Ogham Mcp

Name: Ogham Mcp
Author: ogham-mcp

@ogham-mcp

Ogham Mcp について

Persistent shared memory for AI agents. Hybrid search (pgvector + tsvector), knowledge graph, cognitive scoring, and 16-language temporal extraction. 97.2% Recall@10 on LongMemEval with one PostgreSQL query. Works across Claude Code, Cursor, Codex, OpenClaw, and any MCP client

#persistent #pgvector #ogham #memory

基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

公開者

ogham-mcp

投稿者

Kevin Burns

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "ogham": {
      "command": "uvx",
      "args": [
        "ogham-mcp",
        "serve"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Ogham Mcp?

Ogham Mcp is a persistent, searchable shared memory server for AI coding agents. It enables agents across different client tools (Claude Code, Cursor, Kiro, OpenCode) to retain and search context across sessions. It achieves 97.2% recall@10 on the LongMemEval benchmark using a single PostgreSQL query.

How to use Ogham Mcp?

Install via pip (pip install ogham-mcp), Docker (ghcr.io/ogham-mcp/ogham-mcp), or integrate directly with Claude Code and OpenCode. Configure environment variables for database (Supabase, Neon, or vanilla Postgres), embedding providers, and temporal search. The server exposes MCP tools and can be run with SSE transport for multi-agent setups, or used through its CLI.

Key features of Ogham Mcp

97.2% retrieval recall@10 on LongMemEval benchmark
Single PostgreSQL query – no LLM in search pipeline
Persistent shared memory across different coding clients
Tools for memory, search, graph, profiles, import/export
Three built-in skills: ogham-research, ogham-recall, ogham-maintain
Configurable embedding providers and temporal search

Use cases of Ogham Mcp

AI coding agents sharing context across sessions and client tools
Remembering past decisions, gotchas, and architectural patterns
Reusing agent knowledge without re-explaining the codebase
Multi-agent collaboration with shared memory via SSE transport
Debugging recurring issues by retrieving relevant historical context

FAQ from Ogham Mcp

What runtime does Ogham Mcp require?

Ogham Mcp requires Python 3.13+ and a PostgreSQL database with pgvector and tsvector extensions.

How does Ogham Mcp achieve high retrieval accuracy?

It uses a hybrid search combining pgvector and tsvector (CCF) in a single SQL query, achieving 97.2% recall@10 without neural rerankers, knowledge graphs, or LLMs in the search loop.

What database options are supported?

Supabase, Neon, and vanilla PostgreSQL are supported. Full setup instructions are provided in the README for each option.

Is there a Docker image available?

Yes, a pre‑built Docker image is published at ghcr.io/ogham-mcp/ogham-mcp on GitHub Container Registry.

How can I install Ogham Mcp?

Install via pip (pip install ogham-mcp), Docker, or by integrating directly with MCP clients like Claude Code and OpenCode.

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