hashed-sdk 0.3.4

AI Agent Governance & Security - Professional SDK with CLI

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Governance & Audit Framework for AI Agents

---

⚡ Quick Install

pip install hashed-sdk

🔑 The 3-Line Demo

from hashed import HashedCore, HashedConfig

core = HashedCore(config=HashedConfig(), agent_name="my-agent")

@core.guard("send_email")          # ← policy enforced, signed, logged
async def send_email(to: str): ...

Every call is cryptographically signed (Ed25519), policy-validated, and written to an immutable audit log — automatically.

🔗 Official Links

📦 PyPI	pypi.org/project/hashed-sdk
📖 Documentation	docs/API_REFERENCE.md
🏗️ Protocol Spec	SPEC.md — Hashed Identity Protocol
🐛 Issue Tracker	github.com/Josoriop9/IAMandagent/issues
🔒 Security Policy	SECURITY.md
📋 Changelog	CHANGELOG.md

---

A professional Python SDK that provides cryptographic identity, policy enforcement, and immutable audit logging for AI agents. Built for production-grade AI systems that require accountability, compliance, and security.

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🎯 What is Hashed?

Hashed is a complete governance framework for AI agents that ensures:

• 🔐 Cryptographic Identity: Ed25519 keypairs for agent authentication
• 🛡️ Policy Enforcement: Define and enforce rules for agent operations
• 📝 Immutable Audit Trail: Every operation is cryptographically signed and logged
• 🎛️ Control Dashboard: Web UI for monitoring agents, policies, and operations
• 💾 Persistent Identity: Agents maintain identity across restarts
• 🔄 Real-time Sync: Policies and logs sync automatically with backend

Perfect for: Customer service bots, data analysis agents, autonomous trading systems, or any AI agent that needs governance.

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✨ Key Features

🔐 Identity Management

• Cryptographic Identities: Ed25519 keypairs for each agent
• Persistent Storage: Encrypted key storage with AES-256
• Automatic Registration: Agents self-register with backend on startup
• Signature Verification: All operations cryptographically signed

🛡️ Policy Enforcement

• Declarative Policies: Define max amounts, allow/deny, approval requirements
• Local + Remote: Policies enforced locally and validated with backend
• @guard Decorator: Protect any function with a simple decorator
• Real-time Sync: Policies sync automatically from control plane

📝 Audit Logging

• Immutable Trail: All operations logged with signatures
• Success + Failures: Log both successful and denied operations
• Rich Metadata: Capture context, amounts, timestamps, errors
• Dashboard Visibility: All logs visible in real-time web UI

🎛️ Control Dashboard (Early Access)

• Agent Monitoring: Track all registered agents
• Policy Management: View and create governance rules
• Audit Logs: Search and filter operation history
• Real-time Updates: Auto-refresh every 5 seconds
• 🔗 hashed-dashboard.vercel.app (invite-only — public launch coming soon)

---

🚀 Quick Start

Installation

pip install hashed-sdk

# Optional: secure OS keychain storage for API keys
pip install hashed-sdk[secure]

# Optional: framework integrations
pip install hashed-sdk[langchain]   # LangChain
pip install hashed-sdk[crewai]      # CrewAI
pip install hashed-sdk[strands]     # AWS Strands
pip install hashed-sdk[autogen]     # AutoGen
pip install hashed-sdk[all]         # All frameworks

Basic Usage

import asyncio
from hashed import HashedCore, HashedConfig

# Configure connection to backend
config = HashedConfig(
    backend_url="http://localhost:8000",
    api_key="your_api_key_here"
)

# Create core with identity
core = HashedCore(
    config=config,
    agent_name="My First Agent",
    agent_type="customer_service"
)

async def main():
    # Initialize (registers agent, syncs policies)
    await core.initialize()
    
    # Define a guarded operation
    @core.guard("send_email")
    async def send_email(to: str, subject: str, body: str):
        # Your email logic here
        return {"status": "sent", "to": to}
    
    # Execute - automatically validated and logged
    result = await send_email(
        to="[e-mail]",
        subject="Hello",
        body="Test message"
    )
    
    # Cleanup
    await core.shutdown()

asyncio.run(main())

---

📚 Core Concepts

1. Cryptographic Identity

Each agent has a unique Ed25519 keypair that identifies it:

from hashed import IdentityManager

# Generate new identity (ephemeral)
identity = IdentityManager()
print(f"Public Key: {identity.public_key_hex}")

# Sign operations
signature = identity.sign_message("operation_data")

# Verify signatures
is_valid = identity.verify_signature("operation_data", signature)

2. Persistent Identity

For agents that need to maintain identity across restarts:

from hashed import load_or_create_identity
import os

# Load existing or create new (with encryption)
identity = load_or_create_identity(
    filepath="./secrets/agent_key.pem",
    password=os.getenv("AGENT_PASSWORD")
)

# Use with HashedCore
core = HashedCore(
    config=config,
    identity=identity,  # ← Persistent identity
    agent_name="My Agent"
)

Benefits:

• Same public key across restarts
• Continuous audit trail
• Dashboard shows single agent (not duplicates)
• Policy targeting by specific agent

3. Policy Enforcement

Define rules for what agents can/cannot do:

# Add policies locally
core.policy_engine.add_policy(
    tool_name="send_email",
    allowed=True,
    max_amount=100.0,  # Max 100 emails
    metadata={"description": "Email sending policy"}
)

core.policy_engine.add_policy(
    tool_name="delete_database",
    allowed=False,  # Completely blocked
    metadata={"reason": "Too dangerous"}
)

# Push policies to dashboard (so they're visible)
await core.push_policies_to_backend()

4. Guarded Operations

Protect operations with the @guard decorator:

@core.guard("transfer_money", amount_param="amount")
async def transfer_money(amount: float, to_account: str):
    """
    Transfer money with automatic:
    - Policy validation
    - Operation signing  
    - Audit logging
    - Error handling
    """
    # Your transfer logic
    return {"status": "completed", "amount": amount}

# Execute - policy is checked automatically
try:
    result = await transfer_money(amount=500.0, to_account="12345")
    print("Transfer successful")
except PermissionError as e:
    print(f"Transfer blocked: {e}")

What happens:

1. Local policy check (fast)
2. Backend policy validation (if connected)
3. Operation signing with agent identity
4. Function execution (if allowed)
5. Result logged to backend/ledger
6. If denied: PermissionError raised, denial logged

5. Audit Trail

Every operation is automatically logged:

# Logs are sent automatically, no manual logging needed
await send_email(to="[e-mail]", ...)

# View in dashboard: http://localhost:3000/dashboard/logs
# Or query programmatically via backend API

Each log entry contains:

• Tool name
• Status (success/denied/error)
• Timestamp
• Agent public key
• Cryptographic signature
• Operation parameters (sanitized)
• Result or error message

---

🎨 Examples

Complete Agent with LLM Integration

"""
AI Agent with OpenAI + Hashed Governance
"""
import asyncio
from openai import AsyncOpenAI
from hashed import HashedCore, HashedConfig, load_or_create_identity

# Initialize
openai = AsyncOpenAI()
identity = load_or_create_identity("./secrets/agent.pem", "password123")
core = HashedCore(
    config=HashedConfig(),
    identity=identity,
    agent_name="Customer Service Bot",
    agent_type="customer_service"
)

async def main():
    await core.initialize()
    
    # Define policies
    core.policy_engine.add_policy("send_email", allowed=True, max_amount=50.0)
    core.policy_engine.add_policy("refund", allowed=True, max_amount=500.0)
    core.policy_engine.add_policy("database_delete", allowed=False)
    
    # Push to dashboard
    await core.push_policies_to_backend()
    
    # Define guarded tools
    @core.guard("send_email", amount_param="count")
    async def send_email(to: str, subject: str, count: int = 1):
        # Email logic
        return {"sent": count}
    
    @core.guard("process_refund", amount_param="amount")
    async def process_refund(amount: float, reason: str):
        # Refund logic
        return {"refunded": amount}
    
    # Use with LLM
    tools = [
        {
            "type": "function",
            "function": {
                "name": "send_email",
                "description": "Send email to customer",
                "parameters": { ... }
            }
        },
        # ... more tools
    ]
    
    response = await openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Send refund email"}],
        tools=tools
    )
    
    # Execute tool calls (with automatic governance)
    for tool_call in response.choices[0].message.tool_calls:
        if tool_call.function.name == "send_email":
            result = await send_email(**json.loads(tool_call.function.arguments))
    
    await core.shutdown()

asyncio.run(main())

See examples/ for more:

• quickstart.py - Simple getting started
• persistent_agent.py - Full persistent identity example
• dev_test_agent.py - Development & testing patterns

---

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    Your AI Agent                        │
│  ┌──────────────────────────────────────────────────┐   │
│  │  HashedCore (SDK)                                │   │
│  │  • IdentityManager (Ed25519)                     │   │
│  │  • PolicyEngine (Local validation)               │   │
│  │  • @guard decorator                              │   │
│  │  • AsyncLedger (Logging)                         │   │
│  └──────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘
                          │
                    HTTPS │ (mTLS ready)
                          │
┌─────────────────────────▼───────────────────────────────┐
│              Backend API (FastAPI)                      │
│  • Agent registration                                   │
│  • Policy storage & sync                                │
│  • Audit log persistence                                │
│  • Signature verification                               │
└─────────────────────────────────────────────────────────┘
                          │
                          │ Supabase
                          ▼
┌─────────────────────────────────────────────────────────┐
│               PostgreSQL Database                        │
│  • agents (registry)                                    │
│  • policies (rules)                                     │
│  • ledger_logs (immutable audit trail)                 │
│  • organizations (multi-tenant)                         │
└─────────────────────────────────────────────────────────┘
                          │
                          │ Real-time
                          ▼
┌─────────────────────────────────────────────────────────┐
│            Dashboard (Next.js + React)                  │
│  • Agent monitoring                                     │
│  • Policy management                                    │
│  • Audit log viewer (auto-refresh)                     │
│  • Analytics & reports                                  │
└─────────────────────────────────────────────────────────┘

---

📦 Project Structure

hashed-sdk/
├── src/hashed/
│   ├── __init__.py              # Main exports
│   ├── core.py                  # HashedCore (main class)
│   ├── config.py                # Configuration
│   ├── identity.py              # IdentityManager (Ed25519)
│   ├── identity_store.py        # Persistent identity functions
│   ├── guard.py                 # PolicyEngine + @guard
│   ├── ledger.py                # AsyncLedger (logging)
│   ├── exceptions.py            # Custom exceptions
│   ├── models.py                # Pydantic models
│   ├── client.py                # Legacy client (deprecated)
│   ├── crypto/                  # Cryptography
│   │   ├── __init__.py
│   │   └── hasher.py
│   └── utils/                   # Utilities
│       ├── __init__.py
│       └── http_client.py
├── tests/                       # Test suite
│   ├── test_core.py
│   ├── test_identity.py
│   ├── test_identity_store.py
│   ├── test_guard.py
│   └── ...
├── examples/                    # Usage examples
│   ├── persistent_agent.py
│   ├── basic_usage.py
│   └── secrets/.gitignore
├── server/                      # Backend API
│   ├── server.py               # FastAPI app
│   ├── requirements.txt
│   └── README.md
├── dashboard/                   # Web UI (Next.js)
│   ├── app/
│   ├── lib/
│   └── package.json
├── database/                    # DB schema
│   └── schema.sql
├── docs/                        # Documentation
│   ├── API_REFERENCE.md
│   ├── CLI_GUIDE.md
│   ├── FRAMEWORK_GUIDES.md
│   ├── INTEGRATION.md
│   ├── ROADMAP.md
│   ├── SETUP_GUIDE.md
│   └── USAGE_FROM_OTHER_PROJECT.md
└── pyproject.toml              # Project config

---

🛠️ Development

Setup

# Clone repo
git clone https://github.com/Josoriop9/IAMandagent.git
cd IAMandagent

# Install SDK
pip install -e .

# Start backend
cd server
pip install -r requirements.txt
python server.py

# Start dashboard (separate terminal)
cd dashboard
npm install
npm run dev

# Run tests
pytest

Running the Full Stack

# Terminal 1: Backend
cd server && python server.py

# Terminal 2: Dashboard  
cd dashboard && npm run dev

# Terminal 3: Your agent
python your_agent.py

# Access dashboard (local)
open http://localhost:3000

# Production dashboard (early access)
# https://hashed-dashboard.vercel.app

---

🔒 Security

Identity Storage

Persistent identities are stored encrypted:

# Encrypted with AES-256
identity = load_or_create_identity(
    filepath="./secrets/agent.pem",
    password="strong_password_from_env"  # Never hardcode!
)

# File permissions: 0600 (owner read/write only)
# Password from: Environment variable, secrets manager, etc.

Best Practices

✅ DO:

• Use environment variables for passwords
• Store keys in ./secrets/ with .gitignore
• Use strong passwords (32+ chars, generated)
• Rotate keys periodically
• Use mTLS for production backend
• Review audit logs regularly

❌ DON'T:

• Hardcode passwords in code
• Commit .pem files to git
• Reuse passwords across agents
• Disable signature verification
• Ignore policy violations in logs

See SECURITY.md for complete security guide.

---

📖 Documentation

• API Reference - Complete API docs
• Integration Guide - Integrate with your project
• Setup Guide - Production setup
• Security Guide - Security best practices
• Usage from Other Projects - External usage

---

🔄 Recent Updates

v0.2.1 (Latest)

New Features:

• ✨ CLI banner with # brand logo (appears on hashed with no args)
• ✨ hashed version now reads __version__ dynamically — never drifts
• 🛡️ banner.py — new module, zero extra dependencies (Rich only)

Fixes:

• 🐛 hashed version was hardcoded to 0.2.0 → now uses __version__

v0.2.0

New Features:

• ✨ Persistent identity system (load_or_create_identity)
• ✨ Push policies to dashboard (push_policies_to_backend)
• ✨ Auto-refresh dashboard (5 second polling)
• ✨ 344 tests, 73% coverage, CI gate at 65%
• ✨ API key rotation (hashed rotate-key + /v1/auth/rotate-key)
• ✨ Railway metrics middleware + Slack alerting
• ✨ Supabase RLS enabled on all critical tables
• 🐛 Fixed double logging issue (backend OR local, not both)
• 📝 Complete documentation overhaul

v0.1.0

• 🔐 Cryptographic identity (Ed25519)
• 🛡️ Policy engine with @guard decorator
• 📝 Immutable audit logging
• 🎛️ Web dashboard (Next.js)
• 🔄 Auto-sync with backend

---

🤝 Contributing

Contributions welcome! Please:

1. Fork the repo
2. Create feature branch (git checkout -b feature/amazing)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push branch (git push origin feature/amazing)
5. Open Pull Request

---

📄 License

MIT License - see LICENSE for details.

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🙏 Acknowledgments

Built with:

• cryptography - Ed25519, AES-256
• FastAPI - Backend API
• Next.js - Dashboard UI
• Supabase - Database + Auth
• httpx - HTTP client
• Pydantic - Data validation

---

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• PyPI: hashed-sdk on PyPI

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⚠️ Disclaimer

Note: This is a personal research project developed in my own time, using my own resources.
All code, opinions, and architectural decisions are my own and do not represent
the views, strategies, or position of my current employer.

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Built with ❤️ for responsible AI