Which is more popular, OpenLM or Pipecat?

Pipecat has more GitHub stars (16,142 vs 368).

Which is more actively developed, OpenLM or Pipecat?

Pipecat had more commits in the last 90 days (2,861 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

OpenLM has had no commit in 41 months; Pipecat is actively maintained (2,861 commits in the last 90 days).
Pipecat is growing faster: +833 GitHub stars in the last 30 days vs +-0 for OpenLM.
Pick OpenLM for: openAI-compatible Python client that can call any LLM. Pick Pipecat for: open Source framework for voice and multimodal conversational AI.

From GitHub data refreshed daily.

OpenLMopen-source

OpenAI-compatible Python client that can call any LLM

Open Source framework for voice and multimodal conversational AI

+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

+Voice-first architecture with built-in speech recognition and text-to-speech integration for natural conversational experiences
+Comprehensive ecosystem with client SDKs for multiple platforms and additional tools for structured conversations and UI components
+Modular, composable pipeline system that supports integration with various AI services and transport protocols for flexible development

-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

-Python-only framework which may limit developers working primarily in other languages
-Real-time voice processing complexity may require significant learning curve for developers new to audio/video handling

•Model comparison and evaluation by running identical prompts across multiple LLM providers
•Implementing fallback strategies when primary models are unavailable or rate-limited
•Cost optimization by routing requests to the most economical provider for specific use cases

•Building voice assistants and AI companions for customer support, coaching, or meeting assistance applications
•Creating multimodal interfaces that combine voice, video, and images for interactive storytelling or creative content generation
•Developing business automation agents for customer intake, support workflows, or guided user interactions with structured dialog systems

Which is more popular, OpenLM or Pipecat?: Pipecat has more GitHub stars (16,142 vs 368).
Which is more actively developed, OpenLM or Pipecat?: Pipecat had more commits in the last 90 days (2,861 vs 0).
Should I use OpenLM or Pipecat?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.