
Weasley DeepMind
io.github.potatohoney-pPublished by GitHub account potatohoney-pv1.0.0Updated Oct 24, 2026
Secure, fragment-based long-term memory for MCP-compatible AI agents.
Overview
Self-hosted long-term memory server that lets an AI assistant store, link and recall small typed memory fragments through MCP.
- What you can do
- Store memory fragments, individually or in batches, with type, provenance, importance and lifecycle state
- Recall ranked context within a token budget using keyword, PostgreSQL GIN, graph-neighbor and optional vector search
- Amend, forget and link fragments, and explore the memory graph
- Reflect on and consolidate stored memory, and read memory statistics
- Inspect fragment history, reconstruct history and search traces
- When to use it
- Useful when an assistant should carry context across sessions without replaying whole conversations, and you are willing to run your own PostgreSQL database. Suited to teams that want memory data kept on their own infrastructure.
- Requirements
- Node.js 20.19 or newer and PostgreSQL 15 or newer with the pgvector extension; Redis 7 is optional. Requires the DATABASE_URL connection string, and optionally EMBEDDING_API_KEY for semantic recall. Runs as a local stdio process via npx, or as an HTTP server on loopback with a bearer access key.
Installation
In SourceWeft
- Open Weasley DeepMind in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Weasley DeepMind
Weasley DeepMind is a self-hosted memory server for AI agents. It stores small, typed memory fragments, links related evidence, and recalls the right context through MCP without forcing an entire conversation history back into every prompt.
It is part of the Weasley Open Source family and is released under Apache-2.0.
Why DeepMind
- Useful recall, not a transcript dump. Keyword, PostgreSQL GIN, graph-neighbor, and optional pgvector search are merged and ranked within a token budget.
- Memory with structure. Facts, decisions, procedures, preferences, episodes, and errors keep provenance, importance, links, and lifecycle state.
- MCP-native. Use a local stdio process or the Streamable HTTP endpoint at
/mcp. - Fail-closed HTTP security. The HTTP server rejects requests when no access key is configured. Authentication can only be disabled explicitly for local development.
- Self-hosted data. PostgreSQL is the system of record. Redis and external embedding or LLM providers are optional.
- Operationally ready. Migrations are serialized with a PostgreSQL advisory lock, secrets are redacted from logs, and updates refuse dirty Git checkouts.
Requirements
- Node.js 20.19 or newer
- PostgreSQL 15 or newer with the
pgvectorextension - Redis 7 (optional, for cache and low-latency keyword lookup)
For a reproducible offline check of correction/retraction recall and content-token
budgets, run npm run --silent eval:recall with Node.js 24. This uses production
recall code and synthetic storage, with no database or model required. See the
offline recall evaluation for the
negative control, report definitions and limits.
Install from npm
Create a PostgreSQL database with pgvector, then configure the connection:
The HTTP server listens on http://127.0.0.1:57332 by default. Its MCP endpoint is http://127.0.0.1:57332/mcp.
Add it to an MCP client
The npm package includes a newline-delimited stdio transport. A generic MCP client configuration looks like this:
For an already-running HTTP deployment, point the client at /mcp and send Authorization: Bearer <WEASLEY_DEEPMIND_ACCESS_KEY>.
Run the repository locally
The development Compose file exposes PostgreSQL at localhost:35432 and Redis at localhost:36379. Update .env to match those ports before starting the server.
Core flow
The main tool set includes remember, batch_remember, recall, forget, amend, link, reflect, context, memory_stats, memory_consolidate, graph_explore, fragment_history, reconstruct_history, and search_traces. Tool availability can be limited by API-key permissions and operating mode.
Configuration
See .env.example, configuration, and the installation guide for the complete matrix.
Local transformer embeddings are intentionally excluded from the default install so the core MCP server does not inherit an optional native-image dependency. Follow the pinned setup in docs/embedding-local.md before selecting EMBEDDING_PROVIDER=transformers.
CLI
Run weasley-deepmind --help or see the CLI reference for every command.
Security defaults
- Keep the HTTP listener on loopback unless you have a trusted reverse proxy and an explicit origin allowlist.
- Use a unique, high-entropy
WEASLEY_DEEPMIND_ACCESS_KEYand never commit.env. - Stdio mode trusts the local MCP host process; protect the host account and database credentials accordingly.
- Report vulnerabilities privately using the instructions in SECURITY.md.
Development
Architecture and operations details live in docs/architecture.en.md and docs/operations. Contributions are welcome through CONTRIBUTING.md.
License
Copyright 2026 Weasley Open Source.
Licensed under the Apache License 2.0. See NOTICE for attribution information.
Source: README.md at commit 762f5c7
Tools
0Version history
1- v1.0.0LatestSep 16, 2026
Score
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- Reached in the last 30 days25 pts
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- Auth declared10 pts
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- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts