Workflow Overview: A Multi-Agent Research Assistant Over 4 Context Sources
~12 min read
The book's hands-on build: a multi-agent research assistant gathering context from Documents, Memory, Web search, and arXiv, filtered and synthesized through a CrewAI pipeline.
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Key points
- •The book's hands-on build is a multi-agent research assistant gathering context from 4 sources: Documents, Memory, Web search, and arXiv
- •Workflow: fetch from all 4 sources -> aggregate -> a filtering agent removes irrelevant context -> a synthesizing agent generates the response -> save the response to memory
- •This workflow is a concrete instance of the 4 stages from the companion topic: fetching is Reading, filtering is Compressing, saving the response is Writing
- •Tech stack: Tensorlake (RAG-ready docs), Zep (memory), Firecrawl (web search), Milvus (vector DB), CrewAI (multi-agent orchestration)
- •The book is explicit this is one of many blueprints — the specific tools can vary, but the pattern (multiple sources -> aggregate -> filter -> generate -> persist) generalizes