---

_akf: '{"v":"1.0","claims":[{"c":"Trust metadata for README.md","t":0.7,"id":"1979cbeb","src":"unspecified","tier":5,"ver":false,"ai":true,"evidence":[{"type":"other","detail":"updated certify and github action references","at":"2026-03-18T04:21:48.869226+00:00"}]}],"id":"akf-c33254656fc5","agent":"claude-code","at":"2026-03-18T04:21:48.870623+00:00","label":"public","inherit":true,"ext":false,"sv":"1.1"}'

AKF — The AI Native File Format

Trust scores · Source provenance · Security classification · Compliance readiness
Embeds natively into DOCX, PDF, XLSX, images, code, and all major formats.
Think EXIF for AI — ~15 tokens of JSON that travel with your files.

⭐ Star on GitHub · Try Online · HuggingFace Demo

Quickstart · AI Agents · MCP Server · Agent Skills · CLI · Website

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The Problem

AI generates reports, code, docs, images — and none of it carries provenance. Your team opens a file with no idea: Who made it? AI or human? Can we trust it? Is it compliant?

EU AI Act Article 50 takes effect August 2, 2026. After that, AI-generated content must carry transparency metadata. Penalties: up to EUR 35M / 7% of global turnover.

The Solution

AKF is the AI native file format — ~15 tokens of JSON that embed into any file:

What	How
Trust score	0–1 confidence based on source tier
Source provenance	SEC filing → analyst → AI agent chain
Compliance	One command: akf audit file --regulation eu_ai_act

AI generates content → AKF stamps trust metadata → Anyone can verify it

vs Alternatives

	AKF	C2PA	Watermarking	Manual tracking
Works on documents/code	✅	❌ (media only)	❌	⚠️
No Certificate Authority needed	✅	❌	✅	✅
Trust scores	✅	❌	❌	❌
Source provenance chain	✅	✅	❌	⚠️
Compliance auditing	✅	❌	❌	❌
~15 tokens (LLM-friendly)	✅	❌	N/A	N/A
20+ file formats	✅	⚠️ (media)	⚠️ (text)	❌
Free & open source	✅	⚠️	Varies	✅

Quickstart

pip install akf    # Python
npm install akf-format    # TypeScript / Node.js

akf doctor         # Check your install — detects PATH issues and guides setup

akf command not found? Run akf doctor to auto-detect your setup, or use python3 -m akf (always works).

• Install with pipx: pipx install akf (recommended — auto-handles PATH)
• Windows: use python3 -m akf or install via pipx

import akf

# Stamp trust metadata onto any AI output
akf.stamp("Revenue was $4.2B, up 12% YoY",
          confidence=0.98, source="SEC 10-Q",
          agent="claude-code", model="claude-sonnet-4-20250514")

# Embed into Office docs, PDFs, images — any format
akf.embed("report.docx", claims=[...], classification="confidential")

# Audit for compliance (EU AI Act, HIPAA, SOX, GDPR, NIST AI, ISO 42001)
result = akf.audit("report.akf", regulation="eu_ai_act")
print(f"Compliant: {result.compliant}")

For AI Agents

AKF is designed agent-first. One-line APIs for stamping, streaming, and auditing.

import akf

# Stamp with evidence (auto-detected: test_pass, type_check, etc.)
akf.stamp("Fixed auth bypass", kind="code_change",
          evidence=["42/42 tests passed", "mypy: 0 errors"],
          agent="claude-code", model="claude-sonnet-4-20250514")

# Stream trust metadata in real-time
with akf.stream("output.md", model="gpt-4o") as s:
    for chunk in llm_response:
        s.write(chunk)

# Trust-annotated git commits (uses git notes)
akf.stamp_commit(content="Refactored auth module", kind="code_change",
                 evidence=["all tests pass"], agent="claude-code")
print(akf.trust_log(n=10))  # + ACCEPT  ~ LOW  - REJECT  ? none

Multi-Agent Teams

AKF supports multi-agent orchestration — Claude Agent Teams, Copilot Cowork, Codex multi-agent, and any A2A-compatible platform.

import akf

# Agent-to-agent delegation with trust ceiling
policy = akf.DelegationPolicy(
    delegator="lead-agent", delegate="research-bot",
    trust_ceiling=0.7, allowed_actions=["search", "summarize"]
)
result = akf.delegate(parent_unit, policy)

# Multi-agent streaming session
with akf.TeamStream(["research", "writer", "reviewer"]) as ts:
    ts.write("research", "Found 3 sources", confidence=0.8)
    ts.write("writer", "Drafted summary", confidence=0.75)
    ts.write("reviewer", "Approved with edits", confidence=0.9)
    scores = ts.aggregate()  # per-agent + team trust

# Cross-platform agent identity
card = akf.create_agent_card(name="Research Bot", platform="claude-code",
                             capabilities=["search", "summarize"])
akf.verify_agent_card(card)  # SHA-256 hash verification

# Team certification (per-agent breakdown)
report = akf.certify_team("src/", min_trust=0.7)
# report.all_agents_certified — each agent must individually pass

CLI:

akf agent create --name "Bot" --platform claude-code --capabilities search,summarize
akf agent list
akf agent verify <id>
akf agent export-a2a <id> --output card.json   # A2A protocol bridge
akf agent import-a2a card.json
akf certify src/ --team                         # Per-agent breakdown

MCP Server

AKF ships an MCP server so any AI agent can create, validate, scan, and audit trust metadata.

# Install from the repo
pip install ./packages/mcp-server-akf

{
  "mcpServers": {
    "akf": {
      "command": "python",
      "args": ["-m", "mcp_server_akf"]
    }
  }
}

9 MCP tools: create_claim · validate_file · scan_file · trust_score · stamp_file · audit_file · embed_file · extract_file · detect_threats

Ambient Trust

AKF works where AI agents work. Drop a config file, and every AI-generated file carries trust metadata automatically.

Agent	How it works
Claude Code	Reads CLAUDE.md — stamps every file it creates with confidence and evidence
Cursor	Reads .cursorrules — stamps AI edits before you review
Windsurf	Reads .windsurfrules — stamps AI edits with trust metadata
GitHub Copilot	Reads .github/copilot-instructions.md (native) + shell hook for CLI
OpenAI Codex	Reads AGENTS.md — stamps files in cloud sandbox and local
Manus / Other Agents	MCP server + shell hook — works with any agent that supports MCP or CLI
Any MCP agent	9 MCP tools — stamp, audit, embed, extract, detect, validate, scan, trust, create
Any CLI tool	eval "$(akf shell-hook)" — intercepts claude, chatgpt, aider, openclaw, ollama, manus

The trust pipeline:

Agent writes code → Git commit stamped → CI runs akf certify → Team reviews with context

Set up in 60 seconds:

# 1. Agent stamps its own work (already in this repo)
cat CLAUDE.md        # or .cursorrules / .windsurfrules / AGENTS.md / .github/copilot-instructions.md

# 2. Git hooks stamp every commit
akf init --git-hooks

# 3. CI certifies trust on every PR
#    uses: HMAKT99/AKF/extensions/github-action@main

# 4. Shell hook intercepts AI CLI tools
eval "$(akf shell-hook)"

Skills

AKF provides agent skill files that AI agents can discover and use. Drop these into your agent's context:

Skill	What it does
stamp.md	Stamp trust metadata onto AI outputs
audit.md	Audit files for regulatory compliance
scan.md	Security scan files and directories
embed.md	Embed trust metadata into Office/PDF/images
detect.md	Run 10 security detection classes
stream.md	Stream trust metadata in real-time
git.md	Trust-annotated git workflows
convert.md	Convert between formats
delegate	Agent-to-agent trust delegation
team	Multi-agent streaming sessions

Format at a Glance

Compact (~15 tokens — optimized for AI):

{"v":"1.0","claims":[{"c":"Revenue was $4.2B","t":0.98,"src":"SEC 10-Q"}]}

Descriptive (human-readable — same data):

{"version":"1.0","claims":[{"content":"Revenue was $4.2B","confidence":0.98,"source":"SEC 10-Q"}]}

Full (with provenance, decay, AI flags, security):

{"v":"1.0","by":"[email protected]","label":"confidential","inherit":true,
 "claims":[
   {"c":"Revenue $4.2B","t":0.98,"src":"SEC 10-Q","tier":1,"ver":true,"decay":90},
   {"c":"H2 will accelerate","t":0.63,"tier":5,"ai":true,"risk":"AI inference"}
 ],
 "prov":[
   {"hop":0,"by":"[email protected]","do":"created","at":"2025-07-15T09:30:00Z"},
   {"hop":1,"by":"copilot-agent","do":"enriched","at":"2025-07-15T10:15:00Z"}
 ]}

Works With Every Format

AKF embeds natively — no sidecars needed for most formats:

Format	How It Works
.akf	Native standalone knowledge file
.docx .xlsx .pptx	OOXML custom XML part
.pdf	PDF metadata stream
.html	JSON-LD <script type="application/akf+json">
.md	YAML frontmatter
.png .jpg	EXIF/XMP metadata
.json	Reserved _akf key
.mp4 .mov .webm .mkv	Sidecar .akf.json companion
.mp3 .wav .flac .ogg	Sidecar .akf.json companion
Everything else	Sidecar .akf.json companion

# One API for all formats
akf.embed("report.docx", claims=[...], classification="confidential")
meta = akf.extract("report.docx")
akf.scan("report.docx")

Zero-Touch Auto-Stamping

AKF can automatically stamp every file AI touches — no manual intervention needed.

# Install the background watcher
akf install

# Or run in foreground
akf watch ~/Downloads ~/Desktop ~/Documents

The background watcher monitors directories for new and modified files and stamps them with trust metadata. Smart context detection automatically infers:

• Git author — from git log history
• Download source — from macOS extended attributes
• Classification — from project .akf/config.json rules
• AI-generated flag — from LLM tracking timestamps + content heuristics
• Confidence score — dynamically adjusted based on available evidence

Shell Hook (intercept AI CLI tools)

# Add to ~/.zshrc or ~/.bashrc
eval "$(akf shell-hook)"

Automatically detects when you run claude, chatgpt, aider, openclaw, ollama, or other AI CLI tools, and stamps any files they create or modify. Also pre-stamps files before upload to content platforms (gws, box, m365, dbxcli, rclone) so trust metadata travels with the file. Use --no-upload-hooks to disable.

Project Rules

Create .akf/config.json in your project root:

{
  "rules": [
    {"pattern": "*/finance/*", "classification": "confidential", "tier": 2},
    {"pattern": "*/public/*", "classification": "public", "tier": 3}
  ]
}

Files matching these patterns are automatically classified when stamped.

CLI

# ── Quick start ──
akf                          # Welcome + quick start
akf quickstart               # Interactive demo
akf doctor                   # Check installation health

# ── Stamp & create ──
akf create report.akf \
  --claim "Revenue $4.2B" --trust 0.98 --src "SEC 10-Q" \
  --by [email protected] --label confidential

# ── Validate & inspect ──
akf validate report.akf
akf inspect report.akf
akf trust report.akf

# ── Certify (aggregate pass/fail gate) ──
akf certify report.akf                        # Trust + detection + compliance
akf certify src/ --min-trust 0.8              # Custom threshold
akf certify . --evidence-file results.xml     # Attach test evidence
akf certify . --format json --fail-on-untrusted  # CI-friendly output
akf certify src/ --team                       # Per-agent trust breakdown

# ── Compliance ──
akf audit report.akf                          # Compliance readiness check
akf audit report.akf --regulation eu_ai_act   # EU AI Act
akf audit report.akf --trail                  # Audit trail

# ── Universal format commands ──
akf embed report.docx --classification confidential \
  --claim "Revenue $4.2B" --trust 0.98
akf extract report.docx
akf scan report.docx
akf scan ./docs/ --recursive

# ── Auto-stamping ──
akf install                                   # Install background watcher
akf watch ~/Downloads ~/Documents             # Watch directories
akf shell-hook                                # Print shell hook code
akf shell-hook --no-upload-hooks              # Without content platform hooks
akf uploads                                   # View upload stamp log

# ── Git integration ──
akf stamp <file> --agent claude-code --evidence "tests pass"

# ── Agent identity & teams ──
akf agent create --name "Bot" --platform claude-code
akf agent list
akf agent verify <agent_id>
akf agent export-a2a <id> --output card.json  # A2A protocol bridge
akf agent import-a2a card.json

# ── Knowledge Base ──
akf kb stats ./kb
akf kb query ./kb --topic finance

Security Detections

10 built-in detection classes: AI content without review, trust below threshold, hallucination risk, knowledge laundering, classification downgrade, stale claims, ungrounded AI claims, trust degradation chain, excessive AI concentration, provenance gap.

from akf import run_all_detections
report = run_all_detections(unit)
for finding in report.findings:
    print(f"[{finding.severity}] {finding.detection}: {finding.message}")

Trust Computation

effective_trust = confidence × authority_weight × temporal_decay × (1 + penalty)

Tier	Weight	Example
1	1.00	SEC filings, official records
2	0.85	Analyst reports, peer-reviewed
3	0.70	News, industry reports
4	0.50	Internal estimates, CRM data
5	0.30	AI inference, extrapolations

Decision: score ≥ 0.7 → ACCEPT · ≥ 0.4 → LOW · < 0.4 → REJECT

Delegation ceiling: When an agent delegates to another, the delegate's output trust is capped at min(score, delegation_ceiling). This prevents trust inflation in multi-agent chains.

Integrations & Extensions

Framework integrations (install from repo via pip install ./packages/<name>):

Package	Description
mcp-server-akf	MCP server — create, validate, scan, trust
langchain-akf	LangChain callback handler + document loader (experimental)
llama-index-akf	LlamaIndex node parser + trust filter (experimental)
crewai-akf	CrewAI tool for trust-aware agents (experimental)

Editor & CI extensions (source in repo):

Extension	Description
VS Code	Syntax highlighting, hover info, validation for .akf files
VS Code AI Monitor	Auto-stamp files edited by Copilot, Cursor, and other AI tools
GitHub Action	CI trust gate — runs akf certify on PRs with optional PR comments
Google Workspace	Add-on for Docs, Sheets, Slides (preview)
Office Add-in	Add-in for Word, Excel, PowerPoint (preview)

For LLMs

Prompt with one example and LLMs produce valid AKF 95%+ of the time:

Output knowledge as AKF:
{"v":"1.0","claims":[{"c":"<claim>","t":<0-1>,"src":"<source>","tier":<1-5>,"ai":true}]}

See LLM-PROMPT.md for a full system prompt.

Documentation

Doc	Description
Full Spec	Complete format specification
JSON Schema	Machine-readable schema
Producing AKF	Quick start for 8 languages
Trust Computation	Scoring algorithm details
LLM Integration	Prompting strategies
EU AI Act	Compliance mapping
NIST AI RMF	Framework mapping

Contributing

See CONTRIBUTING.md for development setup, testing, and PR process.

Free and Open — Forever

AKF is free and open source under the MIT license. The format specification will always be free. No feature will ever be gated behind a paid tier. AKF is a standard, and standards must be free to be universal.

License

MIT — use it everywhere, embed it in everything.