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The guardian layer for AI agents — scoped sessions, AES-256-GCM secrets, hash-chained audit, policy proxy.

Project description

Haldir — The Guardian Layer for AI Agents

tests codecov type-checked: mypy Smithery PyPI PyPI Downloads License: MIT Security: SECURITY.md GitHub Stars SafeSkill 89/100

The open-source governance layer for AI agents. Identity, secrets, audit, and policy enforcement — MIT licensed, self-host or use our cloud.

Haldir enforces governance on every AI agent tool call: scoped sessions with spend caps, encrypted secrets the model never sees, hash-chained tamper-evident audit trail, human-in-the-loop approvals, and a proxy that intercepts every MCP call before it reaches your tools. Native SDKs for LangChain, CrewAI, AutoGen, and Vercel AI SDK.

Haldir quickstart: install, create a scoped session, check permission, log the action to the hash-chained audit trail

Haldir architecture: Agent → Proxy → (Gate/Vault/Watch/Policy) → Upstream APIs

CLI

$ haldir overview

  Haldir tenant overview
  acct_xyz123  ·  tier pro  ·  2026-04-19T18:42:11+00:00

  Status     ● ok
  Actions      4,217 / 50,000   ████░░░░░░░░░░░░░░░░    8.4%
  Spend      $ 47.30 this month
  Sessions        12 active  ·  3/10 agents
  Vault            8 secrets  ·  62 accesses this month
  Audit        1,847 entries  ·  0 flagged (7d)  ·  chain ✓
  Webhooks         2 registered  ·  541 deliveries (24h)  ·  99.82% success
  Approvals        1 pending

Install once, drive the whole platform from the terminal:

pip install haldir
haldir login                           # one-time; stashes API key
haldir overview --watch                # top-style live dashboard
haldir status                          # green/yellow/red component pills
haldir ready                           # exits 0/1, perfect for CI
haldir audit tail --agent my-bot       # the last N entries
haldir audit export --format=jsonl --out audit-2026-04.jsonl
haldir audit verify                    # hash chain integrity check
haldir webhooks deliveries             # last 20 retry attempts
haldir migrate up                      # apply pending schema migrations

Every command takes --json for scripts. haldir --help for the full surface.

Two ways to run Haldir

Self-host Cloud (haldir.xyz)
Price Free forever Free tier + paid plans
Features Everything Everything — same API, same SDKs
You run API + Postgres Nothing
Best for Regulated industries, air-gapped, "must own data" "Just make it work"

Self-host in 5 minutes

git clone https://github.com/ExposureGuard/haldir.git
cd haldir
cp .env.example .env
python3 -c 'import base64, os; print(base64.urlsafe_b64encode(os.urandom(32)).decode())'
# paste the output into .env as HALDIR_ENCRYPTION_KEY, then:
docker compose up -d
curl http://localhost:8000/health

Full self-hosting guide: SELF_HOSTING.md

Or use our cloud

pip install haldir

That's it — point at https://haldir.xyz, no signup, live API.


Live now: haldir.xyz · API Docs · OpenAPI Spec · Smithery

🧪 Now accepting 5 design partners. 30 days free, full access, direct line to the founder. If you're shipping AI agents to production, email sterling@haldir.xyz.

Performance

Haldir is fast enough to sit in the hot path of every agent tool call without becoming the bottleneck.

Single-box HTTP throughput (gunicorn 4 workers, 32 concurrent clients, tuned SQLite backend, every request goes through the full middleware stack — auth, validation, idempotency, metrics, structured logging):

Endpoint RPS p50 p95 p99
GET /healthz 1,638 19.1 ms 32.5 ms 41.6 ms
GET /v1/status 1,382 22.2 ms 30.8 ms 45.4 ms
GET /v1/sessions/:id 903 29.2 ms 95.5 ms 172.1 ms
POST /v1/sessions (create) 1,142 27.7 ms 35.2 ms 39.9 ms
POST /v1/audit (hash-chain write) 1,092 28.7 ms 37.6 ms 52.6 ms

Hardware: 12th-gen Intel Core i3-1215U (8 cores, 8 GB RAM). SQLite is configured with WAL + synchronous=NORMAL + 256 MiB mmap + in-memory temp store — the session-lookup p99 dropped by 52 % versus the untuned path. Postgres deployments (configurable pool via HALDIR_PG_POOL_MIN/MAX) flatten the p99 further still; enable via DATABASE_URL=postgresql://....

Primitive cost (pure-Python, no I/O):

Primitive p50 Notes
Vault.store_secret (AES-256-GCM encrypt + AAD binding) < 10 µs in-memory, no DB write
Vault.get_secret (AES-256-GCM decrypt + AAD verify) < 10 µs in-memory
AuditEntry.compute_hash (SHA-256 over canonical payload) < 10 µs
Gate.check_permission over REST ~50-120 ms network + DB round-trip, Cloudflare-fronted
Watch.log_action over REST ~50-150 ms includes chain lookup + DB write
Full governed-tool envelope (check + log) ~100-250 ms

Agents typically wait 500-3000 ms for an LLM completion and 100-1000 ms for an upstream API call, so Haldir's overhead sits inside the noise. Reproduce locally:

# Concurrent HTTP throughput (launches a local gunicorn, ~60s total)
python bench/bench_http.py --duration 10 --concurrency 32 --workers 4

# Primitive cost only (no API key needed)
python bench/bench_primitives.py --local

# End-to-end against the hosted service
export HALDIR_API_KEY=hld_...
python bench/bench_primitives.py

Why Haldir

AI agents are calling APIs, spending money, and accessing credentials with zero oversight. Haldir is the missing layer:

Without Haldir With Haldir
Agent has unlimited access Scoped sessions with permissions
Secrets in plaintext env vars AES-encrypted vault with access control
No spend limits Per-session budget enforcement
No record of what happened Immutable audit trail
No human oversight Approval workflows with webhooks
Agent talks to tools directly Proxy intercepts and enforces policies

Quick Start

pip install haldir
from sdk.client import HaldirClient

h = HaldirClient(api_key="hld_xxx", base_url="https://haldir.xyz")

# Create a governed agent session
session = h.create_session("my-agent", scopes=["read", "spend:50"])

# Store secrets agents never see directly
h.store_secret("stripe_key", "sk_live_xxx")

# Retrieve with scope enforcement
key = h.get_secret("stripe_key", session_id=session["session_id"])

# Authorize payments against budget
h.authorize_payment(session["session_id"], 29.99)

# Every action is logged
h.log_action(session["session_id"], tool="stripe", action="charge", cost_usd=29.99)

# Revoke when done
h.revoke_session(session["session_id"])

Products

Gate — Agent Identity & Auth

Scoped sessions with permissions, spend limits, and TTL. No session = no access.

curl -X POST https://haldir.xyz/v1/sessions \
  -H "Authorization: Bearer hld_xxx" \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "my-bot", "scopes": ["read", "browse", "spend:50"], "ttl": 3600}'

Vault — Encrypted Secrets & Payments

AES-encrypted storage. Agents request access; Vault checks session scope. Payment authorization with per-session budgets.

curl -X POST https://haldir.xyz/v1/secrets \
  -H "Authorization: Bearer hld_xxx" \
  -H "Content-Type: application/json" \
  -d '{"name": "api_key", "value": "sk_live_xxx", "scope_required": "read"}'

Watch — Audit Trail & Compliance

Immutable log for every action. Anomaly detection. Cost tracking. Compliance exports.

curl https://haldir.xyz/v1/audit?agent_id=my-bot \
  -H "Authorization: Bearer hld_xxx"

Proxy — Enforcement Layer

Sits between agents and MCP servers. Every tool call is intercepted, authorized, and logged. Supports policy enforcement: allow lists, deny lists, spend limits, rate limits, time windows.

# Register an upstream MCP server
curl -X POST https://haldir.xyz/v1/proxy/upstreams \
  -H "Authorization: Bearer hld_xxx" \
  -H "Content-Type: application/json" \
  -d '{"name": "myserver", "url": "https://my-mcp-server.com/mcp"}'

# Call through the proxy — governance enforced
curl -X POST https://haldir.xyz/v1/proxy/call \
  -H "Authorization: Bearer hld_xxx" \
  -H "Content-Type: application/json" \
  -d '{"tool": "scan_domain", "arguments": {"domain": "example.com"}, "session_id": "ses_xxx"}'

Approvals — Human-in-the-Loop

Pause agent execution for human review. Webhook notifications. Approve or deny from dashboard or API.

# Require approval for spend over $100
curl -X POST https://haldir.xyz/v1/approvals/rules \
  -H "Authorization: Bearer hld_xxx" \
  -H "Content-Type: application/json" \
  -d '{"type": "spend_over", "threshold": 100}'

MCP Server

Haldir is available as an MCP server with 10 tools for Claude, Cursor, Windsurf, and any MCP-compatible AI:

{
  "mcpServers": {
    "haldir": {
      "command": "haldir-mcp",
      "env": {
        "HALDIR_API_KEY": "hld_xxx"
      }
    }
  }
}

MCP Tools: createSession, getSession, revokeSession, checkPermission, storeSecret, getSecret, authorizePayment, logAction, getAuditTrail, getSpend

MCP HTTP Endpoint: POST https://haldir.xyz/mcp

Architecture

Agent (Claude, GPT, Cursor, etc.)
    │
    ▼
┌─────────────────────────────┐
│       Haldir Proxy          │  ← Intercepts every tool call
│  Policy enforcement layer   │
└──────┬──────────┬───────────┘
       │          │
  ┌────▼────┐ ┌───▼────┐
  │  Gate   │ │ Watch  │
  │identity │ │ audit  │
  │sessions │ │ costs  │
  └────┬────┘ └────────┘
       │
  ┌────▼────┐
  │ Vault   │
  │secrets  │
  │payments │
  └────┬────┘
       │
       ▼
  Upstream MCP Servers
  (your actual tools)

API Reference

Full docs at haldir.xyz/docs

Endpoint Method Description
/v1/keys POST Create API key
/v1/sessions POST Create agent session
/v1/sessions/:id GET Get session info
/v1/sessions/:id DELETE Revoke session
/v1/sessions/:id/check POST Check permission
/v1/secrets POST Store secret
/v1/secrets/:name GET Retrieve secret
/v1/secrets GET List secrets
/v1/secrets/:name DELETE Delete secret
/v1/payments/authorize POST Authorize payment
/v1/audit POST Log action
/v1/audit GET Query audit trail
/v1/audit/spend GET Spend summary
/v1/approvals/rules POST Add approval rule
/v1/approvals/request POST Request approval
/v1/approvals/:id GET Check approval status
/v1/approvals/:id/approve POST Approve
/v1/approvals/:id/deny POST Deny
/v1/approvals/pending GET List pending
/v1/webhooks POST Register webhook
/v1/webhooks GET List webhooks
/v1/proxy/upstreams POST Register upstream
/v1/proxy/tools GET List proxy tools
/v1/proxy/call POST Call through proxy
/v1/proxy/policies POST Add policy
/v1/usage GET Usage stats
/v1/metrics GET Platform metrics

Agent Discovery

Haldir is discoverable through every major protocol:

URL Protocol
haldir.xyz/openapi.json OpenAPI 3.1
haldir.xyz/llms.txt LLM-readable docs
haldir.xyz/.well-known/ai-plugin.json ChatGPT plugins
haldir.xyz/.well-known/mcp/server-card.json MCP discovery
haldir.xyz/mcp MCP JSON-RPC
smithery.ai/server/haldir/haldir Smithery registry
pypi.org/project/haldir PyPI

License

MIT

Links

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