, and validation rules carefully.
tech stack · detected from GitHub

What it's built on#

Languages
JavaScriptPythonTypeScript
Databases
pgvector
Tooling
esbuild
frequently asked

FAQ#

What kinds of memory does Mengram support?

The repository description names semantic, episodic, and procedural memory for AI agents.

Does Mengram integrate with agent frameworks?

Yes. Repository metadata mentions Python and JavaScript SDKs plus LangChain, CrewAI, OpenClaw, and MCP integrations.

Is Mengram open source?

Yes. The GitHub repository reports Apache-2.0 licensing.

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