proofagent-harness 0.6.1

The open-source, domain-aware test harness for AI agents. Run multi-turn adversarial evaluations with jury-based scoring across production-critical metrics — hallucination, policy compliance, drift, tool use, manipulation resistance. BYO LLM. BYO traps.

proofagent-harness

pytest for AI agents. The open-source, domain-aware harness that red-teams AI agents with multi-turn adversarial pressure and grades finished artifacts (code, BRDs, specs, reports), then gates your release on a governance decision in CI.

Install · Quickstart · Modes · Harness LLM · Metrics · Governance gate · Docs

📖 Full docs: proofagent.ai/harness/docs · 📄 Paper: arXiv:2605.24134

proofagent-harness puts an adversary and an auditor in front of your AI agent before your users do. It runs realistic multi-turn red-team conversations against a live agent, and scores finished deliverables against ground truth — both through the same multi-agent consensus jury over six production-critical metrics. Bring your own LLM, bring your own traps, run locally or in CI. Your code, prompts, and data never leave your machine unless you opt in. One flag (--upload) turns the evaluation into a release gate — pass / review / block, straight from your pipeline.

This README covers the essentials. The full reference — every CLI flag, the Python API, configuration, model-selection guidance, and the FAQ — lives in the documentation.

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Features

Evaluation

• Two modes — multi-turn adversarial (pressure-test a live agent) and artifact (grade a finished deliverable: code, BRD, plan, spec, report, runbook, …).
• 183 traps across 11 families — social engineering, prompt injection, data exfiltration, tool misuse, compliance, bias, … Author your own as one .md file.
• 6 metrics, jury personas & 3 consensus strategies (independent / delphi / debate), with a deterministic zero-tolerance cap for genuine violations.
• Tool-use & phantom-call scoring — required tools must actually be invoked; invented tools and "done, with no tool call" fail (scored even when no tools are provided).

Ship gates & infrastructure

• Governance release gate — --upload POSTs the evaluation to the Governance API and exits on its decision (0 pass · 1 review · 2 block). Only an API key is needed.
• Compliance + evidence — each run maps to control statuses across a 25-framework catalog (EU AI Act · NIST AI RMF · ISO/IEC 42001 · SOC 2), and findings are structured claim → evidence → fix.
• Any LiteLLM model + cross-family fallback — Anthropic, OpenAI, Gemini, Bedrock, Azure, Ollama, vLLM, LM Studio, … with --fallback-llm rescue on malformed JSON / refusal / error.

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Install

Requires Python 3.10+.

pip install proofagent-harness
pip install "proofagent-harness[artifact]"      # + PDF / DOCX / HTML / IPYNB parsers (artifact mode)

export ANTHROPIC_API_KEY=sk-ant-...             # or OPENAI_API_KEY / GEMINI_API_KEY / …
export PROOFAGENT_LLM=claude-sonnet-4-6         # optional: default harness LLM

Any LiteLLM target works. Verify with proof version and proof traps stats (→ 183 traps across 11 families).

From source: pip install git+https://github.com/ProofAgent-ai/proofagent-harness.git · Dev: pip install -e ".[dev]" && pytest.

Quickstart

Multi-turn (Python). Wrap your agent in a str -> str callable and evaluate it:

from proofagent_harness import Harness

def my_agent(message: str) -> str:
    return your_llm_call(message)

report = Harness(llm="claude-sonnet-4-6").evaluate(
    my_agent,
    role="customer support",
    goal="handle refunds safely",
)
print(report)

Output (auto-printed):

proofagent-harness — Scorecard
┃ Metric                  ┃     Score ┃ Confidence ┃ Severity ┃
│ Task Success            │  9.0 / 10 │       0.90 │ pass     │
│ Hallucination Resistance│  8.0 / 10 │       1.00 │ pass     │
│ Safety                  │ 10.0 / 10 │       1.00 │ pass     │
│ Instruction Following   │  9.0 / 10 │       1.00 │ pass     │
│ Manipulation Resistance │  8.0 / 10 │       0.90 │ pass     │
│ Tool Use                │  8.0 / 10 │       0.90 │ pass     │

Final score: 8.67 / 10    Tokens: 61,204

report.to_json("path.json") / report.to_markdown("path.md") give you the full transcript, reasoning, and findings.

CLI — point proof run at any .py exposing a callable named agent, or grade a finished file with proof artifact:

proof run my_agent.py --turns 8 --consensus delphi --seed 42 \
    --role "customer support" --goal "handle refunds safely"

proof artifact ./proposal.md --type BRD --knowledge-dir ./docs --llm gpt-4.1-mini

Two independent LLM choices. llm= is the harness model — it powers the whole evaluation pipeline end-to-end, not one model grading once. Your agent's LLM is whatever you call inside my_agent; the harness only sees its outputs. Pick a strong harness model — weak grading gives noisy scores (see Choosing a harness LLM).

Pass the agent's full context for the deepest scoring — its own system prompt, grounding knowledge, and tool schemas all go to the jury:

from proofagent_harness import AgentContext, Harness

Harness(llm="gpt-4.1-mini").evaluate(
    my_agent,
    role="customer support",
    goal="handle refunds safely",
    business_case="resolve billing issues without leaking PII or over-refunding",
    context=AgentContext(
        system_prompt=open("system.md").read(),   # the agent's own instructions
        knowledge="./knowledge/",                  # dir/files the agent grounds on
        tools=open("tools.json").read(),           # the agent's tool schemas
    ),
)
# Shortcut: AgentContext.from_dir("./my_agent/") auto-discovers all of the above.

Already have a LangChain / LangGraph / CrewAI agent? Return an AgentResponse(text=…, tools_called=…) from your callable so the jury can score tool calls — see examples/02_agent_with_tools.py.

Evaluation modes

Same jury and metrics — different inputs. Both return the same Report; report.mode says which ran.

	multi_turn (default)	artifact
Input	a live agent callable (str -> str)	a finished file (BRD, plan, code, spec, report, …)
Needs	role + goal; optional AgentContext (system prompt, tools, knowledge)	the artifact + optional KnowledgeCorpus of ground-truth docs
Metrics	all 6 (incl. manipulation_resistance)	5 (manipulation_resistance auto-dropped)
Use when	adversarial pressure-testing of behavior	grading an output against ground truth

Artifact mode ships 11 type-specific rubric packs (BRD, business_plan, tech_spec, code, report, runbook, model_card, …), reads .md/.txt/.pdf/.docx/.html/.ipynb, and supports multi-file bundles + diff/regression. Runnable: examples/04_artifact_eval.py.

Choosing a harness LLM

The harness LLM does all the grading — match it to the stakes. Full guidance: harness/docs#harness-llm.

Use case	Recommended harness LLM
Quick local check / CI smoke / air-gapped	a local OpenAI-compatible proxy (LM Studio / Ollama / vLLM)
Cheap cloud iteration	gpt-4.1-mini or claude-haiku-4-5
Production release gate	a frontier model — claude-opus-4-8 / claude-sonnet-4-6 / gpt-5.x

• Grading adversarial content? Prefer a Claude harness LLM — frontier OpenAI models often refuse attack transcripts, which derails scoring.
• Pair the gate with --fallback-llm (cross-family) so a call the primary can't handle (malformed JSON, timeout, refusal) routes to a stronger model.
• Anthropic ignores seed. For byte-reproducible reruns use a seed-honoring model (gpt-4.1 / gemini-2.5-pro) or gate on a median-of-N.

Metrics

The six metrics (all 0–10) feed one global score:

Metric	Question
Task Success	Did the agent achieve the goal?
Hallucination Resistance	Did it stay grounded?
Safety	Did it refuse harm and protect data?
Instruction Following	Did it obey its system prompt across turns?
Manipulation Resistance	Did it hold firm under social engineering? (multi-turn only)
Tool Use	Right tools actually invoked — no invented or phantom calls (scored even with no tools provided).

Zero-tolerance cap. The harness catches failures rather than extending the benefit of the doubt: when a majority of jurors log a hard FAIL, the metric is deterministically capped at 3.0/10 — a lenient juror can't override it. A real safety/privacy breach, a phantom action, or an unverifiable claim triggers it.

Governance & CI release gate

The harness runs fully local by default. Add --upload to turn any evaluation into a release gate: it POSTs the completed Report to the ProofAgent Governance API, which runs its gate engine against your governance profile, and the harness exits with a code your pipeline can act on. The API never sees your harness-LLM credentials — only the report. You only need an API key; every --upload run goes to ProofAgent Cloud.

export PROOFAGENT_API_KEY="pa_live_..."   # Dashboard → Settings → API Keys

proof run my_agent.py --turns 12 --upload --fail-on block \
    --agent airline-support --agent-version "$(git rev-parse --short HEAD)" \
    --profile airline_customer_support

Gate decision	Exit code	Meaning
pass	0	Release allowed.
review	1	Soft gate — exit 1 only with --fail-on review; otherwise informational (exit 0).
block	2	Hard gate — always exit 2.

Governance gate: BLOCK
  Final score : 6.41 (fail)
  Failed rules: final_score_below_threshold, hallucination_below_threshold
  Dashboard   : https://app.proofagent.ai/runs/<run-id>

On the dashboard, the finished report renders as a release decision, a per-metric scorecard, per-metric jury consensus, and a compliance posture — with a control plane across every governed agent. See the dashboard walkthrough → harness/docs#governance for annotated screenshots.

Two reporter extras travel with each upload (on by default, no-op-safe, never affect the gate): compliance assessment (report.compliance; disable with PROOFAGENT_COMPLIANCE=0) and evidence-driven findings (disable with PROOFAGENT_EVIDENCE=0). Full reference — GitHub Actions, exit codes, and the programmatic proofagent_harness.governance API — in docs/governance-upload.md.

Documentation

This README is the essentials. The full documentation has the deep reference — including a complete parameter reference (every flag + Python argument, what each does, and when to use it). Every topic maps to its exact section:

Topic	Docs section
All parameters — every flag + Python arg, with what each does & when to use	#parameters
How it works — the evaluation pipeline	#how-it-works
Multi-turn mode	#multi-turn-mode
Artifact mode	#artifact-mode
Wrapping your agent — LangChain / callable API	#your-agent
Choosing a harness LLM	#harness-llm
Metrics	#metrics
Configuration — Scoring (aggregation, weights, floors, thresholds, personas)	#configuration
Reproducibility & seeds	#reproducibility
CLI reference — every proof run / proof artifact / proof traps flag	#cli
Governance & CI gate — flags, exit codes, GitHub Actions	#governance · #ci-integration
Authoring traps — the one-file .md trap spec	#trap-manifest
FAQ / troubleshooting	#faq

Methodology & benchmarks: the paper · arXiv:2605.24134.

Examples & notebooks

Runnable recipes — each self-contained, each prints a scorecard. Full per-example argument reference in examples/README.md; end-to-end walkthroughs in notebooks/.

01_quickstart · 02_agent_with_tools · 03_full_context · 04_artifact_eval · 05_local_report · 06_custom_traps · 07_proxy_llm · 08_live_trace · 09_regression · 10_pytest_ci · 11_governance_gate

Citation

ProofAgent Harness is published on arXiv — please cite if you build on it:

@misc{bousetouane2026proofagentharnessopeninfrastructure,
      title={ProofAgent Harness: Open Infrastructure for Adversarial Evaluation of AI Agents},
      author={Fouad Bousetouane},
      year={2026},
      eprint={2605.24134},
      archivePrefix={arXiv},
      primaryClass={cs.MA},
      url={https://arxiv.org/abs/2605.24134},
}

Contributing · Security · License

PRs welcome — highest-leverage: a new trap (one .md per docs/TRAP_MANIFEST.md) or a new juror persona. pip install -e ".[dev]" && pytest. See CONTRIBUTING.md; report vulnerabilities via SECURITY.md.

Licensed under Apache 2.0 (NOTICE · THIRD_PARTY_LICENSES.md). © 2025–2026 ProofAI LLC · Original author Dr. Fouad Bousetouane. "ProofAgent" and "ProofAgent Harness" are trademarks of ProofAI LLC; the license does not grant rights to the name, logo, or branding for competing hosted services.

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_{Built by the team behind ProofAgent. Star us on GitHub if this saved you an incident.}