agents vs OpenLM
Side-by-side comparison of two AI agent tools
Short answer
- OpenLM has had no commit in 41 months; agents is actively maintained (528 commits in the last 90 days).
- agents is growing faster: +1,358 GitHub stars in the last 30 days vs +-0 for OpenLM.
- Pick agents for: a framework for building realtime voice AI agents. Pick OpenLM for: openAI-compatible Python client that can call any LLM.
From GitHub data refreshed daily.
agentsopen-source
A framework for building realtime voice AI agents π€ποΈπΉ
OpenLMopen-source
OpenAI-compatible Python client that can call any LLM
Metrics
| agents | OpenLM | |
|---|---|---|
| Stars | 14.4k | 368 |
| Star velocity /mo | 1.4k | -0.47619047619047616 |
| Commits (90d) | 528 | 0 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8605320671263966 | 0.12773224683762688 |
Pros
- +Comprehensive multi-modal capabilities with flexible integrations for STT, LLM, TTS, and Realtime APIs in a single framework
- +Built-in telephony integration allows agents to make and receive phone calls through LiveKit's telephony stack
- +Advanced semantic turn detection using transformer models helps reduce interruptions and improve conversation flow
- +Drop-in OpenAI compatibility requires minimal code changes (single import line)
- +Multi-provider support enables batch processing across different models and providers simultaneously
- +Lightweight architecture calls APIs directly without bloated SDK dependencies
Cons
- -Requires server infrastructure and technical expertise to deploy and maintain realtime voice agents
- -Complex setup with multiple integration points may have a steep learning curve for newcomers
- -Real-time voice processing demands significant computational resources and low-latency networking
- -Currently limited to Completion endpoint only, lacking support for newer OpenAI features like Chat completions
- -Relatively small community with 371 GitHub stars compared to official SDKs
- -May lag behind latest provider API updates due to abstraction layer maintenance overhead
Use Cases
- β’Customer service automation with voice-enabled agents that can handle phone calls and web-based interactions
- β’Virtual assistants for healthcare or education that need to see, hear, and respond in real-time conversations
- β’Interactive voice response (IVR) systems that integrate with existing telephony infrastructure for business applications
- β’Model comparison and evaluation by running identical prompts across multiple LLM providers
- β’Implementing fallback strategies when primary models are unavailable or rate-limited
- β’Cost optimization by routing requests to the most economical provider for specific use cases
FAQ
- Which is more popular, agents or OpenLM?
- agents has more GitHub stars (14,447 vs 368).
- Which is more actively developed, agents or OpenLM?
- agents had more commits in the last 90 days (528 vs 0).
- Should I use agents or OpenLM?
- 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.