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Provide self-hosted memory and hybrid retrieval for AI development tools.
The available materials describe an open-source MIT MCP memory layer with no declared required secrets or fixed remote endpoints, and no clear high-risk red flags are evident. However, its stated self-hosted Supabase/Vercel memory storage and retrieval model may imply local execution, persistent data handling, and possible network egress in real deployments, so caution is warranted where details are missing.
The materials explicitly state that no keys or environment variables are required. There is no indication that API tokens, account credentials, or other highly sensitive secrets are needed, so credential exposure risk appears low.
Although no remote endpoints are declared, the description mentions being 'self-hosted on Supabase + Vercel,' which suggests that real deployments may communicate with self-hosted cloud services and transfer memory data. The materials do not specify hosts, data flows, or whether outbound transfer happens by default, so deployment details should be verified.
The system check marks this tool as executes-code, indicating standard MCP capability to run code or processes locally. This is a normal property for this class of tool; the available materials do not show requests for unusual system privileges or actions clearly unrelated to its stated purpose.
As a 'memory layer,' its function inherently involves storing, indexing, and retrieving user or agent interaction content, which may create a persistent memory dataset. The materials do not specify exact read/write scope, isolation boundaries, retention policy, or deletion mechanisms, so attention should be paid to whether sensitive context is stored and where it persists locally or in the cloud.
Positive signals include open-source code, an MIT license, and auditability, which materially reduce supply-chain risk. Points to watch are that the source is a third-party registry, the repository has 0 stars, and maintenance status is unknown, so trust is moderate and code/dependency review is advisable before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-Agentmemory" yet — see the docs or source repo.
Explain how to connect mcp-Agentmemory to Claude Code, including deployment steps for Supabase and Vercel, required environment variables, and how to verify memory is working.
A complete integration and deployment guide with setup steps, environment variables, and verification workflow.
Design a workflow using mcp-Agentmemory so that when I edit a project in Cursor, it automatically retrieves relevant past decisions, code snippets, and task logs, then compiles them into a context summary for the AI.
An actionable memory retrieval workflow describing triggers, retrieved content, and the summary output format.
Explain in simple terms the roles of BM25, pgvector, and graph retrieval in mcp-Agentmemory, and describe the advantages and use cases of combining them versus using vector search alone.
A clear explanation of the concepts and scenario comparison to help decide when hybrid retrieval is most useful.
Share, search, and reuse local memory across multiple AI coding agents.
Help MCP clients remember preferences and retrieve key context across chats.
Give Claude Code persistent memory and semantic context retrieval across sessions.
Persist Claude Code conversations and retrieve relevant context across sessions.
Provide shared memory for Claude across apps with sessions, handoffs, and artifacts.
Provide local-first memory graph and hybrid search for Claude Code and MCP clients.