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How to research a question with a parallel agent team with Frontier Agent

A coordinator splits a broad question into parallel sub-agent tasks and returns one synthesized report.

Credentials. Your model API key goes into the .env file, so keep that file out of shared repositories and screenshots.

Who it is forA researcher or analyst with a broad question that needs several sources checked at once.

The problemOne agent working alone checks sources one at a time, which is slow for broad questions with many angles.

The outcomeA final report built from structured sub-agent findings, with deliverables and the run trace saved in the run directory.

What you need first

  • Git
  • Python 3.12
  • uv
  • An OpenAI-compatible model endpoint with its API key, base URL, and model name
  • Docker (optional, only for the container path)

Steps

  1. git clone https://github.com/ApodexAI/FrontierAgent.git
  2. cd FrontierAgent
  3. uv sync --python 3.12 --extra dev
  4. cp .env.example .env, then set OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL in .env
  5. uv run frontier-agent --mode agent_team --cwd /path/to/project "Research the alternatives, verify the evidence, and write a report" (inferred, check the README)

Prompt for your agent

Using the FrontierAgent repo at <repo path>, run agent_team mode with --cwd set to <project path> and the question <question>. The expected output is a final report plus deliverables in the run's /outputs folder. Verify that the report cites evidence the sub-agents collected and that the run directory contains the checkpoint and trace.

Caveat. Agent Team sub-agents run in parallel on top of benchmark concurrency, so total model calls can grow quickly; the README advises starting with --concurrency 1 when evaluating.

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