Principal (Agentic) Investigator
Developed by the Poggio Lab team at MIT
We're building pAI, a multi-agent AI system that turns research hypotheses into high-quality, literature-grounded, experiment-backed papers with minimal human steering.
Current AI systems require on the order of 102 to 103 prompts to go from a research idea to a written paper. pAI aims to reduce this to up to 10 human interactions.
This is not about fully automating research or replacing human creativity. pAI focuses on the rigorous establishment of ideas: matching theory with experiments, distinguishing correlation from causation, proposing parallel explanations and testing them. The human provides the idea. The system does the structured work of turning it into a solid manuscript.
Cost: ~$2-10
Time: 15-40 min
Requires: one API key
# 1. Bootstrap environment (one-time, ~5 min)
./scripts/bootstrap.sh researchlab minimal
conda activate researchlab
# 2. Set your API key (only one provider needed)
cp .env.example .env
echo "ANTHROPIC_API_KEY=your_key_here" >> .env
# 3. Validate setup without spending tokens
python launch_multiagent.py --task "test" --dry-run
# 4. Run the included quickstart example (~$3)
python launch_multiagent.py \
--task "$(cat examples/quickstart/task.txt)" \
--output-format markdown \
--no-counsel \
--no-log-to-filesRecent notes, releases, and research updates from the team.
Tue Mar 31, 2026
Two new ways to run the pAI/MSc research pipeline are now available: a Claude Code skill and an OpenClaw plugin. Both wrap the same core pipeline — 22 agents, 29 backtested prompts, multi-round person...
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Sat Mar 21, 2026
pAI/MSc is now online. You can read the technical report or browse the GitHub repository. This first public release is an open-source, customizable, modular multi-agent system for academic research wo...
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