MemoMind Memory Layer
A self-hosted, cross-session memory system for coding agents.
What does this agent do, and when should you use it?
MemoMind is a local persistent-memory system for AI coding agents built around retain, recall, and reflect. It stores extracted facts, entities, tags, relationships, and temporal information in PostgreSQL, pgvector, and a knowledge graph, then retrieves memory through four search paths. The project exposes a stdio MCP integration for Claude Code, a HindsightClient Python SDK, REST endpoints, and a web dashboard. Its database and embedding models run locally, while fact extraction can be configured for OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio, or OpenAI-compatible services. It also imports ChatGPT and Gemini histories plus DayLife activity data, and exports memory and graph data as JSON.
Claude Code can call retain, recall, and reflect through the stdio MCP server in mcp_stdio.py. retain extracts facts from new context and writes them to a selected memory bank; recall combines semantic similarity, BM25 keyword search, knowledge-graph search, and temporal search; reflect synthesizes across stored memories. MemoMind uses bge-m3 for 1024-dimensional embeddings and can use an NVIDIA GPU for embedding and reranking, while dashboard.py provides visual browsing, filtering, search, and graph inspection. It can import ChatGPT/Gemini conversations and DayLife events, then export memories and graph data through the dashboard or backup-memomind.py.
- A Claude Code user who wants a new coding session to retrieve prior architectural decisions, naming conventions, and stack choices.
- An engineer maintaining a long-lived codebase who needs to retain past debugging attempts, outcomes, and review-feedback patterns.
- A consultant handling multiple clients or projects who needs separate memory banks for isolated contexts.
- A user with substantial ChatGPT or Gemini history who wants extracted facts in one knowledge graph with links back to original conversations.
- A DayLife user who wants personal activity events synchronized into a searchable timeline for AI analysis.
What are this agent's strengths and limitations?
- Stores structured memory in PostgreSQL, pgvector, and a knowledge graph instead of relying solely on static Markdown rule files.
- Separates writing, four-path retrieval, and cross-memory synthesis through `retain`, `recall`, and `reflect`.
- Offers Claude Code stdio MCP, a Python SDK, REST API, and a dashboard for integration and human review.
- Uses multilingual bge-m3 embeddings with optional local GPU acceleration and exports memories, entities, and relationships as JSON.
- Supports memory-bank isolation by user or project and imports data from ChatGPT, Gemini, and DayLife.
- Setup is not one-command: it requires Python, PostgreSQL 17, pgvector, and an LLM provider; native Windows also requires Visual Studio Build Tools.
- Storage and embeddings can stay local, but `retain` performs LLM-based fact extraction; unless using a local option such as Ollama, data handling and cost depend on the chosen API.
- Each open Claude Code MCP session loads an embedding model; the documented MCP-process memory use is about 1.5GB.
- The installation instructions focus on Windows, WSL2, and Linux; macOS appears in prerequisites but has no complete native setup procedure.
- The health-check setup uses port 19999, while the Python SDK and REST examples use 8888, so adopters need to verify their deployed configuration.
How do you install or deploy this agent?
The documented Linux/WSL2 path is:git clone https://github.com/24kchengYe/MemoMind.gitcd MemoMindsudo bash install.sh
Then edit /opt/memomind-env/serve.py and set LLM_API_KEY, LLM_BASE_URL, and LLM_MODEL; for an OpenAI-compatible service, one documented example is LLM_BASE_URL = "https://openrouter.ai/api/v1". Start the service with:sudo systemctl start memomind
Verify it with:curl http://localhost:19999/health
The expected response is JSON containing "status":"healthy" and "database":"connected". Native Windows setup additionally requires Python 3.11+, Visual Studio Build Tools, PostgreSQL 17, and pgvector compiled from source.
How do you use this agent?
Register the service with Claude Code:claude mcp add --scope user --transport stdio memomind -- /opt/memomind-env/bin/python3 /opt/memomind-env/mcp_stdio.py
Alternatively, install the Python client:pip install hindsight-client
Then call:from hindsight_client import HindsightClientclient = HindsightClient(base_url="http://localhost:8888")client.retain("default", "User prefers FastAPI over Express for new projects")results = client.recall("default", "What framework should I use?")insight = client.reflect("default", "What patterns do you see in my tech choices?")
Create an isolated bank with client.create_bank("user-alice", name="Alice's Memories").
How does this agent compare with similar options?
Against Claude Code's CLAUDE.md and MEMORY.md, MemoMind is positioned for changing facts, entity relationships, and on-demand recall, while static project rules can remain in project files. The repository also names Mem0, Graphiti/Zep, Letta, Cognee, Hindsight, and MemOS as alternatives, but it does not provide a migration procedure from them or MemoMind benchmark results.