Ogham MCP Server

@ogham-mcp

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

ogham-mcp

提交者

Kevin Burns

关于 Ogham MCP Server

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

Ogham MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Ogham MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

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.

记忆与知识分类下的更多 MCP 服务器

AOCI-CODE

aoci-spec

Local MCP server and CLI that keeps a Git-versioned, model-written index of a codebase and its database schema, so coding agents start each task with the whole system in view.

1.7K

Semantic Ui React

Semantic-Org

The official Semantic-UI-React integration

13.2K

Context7 MCP - Up-to-date Docs For Any Cursor Prompt

upstash

Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors

58.7K

Leviathan Systems Public Content MCP

Leviathan Systems

Search published GPU deployment, rack integration and structured cabling resources; read canonical documents and Leviathan Systems company service scope. Anonymous, read-only Streamable HTTP MCP. No credentials required.

agentleFS

One permissioned folder your team and your AI agents both log into.