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

LangChain has more GitHub stars (147,399 vs 479).

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

LangChain had more commits in the last 90 days (542 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; LangChain is actively maintained (542 commits in the last 90 days).
LangChain is growing faster: +23,097 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
Pick LangChain for: the agent engineering platform. Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram.

From GitHub data refreshed daily.

LangChainopen-source

The agent engineering platform

Deploy LangChain Agents and connect them to Telegram

	LangChain	Multi-Modal LangChain agents in Production
Stars	147.4k	479
Star velocity /mo	23.1k	0.3157894736842105
Commits (90d)	542	0
Releases (6m)	10	0
Overall score	0.8918400192125109	0.1390646436413974

+Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
+Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
+Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript

+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

-Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
-Potential over-engineering for simple use cases that might be better served by direct API calls
-Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns

-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

•Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
•Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
•Developing chatbots and conversational AI with memory, context management, and integration with external data sources

•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, LangChain or Multi-Modal LangChain agents in Production?: LangChain has more GitHub stars (147,399 vs 479).
Which is more actively developed, LangChain or Multi-Modal LangChain agents in Production?: LangChain had more commits in the last 90 days (542 vs 0).
Should I use LangChain 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.