FastAgency vs LangStream
Side-by-side comparison of two AI agent tools
Short answer
- FastAgency is growing faster: +3 GitHub stars in the last 30 days vs +1 for LangStream.
- Pick FastAgency for: the fastest way to bring multi-agent workflows to production. Pick LangStream for: langStream.
From GitHub data refreshed daily.
FastAgencyopen-source
The fastest way to bring multi-agent workflows to production.
LangStreamopen-source
LangStream. Event-Driven Developer Platform for Building and Running LLM AI Apps. Powered by Kubernetes and Kafka.
Metrics
| FastAgency | LangStream | |
|---|---|---|
| Stars | 548 | 427 |
| Star velocity /mo | 2.526315789473684 | 0.9473684210526316 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 396 | — |
| Overall score | 0.16848510206411044 | 0.15325383313942129 |
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
- +Production-ready platform with Kubernetes and Kafka backing for enterprise-scale LLM applications
- +Event-driven architecture optimized for handling streaming AI workloads and real-time interactions
- +Comprehensive tooling including CLI, VS Code extension, and sample applications for rapid development
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
- -Requires Java 11+ runtime dependency which adds complexity to deployment environments
- -Relatively new project with limited community adoption (421 GitHub stars)
- -Opinionated architecture that may not suit all AI application patterns beyond event-driven use cases
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
- •Building real-time chat completion applications with OpenAI integration and streaming responses
- •Deploying scalable LLM applications on Kubernetes clusters with event-driven processing
- •Developing AI applications that require integration between multiple data sources and LLM services
FAQ
- Which is more popular, FastAgency or LangStream?
- FastAgency has more GitHub stars (548 vs 427).
- Which is more actively developed, FastAgency or LangStream?
- FastAgency had more commits in the last 90 days (0 vs 0).
- Should I use FastAgency or LangStream?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.