research-evidence-agent is a local-first provenance and claim–evidence audit
tool for computational and experimental research bundles.
It keeps four evidence layers explicit:
- raw experiment;
- reprocessed data;
- model output or inference; and
- synthetic illustration.
The deterministic core creates SHA-256 manifests, applies user-reviewable path rules, flags unclassified or sensitive-looking files, and checks whether a claim ledger cites evidence of a compatible type. It never interprets scientific values.
An optional OpenAI Agents SDK layer can review aggregate counts and issue codes. By default, the agent receives neither file contents nor individual filenames.
python -m pip install -e ".[dev]"
research-evidence-agent demo --output-dir outputs/demo-bundle
pytestScan a real local bundle without uploading it:
research-evidence-agent scan ./my-study --output manifest.json
research-evidence-agent audit manifest.json claims.json{
"claims": [
{
"id": "claim-1",
"text": "A directly observed trend.",
"claim_type": "observation",
"evidence": ["ev-0123456789ab"]
}
]
}An observation without raw evidence fails. Model-only support cannot be silently
rewritten as an observation. Warnings and errors are machine-readable so the tool
can run in CI before a report or data package is released.
python -m pip install -e ".[agent]"
export OPENAI_API_KEY="..."
export OPENAI_MODEL="gpt-5.6-terra"
research-evidence-agent agent ./my-studyThe Agents SDK wraps one aggregate-only function tool. The model organizes the risk report; deterministic code owns hashing, classification and validation. See the agent boundary.
This package helps prevent category errors and provenance loss. It does not prove that an experiment is correct, a model is valid, a paper claim is causal, or a dataset may legally be redistributed. Human domain review remains required.
BSD-3-Clause licensed.
Companion repositories:
tailaware-rve— tail-aware scientific surrogate diagnostics;engineering-run-agent— read-only solver and scheduler triage; andsynthetic-gpa-agent— synthetic microscopy-method validation.