Star Growth
Overview
FastAgency is a Python framework that provides a unified programming interface for deploying multi-agent workflows built with AG2 (formerly AutoGen) to production environments. Rather than being another agentic AI framework, FastAgency focuses on bridging the gap between development and production deployment of existing AG2 workflows. It enables developers to create web chat applications and REST API services that interact with AI agents using just a few lines of code. The framework is designed to handle scaling challenges by supporting fully distributed systems with internal message brokers that can coordinate multiple machines across multiple datacenters. FastAgency addresses the common problem of moving from prototype agent workflows to production-ready applications, providing the infrastructure and tooling needed to deploy agent-based systems at scale. With support for multiple Python versions and active CI/CD pipelines, it aims to be a reliable production deployment solution for multi-agent AI applications.
Deep Analysis
vs raw AutoGen/AG2: production deployment framework with unified interface, built-in testing, and FastAPI/NATS.io adapters for scaling agent workflows
⚡ Capabilities
- • Unified programming interface for multi-agent AI workflows
- • Prototype-to-production deployment bridge
- • External API integration via OpenAPI specs
- • Built-in Tester Class for automated workflow testing
- • CLI for workflow orchestration and monitoring
- • Console and web UI interfaces
- • CI/CD integration support
🔗 Integrations
✓ Best For
- ✓ Teams deploying AG2/AutoGen workflows to production
- ✓ Projects needing unified console + web interfaces for agent workflows
✗ Not Ideal For
- ✗ Teams using LangChain/CrewAI (AG2 only currently)
- ✗ Simple single-agent applications
Languages
Deployment
⚠ Known Limitations
- ⚠ Currently supports only AG2 as runtime framework
- ⚠ Multi-framework support planned but not yet available
- ⚠ Python 3.10-3.12 only
- ⚠ Web UI limited to MesopUI option
Pros
- + Unified interface for deploying AG2 workflows to production with minimal code changes
- + Supports both web chat applications and REST API services from the same codebase
- + Built-in scaling capabilities with distributed architecture and message broker coordination
Cons
- - Dependent on AG2 framework, limiting flexibility to other agent frameworks
- - Relatively small community with 532 GitHub stars compared to major frameworks
- - Limited documentation available in the provided materials for advanced features
Use Cases
- • Deploying AG2 multi-agent chatbots as web applications for customer service or support
- • Creating REST API services that expose agent workflows for integration with existing systems
- • Building scalable distributed agent systems that coordinate across multiple servers or datacenters