https://github.com/the-swarm-corporation/agentapiproduction
A production-grade REST API for deploying and managing AI agents in the cloud. This API provides a scalable and secure way to create, manage, and interact with AI agents.
https://github.com/the-swarm-corporation/agentapiproduction
agents ai api cloudrun docker fastapi ml swarms
Last synced: 6 months ago
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A production-grade REST API for deploying and managing AI agents in the cloud. This API provides a scalable and secure way to create, manage, and interact with AI agents.
- Host: GitHub
- URL: https://github.com/the-swarm-corporation/agentapiproduction
- Owner: The-Swarm-Corporation
- License: mit
- Created: 2025-01-02T09:08:21.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T14:03:05.000Z (9 months ago)
- Last Synced: 2025-04-24T02:07:32.529Z (6 months ago)
- Topics: agents, ai, api, cloudrun, docker, fastapi, ml, swarms
- Language: Python
- Homepage: https://swarms.ai
- Size: 79.1 KB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Swarms Agent API
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
A production-grade REST API for deploying and managing AI agents in the cloud. This API provides a scalable and secure way to create, manage, and interact with AI agents.
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://www.docker.com/)
## Features
- 🚀 Create and manage multiple AI agents
- 🔄 Process completions with rate limiting and error handling
- 📊 Track agent metrics and performance
- 🔒 Built-in security features
- 🎯 Horizontal scaling support
- 🛠️ Easy deployment with Docker
- 💫 Graceful shutdown handling
- 🔍 Comprehensive logging
- ⚡ High-performance FastAPI backend
## Quick Start
### Prerequisites
- Python 3.11+
- Docker (optional)
- OpenAI API key (or other LLM provider key)
### Local Development Setup
1. Clone the repository:
```bash
git clone https://github.com/The-Swarm-Corporation/AgentAPIProduction.git
cd agent-api
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Create a `.env` file:
```env
OPENAI_API_KEY=your_api_key_here
PORT=8080
WORKSPACE_DIR=agent_workspace
```
5. Run the server:
```bash
python -m uvicorn api.api:app --host 0.0.0.0 --port 8080 --reload
```
### Docker Deployment
1. Build the Docker image:
```bash
docker build -t agent-api .
```
2. Run the container:
```bash
docker run -d \
-p 8080:8080 \
-e OPENAI_API_KEY=your_api_key_here \
-e PORT=8080 \
--name agent-api \
agent-api
```
## API Documentation
### Base URL
```
http://localhost:8080/v1
```
### Endpoints
#### Create Agent
```http
POST /v1/agent
```
```json
{
"agent_name": "string",
"model_name": "string",
"description": "string",
"system_prompt": "string",
"temperature": 0.1,
"max_loops": 1
}
```
#### List Agents
```http
GET /v1/agents
```
#### Process Completion
```http
POST /v1/agent/completions
```
```json
{
"prompt": "string",
"agent_id": "uuid",
"max_tokens": null,
"temperature_override": 0.5
}
```
#### Get Agent Metrics
```http
GET /v1/agent/{agent_id}/metrics
```
For full API documentation, visit `/v1/docs` or `/v1/redoc` after starting the server.
## Rate Limiting
The API implements rate limiting with the following defaults:
- 2 requests per second
- 30 requests per minute
- 1000 requests per hour
- 10000 requests per day
## Deployment Considerations
### Production Environment
For production deployment, consider:
1. Using a reverse proxy (e.g., Nginx)
2. Implementing SSL/TLS
3. Setting up monitoring (e.g., Prometheus + Grafana)
4. Using environment-specific configuration
5. Implementing proper backup strategies
Example `docker-compose.yml` for production:
```yaml
version: '3.8'
services:
agent-api:
build: .
ports:
- "8080:8080"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- PORT=8080
volumes:
- agent_data:/app/agent_workspace
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
interval: 30s
timeout: 3s
retries: 3
restart: unless-stopped
volumes:
agent_data:
```
### Cloud Deployment
The API can be deployed to various cloud providers:
#### AWS Elastic Beanstalk
```bash
eb init -p docker agent-api
eb create production
```
#### Google Cloud Run
```bash
gcloud builds submit --tag gcr.io/PROJECT_ID/agent-api
gcloud run deploy agent-api --image gcr.io/PROJECT_ID/agent-api
```
#### Azure Container Instances
```bash
az container create \
--resource-group myResourceGroup \
--name agent-api \
--image your-registry.azurecr.io/agent-api \
--dns-name-label agent-api \
--ports 8080
```
## Error Handling
The API implements comprehensive error handling:
- Rate limit exceeded: 429
- Not found: 404
- Internal server error: 500
Error responses follow this format:
```json
{
"detail": {
"error": "string",
"message": "string"
}
}
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
For support and questions:
- Open an issue on GitHub
- Join our Discord community
- Email support@yourdomain.com
## Acknowledgments
- FastAPI for the excellent web framework
- Swarms for the agent implementation
- The open-source community
---
Made with ❤️ by [swarms.ai](https://www.swarms.ai)