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Directories / Use Cases / Build a Multi-Agent Research Team

Use case

Build a Multi-Agent Research Team

Run parallel research across multiple sources, synthesise the findings into a single cited output, and keep every step reproducible.

The hard part of multi-source research is not gathering information. It is keeping parallel agent runs from contradicting each other, losing their citations, or producing summaries that cannot be traced back to a source. Solo developers and small analyst teams build this because they need more throughput than one prompt can give them, but they run into state management immediately. Each sub-agent needs to write its findings somewhere the others can read, the synthesiser needs to know which claim came from which source, and the whole run needs to be replayable when a finding looks wrong. That is three coordination problems before you write any research logic.

The stack

Each pick is a real entry on the index. Click any one for the full detail page.

  1. 1
    O OSS Crew Orchestration

    CrewAI

    by CrewAI

    Why this: CrewAI's role-based mental model maps directly onto a research team: a planner agent scopes the question, researcher agents fan out in parallel across sources, and a writer agent synthesises the output. The crew abstraction handles turn-taking and result handoff between roles without you wiring that manually.

    Full entry
  2. 2
    A Agents Driver + Sub-agent Runtime

    Claude Code

    by Anthropic

    Why this: Claude Code runs each crew member as a headless sub-agent. Skills and hooks let you enforce hard rules across every agent in the crew, such as requiring a citation field before a finding is written to memory. The sub-agent primitives in Claude 4.x are the cheapest way to run genuine parallel research without a separate orchestration server.

    Full entry
  3. 3
    M MCP Web Search

    Brave Search MCP

    by Brave (reference implementation)

    Why this: Each researcher agent needs a search surface that does not require managing quotas or custom backends. Brave Search MCP is the reference implementation that drops into any MCP-compatible agent in minutes. The privacy-respecting results are cleaner than scraping, and the same key works across every agent in the crew.

    Full entry
  4. 4
    M MCP Source Reader

    Fetch MCP Server

    by Anthropic (reference implementation)

    Why this: Finding a source via search and actually reading it are two separate operations. Fetch MCP converts any URL to clean markdown the agent can reason from without HTML boilerplate. When a researcher agent needs to pull the full text of a paper, filing, or report, this is the tool that does it cleanly.

    Full entry
  5. 5
    M MCP Shared Findings Store

    Memory MCP Server

    by Model Context Protocol (reference)

    Why this: Parallel agents need a shared write surface so the synthesiser can see what every researcher found. Memory MCP stores findings as a knowledge graph with entities and relations, not a flat list of notes. That structure means the synthesiser can query by claim type or source rather than reading every agent's raw output.

    Full entry
  6. 6
    S Skills Research Loop

    Deep Research Skill

    by Community

    Why this: The Deep Research skill locks in the plan-search-synthesize-cite loop for each researcher agent. Without a skill enforcing this structure, agents drift into free-form summaries that the synthesiser cannot reconcile. This skill forces each agent to produce structured notes with citations before passing output to the memory layer.

    Full entry
Why we picked this stack

Get this running with Enterprise DNA.

Enterprise DNA gives the crew a real operating layer. Each research run creates a work item in OPM so you can see what ran, when, and whether the synthesis passed the citation threshold. Agent secrets (API keys for Brave Search, the Fetch proxy, the memory store) live in Infisical under the EDNA-CC project rather than scattered across environment files. When the synthesiser finishes, the briefing lands in the inbox via the sealed Omni-Mail client, attributed to the project it was researching. No five-tab audit to know what shipped.

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Alternative stacks

Different angles on the same outcome.

Alternative

Single-agent version

If parallel agents are more complexity than the workload justifies, a single LangGraph agent with the same search and memory tools covers most research jobs.

See the alternative Alternative

Content publishing pipeline

Once the research is synthesised, this stack turns the output into a scheduled cross-channel publish with the right cut for each platform.

See the alternative Alternative

Code review bot

The same parallel-agents-then-synthesiser pattern applied to PR review instead of research, with security and style passes running in parallel before a single verdict.

See the alternative

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