Proofagent Harness

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.

[](https://pypi.org/project/proofagent-harness/) [](https://pypi.org/project/proofagent-harness/) [](LICENSE) [](https://github.com/ProofAgent-ai/proofagent-harness/actions/workflows/ci.yml) [](https://arxiv.org/abs/2605.24134)

[Install](#install) · [Quickstart](#quickstart) · [Modes](#evaluation-modes) · [Harness LLM](#choosing-a-harness-llm) · [Metrics](#metrics) · [Governance gate](#governance--ci-release-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. The agent and the domain are two separate inputs:

# Multi-turn — the AGENT via --context-dir, the DOMAIN via --domain-knowledge-dir
proof run my_agent.py \
    --context-dir ./my_agent/ \            # system_prompt.md + tools.json + memory.jsonl + agent.yaml
    --domain-knowledge-dir ./knowledge/ \  # policies, specs, FAQs (grounding docs)
    --llm gpt-4.1-mini --consensus delphi --assess-context

# Artifact — grade a finished deliverable against a ground-truth corpus
proof artifact ./proposal.md \
    --type BRD --domain-knowledge-dir ./docs --llm gpt-4.1-mini

--context-dir loads the full AgentContext (system prompt + tool schemas + memory + an optional agent.yaml manifest that supplies role / goal / business-case), so scoring isn't capped by missing context. --turns defaults to 15. Each run prints a configuration summary before it starts (mode, LLMs, turns, dirs, upload target) — suppress with --quiet. A complete, copy-me project is in [examples/credit_agent/](examples/credit_agent/).

> 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](#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.

Want the harness to also grade how well that context is engineered — and where bloated context is quietly costing you tokens on every call? Add assess_context=True (CLI: --assess-context). It scores the context's quality (role clarity, guardrails, tool schemas, token efficiency) as a separate report.context_engineering sub-score that never affects the metric scores or the gate — with a token_impact verdict and a token-savings estimate on every finding. (Why it matters + how it works →)

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](examples/02agentwith_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](examples/04artifacteval.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 --upload --fail-on block \
    --context-dir ./my_agent/ --domain-knowledge-dir ./knowledge/ \
    --agent airline-support \                      # ← the name shown on the governance dashboard
    --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/

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](docs/governance-upload.md).

CLI reference

Every flag for the two evaluation commands, with its default. Both share the same governance / upload group (below). For the full parameter reference — each flag and its Python-API equivalent, with guidance on when to reach for it — see the documentation.

proof run — multi-turn evaluation

proof run AGENT_FILE [OPTIONS]   # AGENT_FILE = a .py exposing a callable named `agent`

| Flag | Default | What it does | |---|---|---| | AGENT_FILE | (required) | Python file exposing a callable named agent | | --entry | agent | Name of the callable inside the file | | --context-dir | — | Directory that defines the agent, loaded via AgentContext.from_dir(): system_prompt.md, tools.json, memory.jsonl, and an optional agent.yaml manifest (role / goal / business-case). Lifts the limited-context ceilings on instruction-following & safety | | --domain-knowledge-dir | — | Directory of domain knowledge the agent is grounded on (policies, specs, FAQs — .md/.txt/.json/.yaml). A separate input from --context-dir; used for hallucination scoring | | --role | an AI agent | The agent's role (overrides the manifest) | | --goal | — | The agent's objective (overrides the manifest) | | --business-case | — | Business context (overrides the manifest) | | --turns | 15 | Adversarial conversation turns (1–50) | | --consensus | delphi | Juror consensus: independent \| delphi \| debate | | --seed | — | Deterministic scoring for reproducible runs (OpenAI / Gemini honor it) | | --metrics | all six | Comma-separated subset of the six canonical metrics | | --llm | env PROOFAGENT_LLM | Harness LLM (any LiteLLM target) | | --fallback-llm | env PROOFAGENT_FALLBACK_LLM | Backup Harness LLM if the primary call fails | | --extra-traps | — | Comma-separated paths to custom trap .md files or dirs | | --trap-packs | — | Comma-separated community trap packs | | --pin-traps | — | Force-include specific traps by name | | --assess-context | off | Add the context-engineering sub-score (additive, never gates) | | --json | — | Write the report JSON to this path | | --markdown | — | Write the report Markdown to this path | | --quiet | off | Suppress the config summary + live progress UI | | governance / upload group | | (see below) |

proof artifact — artifact evaluation

proof artifact ARTIFACT_PATH [OPTIONS]   # grade a finished deliverable (no live agent)

| Flag | Default | What it does | |---|---|---| | ARTIFACT_PATH | (required) | The deliverable to grade (.md/.txt/.pdf/.docx/.html/.json/…) | | --type / -t | BRD | Rubric pack: BRD \| report \| business_plan \| tech_spec \| requirements \| code \| runbook \| data_contract \| model_card \| … | | --domain-knowledge-dir / -k | — | Ground-truth corpus to grade the artifact against (--knowledge-dir is a back-compat alias) | | --role | an AI agent producing a deliverable | The producing agent's role | | --business-case | — | Business context for the deliverable | | --consensus | delphi | independent \| delphi \| debate | | --seed | 42 | Deterministic scoring | | --llm / --fallback-llm | env | Harness

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ProofAgent-ai
• Source: ProofAgent-ai/proofagent-harness
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.