hivegate-ai/hivegate

**Run your Agno agents as a production API.**
HiveGate is an open-source FastAPI service built on Agno 3. You write the agent, and HiveGate gives it what production needs:
- per-user sessions
- OAuth tokens for Google and Microsoft
- human approval for risky tool calls
- multi-agent teams with a supervisor
- versioned prompts
- token usage and cost metering
- tracing
**Website** · **Docs** · **Deploy guide** · **Discussions** · **Roadmap**
See the Quickstart for the full steps.
Or deploy the prebuilt image in one click:
> HiveGate is not a request-routing API gateway like Kong. It runs AI agents and exposes them over a REST API.
Why HiveGate, and when you don't need it
Agno gives you the agent itself: model calls, tools, memory and teams. Its own AgentOS can also serve agents over HTTP, with sessions, human approval, RBAC and tracing. **If your agents are defined in code and AgentOS covers what you need, use AgentOS.**
HiveGate is for when your agents act *for your users*, in their own accounts, and are managed as data rather than code:
- **Per-user OAuth tokens.** Store each user's Google or Microsoft tokens once. HiveGate refreshes them, and the Gmail, Calendar, Contacts and Drive toolkits act as that user.
- **Approval before risky actions.** A tool call such as sending an email pauses the run until someone approves or denies it through the API.
- **Agents, prompts and skills as data.** Create and version them through the REST API, with no redeploy.
- **Per-tenant knowledge.** Documents are kept per tenant and per collection in Qdrant.
- **Supervisor teams with container workers.** Jobs run in Docker or Kubernetes, including Claude Code and managed-agent workers.
- **A ready-to-deploy service.** A prebuilt image with one-click deploys to Render, Railway and Koyeb, plus Kubernetes and cloud manifests.
Features
- **Agent Management** - Create, configure, and manage AI agents via REST API
- **Team Orchestration** - Compose agents into teams for multi-agent workflows
- **Supervisor Platform** - Supervisor/worker execution with job queues, approval flows, and containerized runners (Docker & Kubernetes)
- **Skills & Evaluations** - Reusable skill definitions and a built-in evaluation framework for agent quality
- **Knowledge Base** - Store and index documents for agent retrieval (Qdrant)
- **Prompts Service** - Versioned prompt templates with pluggable storage (PostgreSQL, LangSmith)
- **Token Management** - Secure OAuth token storage with auto-refresh
- **Toolkits** - Pre-built integrations for Calendar, Email, Contacts, Drive (Google & Microsoft), plus Claude Code and managed-agent providers
- **Usage & Cost Metering** - Every model call recorded with its tokens and USD cost, queryable through the /usage API
- **Prompt Caching** - Claude models cache tools, the system prompt and the conversation, so repeat input bills at a fraction of the price
- **Observability** - Pluggable tracing and logging (OpenTelemetry, Sentry, OTLP)
Quickstart
> Prerequisites: Docker Desktop installed and running, Python 3.11+.
1. Clone and configure
Edit .env **before starting the server**. The server reads it once at startup, so a key exported in another terminal won't reach it.
- Set at least one model provider key. The demo agents use Gemini by default, so set GOOGLE_API_KEY. OPENAI_API_KEY, ANTHROPIC_API_KEY, XAI_API_KEY, ZAI_API_KEY and DEEPSEEK_API_KEY also work.
- For local use, uncomment AUTH_DISABLED=true so you can call the API without creating an API key first. Never enable it on a deployment other people can reach.
The database settings in .env.example already match the Postgres that Docker Compose starts.
2. Start
3. Explore
4. Chat with an agent
If the reply mentions an invalid API key, check the model key in .env and restart the server. To pick a different model, see Choosing a model.