Summary
If you're running multiple AI coding agents on the same machine and want them to reference each other's work, this server exposes two MCP tools that return local session file paths. Get agent sessions scans ~/.claude, ~/.codex, and ~/.gemini directories for recent JSONL or JSON session logs tied to a project path. Get agent session does the same for a single agent. The agent then reads those files directly using its own file access. No relay, no transformation, just discovery. Useful when you want Codex to review what Claude Code just worked on, or vice versa, without manually hunting down session logs or switching contexts.
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OpenHarness
AI 코딩 에이전트의 최근 세션 파일을 찾아 서로 참조하게 해주는 로컬 MCP 서버.
전용 하네스 없이 하네스처럼 동작한다. 기존 도구 환경을 그대로 유지하면서, 에이전트 간 세션을 연결하는 디스커버리 레이어.
Supported Agents (MVP)
| Agent | Session Path | Format |
|---|---|---|
| Claude Code | ~/.claude/projects/ | JSONL |
| Codex CLI | ~/.codex/sessions/ | JSONL |
| Gemini CLI | ~/.gemini/tmp/<hash>/chats/ | JSON |
Prerequisites
- Each agent must be able to read local files directly.
- OpenHarness only provides paths and metadata — it does not transform, relay, or store session content.
Install
npm install -g @freebird-ai/openharness
openharness init
This registers OpenHarness as an MCP server in each agent's config.
MCP Tools
get_agent_sessions
Returns recent session paths for all agents, filtered by project.
Input:
{ "project": "/Users/me/my-app" }
Output:
[
{
"agent": "claude-code",
"path": "/Users/me/.claude/projects/-Users-me-my-app/session.jsonl",
"format": "jsonl",
"updatedAt": "2026-03-17T14:32:00Z",
"matchedProject": "/Users/me/my-app",
"matchedBy": "directory-name"
}
]
get_agent_session
Returns the recent session path for a specific agent.
Input:
{ "agent": "claude-code", "project": "/Users/me/my-app" }
Output:
{
"agent": "claude-code",
"path": "/Users/me/.claude/projects/-Users-me-my-app/session.jsonl",
"format": "jsonl",
"updatedAt": "2026-03-17T14:32:00Z",
"matchedProject": "/Users/me/my-app",
"matchedBy": "directory-name"
}
Returns null if no session is found.
Project Matching
| Agent | Strategy |
|---|---|
| Claude Code | Project path escaped with - as directory name |
| Codex CLI | Workspace field inside session JSONL |
| Gemini CLI | Project path hash matching tmp/<hash>/ |
How It Works
User → Codex: "Review what Claude Code worked on recently"
Codex → OpenHarness MCP: get_agent_session({ agent: "claude-code", project: "..." })
Codex ← receives session path + metadata
Codex → reads the JSONL file directly and performs review
No rules needed in agent instruction files. MCP tool descriptions are enough for agents to use naturally.
Manual MCP Configuration
If you prefer to configure manually instead of using init:
{
"mcpServers": {
"openharness": {
"command": "openharness",
"args": []
}
}
}
Add this to your agent's MCP config file:
| Agent | Config File |
|---|---|
| Claude Code | ~/.claude/mcp.json or project .mcp.json |
| Codex CLI | project .codex/ |
| Gemini CLI | ~/.gemini/settings.json |
License
MIT
Featured
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →