Week 45, updated 2 Nov 2026

AI use cases Automation & agent frameworks Multi-agent AI teams

Multi-agent AI teams

Orchestrate several AI agents with different roles that hand work to each other. Example prompts, workflow and ranked agent frameworks.

Tools compared
4
Open source
4of 4
Reviewed
16 Oct

For: Developers

Best-scored tools for this jobscore, safety
  1. 1CrewAIAgent framework57Caution
  2. 2LangGraphAgent framework44No issues found
  3. 3AutoGenAgent framework40Caution
  4. 4OpenAI Agents SDK (Python)Agent framework39Caution
What the score and shapes mean

Score = public maintenance, adoption and docs signals, as a percentile of 52 tools. Nobody here tested these hands-on. Shapes are the separate safety label: No issues found, No dependencies scanned, Caution.

What it does for you

Developers building agent features who have outgrown a single prompt.

  • Split a task into planner, worker and reviewer roles
  • Run agents in parallel and merge their results
  • Add human approval between steps
  • Build stateful, resumable agent graphs
  • Prototype role-playing agent crews
More about this job

A multi-agent setup splits a job across specialised agents, for example a researcher, a writer and a reviewer, with a framework handling the hand-offs. It can help on complex tasks, and it costs more and is harder to debug than a single agent.

Workflow in 5 steps

  1. Solve the task with one agent first.
  2. Define each role with a narrow job and a structured output.
  3. Choose a framework and implement the hand-offs as an explicit graph or sequence.
  4. Add step limits, timeouts and logging to every agent.
  5. Evaluate on a fixed set of examples before changing roles.
Full steps
  1. Solve the task with one agent first; only split if one agent fails for a clear reason.
  2. Define each role with a narrow job and a structured output.
  3. Choose a framework and implement the hand-offs as an explicit graph or sequence.
  4. Add step limits, timeouts and logging to every agent.
  5. Evaluate on a fixed set of examples before changing roles.

Prompts to copy

Starting points we wrote, not tested results.

You are the reviewer agent. Check the draft below against the brief and list every requirement it misses. Do not rewrite it.

2 more prompts

You are the planner. Break this goal into at most five tasks, each with an owner role and a clear done condition. Output JSON.

Compare the two answers from the worker agents and pick the better one, explaining the deciding difference in one sentence.

Watch out for

  • Agent loops can burn through paid API tokens quickly.
  • Agents that can run commands can delete or overwrite files.
  • AI output can be fluent and wrong.
  • Agents can agree with each other and still be wrong.
  • CautionCrewAI8 critical-severity known vulnerabilities in runtime dependencies (OSV)
  • No issues foundLangGraphNo issues found in this week’s scan
  • CautionAutoGen33 critical-severity known vulnerabilities in runtime dependencies (OSV)
  • CautionOpenAI Agents SDK (Python)3 critical-severity known vulnerabilities in runtime dependencies (OSV)
Why these warnings?
  • Agent loops can burn through paid API tokens quickly. Set spending limits with your model provider and watch the first few runs.
  • Agents that can run commands can delete or overwrite files. Work in a branch or sandbox, commit first, and approve destructive commands by hand.
  • AI output can be fluent and wrong. Check facts, code and citations yourself before you rely on them or publish them.
  • Agents can agree with each other and still be wrong. A reviewer agent is not independent verification.
  • CrewAI: 8 critical-severity known vulnerabilities in runtime dependencies (OSV)
  • AutoGen: 33 critical-severity known vulnerabilities in runtime dependencies (OSV)
  • OpenAI Agents SDK (Python): 3 critical-severity known vulnerabilities in runtime dependencies (OSV)

Recommended tools

From our ranked data, highest score first. A high score does not mean a clean scan.

Related jobs

Sources for tool facts

crewAIInc/crewAI · langchain-ai/langgraph · microsoft/autogen · openai/openai-agents-python. Data fetched 2 Nov 2026. Scores and labels are automated, relative and not a hands-on test.

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