Multi-Modal LangChain agents in Production vs RasaGPT
Side-by-side comparison of two AI agent tools
Short answer
- RasaGPT is growing faster: +0 GitHub stars in the last 30 days vs +0 for Multi-Modal LangChain agents in Production.
- Pick Multi-Modal LangChain agents in Production for: deploy LangChain Agents and connect them to Telegram. Pick RasaGPT for: headless LLM chatbot platform built on Rasa and LangChain for indexing, retrieval, and context injection.
From GitHub data refreshed daily.
Deploy LangChain Agents and connect them to Telegram
RasaGPTopen-source
Headless LLM chatbot platform built on Rasa and LangChain for indexing, retrieval, and context injection
Metrics
| Multi-Modal LangChain agents in Production | RasaGPT | |
|---|---|---|
| Stars | 479 | 2.5k |
| Star velocity /mo | 0.3157894736842105 | 0.4736842105263158 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Overall score | 0.1390646436413974 | 0.14408998147900343 |
Pros
- +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
- +开箱即用的完整解决方案，解决了 Rasa 与 LLM 集成的所有技术痛点，包括库冲突、元数据传递等问题
- +提供完整的技术栈集成，包括 FastAPI 后端、文档上传训练管道、Docker 支持和多平台部署能力
- +实现了自定义 pgvector 集成和多租户架构，比使用 Langchain 原生方案更加灵活可控
Cons
- -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
- -作者明确表示这不是生产级代码，存在 prompt injection 和多种安全漏洞风险
- -作为概念验证项目，缺乏企业级的安全性、稳定性和性能优化
- -学习成本较高，需要同时掌握 Rasa、Langchain 和 FastAPI 等多个框架
Use Cases
- •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
- •企业内部知识库问答系统，需要结合传统规则对话和 LLM 生成能力的客服场景
- •多渠道聊天机器人部署，特别是需要同时支持 Telegram、Slack 等平台的应用
- •需要文档索引和检索功能的智能助手，如技术文档查询、产品说明书问答等场景
FAQ
- Which is more popular, Multi-Modal LangChain agents in Production or RasaGPT?
- RasaGPT has more GitHub stars (2,464 vs 479).
- Which is more actively developed, Multi-Modal LangChain agents in Production or RasaGPT?
- Multi-Modal LangChain agents in Production had more commits in the last 90 days (0 vs 0).
- Should I use Multi-Modal LangChain agents in Production or RasaGPT?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.