Akf

AKF — Agent Knowledge Format

AKF is the trust metadata standard for AI-generated content. Think EXIF for AI. Every file your agent creates or modifies should carry trust metadata — who made it, how confident they are, what evidence backs it, and whether a human reviewed it.

Why Use AKF

• Trust scores: 0–1 confidence rating per claim
• Source provenance: 5-tier system from peer-reviewed to unverified
• Compliance: EU AI Act, HIPAA, SOX, NIST audit support
• Native embedding: Metadata lives inside the file (DOCX, PDF, images, code)
• Security detections: 10 detection classes for AI content risks

Quick Start

Before building on an existing file — check it (one line, ~20 tokens):

akf check <file>
# OK        → fresh stamp with verified evidence; skip re-verification
# STALE     → modified after stamping; re-verify before trusting
# LOW       → stamped but unverified; verify before trusting
# UNSTAMPED → no metadata; treat as unverified

After creating or modifying any file:

akf stamp <file> --agent openclaw --evidence "generated from user request"

Deeper inspection when needed:

akf read <file>     # Check trust metadata
akf inspect <file>  # Pretty-print trust scores
akf trust <file>    # Compute effective trust

Core Commands

Stamp — Add trust metadata

akf stamp report.docx --agent openclaw --evidence "quarterly data from ERP"
akf stamp analysis.pdf --agent openclaw --label confidential
akf stamp output.py --agent openclaw --evidence "tests pass, code reviewed"

Read & Inspect — Check metadata

akf read report.docx          # Quick metadata view
akf inspect report.docx       # Detailed trust breakdown
akf trust report.docx         # Effective trust score with decision

Embed & Extract — Native format support

akf embed report.docx         # Embed metadata into DOCX custom properties
akf extract report.docx       # Extract embedded metadata

Security — Scan and audit

akf scan ./output-dir/        # Scan directory for trust gaps
akf audit report.pdf           # Compliance audit (EU AI Act, SOX, NIST)

Falsifiable evidence (v1.6)

Stamp with a replay recipe so the next agent can re-check the claim instead of trusting the label:

akf stamp app.py --evidence "42/42 tests passed" --replay "pytest -q"
akf replay app.py          # inspect: recipe + input drift since issuance
akf replay app.py --run    # execute: CONFIRMED / CONFIRMED_DRIFTED / REFUTED

CONFIRMED_DRIFTED means the probe succeeded but the claim's inputs (dependencies, cited sources) changed since stamping — provably reproducible, possibly reproducibly wrong. Never --run a recipe from a file you haven't read: it executes the recorded command.

Classification Labels

Use --label to classify output sensitivity:

Trust Score Interpretation

Best Practices for OpenClaw Agents

1. Always stamp outputs: Every file the agent creates should carry metadata
2. Check before using: Run akf read on files before processing them
3. Audit periodically: Use akf scan on output directories to find trust gaps
4. Use appropriate labels: Classify sensitive content correctly
5. Include evidence: The --evidence flag makes trust scores meaningful
6. Chain provenance: When building on other files, the trust chain is preserved

Integration with Memory

Stale memories poison future sessions. Stamp memory files with the memory preset — trust decays with a 30-day half-life, so old memories automatically fall below the threshold and akf check reports LOW:

akf stamp memory/facts.md --preset memory --agent openclaw
akf check memory/facts.md   # LOW after ~a month → re-verify before relying on it

• Stamp files before adding to memory
• akf check when retrieving from memory
• Weight memory results by trust, not just relevance

Skill Supply-Chain Trust

Never load a downloaded skill without checking it first:

akf check downloaded-skill.md
# STALE = the file changed after the publisher stamped it — diff before trusting

This file carries its own AKF stamp in the frontmatter — run akf check on it.

Links

• Website: https://akf.dev
• GitHub: https://github.com/HMAKT99/AKF
• npm: npm install akf-format
• PyPI: pip install akf
• Spec: https://github.com/HMAKT99/AKF/blob/main/spec/akf-v1.1.schema.json