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

AgentScope has more GitHub stars (32,703 vs 479).

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

AgentScope had more commits in the last 90 days (304 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; AgentScope is actively maintained (304 commits in the last 90 days).
AgentScope is growing faster: +1,829 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
Pick AgentScope for: build and run agents you can see, understand and trust. Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram.

From GitHub data refreshed daily.

AgentScopeopen-source

Build and run agents you can see, understand and trust.

Deploy LangChain Agents and connect them to Telegram

	AgentScope	Multi-Modal LangChain agents in Production
Stars	32.7k	479
Star velocity /mo	1.8k	0.3157894736842105
Commits (90d)	304	0
Releases (6m)	10	0
Downloads (30d, npm + PyPI)	296.7K	—
Overall score	0.8294203381821088	0.1390646436413974

+Production-ready with multiple deployment options including local, serverless, and Kubernetes with built-in observability
+Comprehensive built-in features including ReAct agents, memory, planning, voice interaction, and model finetuning capabilities
+Flexible multi-agent orchestration through message hub architecture with support for complex workflows and agent communication

+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

-Python-only framework limits usage for teams working in other programming languages
-Requires Python 3.10+ which may not be compatible with all existing environments
-As a comprehensive framework, may have a steeper learning curve compared to simpler agent libraries

-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 production AI agent systems that require transparency, debugging capabilities, and human oversight
•Developing multi-agent workflows where agents need to collaborate, communicate, and orchestrate complex tasks
•Creating conversational AI applications with realtime voice interaction and custom model finetuning requirements

•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, AgentScope or Multi-Modal LangChain agents in Production?: AgentScope has more GitHub stars (32,703 vs 479).
Which is more actively developed, AgentScope or Multi-Modal LangChain agents in Production?: AgentScope had more commits in the last 90 days (304 vs 0).
Should I use AgentScope 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.