Which is more popular, agents or Multi-Modal LangChain agents in Production?

agents has more GitHub stars (14,454 vs 479).

Which is more actively developed, agents or Multi-Modal LangChain agents in Production?

agents had more commits in the last 90 days (532 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

Multi-Modal LangChain agents in Production has had no commit in 38 months; agents is actively maintained (532 commits in the last 90 days).
agents is growing faster: +1,352 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
Pick agents for: a framework for building realtime voice AI agents. Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram.

From GitHub data refreshed daily.

agentsopen-source

A framework for building realtime voice AI agents 🤖🎙️📹

Deploy LangChain Agents and connect them to Telegram

	agents	Multi-Modal LangChain agents in Production
Stars	14.5k	479
Star velocity /mo	1.4k	0.3157894736842105
Commits (90d)	532	0
Releases (6m)	10	0
Overall score	0.849107226184685	0.1390646436413974

+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

+Production-ready infrastructure with built-in memory management and deployment tooling via Steamship platform
+Multi-modal support including voice capabilities and embeddable chat windows for versatile user interactions
+Telegram integration and monetization features built-in, enabling immediate deployment and revenue generation

-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

-Platform dependency on Steamship creates vendor lock-in and limits deployment flexibility
-Limited documentation beyond basic setup may create learning curve for complex customizations
-Focused primarily on Telegram integration, which may not suit all chatbot deployment scenarios

•Customer service automation with voice-enabled agents that can handle phone calls and web-based interactions
•Virtual assistants for healthcare or education that need to see, hear, and respond in real-time conversations
•Interactive voice response (IVR) systems that integrate with existing telephony infrastructure for business applications

•Building production-ready Telegram chatbots with persistent memory for customer service or community engagement
•Creating voice-enabled AI companions or assistants that can be monetized through subscription or usage fees
•Rapid prototyping and deployment of LangChain agents for businesses needing immediate conversational AI solutions

Which is more popular, agents or Multi-Modal LangChain agents in Production?: agents has more GitHub stars (14,454 vs 479).
Which is more actively developed, agents or Multi-Modal LangChain agents in Production?: agents had more commits in the last 90 days (532 vs 0).
Should I use agents or Multi-Modal LangChain agents in Production?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.