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

Cheshire Cat AI has more GitHub stars (3,094 vs 479).

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

Cheshire Cat AI had more commits in the last 90 days (13 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; Cheshire Cat AI is actively maintained (13 commits in the last 90 days).
Cheshire Cat AI is growing faster: +14 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
Pick Cheshire Cat AI for: aI agent microservice. Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram.

From GitHub data refreshed daily.

AI agent microservice

Deploy LangChain Agents and connect them to Telegram

	Cheshire Cat AI	Multi-Modal LangChain agents in Production
Stars	3.1k	479
Star velocity /mo	14.36842105263158	0.3157894736842105
Commits (90d)	13	0
Releases (6m)	5	0
Downloads (30d, npm + PyPI)	210	—
Overall score	0.3830458573070783	0.1390646436413974

+Complete microservice architecture with WebSocket and REST API support makes integration seamless
+Built-in RAG with Qdrant vector database provides out-of-the-box knowledge management capabilities
+Extensive plugin system with hooks and tools allows deep customization of agent behavior

+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 Docker knowledge and infrastructure for deployment and management
-Python-only plugin development may limit accessibility for teams using other languages
-Complexity of features may create a steep learning curve for simple chatbot use cases

-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

•Adding conversational AI capabilities to existing web applications through API integration
•Building knowledge-aware customer support bots that can query internal documentation
•Creating specialized AI agents with custom tools and workflows for business process automation

•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, Cheshire Cat AI or Multi-Modal LangChain agents in Production?: Cheshire Cat AI has more GitHub stars (3,094 vs 479).
Which is more actively developed, Cheshire Cat AI or Multi-Modal LangChain agents in Production?: Cheshire Cat AI had more commits in the last 90 days (13 vs 0).
Should I use Cheshire Cat AI 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.