Chaindesk vs RasaGPT
Side-by-side comparison of two AI agent tools
Chaindeskfree
The no-code platform for building custom LLM Agents
RasaGPTopen-source
💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
Metrics
| Chaindesk | RasaGPT | |
|---|---|---|
| Stars | 3.0k | 2.5k |
| Star velocity /mo | 4.010695187165775 | 0.32085561497326204 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Overall score | 0.2546416508464133 | 0.20033130539218696 |
Pros
- +No-code approach potentially makes LLM agent creation accessible to non-developers
- +Moderate GitHub community interest with 2940 stars
- +Focuses specifically on custom LLM agents rather than general AI tools
- +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
- -Extremely limited documentation makes evaluation difficult
- -Unclear what specific features or capabilities are actually provided
- -Cannot assess reliability, performance, or production readiness from available information
- -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 chatbots or conversational agents without coding
- •Creating custom AI assistants for specific business needs
- •Prototyping LLM-powered applications through visual interfaces
- •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