Back to Ecosystem Pulse
ToolExperimental
crewai-rag-langchain-qdrant
by benitomartin
CrewAI + LangChain RAG demo with Qdrant-backed multi-agent retrieval
Jupyter Notebook
Updated May 19, 2024
Share:
Summary
Demonstrates a collaborative multi-agent RAG (retrieval-augmented generation) workflow using CrewAI and LangChain in a Jupyter notebook. Orchestrates multiple agents that query a Qdrant vector store and combine retrieved context with LLM responses to solve user queries. Includes runnable examples and wiring between CrewAI agent coordination, LangChain prompts, and Qdrant-backed retrieval. Tool Use Pattern.
Why It Matters
As agent systems start delegating retrieval and synthesis across specialist agents, reproducible examples for wiring RAG pipelines are essential for trust and evaluation. This notebook makes the interaction patterns explicit so you can inspect where failures or hallucinations come from and iterate on retrieval quality or agent roles. It’s useful for teams experimenting with agent delegation, retrieval tuning, and early-stage agent-to-agent evaluation workflows. This is also where guardrails help ensure safe, predictable behavior Guardrails Pattern, and where ongoing monitoring matters for detecting drift or failures Emergence-Aware Monitoring Pattern.
Ideal For
Practitioners prototyping multi-agent RAG pipelines who want a runnable example tying CrewAI orchestration to LangChain and Qdrant retrieval. This aligns with approaches in Dynamic Task Routing Pattern to flexibly assign subtasks across agents.
How It's Used
- Prototyping multi-agent RAG workflows that split retrieval and synthesis across agents
- Evaluating how retrieval quality affects agent outputs and tracking failure modes
- Learning how to connect CrewAI orchestration, LangChain prompts, and Qdrant vector search
Works With
crewailangchainqdrantopenai
Topics
crewaijupyter-notebooklangchainllmpyhtonqdrantrag
Similar Tools
autogenlangchain
Keywords
multi-agent orchestrationmulti-agent trustragagent-delegation