AI代理知识服务器——通过MCP将你的文档、代码、Notion页面、Slack帖子和Discord论坛索引为可搜索、代理可访问的知识。一个配置文件、一个命令，即可支持任何AI编码代理。

快速启动

或者用 Docker 使用：

对于 Docker 中的本地（transformers.js）嵌入，使用 ghcr.io/copilotkit/pathfinder:latest-local 镜像——它预装了 @xenova/transformers。默认镜像保持纤薄，如果设置 embedding.provider: local 但没有依赖，启动时会大声失败。

然后连接你的AI代理：

试试看——《探路者》自创文档

本文档由一个实时的Pathfinder实例索引。连接您的代理以尝试：

它的作用

Pathfinder 将你的 GitHub 仓库——文档（Markdown、MDX、HTML）和源代码——索引到一个 PostgreSQL 向量数据库中。支持 OpenAI、Ollama 和本地 transformers.js 嵌入——本地提供者无需 API 密钥。它通过 MCP 提供可配置的搜索和文件系统探索工具，使 AI 代理能够语义搜索你的文档并用 bash 命令浏览文件。

|工具类型 |功能 |示例 |

pathfinder

The knowledge server for AI agents — index your docs, code, Notion pages, Slack threads, and Discord forums into searchable, agent-accessible knowledge via MCP. One config file, one command, works with any AI coding agent.

Quick Start

npx @copilotkit/pathfinder init
npx @copilotkit/pathfinder serve

Or with Docker:

docker pull ghcr.io/copilotkit/pathfinder
docker run -v ./pathfinder.yaml:/app/pathfinder.yaml \
  -v ./docs:/app/docs -p 3001:3001 \
  ghcr.io/copilotkit/pathfinder

For local (transformers.js) embeddings in Docker, use the ghcr.io/copilotkit/pathfinder:latest-local image — it ships @xenova/transformers preinstalled. The default image stays slim and fails loudly at startup if embedding.provider: local is set without the dependency.

Then connect your AI agent:

{
  "mcpServers": {
    "my-docs": { "url": "http://localhost:3001/mcp" }
  }
}

Try It — Pathfinder on Its Own Docs

This documentation is indexed by a live Pathfinder instance. Connect your agent to try it:

# Claude Code
claude mcp add pathfinder-docs --transport http https://mcp.pathfinder.copilotkit.dev/mcp

// Claude Desktop / Cursor / any MCP client
{
  "mcpServers": {
    "pathfinder-docs": {
      "url": "https://mcp.pathfinder.copilotkit.dev/mcp"
    }
  }
}

What It Does

Pathfinder indexes your GitHub repos — docs (Markdown, MDX, HTML) and source code — into a PostgreSQL vector database. Supports OpenAI, Ollama, and local transformers.js embeddings — no API key required for local providers. It serves configurable search and filesystem exploration tools via MCP, so AI agents can search your docs semantically and browse files with bash commands.

Features

• Semantic Search — pgvector RAG with configurable chunk sizes, overlap, and score thresholds
• Filesystem Exploration — QuickJS WASM sandbox with session state, qmd semantic grep, related files