assay-ai 1.20.0

AI evidence that's harder to fake and easier to verify

Assay

Signed evidence for tool-using AI.

Assay turns AI runs into signed evidence packets another team can verify offline — no server, no trust required.

Agents talk via MCP. Agents prove via Assay.

pip install assay-ai
assay try-mcp

Three MCP tool calls. Receipted. Signed. Verified. 30 seconds. No API key.

Verify a packet in your browser — no install, no account. Drag in a proof pack, see the result.

See the before/after specimen — when a decision is disputed, the difference between reconstruction and verification.

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Exit codes

Exit	State	Meaning
0	pass	Authentic evidence, standards met
1	honest fail	Authentic evidence, standards not met
2	tampered	Evidence altered after signing

A signed failure is stronger evidence than a vague pass.

What a proof pack contains

proof_pack/
  receipt_pack.jsonl      # One receipt per tool call, model invocation, or policy check
  pack_manifest.json      # SHA-256 hashes of every file + Ed25519 signature
  pack_signature.sig      # Raw signature bytes
  verify_report.json      # Machine-readable verification result
  verify_transcript.md    # Human-readable transcript

Change one byte in any file. Verification fails.

Try more

assay try                  # General proof pack demo (15 seconds)
assay demo-challenge       # Good pack + tampered pack side by side
assay start                # Guided setup for your project

Add to your project

assay scan . --report      # Find uninstrumented LLM call sites
assay patch .              # Auto-insert SDK integration
assay run -- python my.py  # Run + build signed proof pack
assay verify-pack ./proof_pack_*/   # Verify offline

Boundary: Assay proves the evidence artifact has not been altered after signing. It does not prove every upstream input was authentic. Trust tiers.

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Why this exists

We scanned 30 AI projects and found 231 high-confidence LLM call sites. None had Assay-compatible tamper-evident instrumentation. (Many have logging — this measures cryptographic evidence specifically.) Full results.

Adversarial testing: 16 deterministic tampering attacks, all caught, zero false passes. Full report.

Self-scan note: Running assay score . on this repo returns a low score. That is expected — Assay instruments AI workflows, not itself. This repo is the instrument, not the subject.

For the ecosystem map, see docs/REPO_MAP.md.

Install details (Windows, PATH issues, deterministic setup)

# Windows
py -m pip install assay-ai

Assay requires Python 3.9+.

If pip is not on your PATH, use python3 -m pip on macOS/Linux or py -m pip on Windows.

Validation status:

• CI smoke-tests the first CLI path on Linux, macOS, and Windows using assay version and assay try.
• The deeper SDK compatibility suite currently runs on Ubuntu.

If assay is not recognized after install, open a new terminal first. On Windows, the usual fix is adding Python's Scripts directory to PATH.

For deterministic environment setup, see docs/START_HERE.md.

Shell completions (bash/zsh/fish/PowerShell):

assay --install-completion

Restart your shell after installing. Tab completion works for all commands and options.

See it — then understand it (demo-challenge, demo-incident, honest failure)

See It -- Then Understand It

assay try (above) gives you the 15-second version. For the full specimen with file output and manual verification, use the challenge demo:

assay demo-challenge    # creates challenge_pack/ with good + tampered packs

Two packs, one byte changed ("gpt-4" -> "gpt-5" in the receipts). Here's what happens (pack IDs and timestamps will differ on your machine):

$ assay verify-pack challenge_pack/good/

  VERIFICATION PASSED

  Pack ID:    pack_20260222_ca2bb665
  Integrity:  PASS
  Claims:     PASS
  Receipts:   3
  Signature:  Ed25519 valid

  Exit code: 0

$ assay verify-pack challenge_pack/tampered/

  VERIFICATION FAILED

  Pack ID:    pack_20260222_ca2bb665
  Integrity:  FAIL
  Error:      Hash mismatch for receipt_pack.jsonl

  Exit code: 2

One byte changed. Verification fails. No server access needed. No trust required. Just math.

Now try the policy violation demo:

assay demo-incident     # two-act scenario: honest PASS vs honest FAIL

  Act 1: Agent uses gpt-4 with guardian check
  Integrity: PASS    Claims: PASS    Exit code: 0

  Act 2: Someone swaps model to gpt-3.5-turbo, removes guardian
  Integrity: PASS    Claims: FAIL    Exit code: 1

Act 2 is an honest failure -- authentic evidence proving the run violated its declared standards. The evidence is real. The failure is real. Nobody can edit the history. Exit code 1.

Honest failure is a feature, not an embarrassment. Exit 1 is audit gold: a control failed, the failure is detectable and retained, and the evidence is authentic. A signed failure is stronger evidence than a vague pass. Auditors, regulators, and buyers trust systems that can show what went wrong -- not systems that only ever claim success.

How that works

Assay separates two questions on purpose:

• Integrity: "Were these bytes tampered with after creation?" (signatures, hashes, required files)
• Claims: "Does this evidence satisfy our declared governance checks?" (receipt types, counts, field values)

Integrity	Claims	Exit	Meaning
PASS	PASS	0	Evidence checks out, declared standards pass
PASS	FAIL	1	Honest failure: authentic evidence of a standards violation
FAIL	--	2	Tampered evidence
--	--	3	Bad input (missing files, invalid arguments)

The split is the point. Systems that can prove they failed honestly are more trustworthy than systems that always claim to pass.

With real calls: assay scan . finds your actual OpenAI / Anthropic / Gemini / LiteLLM / LangChain call sites. assay patch . instruments them. Every real LLM call emits a signed receipt. The demos above use synthetic data so you can see verification without configuring anything.

How Assay captures evidence

Installing Assay gives you the CLI, receipt store, and proof-pack builder. It does not automatically record your app.

Receipts are emitted only when your runtime is instrumented:

• assay patch . inserts the right Assay integration for supported SDKs
• patch() wrappers emit receipts when model calls happen
• AssayCallbackHandler() does the same for LangChain callback flows
• emit_receipt(...) lets you record events manually in any stack

assay run -- <your command> then does three things:

1. creates a trace id
2. runs your app with ASSAY_TRACE_ID in the environment
3. packages any emitted receipts into proof_pack_<trace_id>/

The result is a signed, offline-verifiable artifact:

app execution
  -> instrumented SDK or emit_receipt(...)
  -> receipts written to ~/.assay/...
  -> assay run packages them into proof_pack_<trace_id>/
  -> assay verify-pack checks the artifact offline

Three operating modes (wrapper, runtime SDK, settlement)

Three Operating Modes

Assay is an evidence substrate, not just a CLI wrapper. It operates in three modes depending on your runtime shape.

Boundary note: Mode 1 is the primary public story in this charter. Modes 2 and 3 are real advanced capability and bridge primitives. They should not replace the first-contact explanation of scan -> patch -> run -> proof pack -> verify.

Mode 1: Wrapper

For scripts, CI jobs, and bounded workflows. Lowest friction.

assay run -c receipt_completeness -- python my_app.py
assay verify-pack ./proof_pack_*/

One process, one proof pack, one verification. This is the fastest path to a verifiable artifact.

Mode 2: Runtime

For services, agents, and long-lived processes. Episode-native Python SDK.

import assay

with assay.open_episode(policy_version="v2.1") as episode:
    episode.emit("model.invoked", {"model": "gpt-4", "tokens": 800})
    episode.emit("tool.invoked", {"tool": "knowledge_base"})
    episode.emit("guardian.approved", {"action": "send_reply"})

    checkpoint = episode.seal_checkpoint(reason="before_send_reply")
    verdict = assay.verify_checkpoint(checkpoint)

    if verdict.ok:
        send_reply()
        episode.emit("action.settled", {"action": "send_reply"})
    elif verdict.honest_fail:
        escalate()
        episode.emit("action.denied", {"reason": "honest_fail"})

Emit receipts continuously during execution. Seal checkpoints at review boundaries. The episode is the constitutional unit, not the Unix process.

Mode 3: Settlement

For high-consequence actions (payments, approvals, publishing, deletions).

checkpoint = episode.seal_checkpoint(reason="before_payout")
verdict = assay.verify_checkpoint(checkpoint)

if not verdict.ok:
    # Do not proceed. Evidence posture is insufficient.
    if verdict.honest_fail:
        escalate(verdict.errors)  # authentic evidence of a gap
    else:
        alert(verdict.errors)     # tampered or missing evidence
    episode.emit("action.denied", {"reason": verdict.errors})
else:
    execute_payout()
    episode.emit("action.settled", {"action": "payout"})

Consequences require verified evidence posture before the world changes. verify_checkpoint() is the settlement gate.

The law: Receipt boundaries follow semantic events. Proof boundaries follow review boundaries. Settlement boundaries follow consequence boundaries.

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Full project setup (scan, patch, run, verify, CI gate)

Add to Your Project

# 1. Find uninstrumented LLM calls
assay scan . --report

# 2. Patch (one line per SDK, or auto-patch all)
assay patch .

# 3. Run + build a signed evidence pack
# -c receipt_completeness runs the built-in completeness check (see `assay cards list` for all options)
# everything after -- is your normal run command
assay run -c receipt_completeness -- python my_app.py

# 4. Verify (CLI or browser — no install needed for browser)
assay verify-pack ./proof_pack_*/
# Or verify in your browser: https://haserjian.github.io/assay-proof-gallery/verify.html

# 5. Generate report artifacts for security/compliance review
assay report . -o evidence_report.html --sarif

# 6. Optional: set and enforce score gates in CI
assay gate save-baseline
assay gate check . --min-score 60 --fail-on-regression

Command discovery: assay --help shows the most-used entry points. Commands used in this guide — gate, report, ci, diff, analyze, lock, and vendorq — are available but not listed there by default. Run assay <command> --help for full options.

assay scan . --report detects LLM call sites via static AST analysis (OpenAI, Anthropic, Google Gemini, LiteLLM, LangChain) and generates a self-contained HTML gap report. Dynamic dispatch, eval, subprocess, and raw HTTP calls are not covered — see SCANNER_LIMITATIONS.md. assay patch inserts the two-line integration. assay run wraps your command, collects receipts, and produces a signed 5-file evidence pack. assay verify-pack checks integrity + claims and exits with one of the four codes above. Then run assay explain on any pack for a plain-English summary.

Local models: Any OpenAI-compatible server (Ollama, LM Studio, vLLM, llama.cpp) works automatically -- Assay patches the OpenAI SDK at the class level, so OpenAI(base_url="http://localhost:11434/v1") emits receipts like any other provider. LiteLLM users get the same coverage via the LiteLLM integration (ollama/llama3, etc.).

Why now: EU AI Act Article 12 requires automatic logging for high-risk AI systems; Article 19 requires providers to retain automatically generated logs for at least 6 months. High-risk obligations apply from 2 Aug 2026 (Annex III) and 2 Aug 2027 (regulated products). SOC 2 CC7.2 requires monitoring of system components and analysis of anomalies as security events. "We have logs on our server" is not independently verifiable evidence. Assay produces evidence that is. See compliance citations for exact references.

CI Gate

Fastest path (recommended):

assay ci init github --run-command "python my_app.py" --min-score 60

This generates a 3-job GitHub Actions workflow:

• assay-gate (score enforcement, regression checks, JSON gate report artifact)
• assay-verify (proof pack generation + cryptographic verification)
• assay-report (HTML evidence report artifact + SARIF upload)

Manual path (advanced):

assay gate save-baseline
assay gate check . --min-score 60 --fail-on-regression --save-report assay_gate_report.json --verbose --json
assay run -c receipt_completeness -- python my_app.py
assay verify-pack ./proof_pack_*/ --lock assay.lock --require-claim-pass
assay report . -o evidence_report.html --sarif

The lockfile catches config drift. Verify-pack catches tampering. Gate enforces score regressions. Report produces the shareable artifact + SARIF. assay diff remains useful for deep forensics and budget/drift analysis. See Decision Escrow for the protocol model.

# Lock your verification contract
assay lock write --cards receipt_completeness -o assay.lock

Daily use after CI is green

Regression forensics:

assay diff ./proof_pack_*/ --against-previous --why

--against-previous auto-discovers the baseline pack. --why traces receipt chains to explain what regressed and which call sites caused it.

Cost/latency drift (from receipts):

assay analyze --history --since 7

Shows cost, latency percentiles, error rates, and per-model breakdowns from your local trace history.

VendorQ (verifiable vendor questionnaires)

VendorQ: Verifiable Vendor Questionnaires

Enterprise customers ask AI governance questions in security questionnaires. VendorQ compiles evidence-backed answer packets from Assay proof packs. Every answer traces to a signed receipt. Every modification is detectable.

For the buyer-facing wrapper around that proof material, see docs/reviewer-packets.md.

Quick path:

assay vendorq ingest --in questionnaire.csv --out .assay/vendorq/questions.json
assay vendorq compile --questions .assay/vendorq/questions.json --pack ./proof_pack_* --policy conservative --out .assay/vendorq/answers.json
assay vendorq export-reviewer --proof-pack ./proof_pack_* --out reviewer_packet
assay reviewer verify reviewer_packet

Use VendorQ when the pain is: "we have to answer AI-governance questions and we cannot hand the reviewer a verifiable artifact."

# Ingest a questionnaire, compile answers against evidence, lock, verify
assay vendorq ingest --in questionnaire.csv --out questions.json
assay vendorq compile --questions questions.json --pack ./proof_pack --out answers.json
assay vendorq lock write --answers answers.json --pack ./proof_pack --out vendorq.lock
assay vendorq verify --answers answers.json --pack ./proof_pack --lock vendorq.lock --strict

10 deterministic verification rules. Tamper one answer and verification fails with exit code 2. The packet is forwardable to your customer's security team — they verify it offline with a public key.

See it live: Proof Gallery — three real proof packs demonstrating pass, honest fail, and tamper detection. All three are independently verifiable without any account or API key.

Adversarial testing: 16 attack scenarios, 16 catches, 0 false passes.

Reviewer packets (cross-boundary evidence handoff)

Reviewer-Ready Evidence Packets

A reviewer-ready evidence packet is the buyer-facing wrapper around a signed proof pack. Assay produces the proof pack. The evidence packet makes that proof usable across an organizational boundary: scope, coverage, review state, and the nested proof-pack verification path in one forwardable artifact.

# Compile a reviewer packet from a proof pack plus declarative packet inputs
assay vendorq export-reviewer \
  --proof-pack tests/fixtures/reviewer_packet/sample_proof_pack \
  --boundary tests/fixtures/reviewer_packet/sample_boundary.json \
  --mapping tests/fixtures/reviewer_packet/sample_mapping.json \
  --out reviewer_packet_demo

# Verify the reviewer packet and derive the settlement
assay reviewer verify reviewer_packet_demo
assay reviewer verify reviewer_packet_demo --json

Canonical handoff flow:

proof pack -> reviewer packet -> assay reviewer verify -> browser verify

Buyer verdicts and CLI exit codes are different layers:

• Buyer verdicts: VERIFIED, VERIFIED_WITH_GAPS, INCOMPLETE_EVIDENCE, EVIDENCE_REGRESSION, TAMPERED, OUT_OF_SCOPE
• CLI exit codes: 0/1/2/3 for PASS, HONEST_FAIL, TAMPERED, and bad input

Use the proof pack when you need cryptographic verification. Use the evidence packet when another team needs a bounded artifact they can inspect, forward, and challenge.

Verify online: Browser verifier — drop in a proof pack or reviewer packet and check it client-side.

Passports (portable signed evidence credentials)

Passports: Portable Signed Evidence

A passport is a signed, content-addressed JSON object that summarizes what was verified about an AI system: claims, coverage, reliance class, and a validity window. Built from proof pack evidence, not asserted by hand.

Try the seeded lifecycle demo (no API key, no repo context needed):

pip install assay-ai
assay passport demo

The demo intentionally starts with a weak passport, then challenges and supersedes it. The initial X-Ray grade (D) is part of the lifecycle, not a product failure.

12 commands (assay passport --help). The 6 you'll use most:

Command	Question
verify	Is this artifact authentic and untampered?
status	Should I rely on it under my policy? (PASS/WARN/FAIL)
xray	How strong is the evidence posture? (A-F grade)
challenge	Record a governance objection against a passport
supersede	Link the old passport to an improved successor
diff	What changed between two passport versions?

Also: mint, sign, show, render, revoke, demo.

Full command set:

# Mint a passport from a proof pack, sign it, verify it
assay passport mint --pack ./proof_pack/ --subject-name "MyApp" \
  --system-id "my.app.v1" --owner "My Org" --output passport.json
assay passport sign passport.json
assay passport verify passport.json

# Check reliance posture under a policy mode
assay passport status passport.json --mode buyer-safe --json

# X-Ray diagnostic: structural grade (A-F) and improvement path
assay passport xray passport.json --report xray.html

# Lifecycle governance (all cryptographically signed)
assay passport challenge passport.json --reason "Missing coverage"
assay passport supersede old.json new.json --reason "Addressed gap"
assay passport diff old.json new.json --report diff.html

Worked example: Seeded referee gallery — pre-built signed passports, governance receipts, X-Ray diagnostic, and trust diff. All artifacts are regenerable via python3 docs/passport/generate_gallery.py.

Deeper docs: Passport guide | Verification ritual | Gallery manifest

What this proves today:

• Signed, content-addressed passport artifacts with Ed25519 signatures
• Deterministic lifecycle governance: challenge, supersede, revoke, diff
• Reproducible worked examples on seeded reference artifacts
• Offline verification without network access

What is future scope:

• Arbitrary external trust-surface scanning (URLs, PDFs, vendor pages)
• Minting from external vendor documents (currently proof-pack only)
• Generalized trust analysis across messy real-world inputs
• Enterprise diff workflows (primitive exists, product does not)

AI Decision Credentials (ADC)

AI Decision Credentials (ADC)

ADC is a structured schema for packaging AI decision evidence into verifiable, time-bounded credentials. An ADC wraps the proof pack with decision metadata: what was decided, by whom, under what policy, with what evidence, and how long the credential remains valid.

# Verify a pack with expiry enforcement
assay verify-pack ./proof_pack_*/ --check-expiry

# ADC v0.1 schema: 35 properties, 17 required, additionalProperties: false
# Schema: src/assay/schemas/adc_v0.1.schema.json

The conformance corpus includes 10 canonical packs (including stale_01 for expired credentials and superseded_01 for replaced decisions).

What becomes harder to fake

What Becomes Harder to Fake

Assay is not a truth oracle. It is an evidence-hardening layer.

If someone tries to...	Without Assay	With Assay
Edit evidence after a run	Hard to notice	Verification fails
Drop or weaken locked checks	Easy to hide	Lock mismatch exposes it
Omit covered call sites	Easy to hand-wave	Completeness checks catch it
Hand buyer internal logs, ask for trust	Buyer must trust the operator	Buyer verifies offline
Fabricate a complete run from scratch	Possible	Still possible at base tier; stronger deployment raises the cost

Why there is no quiet edit. Every file in a proof pack is fingerprinted. The fingerprints are recorded in a manifest. The manifest is digitally signed. Change a file -- the fingerprint won't match. Fix the manifest to cover it -- the signature breaks. Re-sign the manifest -- the signer identity changes. Every path to tampering leaves a visible trace.

Assay proves the evidence artifact has not been quietly changed after the fact. It does not, by itself, prove every upstream component was honest.

Deployment ladder -- start at Base, strengthen as your trust requirements grow:

• Base -- self-signed artifact, offline-verifiable, tamper-evident
• Hardened -- CI-held signing key + branch protection (separates signer from developer)
• Anchored -- transparency ledger + external timestamping (RFC 3161)

Completeness is enforced relative to call sites enumerated by the scanner and/or declared by policy. Undetected call sites are a known residual risk, reduced via multi-detector scanning and CI gating.

Assay doesn't make fraud impossible -- it makes fraud expensive, fragile, and much easier to catch.

Evidence compiler model + full command reference

The Evidence Compiler

Assay is an evidence compiler for AI execution. If you've used a build system, you already know the mental model:

Concept	Build System	Assay
Source	.c / .ts files	Receipts (one per LLM call)
Artifact	Binary / bundle	Evidence pack (5 files, 1 signature)
Tests	Unit / integration tests	Verification (integrity + claims)
Lock	package-lock.json	assay.lock
Gate	CI deploy check	CI evidence gate

Commands

The core path is 6 commands:

assay try                 # 60-second demo (sign, tamper, catch)
assay scan / assay patch  # instrument
assay run                 # produce evidence
assay verify-pack         # verify evidence
assay diff                # catch regressions
assay score               # evidence readiness (0-100, A-F)

Full command reference:

Getting started

Command	Purpose
assay try	60-second demo: sign, tamper, catch
assay status	One-screen operational dashboard
assay start demo|ci|mcp	Guided entrypoints for trying, CI setup, or MCP auditing
assay onboard	Guided setup: doctor -> scan -> first run plan
assay doctor	Preflight check: is Assay ready here?
assay version	Print installed version

Instrument + produce evidence

Command	Purpose
assay scan	Find uninstrumented LLM call sites (--report for HTML)
assay patch	Auto-insert SDK integration patches into your entrypoint
assay run	Wrap command, collect receipts, build signed evidence pack

Verify + analyze

Command	Purpose
assay verify-pack	Verify integrity + claims (the 4 exit codes)
assay verify-signer	Extract and verify signer identity from a pack manifest
assay explain	Plain-English summary of an evidence pack
assay analyze	Cost, latency, error breakdown from pack or --history
assay diff	Compare packs: claims, cost, latency (--against-previous, --why, --gate-*)
assay score	Evidence Readiness Score (0-100, A-F) with anti-gaming caps

Workflows + CI

Command	Purpose
assay flow try|adopt|ci|mcp|audit	Guided workflow executor (dry-run by default, --apply to execute)
assay ci init github	Generate a GitHub Actions workflow
assay ci doctor	CI-profile preflight checks
assay audit bundle	Create portable audit bundle (tar.gz with verify instructions)
assay compliance report	Generate compliance evidence report

Pack + baseline management

Command	Purpose
assay packs list	List local proof packs
assay packs show	Show pack details
assay packs pin-baseline	Pin a pack as the diff baseline
assay baseline set|get	Set or get the baseline pack for diff

Key management

Command	Purpose
assay key generate	Generate a new Ed25519 signing key
assay key list	List local signing keys and active signer
assay key info	Show key details (fingerprint, creation date)
assay key set-active	Set active signing key for future runs
assay key rotate	Generate a new key and switch active signer
assay key export|import	Export or import keys for CI or team sharing
assay key revoke	Revoke a signing key

Lockfile + cards

Command	Purpose
assay lock write	Freeze verification contract to lockfile
assay lock check	Validate lockfile against current card definitions
assay lock init	Initialize a new lockfile interactively
assay cards list	List built-in run cards and their claims
assay cards show	Show card details, claims, and parameters

MCP + policy

Command	Purpose
assay mcp-proxy	Transparent MCP proxy: intercept tool calls, emit receipts
assay mcp policy init	Generate a starter MCP policy YAML file
assay mcp policy validate	Validate a policy file against the schema
assay policy impact	Analyze policy impact on existing evidence

Incident forensics

Command	Purpose
assay incident timeline	Build incident timeline from receipts
assay incident replay	Replay an incident from receipt chain

Demos

Command	Purpose
assay demo-incident	Two-act scenario: passing run vs failing run
assay demo-challenge	CTF-style good + tampered pack pair
assay demo-pack	Generate demo packs (no config needed)

Documentation

• Start Here -- 6 steps from install to evidence in CI
• Evidence Packets -- compile, verify, and hand off reviewer-ready evidence packets
• What Assay Does Today -- the plain-language founder memo
• Boundary Map -- Assay vs VendorQ vs AgentMesh vs Loom/CCIO
• Full Picture -- architecture, trust tiers, repo boundaries, release history
• Quickstart -- install, golden path, command reference
• For Compliance Teams -- what auditors see, evidence artifacts, framework alignment
• Compliance Citations -- exact regulatory references (EU AI Act, SOC 2, ISO 42001)
• Decision Escrow -- protocol model: agent actions don't settle until verified
• Roadmap -- phases, product boundary, execution stack
• Repo Map -- what lives where across the Assay ecosystem
• Pilot Program -- early adopter program details

Common Issues

• "No receipts emitted" after assay run: First, check whether your code has call sites: assay scan . -- if scan finds 0 sites, you may not be using a supported SDK yet. Installing Assay alone does not emit receipts; your runtime must be instrumented. If scan finds sites, check: (1) Is # assay:patched in the file, or did you add patch() / a callback? Run assay scan . --report to see patch status per file. (2) Did you install the SDK extra (python3 -m pip install "assay-ai[openai]")? (3) Did patch() execute before the first model call? (4) Did you use -- before your command (assay run -- python app.py)? Run assay doctor for a full diagnostic.

• LangChain projects: assay patch auto-instruments OpenAI and Anthropic SDKs but not LangChain (which uses callbacks, not monkey-patching). For LangChain, add AssayCallbackHandler() to your chain's callbacks parameter manually. See src/assay/integrations/langchain.py for the handler.

• assay run python app.py gives "No command provided": You need the -- separator: assay run -c receipt_completeness -- python app.py. Everything after -- is passed to the subprocess.

• Quickstart blocked on large directories: assay quickstart guards against scanning system directories (>10K Python files). Use --force to bypass: assay quickstart --force.

• macOS: ModuleNotFoundError inside assay run but works outside it: On macOS, python3 on PATH may point to a different Python version than where assay and your SDK are installed (e.g. python3 → 3.14, but packages are in 3.11). Use a virtual environment (recommended), or specify the exact interpreter: assay run -- python3.11 app.py. Check with python3 --version and compare to the Python where you installed Assay.

Get Involved

• Try it: python3 -m pip install assay-ai && assay try
• Questions / feedback: GitHub Discussions
• Bug reports: Issues
• Want this in your stack in 2 weeks? Pilot program -- we instrument your AI workflows, set up CI gates, and hand you a working evidence pipeline. Open a pilot inquiry.

Related Repos

Repo	Purpose
assay	Core CLI, SDK, conformance corpus (this repo)
assay-verify-action	GitHub Action for CI verification
assay-ledger	Public transparency ledger
assay-proof-gallery	Live demo packs (PASS / HONEST FAIL / TAMPERED)

License

Apache-2.0