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Build a Multi-Agent Research Team
5 specialist agents, 3 running in parallel, one cited report
1 phase. 6 lessons. 6 labs. Build a multi-agent research system with LangGraph: a Planner that decomposes your question, 3 specialists (Web, Technical, Market) that research in parallel, and a Synthesizer that resolves conflicts and produces a cited Markdown report in 2-4 minutes.
6 lessons · 6 labs · 5 agents · 1 real report
Outcomes you will have by the end
- A real, cited research report on a real question — Not a demo — a Markdown report on something you actually wanted to know, produced by 5 agents (1 planning, 3 researching in parallel, 1 synthesizing) in 2-4 minutes, with real sources you can check.
- A working mental model of graph-based multi-agent systems — Nodes, edges, shared state, reducers, and fan-out/fan-in are the actual architecture behind serious production multi-agent systems — not just a LangGraph API you called once.
- A verified, real parallel-execution pattern — Async nodes, a semaphore-limited API call, and measured timing logs proving concurrency actually happened — reusable for any multi-agent system, not just research.
- Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the graph-architecture, state-design, and synthesis skills you built.
What you will be able to do
Graph-Based Agent Architecture · State Schema Design · Task Decomposition · Parallel Agent Execution · Conflict-Resolving Synthesis
Every phase, every lesson, every project
- Build a Multi-Agent Research Team (6 lessons) — Graph architecture vs. chains, shared state design and reducers, the Planner agent, Web and Technical specialists, the Market Analyst and real parallel execution, the Synthesizer and report generation
The technologies you will use
LangGraph · Anthropic Claude API · Serper API · Pydantic
Roles this course prepares you for
- Founder / Analyst (Time saved on research-heavy work) — Run your own research team on competitive analysis, technology evaluation, and market questions instead of doing multi-hour manual research sessions.
- AI Systems Consultant ($100-250/hr) — Design and build custom multi-agent research or analysis systems for teams that need domain-specific research automation.
- Open-Source Contributor (N/A — reputation and portfolio value) — Extend this pattern with more specialists or domain-specific research agents and publish it.
Common questions
Do I need Course 01 first?
Recommended, not required. This course assumes you're comfortable with the basic tool-use/agent pattern; Course 01 builds that from scratch if you haven't already.
Why LangGraph instead of just asyncio.gather like Course 03?
Course 03's orchestrator works well for independent generators with no shared state. This course's agents share a genuinely complex state object (8 fields, some needing reducers) and have real dependencies (specialists depend on the Planner's output) — LangGraph's graph model handles that state management and dependency structure explicitly, instead of you hand-rolling it.
How much does this cost to run?
A full run uses 5 LLM calls (1 planner, 3 specialists, 1 synthesizer) plus web searches — a few cents per report at Haiku/Sonnet pricing, far less than Course 06's coding agent since there's no iterative fix-loop retry cost.
What do I end up with?
A real multi-agent system, `research_team.py "<question>"`, producing a structured, cited Markdown report on any research question in 2-4 minutes — with 3 specialists having genuinely researched it in parallel.
Key terms in this course
Agent
Continue your learning path
Agentic AI Engineering
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