Hatchet MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Hatchet MCPlist my Kubernetes pods"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Personal Agentic AI - Hatchet MCP
This is my personal local AI agent setup that can run durable and fully traceable agents (with LangGraph), utilizing a universal MCP interface. The MCP directly talks to a Hatchet server, which then triggers my custom tasks/workflows, making it trivial to hook up any local LLM chat interface (LMStudio, OpenCode, Claude Code, Open WebUI, etc) and run custom AI workflows. This has the added benefit of full logging, custom loops, orchestration, retries, tracing, and all other goodies provided by Hatchet.
Quick start
uv sync # install deps (this is only required for local dev)
just docker-start # start Hatchet orchestration server in Docker (localhost:8888)
just worker # start local worker (runs LangGraph locally, no Docker)
just dev # run LangGraph Studio to visualize and debug graphsRelated MCP server: Agentic Control Framework (ACF)
Currently available agents
Agent | How to trigger |
Knowledge Management | ingests files, extracts text, vector embeddings, deep inspection with LLM, RAG retrieval, file indexing |
K8s DevOps Troubleshooter | full LangGraph agent loop: check cluster, diagnose with LLM, auto-fix via kubectl, verify, self-correct |
K8s Direct Tools | individual k8s ops (check_pods, get_logs, describe_pod, run_kubectl, etc.) routed through Hatchet |
Architecture
LLM client (opencode, claude, etc.)
│
├── MCP Server (kb_server.py) ◄── stdio
│ ├── ingest_document ──▶ event push to Worker
│ ├── search, get_document
│ ├── list_documents, search_documents
│ └── delete_document
│
└── MCP Server (k8s_server.py) ◄── stdio
├── check_pods, get_logs, describe_pod
├── get_events, debug_pod, run_kubectl
├── get_deployments, get_statefulsets, get_daemonsets
├── get_services, get_ingresses, get_configmaps
├── get_secrets, exec_in_pod
└── run_devops_agent ──▶ event push to Worker
┌──────────────────────────────┐
│ Hatchet Worker │
│ ├─ knowledge_ingestion │
│ ├─ k8s_devops │
│ └─ k8s_tool │
│ All runs visible in dashboard │
└──────────────────────────────┘MCP config
Register the servers in your LLM client:
{
"mcpServers": {
"knowledge-base": {
"command": "uv",
"args": ["run", "python", "src/mcp/kb_server.py"]
},
"k8s-devops": {
"command": "uv",
"args": ["run", "python", "src/mcp/k8s_server.py"]
}
}
}Adding agents and MCP tools
Create
src/langgraph/agents/<name>.pywithStateGraph(...)and agraphvariableCreate
src/hatchet_worker/workflows/<name>.py— wraps the graph in a Hatchet taskRegister it in
src/hatchet_worker/worker.pywith anon_events=[...]trigger(Optional) Create
src/mcp/<name>_server.pywithFastMCPand@server.tool()functions
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