Built a Clinical Research Orchestrator with LangGraph – Critic loop, HITL, and stateful multi-agent flow (open source)
Hey r/LangChain,
Just open-sourced a multi-agent research system built with LangGraph.
**What it does:**
You give it a complex clinical/research question. A network of AI agents
(Orchestrator → Researcher → Critic → Writer) researches the topic, critiques
data quality, loops back if insufficient, and only generates the final report
after human approval (HITL).
**Key architectural decisions:**
- LangGraph over CrewAI — explicit control over edges, state transitions, and interrupt points
- `operator.add` on `research_data` — append-only accumulation across critic revision cycles
- `interrupt_before=["writer"]` — human approves before report generation (true HITL)
- DeepSeek via OpenAI-compatible API — cost-efficient drop-in for GPT-4
**Stack:** LangGraph · LangChain · DeepSeek · Tavily · Pydantic · Python
The repo includes a real example output (clinical_report.md) generated with:
*"Latest evidence on semaglutide for obesity treatment in CKD patients"*
GitHub: https://github.com/Armandogith/langgraph-research-orchestrator
Happy to discuss the architecture — particularly around the critic loop design
and state checkpointing. What patterns are you all using for quality control
in multi-agent pipelines?