DeepSecure: Effortless Identity & Auth for AI Agents
**Give every AI agent a cryptographic identity and authenticated ephemeral credentials.
Handle auth, delegation, policy enforcement, and secure proxying automatically. Effortlessly add identity and auth to any AI agent -- regardless of any platform, any framework, and any model.**
[**π Documentation**](docs/) [**π― Examples**](examples/) [**π¬ Community**](https://discord.gg/SUbswk8T)
## π― Why DeepSecure?
### The Problem: AI Agents Are Security Nightmares
```python
# β Current state: Security chaos
# π API keys scattered everywhere
os.environ["OPENAI_API_KEY"] = "sk-..." # Same key shared across all agents
# π€ No agent identity - who did what? which actions?
agent1 = YourFavoriteFramework() # Anonymous agent
agent2 = AnotherFramework() # Another anonymous agent
# π« All-or-nothing permissions
agent.call_internal_api() # Full admin access to everything
agent.call_external_api() # Full admin access to everything
# No delegation, no policy enforcement, no audit trail
# Result: One breach = Complete system compromise
```
### The Solution: Comprehensive Zero-Trust for AI Agents
```python
# β
With DeepSecure: Complete security transformation
# π Cryptographic identity per agent
client = deepsecure.Client()
agent = client.agent("financial-analyst", auto_create=True) # Ed25519 identity
# π Fine-grained policy enforcement happens automatically
# When agent fetches secrets, gateway validates JWT claims and enforces policy
secret = client.get_secret(
agent_id=agent.id,
secret_name="openai-api",
path="/v1/chat/completions"
)
# Gateway enforces: Does agent have OpenAI access? Rate limits? Business hours?
# Policy controls which agents can access which APIs, when, and how often
# π Secure delegation between agents
delegation_token = client.delegate_access(
delegator_agent_id=agent.id,
target_agent_id="data-processor",
resource="financial-data",
permissions=["read"],
ttl_seconds=1800)
# π Complete audit trail + policy enforcement
# Every action logged, every access controlled, every delegation tracked
# Result: Zero-trust security with full visibility and control
```
## π₯ From Security Nightmare to Zero-Trust Security
| **Without DeepSecure** | **With DeepSecure** |
|---|---|
| π **Shared API keys** | π‘οΈ **AI Agents don't have access to API keys** |
| π€ **No Agent Identity** | π **AI Agents get Ed25519 Cryptographic Identity** |
| π« **No Access Control** | π **AI Agents with Fine-Grained Policies** |
| π **No delegation and tracking** | π **AI Agents with crypotographic delegation and audit trail** |
| π **Production Blockers** | π **Enterprise-Ready** |
## βοΈ Getting Started
Get fully set up with DeepSecure in under 5 minutesβsecure your AI agents instantly!
### Prerequisites
- **Python 3.9+**
- **pip** (Python package installer)
- **Access to an OS keyring** (macOS Keychain, Windows Credential Store, or Linux keyring) for secure agent private key storage
- **Docker and Docker Compose** for running the backend services
### 1. Install DeepSecure
```bash
pip install deepsecure
```
### 2. Backend Services Setup
DeepSecure uses a dual-service architecture:
- **`deeptrail-control`** - Control Plane (manages agents, policies, credentials)
- **`deeptrail-gateway`** - Data Plane (enforces policies, injects secrets)
#### Quick Start with Docker Compose
```bash
# Clone the repository
git clone https://github.com/DeepTrail/deepsecure.git
cd deepsecure
# Start both services
docker-compose up -d
# Verify services are running
docker-compose ps
```
This will start:
- **Control Plane** at `http://localhost:8000`
- **Gateway** at `http://localhost:8001`
- **PostgreSQL** database for persistent storage
### 3. Configure DeepSecure CLI
```bash
# Set the control plane URL
deepsecure configure set-url http://localhost:8000
# Verify connection
deepsecure health
```
### 4. Verify Installation
```bash
# Check version
deepsecure --version
# Test agent creation
deepsecure agent create --name "test-agent"
```
π **You're all set!** Your secure AI agent infrastructure is now running.
**Next Steps:**
- Try the [30-second quickstart](#-30-second-quickstart) below
- Explore our [examples](#-examples) for real-world use cases
- Read the [Architecture Guide](docs/design/deepsecure-technical-overview.md) to understand the system
---
## β‘ 30-Second Quickstart
```bash
# 1. Install DeepSecure
pip install deepsecure
# 2. Connect to your security control plane
# For local development:
deepsecure configure set-url http://localhost:8001
# For production (your deployed instance):
# deepsecure configure set-url https://deepsecure.yourcompany.com
# 3. Create your first AI agent identity
deepsecure agent create --name "my-ai-agent"
# 4. Use in your AI code
import deepsecure
client = deepsecure.Client()
agent = client.agent("my-ai-agent", auto_create=True)
secret = client.get_secret(name="openai-api", agent_name=agent.name)
# That's it! Your agent now has secure, audited access to OpenAI
```
**π― What you just achieved:**
- β
**Centralized Security**: All your AI agents use one security control plane
- β
**Zero Hardcoded Secrets**: Agents get ephemeral credentials automatically
- β
**Unique Identity**: Each agent has cryptographic identity (Ed25519)
- β
**Complete Audit Trail**: Every action is logged for compliance and debugging
- π‘οΈ **Policy Enforcement Ready**: Fine-grained access control available via `deepsecure policy` commands
## ποΈ Architecture: Control Plane + Data Plane
DeepSecure implements a **dual-service architecture** designed for production scale:
### π§ Control Plane (`deeptrail-control`)
- **Agent Identity Management**: Ed25519 cryptographic identities
- **Policy Engine**: Fine-grained RBAC with delegation support
- **Credential Issuance**: Ephemeral, time-bound access tokens
- **Audit Logging**: Immutable security event tracking
### π Data Plane (`deeptrail-gateway`)
- **Secret Injection**: Automatic API key insertion at runtime
- **Policy Enforcement**: Real-time access control decisions
- **Split-Key Security**: Client/backend key reassembly for ultimate protection
- **Request Proxying**: Transparent handling of all agent tool calls
```mermaid
graph TB
A[AI Agent/Developer] --> B[DeepSecure SDK]
%% Management Flow - Direct to Control
B -->|Management Operations
Agent/Policy CRUD| D[Control Plane
deeptrail-control]
%% Runtime Flow - Through Gateway
B -->|Runtime Operations
Tool Calls| C[Gateway
deeptrail-gateway]
C --> D
C --> E[External APIs
OpenAI, AWS, etc.]
D --> F[Policy Engine]
D --> G[Split-Key Store]
D --> H[Audit Log]
%% Labels for clarity
B -.->|"deepsecure agent create
deepsecure policy create"| D
B -.->|"agent.call_openai()
with secret injection"| C
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
style E fill:#fff3e0
```
## π¬ Examples
Explore our comprehensive example collection:
| Example | Description | Framework |
|---|---|---|
| [**Basic Agent Creation**](examples/01_create_agent_and_issue_credential.py) | Create your first secure agent | Core SDK |
| [**LangChain Integration**](examples/05_langchain_secure_tools.py) | Secure LangChain agents | LangChain |
| [**CrewAI Team Security**](examples/03_crewai_secure_tools.py) | Multi-agent crew with delegation | CrewAI |
| [**Gateway Injection**](examples/08_gateway_secret_injection_demo.py) | Automatic secret injection | Core SDK |
| [**Advanced Delegation**](examples/11_advanced_delegation_patterns.py) | Complex delegation workflows | Core SDK |
| [**Platform Bootstrap**](examples/12_platform_expansion_bootstrap.py) | Kubernetes/AWS agent bootstrapping | Infrastructure |
## π What's Next?
You've now seen the core workflow! Ready to dive deeper?
### π Documentation
| Resource | Description |
|---|---|
| [**π Getting Started**](docs/getting-started.md) | Complete setup guide with examples |
| [**π§ CLI Reference**](docs/cli-reference.md) | All commands and options |
| [**π SDK Documentation**](docs/sdk/) | Python SDK with full API reference |
| [**ποΈ Architecture Guide**](docs/design/deepsecure-technical-overview.md) | Deep dive into system design |
| [**π Security Model**](docs/security-model.md) | Cryptographic foundations |
| [**π Deployment Guide**](docs/deployment/) | Production deployment patterns |
For hands-on examples, explore our [`examples/`](examples/) directory with LangChain, CrewAI, and multi-agent patterns.
## π€ Contributing
DeepSecure is open source, and your contributions are vital! Help us build the future of AI agent security.
π **Star our GitHub Repository!**
π **Report Bugs or Feature Requests**: Use [GitHub Issues](https://github.com/yourusername/deepsecure-cli/issues).
π‘ **Suggest Features**: Share ideas on [GitHub Issues](https://github.com/yourusername/deepsecure-cli/issues) or [GitHub Discussions](https://github.com/yourusername/deepsecure-cli/discussions).
π **Improve Documentation**: Help us make our guides clearer.
π» **Write Code**: Tackle bugs, add features, improve integrations.
For details on how to set up your development environment and contribute, please see our [Contributing Guide](CONTRIBUTING.md).
## π« Community & Support
**GitHub Discussions**: The primary forum for questions, sharing use cases, brainstorming ideas, and general discussions about DeepSecure and AI agent security. This is where we want to build our community!
**GitHub Issues**: For bug reports and specific, actionable feature requests.
We're committed to fostering an open and welcoming community.
## π License
This project is licensed under the terms of the [Apache 2.0 License](LICENSE).
---
**β Star us on GitHub if DeepSecure helps secure your AI agents!**
[**π Get Started**](#-30-second-quickstart) β’ [**π Documentation**](docs/) β’ [**π¬ Join Discord**](https://discord.gg/deepsecure)
*Built with β€οΈ for the AI agent developer community*