If you’re evaluating agent frameworks, you’ll likely touch LangChain, LlamaIndex, CrewAI, and AutoGPT. Here’s how they differ—and where Draft’n run fits if you want less glue code and more production.
Internal resources: Agentic AI, AI workflow builder, Integration, Custom AI, Demo.
LangChain
A modular toolbox with a vast ecosystem of tools, memory modules, and agents (ReAct). Great for prototyping and custom pipelines.
- External site: LangChain
- Strengths: integrations galore, Python/TS parity, community examples.
- Trade‑offs: autonomy can be brittle without structure; add LangGraph/LangSmith to harden.
LlamaIndex
Focused on data + RAG: ingestion, parsing (LlamaParse), indexes, and query engines. Now includes Workflows and cloud options.
- External site: LlamaIndex
- Strengths: top‑tier data connectors and indexing patterns; strong RAG ergonomics.
- Trade‑offs: more code to orchestrate end‑to‑end unless you adopt their cloud.
CrewAI
Built for multi‑agent collaboration with roles and coordination. Open core plus a studio for build/deploy/track.
- External site: CrewAI
- Strengths: patterns like manager/worker/validator; good for complex teamwork.
- Trade‑offs: still need to stitch observability, deployment, and governance your side.
AutoGPT
The open source autonomy pioneer. Powerful, extensible, and hands‑on. Now with a visual canvas, but still requires careful guardrails for production.
- External site: AutoGPT
- Strengths: exploration, plugins, internet/code/folders access.
- Trade‑offs: reliability and cost control are on you.
Where Draft’n run helps
- Visual, explicit workflows that still allow LLM decisions where needed.
- Built‑in traces, token/cost tracking, alerts, and human‑in‑the‑loop steps.
- One‑click API deployment and RBAC, with open source or managed hosting.
- Works with your stack: reuse LangChain tools, call LlamaIndex, hit any REST/DB.
See how teams ship faster in the case studies or try a demo.
