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

FastAgency has more GitHub stars (548 vs 479).

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

FastAgency had more commits in the last 90 days (0 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

FastAgency is growing faster: +3 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
Pick FastAgency for: the fastest way to bring multi-agent workflows to production. Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram.

From GitHub data refreshed daily.

FastAgencyopen-source

The fastest way to bring multi-agent workflows to production.

Deploy LangChain Agents and connect them to Telegram

	FastAgency	Multi-Modal LangChain agents in Production
Stars	548	479
Star velocity /mo	2.526315789473684	0.3157894736842105
Commits (90d)	0	0
Releases (6m)	0	0
Overall score	0.16848510206411044	0.1390646436413974

+Unified interface for deploying AG2 workflows to production with minimal code changes
+Supports both web chat applications and REST API services from the same codebase
+Built-in scaling capabilities with distributed architecture and message broker coordination

+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

-Dependent on AG2 framework, limiting flexibility to other agent frameworks
-Relatively small community with 532 GitHub stars compared to major frameworks
-Limited documentation available in the provided materials for advanced features

-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

•Deploying AG2 multi-agent chatbots as web applications for customer service or support
•Creating REST API services that expose agent workflows for integration with existing systems
•Building scalable distributed agent systems that coordinate across multiple servers or datacenters

•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, FastAgency or Multi-Modal LangChain agents in Production?: FastAgency has more GitHub stars (548 vs 479).
Which is more actively developed, FastAgency or Multi-Modal LangChain agents in Production?: FastAgency had more commits in the last 90 days (0 vs 0).
Should I use FastAgency 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.