autonomous_research_graph
Runs a multi-agent research graph that automates literature search, hypothesis testing, code execution, and verification, producing an audited research output with cryptographic proof.
Instructions
Run the multi-stage research DAG (agent swarm) instead of the linear loop.
Chains specialized agents — literature mining, a parallel hypothesis swarm, feasibility screening, experiment design/code generation, a hardened sandbox + static methodology (p-hacking/data-dredging) audit, citation verification (reusing the retraction-aware verifier), adversarial review, drafting, LaTeX, and a signed cryptographic proof-of-rigor attestation (SHA-256 content hash + signature, optional IPFS/on-chain anchoring) — logging every agent action to an audit trail and checkpointing each stage.
Requires an LLM API key with quota for the reasoning agents (OPENAI_API_KEY /
ANTHROPIC_API_KEY / GOOGLE_API_KEY); citation verification and proof signing are keyless.
Set enable_sandbox=True to actually execute the generated analysis code in a hardened,
network-less Docker container (off by default since the code is model-written; requires
Docker). check_compliance, publish_preprints, and patent_freedom remain honest
placeholders. Optionally pass breakpoints (stage names) to pause for human approval.
Returns {status, session_id, checkpoint, final_state, audit_trail}.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | gpt-4o-mini-2024-07-18 | |
| topic | Yes | ||
| provider | No | ||
| breakpoints | No | ||
| enable_sandbox | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |