Content
# Station
**Open-Source Runtime for Infrastructure Management Agents**
Deploy AI agents on your infrastructure. Keep sensitive data secure. Maintain full control.
[Quick Start](#quick-start) | [Documentation](./docs/station/) | [Examples](./docs/station/examples.md)
---
## Why Station?
AI agents can automate infrastructure management—cost optimization, security compliance, deployments—but most solutions require sharing credentials and sensitive data with third-party platforms.
**Station gives you control:**
- ✅ **Run on your infrastructure** - Deploy agents wherever you need them (AWS, GCP, on-prem, local)
- ✅ **Keep data private** - Agents access your tools directly, no data leaves your environment
- ✅ **Simple agent development** - Declarative dotprompt format, develop and test locally
- ✅ **Fine-grained security** - Control exactly which tools each agent can use (read vs write)
- ✅ **Share and collaborate** - Bundle agents with MCP configs for easy distribution
- ✅ **Open source** - Full transparency, audit the code yourself
[Learn more about Station's architecture →](./docs/station/architecture.md)
---
## How Simple Are Agents?
Here's a complete FinOps agent in dotprompt format:
```yaml
---
metadata:
name: "AWS Cost Spike Analyzer"
description: "Detects unusual cost increases and identifies root causes"
model: gpt-4o-mini
max_steps: 5
tools:
- "__get_cost_and_usage" # AWS Cost Explorer - read only
- "__list_cost_allocation_tags"
- "__get_savings_plans_coverage"
---
{{role "system"}}
You are a FinOps analyst specializing in AWS cost anomaly detection.
Analyze cost trends, identify spikes, and provide actionable recommendations.
{{role "user"}}
{{userInput}}
```
That's it. Station handles:
- MCP tool connections (AWS Cost Explorer, Stripe, Grafana, etc.)
- Template variables for secrets/config (`{{ .AWS_REGION }}`)
- Multi-environment isolation (dev/staging/prod)
- Execution tracking and structured outputs
[See more agent examples →](./docs/station/examples.md)
---
## Quick Start
### 1. Install Station
```bash
curl -fsSL https://raw.githubusercontent.com/cloudshipai/station/main/install.sh | bash
```
### 2. Start Station
```bash
# Set your OpenAI API key
export OPENAI_API_KEY=sk-your-key-here
# Start Station (automatically configures .mcp.json for Claude Code/Cursor)
stn up --provider openai
```
**More provider options:**
```bash
# OpenAI with specific model
stn up --provider openai --model gpt-4o
# Anthropic Claude
stn up --provider anthropic --api-key sk-ant-...
# Google Gemini
stn up --provider gemini --api-key your-key --model gemini-2.0-flash-exp
# Custom provider (Ollama, etc.)
stn up --provider custom --base-url http://localhost:11434/v1 --model llama3.2
# With CloudShip registration for centralized management
stn up --provider openai --cloudshipai-registration-key your-registration-key
```
**Stop Station:**
```bash
stn down
```
**That's it!** Station is now running with:
- ✅ Web UI at `http://localhost:8585` for managing tools, bundles, and builds
- ✅ MCP server at `http://localhost:8586/mcp` configured for Claude Code/Cursor
- ✅ Dynamic Agent MCP at `http://localhost:3030/mcp`
- ✅ `.mcp.json` automatically created for seamless Claude integration
[Full installation guide →](./docs/station/installation.md)
---
## Development Workflow
Station provides a complete agent development workflow using Claude Code or Cursor:
### 1. Add MCP Tools (via UI)
Open the Web UI at `http://localhost:8585`:
- Browse available MCP servers (AWS, Stripe, Grafana, filesystem, security tools)
- Add MCP tools to your environment
- Configure template variables for secrets
### 2. Connect Claude Code/Cursor
Station automatically creates `.mcp.json` when you run `stn up`:
```json
{
"mcpServers": {
"station": {
"type": "http",
"url": "http://localhost:8586/mcp"
}
}
}
```
Restart Claude Code/Cursor to connect to Station.
### 3. Create & Manage Agents (via Claude)
Use Claude Code/Cursor with Station's MCP tools to:
- **Create agents** - Write dotprompt files with agent definitions
- **Run agents** - Execute agents and see results in real-time
- **List agents** - View all agents in your environments
- **Update agents** - Modify agent configs and tools
- **Create environments** - Set up dev/staging/prod isolation
- **Sync environments** - Apply changes and resolve variables
Example interaction with Claude:
```
You: "Create a FinOps agent that analyzes AWS costs using the cost explorer tools"
Claude: [Uses Station MCP tools to create agent with proper dotprompt format]
You: "Run the agent to analyze last month's costs"
Claude: [Executes agent and shows cost analysis results]
```
### 4. Bundle & Deploy (via UI)
Back to the Web UI at `http://localhost:8585`:
- **Create bundles** - Package agents + MCP configs for distribution
- **Share bundles** - Export bundles to share with team
- **Build Docker images** - Create production containers from environments
- **Install bundles** - Import bundles from registry or files
[Agent Development Guide →](./docs/station/agent-development.md) | [Bundling & Distribution →](./docs/station/bundles.md)
---
## MCP Tools & Templates
Station uses the Model Context Protocol (MCP) to give agents access to tools—AWS APIs, databases, filesystems, security scanners, and more.
**Fine-grained control over agent capabilities:**
```yaml
tools:
- "__get_cost_and_usage" # AWS Cost Explorer - read only
- "__list_cost_allocation_tags" # Read cost tags
- "__read_text_file" # Filesystem read
# No write permissions - agent can analyze but not modify
```
**Template variables for secure configuration:**
```json
{
"mcpServers": {
"aws-cost-explorer": {
"command": "mcp-server-aws",
"env": {
"AWS_REGION": "{{ .AWS_REGION }}",
"AWS_PROFILE": "{{ .AWS_PROFILE }}"
}
}
}
}
```
Variables are resolved at runtime from `variables.yml`—never hardcoded in configs.
[MCP Tools Documentation →](./docs/station/mcp-tools.md) | [Template Variables Guide →](./docs/station/templates.md)
---
## Zero-Config Deployments
Deploy Station agents to production without manual configuration. Station supports zero-config deployments that automatically:
- Discover cloud credentials and configuration
- Set up MCP tool connections
- Deploy agents with production-ready settings
**Deploy to Docker Compose:**
```bash
# Build environment container
stn build env production
# Deploy with docker-compose
docker-compose up -d
```
Station automatically configures:
- AWS credentials from instance role or environment
- Database connections from service discovery
- MCP servers with template variables resolved
**Supported platforms:**
- Docker / Docker Compose
- AWS ECS
- Kubernetes
- AWS Lambda (coming soon)
[Zero-Config Deployment Guide →](./docs/station/zero-config-deployments.md) | [Docker Compose Examples →](./docs/station/docker-compose-deployments.md)
---
## Use Cases
**FinOps & Cost Optimization:**
- Cost spike detection and root cause analysis
- Reserved instance utilization tracking
- Multi-cloud cost attribution
- COGS analysis for SaaS businesses
**Security & Compliance:**
- Infrastructure security scanning
- Compliance violation detection
- Secret rotation monitoring
- Vulnerability assessments
**Deployment & Operations:**
- Automated deployment validation
- Performance regression detection
- Incident response automation
- Change impact analysis
[See Example Agents →](./docs/station/examples.md)
---
## System Requirements
- **OS:** Linux, macOS, Windows
- **Memory:** 512MB minimum, 1GB recommended
- **Storage:** 200MB for binary, 1GB+ for agent data
- **Network:** Outbound HTTPS for AI providers
---
## Resources
- 📚 **[Documentation](./docs/station/)** - Complete guides and tutorials
- 🐛 **[Issues](https://github.com/cloudshipai/station/issues)** - Bug reports and feature requests
- 💬 **[Discord](https://discord.gg/station-ai)** - Community support
---
## License
**Apache 2.0** - Free for all use, open source contributions welcome.
---
**Station - Open-Source Runtime for Infrastructure Management Agents**
*Deploy AI agents on your infrastructure. Keep data secure. Maintain control.*
