Get from zero to your first conjecture in 5 minutes. For a narrative walkthrough of a complete research session, see TUTORIAL.md.
git clone <repo-url> && cd agentic-research
pip install -e ".[dev]"export ANTHROPIC_API_KEY="sk-ant-..."Or for Vertex AI:
export CLAUDE_CODE_USE_VERTEX=1
export ANTHROPIC_VERTEX_PROJECT_ID="my-gcp-project"agentic-research explore 'every sufficiently large even number is the sum of two primes'This runs the ExplorationAgent and ConjectureGenerator. You'll see a table of ranked conjectures with confidence scores and difficulty estimates. Default budget: $2.00.
Expected output:
Generated Conjectures
┌───┬──────────────────────────────────────────┬────────────┬────────────┐
│ # │ Statement │ Confidence │ Difficulty │
├───┼──────────────────────────────────────────┼────────────┼────────────┤
│ 1 │ For all n > 2, 2n = p + q for primes... │ 0.85 │ 8 │
│ 2 │ Every even number > 4 has at least... │ 0.72 │ 6 │
│ 3 │ ... │ 0.60 │ 5 │
└───┴──────────────────────────────────────────┴────────────┴────────────┘
Domain: number_theory | Concepts found: 12 | Directions: 4
Cost Summary
┌────────────┬────────────────┐
│ Total cost │ $0.0842 │
│ Budget │ $2.00 │
│ Status │ Within budget │
└────────────┴────────────────┘
Take one of the generated conjectures and formalize it into Lean 4:
agentic-research formalize 'the square root of 2 is irrational' --budget 3.00This runs the FormalizationPipeline (type planning → type formalization → theorem formalization) and then the IntentJudge to verify the Lean statement captures your idea.
Before investing in proof search, check if the statement can be disproved:
agentic-research check 'theorem foo : ∀ n : Nat, n + 0 = n' --budget 2.00If the check returns PLAUSIBLE, attempt a proof:
agentic-research prove 'theorem foo : ∀ n : Nat, n + 0 = n' --budget 10.00 --timeout 600This is the most expensive operation — it runs the full proof pipeline with recursive decomposition, iterative refinement, and Lean REPL verification.
agentic-research statusShows your session's conjecture history, proof outcomes, and memory tier usage.
- Tutorial — narrative walkthrough of a complete research session (~30 min)
- API Guide — use ProofPartner as a Python library in Jupyter notebooks or batch scripts
- Architecture — full pipeline description and agent inventory
- Reproducibility — model versions, cost estimates, hardware requirements
- FAQ — common questions and troubleshooting
- Run
agentic-research --helpfor all CLI options - Install elan for real Lean 4 proof verification (without it, Lean operations use mocked backends)
- Set
AGENTIC_RESEARCH_MODELto use a different Claude model