Vaara is the tamper-evident runtime evidence layer for AI systems. It turns every action an agent takes into a record an outside party can verify without trusting you, then binds that record to the machine's own TPM 2.0 + IMA attestation. EU AI Act compliance, and any other case where you have to prove what an agent actually did.

Open source. No SaaS. No telemetry. No signup.

$ vaara verify-record someone-elses-record.json

  schema             ok    well-formed SEP-2828 execution record
  result commitment  ok    projectionDigest == sha256(projection)
  verdict            CONFORMS

no signing key, no access to the system that produced it

What's shipped

• Check any record yourself: vaara verify-record tests any JSON against the published SEP-2828 format, including a record Vaara never produced, with no signing key and no access to the system that made it. vaara verify-bundle runs the full evidence set and prints a single pass or fail, fail-closed on authenticity.
• One sink for any stack's evidence: vaara ingest seals a record from another format, an MCP execution record or a did:web catalog entry, into one canonical, content-addressed envelope with an honest gap report, so a dropped record is a provable hole. The conformance corpus is reproduced by a checker that imports no Vaara.
• Hash-chained, tamper-evident audit trail (SHA-256, optional Ed25519, optional post-quantum ML-DSA-65), anchored to an RFC 3161 / eIDAS qualified timestamp so a record cannot be backdated against a clock you do not control. An auditor verifies it offline with a public key.
• Hardware-rooted binding: vaara verify-tpm-binding and vaara verify-tpm-chain tie an execution record to the machine's own TPM 2.0 quote and IMA measurements.
• Sovereign inference: run a local model through Vaara and every answer carries a signed receipt bound to the machine's TPM. Open under AGPL-3.0.
• One-command regulator package: vaara trail export-article12 writes the signed trail, per-article EU AI Act evidence, and the time anchor as Article 19 existence-in-time, in one file an authority checks offline.
• Policy gating on every tool call: allow, block, or escalate each agent action against your own policy before it runs, through a transparent MCP proxy with native hooks for LangChain, CrewAI, and the OpenAI Agents SDK. TypeScript client on npm, Claude Code plugin in the same repo.
• Authored SEP-2828, the Model Context Protocol server-side execution-record proposal, reproduced in full by an independent developer from a clean checkout. Releases are SLSA Build Level 3 and Sigstore-signed, with continuous fuzzing on the decoder, audit, and policy loader.

Adoption (live)

• - PyPI downloads, last 30 days
• - PyPI downloads, last 7 days
• - npm downloads, last 7 days (@vaara/client)

Acknowledged by

• IMDA Model AI Governance Framework for Agentic AI v1.5 (Singapore, 20 May 2026), industry contributors
• AMD developer testimonial (May 2026)
• OpenSSF Best Practices Project 12612

Where