AI & Agents · Pro
Multi-Agent Research Swarm
An orchestrator-worker system where a lead agent spawns parallel sub-agents that fan out web searches, critique each other, and merge a cited report
You build a multi-agent pipeline where a root orchestrator decomposes a research question into parallel subtasks, spawns specialized worker agents to retrieve and synthesize web content, routes their outputs through a critic agent for factual cross-checking, then merges everything into a single structured, citation-backed report. The project is worth building because it demonstrates production patterns for multi-agent coordination, async tool orchestration, and output reliability that appear repeatedly in real AI engineering work. Moving from a single LLM call to a stateful agent graph forces you to reason about concurrency, partial failure, and structured output schemas in ways that toy examples never surface.
What you build
- Orchestrator agent decomposes an input query into N parallel research subtasks
- Worker agents fan out concurrently, each running web searches and scraping source content
- Critic agent reviews each worker draft for factual consistency and flags contradictions
- Vector store deduplicates and indexes retrieved chunks across all workers
- Final merge agent synthesizes a coherent report with inline citations and a source list
- Structured output schema enforces section headers, confidence scores, and citation format
- LangSmith or built-in tracing captures the full agent graph execution for debugging
What it teaches
- Dynamic fan-out and fan-in with LangGraph's Send API and conditional edges
- Designing shared agent state schemas that multiple nodes read and write safely
- Prompt engineering for critic agents that compare multiple drafts rather than generate from scratch
- Async tool orchestration patterns including parallel execution and partial failure handling
- Structured output enforcement with Pydantic or JSON schema to keep multi-agent outputs mergeable
- Distributed tracing of agent graphs using LangSmith for debugging non-deterministic execution paths
How it works
- 1
User Query
- Raw research question
- Scope constraints
↓ decompose
- 2
Orchestrator
- Subtask splitter
- Send API fan-out
↓ parallel
- 3
Worker Agents
- Web search tool
- Chunk + embed
- Draft + sources
↓ collect
- 4
Critic Agent
- Cross-check drafts
- Flag contradictions
↓ revise or pass
- 5
Merge Agent
- Synthesize sections
- Inline citations
- Confidence scores
↓ output
- 6
Final Report
- Structured JSON
- Cited markdown
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Take it further
- Add a confidence-weighted voting step so the merge agent down-ranks claims that only one worker found
- Persist the vector store between runs so repeated queries reuse prior research chunks and reduce API cost
- Expose the graph as a streaming HTTP endpoint and build a minimal UI that shows each agent node status in real time
