Identity & policy enforcement for AI agents — an open protocol that answers one question before any agent tool call runs: Cryptographic identity (Ed25519, W3C DID), deterministic policy decisions, and a signed, hash-chained receipt for every allow and every deny. Built on OAuth 2.1, with delegation chains and tamper-evident audit trails.
Model Context Protocol server exposing Airlock gateway tools (stdio)
2026-11-14
ดาว
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เครื่องมือ
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อัปไทม์
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who is this agent, and is it authorized to do that?
Works with Claude (Anthropic SDK integration included), OpenAI, LangChain, or your own agents — one identity & policy layer across all of them.
AI agents now take real actions — they call tools, move data, run commands, execute transactions. A prompt injection or a misbehaving model turns a helpful agent into a confused deputy: a legitimate operation performed by the wrong agent, and no content guardrail will catch it. Guardrails filter content; they cannot distinguish an authorized tool call from an unauthorized one. There is no standard mechanism for verifying agent identity and enforcing authorization before the action runs.
The Solution
Airlock is an access-control layer that sits between the agent and its tools. Every tool call is checked — deterministically, whatever model is driving:
Agent ──tool call──> Airlock ──allowed──> Tool executes
│
├── Identify who is this agent? (Ed25519 / OAuth 2.1, verified not asserted)
├── Decide does policy allow it? (deterministic — same answer, any model)
├── Enforce allow or deny — before the tool ever runs
└── Receipt signed, hash-chained record of every decision
The receipt chain is tamper-evident (Ed25519 signatures + SHA-256 hash chain) — it's the artifact you hand an auditor. OAuth 2.1 provides identity; Airlock adds trust scoring, delegation with scope narrowing (RFC 8693), and the enforcement + audit layer on top.
pip install airlock-protocol
# Verify an agent in 7 lines
python -c "
from airlock import AirlockClient
client = AirlockClient() # defaults to api.airlock.ing
result = client.verify('did:key:z6MkhaXgBZDvotDkL5257faiztiGiC2QtKLGpbnnEGta2doK')
print(f'Verified: {result.verified}, Score: {result.trust_score}')
"
CLI
# Verify an agent from the command line
airlock verify did:key:z6Mk...
# Start a local gateway for development
airlock serve
# Scaffold a new Airlock-protected project
airlock init
Self-hosting
# Clone and run locally
git clone https://github.com/airlock-protocol/airlock.git
cd airlock
pip install -e ".[dev]"
python demo/run_demo.py # 3-agent demo, no external services needed
python -m pytest tests/ -v # 1,087 tests
from airlock import AirlockClient
# Default — routes through central Airlock registry (api.airlock.ing)
client = AirlockClient()
result = client.verify("did:key:z6Mk...")
if result.verified:
print(f"Trusted: {result.agent_name}, Score: {result.trust_score}")
# Self-hosted — point to your own gateway
client = AirlockClient(gateway_url="http://localhost:8000")
# Async support
result = await client.averify("did:key:z6Mk...")
TypeScript client (airlock-client)
The npm workspace under sdks/typescript exposes the same REST operations via fetch (Node 18+). See sdks/typescript/README.md. Published PyPI name remains airlock-protocol (Python); the TS package is airlock-client on npm when released.
MCP adapter (airlock-mcp)
integrations/airlock-mcp is a stdio Model Context Protocol server that surfaces gateway tools (health, resolve, session, reputation, etc.) to MCP hosts. Build from repo root: npm install && npm run build:mcp.
When you publish: see RELEASING.md (PyPI OIDC, npm NPM_TOKEN, workflows).