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Give AI agents human-like memory and learning for better long-term automation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mengram" yet — see the docs or source repo.
Explain how to integrate mengram into my Python AI agent so it has semantic, episodic, and procedural memory. Include installation steps, sample code, and how to write and retrieve memories after conversations.
An integration guide with dependency setup, sample code, and the basic memory write and retrieval flow.
I have an AI agent that performs web tasks. Design a failure-learning workflow with mengram: when a task fails, record the reason, summarize reusable steps, and prioritize that experience in similar future tasks. Provide an implementation approach and pseudocode.
A failure feedback loop showing how to log failures, distill procedures, and reuse them in later tasks.
Provide an example of integrating mengram with LangChain or CrewAI for a multi-step research assistant. Show how the agent stores task history, reads past experience, and uses it to improve later responses.
A framework integration example showing how a memory layer improves continuity and response quality in multi-step agents.
Provide persistent memory for AI agents across sessions and tasks.
Provide Git-backed Markdown memory and clean search for AI agents.
Give AI agents cross-session memory through compressed histories and prior recall.
Give AI agents persistent memory with semantic search and automatic memory management.
Give AI coding agents persistent local shared memory across agents.
Give AI agents persistent memory with retrieval and relevance ranking.