Who it is forAn engineer or AI agent evaluating how a research agent is architected before adopting or forking its design.
The problemArchitecture claims are hard to judge without reading the code and checking that the tests pass.
The outcomeA checked-out copy of Poirot with its test suite run and a short map of the middleware and memory layers.
What you need first
- Python 3.12 or newer
- A DeepSeek API key (the README says to set DEEPSEEK_API_KEY in .env)
- Git and a terminal
- Docker only if you want the container sandbox
Steps
- Clone the repo from https://github.com/HezaoHezao/poirot and change into the project folder. (inferred, check the README)
- Create a virtual environment with
python -m venv .venv. - Activate it with
source .venv/bin/activateon Linux or macOS, or.venv\Scripts\activateon Windows. - Install the dev extras with
pip install -e ".[dev]". - Run the test suite with
pytest. (inferred, check the README) - Read the Core Modules and Architecture sections of the README, then list the middleware in the agents package.
Prompt for your agent
In the Poirot repo at <path to poirot checkout>, install the dev extras, run the test suite, and report the pass and fail counts. Expected output: a short summary of the test result and a list of the middleware named in the README. Verify that the README's claimed test count of 2400+ roughly matches the number of tests collected.
Caveat. The README gives no test command, so the pytest step is inferred and may need adjusting to the project's test setup.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for HezaoHezao/poirot and "Study the agent kernel and run its test suite"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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