Station - AI Agent Orchestration Platform

Build, test, and deploy intelligent agent teams. Self-hosted. Git-backed. Production-ready.

Quick Start | Real Example | Deploy | Documentation

Why Station?

Build multi-agent systems that coordinate like real teams. Test with realistic scenarios. Deploy on your infrastructure.

Station gives you: - ✅ Multi-Agent Teams - Coordinate specialist agents under orchestrators - ✅ Built-in Evaluation - LLM-as-judge tests every agent automatically
- ✅ Git-Backed Workflow - Version control agents like code - ✅ One-Command Deploy - Push to production with stn deploy - ✅ Full Observability - Jaeger traces for every execution - ✅ Self-Hosted - Your data, your infrastructure, your control

Quick Start (2 minutes)

Prerequisites

• Docker - Required for Jaeger (traces and observability)
• AI Provider - Choose one:
• CloudShip AI (Recommended) - STN_CLOUDSHIP_KEY or CLOUDSHIPAI_REGISTRATION_KEY
• OPENAI_API_KEY - OpenAI (gpt-5-mini, gpt-5, etc.)
• GEMINI_API_KEY - Google Gemini
• ANTHROPIC_API_KEY - Anthropic (claude-sonnet-4-20250514, etc.)

1. Install Station

curl -fsSL https://raw.githubusercontent.com/cloudshipai/station/main/install.sh | bash

2. Initialize Station

Choose your AI provider:

CloudShip AI (Recommended)

Use CloudShip AI for optimized inference with Llama and Qwen models. This is the default when a registration key is available.

# Set your CloudShip registration key
export CLOUDSHIPAI_REGISTRATION_KEY="csk-..."
# Or use: export STN_CLOUDSHIP_KEY="csk-..."

stn init --provider cloudshipai --ship  # defaults to cloudship/llama-3.1-70b

Available models: - cloudship/llama-3.1-70b (default) - Best balance of performance and cost - cloudship/llama-3.1-8b - Faster, lower cost - cloudship/qwen-72b - Alternative large model

Claude Max/Pro Subscription (⚠️ DEPRECATED)

⚠️ DEPRECATED: Anthropic OAuth is currently unavailable.

Anthropic has restricted third-party use of OAuth tokens. This authentication method is not working until further notice.

Please use one of the following alternatives: - OpenAI API Key (recommended) - Google Gemini API Key - Anthropic API Key (pay-per-token, not subscription-based)

~~Use your existing Claude Max or Claude Pro subscription - no API billing required.~~

# ❌ NOT WORKING - Anthropic OAuth disabled
# stn init --provider anthropic --ship
# stn auth anthropic login

OpenAI (API Key)

export OPENAI_API_KEY="sk-..."
stn init --provider openai --ship  # defaults to gpt-5-mini

Google Gemini (API Key)

export GEMINI_API_KEY="..."
stn init --provider gemini --ship

This sets up: - ✅ Your chosen AI provider - ✅ Ship CLI for filesystem MCP tools - ✅ Configuration at ~/.config/station/config.yaml

3. Start Jaeger (Tracing)

Start the Jaeger tracing backend for observability:

stn jaeger up

This starts Jaeger UI at http://localhost:16686 for viewing agent execution traces.

4. Connect Your MCP Client

Choose your editor and add Station:

Claude Code CLI

claude mcp add station -e OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 --scope user -- stn stdio

Verify with claude mcp list.

OpenCode

Add to opencode.jsonc:

{
  "mcp": {
    "station": {
      "enabled": true,
      "type": "local",
      "command": ["stn", "stdio"],
      "environment": {
        "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318"
      }
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project (or ~/.cursor/mcp.json for global):

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["stdio"],
      "env": {
        "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318"
      }
    }
  }
}

Claude Desktop

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["stdio"],
      "env": {
        "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318"
      }
    }
  }
}

Optional GitOps: Point to a Git-backed workspace:

"command": ["stn", "--config", "/path/to/my-agents/config.yaml", "stdio"]

5. Install Editor Plugins (Optional)

Get skills, slash commands, and enhanced documentation for your AI editor:

Claude Code Plugin

Adds /station commands, skills for agent creation, and MCP server config.

# Add Station marketplace and install plugin
/plugin marketplace add cloudshipai/station
/plugin install station@cloudshipai-station

Or install from local clone:

/plugin install ./station/claude-code-plugin

OpenCode Skill

Adds Station CLI reference skill with agent, workflow, and deployment docs.

# Copy skill to your project
cp -r station/opencode-plugin/.opencode .

# Or install globally
cp -r station/opencode-plugin/.opencode ~/.config/opencode/

Restart OpenCode - skill auto-loads.

6. Start Building

Restart your editor. Station provides: - ✅ Web UI at http://localhost:8585 for configuration - ✅ Jaeger UI at http://localhost:16686 for traces - ✅ 41 MCP tools available in your AI assistant

Try your first command:

"Show me all Station MCP tools available"

Interactive Onboarding Guide (3-5 min tutorial)

Copy this prompt into your AI assistant for a hands-on tour:

You are my Station onboarding guide. Walk me through an interactive hands-on tutorial.

RULES:
1. Create a todo list to track progress through each section
2. At each section, STOP and let me engage before continuing
3. Use Station MCP tools to demonstrate - don't just explain, DO IT
4. Keep it fun and celebrate wins!

THE JOURNEY:

## 1. Hello World Agent
- Create a "hello-world" agent that greets users and tells a joke
- Call the agent and show the result
[STOP for me to try it]

## 2. Faker Tools & MCP Templates
- Explain Faker tools (AI-generated mock data for safe development)
- Note: Real MCP tools are added via Station UI or template.json
- Explain MCP templates - they keep credentials safe when deploying
- Create a "prometheus-metrics" faker for realistic metrics
[STOP to see the faker]

## 3. DevOps Investigation Agent
- Create a "metrics-investigator" agent using our prometheus faker
- Call it: "Check for performance issues in the last hour"
[STOP to review the investigation]

## 4. Multi-Agent Hierarchy
- Create an "incident-coordinator" that delegates to:
  - metrics-investigator (existing)
  - logs-investigator (new - create a logs faker)
- Show hierarchy structure in the .prompt file
- Call coordinator: "Investigate why the API is slow"
[STOP to see delegation]

## 5. Inspecting Runs
- Use inspect_run to show detailed execution
- Explain: tool calls, delegations, timing
[STOP to explore]

## 6. Workflow with Human-in-the-Loop
- Create a workflow: investigate → switch on severity → human_approval if high → report
- Make it complex (switch/parallel), not sequential
- Start the workflow
[STOP for me to approve/reject]

## 7. Evaluation & Reporting
- Run evals with evaluate_benchmark
- Generate a performance report
[STOP to review]

## 8. Grand Finale
- Direct me to http://localhost:8585 (Station UI)
- Quick tour: Agents, MCP servers, Runs, Workflows
- Celebrate!

## 9. Want More? (Optional)
Briefly explain these advanced features (no demo needed):
- **Schedules**: Cron-based agent scheduling
- **Sandboxes**: Isolated code execution (Python/Node/Bash)
- **Notify Webhooks**: Send alerts to Slack, ntfy, Discord
- **Bundles**: Package and share agent teams
- **Deploy**: `stn deploy` to Fly.io, Docker, K8s
- **CloudShip**: Centralized management and team OAuth

Start now with Section 1!

Running Station with stn up

The easiest way to run Station is with stn up - a single command that starts Station in a Docker container with everything configured.

Primary Use Case: Running Bundles

stn up is designed to make it trivial to run agent bundles from your CloudShip account or the community:

# Run a bundle from CloudShip (by ID or name)
stn up --bundle finops-cost-analyzer

# Run a bundle from URL
stn up --bundle https://example.com/my-bundle.tar.gz

# Run a local bundle file
stn up --bundle ./my-custom-agents.tar.gz

This is the recommended way for most users to get started - just pick a bundle and go.

Secondary Use Case: Testing Local Configurations

Developers can also use stn up to test their local agent configurations in an isolated container environment:

# Test your local workspace in a container
stn up --workspace ~/my-agents

# Test with a specific environment
stn up --environment production

# Test with development tools enabled
stn up --develop

This lets you validate that your agents work correctly in the same containerized environment they'll run in production.

Quick Commands

# Start Station (interactive setup on first run)
stn up

# Start with specific AI provider
stn up --provider openai --model gpt-5

# Check status
stn status

# View logs
stn logs -f

# Stop Station
stn down

# Stop and remove all data (fresh start)
stn down --remove-volume

What stn up Provides

See Container Lifecycle for detailed architecture.

AI Provider Authentication

Station supports multiple authentication methods for AI providers.

API Keys (Default)

The simplest way to authenticate - set your API key as an environment variable:

# CloudShip AI (Recommended - auto-detected when registration key is set)
export CLOUDSHIPAI_REGISTRATION_KEY="csk-..."
# Or: export STN_CLOUDSHIP_KEY="csk-..."

# OpenAI
export OPENAI_API_KEY="sk-..."

# Google Gemini
export GEMINI_API_KEY="..."

# Anthropic (API billing)
export ANTHROPIC_API_KEY="sk-ant-api03-..."

Anthropic OAuth (Claude Max/Pro Subscription) - ⚠️ DEPRECATED

⚠️ DEPRECATED: Anthropic OAuth is currently unavailable.

Anthropic has restricted third-party use of OAuth tokens. This authentication method is not working until further notice.

Use these alternatives instead: - OPENAI_API_KEY for OpenAI models (recommended) - GEMINI_API_KEY for Google Gemini models - ANTHROPIC_API_KEY for Anthropic API (pay-per-token billing)

Previous OAuth documentation (for reference only)

~~Use your Claude Max or Claude Pro subscription instead of pay-per-token API billing.~~

Setup (NOT WORKING):

# ❌ DEPRECATED - Anthropic OAuth disabled
# stn auth anthropic login

Authentication Priority: | Priority | Method | Description | |----------|--------|-------------| | 1 | STN_AI_AUTH_TYPE=api_key | Force API key mode (override) | | ~~2~~ | ~~Station OAuth tokens~~ | ~~From stn auth anthropic login~~ DEPRECATED | | ~~3~~ | ~~Claude Code credentials~~ | ~~From ~/.claude/.credentials.json~~ DEPRECATED | | 4 | ANTHROPIC_API_KEY env var | Standard API key (USE THIS) |

For Anthropic models, use API key authentication:

# Set Anthropic API key
export ANTHROPIC_API_KEY="sk-ant-api03-..."

# Or in Docker
docker run \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  -e STN_AI_PROVIDER=anthropic \
  station:latest

How You Interface: MCP-Driven Platform

Station is driven entirely through MCP tools in your AI assistant. Natural language requests use 41+ available MCP tools.

MCP Tool Categories

Example interaction:

You: "Create a logs analysis agent that uses Datadog and Elasticsearch"
Claude: [Using create_agent tool...] ✅ Created logs_investigator

You: "Run the incident coordinator on the API timeout issue"  
Claude: [Using call_agent...] [Full investigation with multi-agent delegation]

Discover all tools: Ask your AI assistant "Show me all Station M