Build with AI Identity

Your App  →  AI Identity Gateway  →  LLM Provider (OpenAI, Anthropic, Gemini, Cohere, Mistral, etc.)
                     │
                     ├─ Validate agent identity
                     ├─ Check policy (rate limit, model access, budget)
                     ├─ Inject audit headers (X-Agent-ID, X-Request-ID)
                     ├─ Log request metadata
                     └─ Forward to upstream provider

from openai import OpenAI

# Point the OpenAI client at the AI Identity gateway
client = OpenAI(
    base_url="https://ai-identity-gateway.onrender.com/v1",
    api_key="aid_sk_your_agent_key",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize today's news"}],
)
print(response.choices[0].message.content)

from openai import OpenAI

# Same gateway, different model — Anthropic Claude
client = OpenAI(
    base_url="https://ai-identity-gateway.onrender.com/v1",
    api_key="aid_sk_your_agent_key",
)

response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://ai-identity-gateway.onrender.com/v1",
  apiKey: "aid_sk_your_agent_key",
});

// Works with any supported model — OpenAI, Anthropic, Gemini, etc.
const response = await client.chat.completions.create({
  model: "gemini-2.5-pro",  // or "gpt-4o", "claude-sonnet-4-20250514", etc.
  messages: [{ role: "user", content: "Summarize today's news" }],
});
console.log(response.choices[0].message.content);

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    base_url="https://ai-identity-gateway.onrender.com/v1",
    api_key="aid_sk_your_agent_key",
)

response = llm.invoke("What is AI Identity?")
print(response.content)

import os
os.environ["OPENAI_API_BASE"] = "https://ai-identity-gateway.onrender.com/v1"
os.environ["OPENAI_API_KEY"] = "aid_sk_your_agent_key"

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Find the latest AI governance news",
    backstory="You are an expert AI policy analyst.",
    llm="gpt-4o",
)

task = Task(
    description="Summarize recent developments in AI regulation",
    expected_output="A concise briefing document",
    agent=researcher,
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
print(result)

from autogen import AssistantAgent, UserProxyAgent

config_list = [{
    "model": "gpt-4o",
    "base_url": "https://ai-identity-gateway.onrender.com/v1",
    "api_key": "aid_sk_your_agent_key",
}]

assistant = AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list},
)

user_proxy = UserProxyAgent(
    name="user",
    human_input_mode="NEVER",
    code_execution_config=False,
)

user_proxy.initiate_chat(
    assistant, message="Explain how AI identity governance works."
)

# List your agents
curl https://ai-identity-gateway.onrender.com/v1/agents \
  -H "Authorization: Bearer YOUR_API_KEY"

# Create a chat completion through the gateway (any provider)
curl -X POST https://ai-identity-gateway.onrender.com/v1/chat/completions \
  -H "Authorization: Bearer aid_sk_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-pro",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# Check agent usage and forensic logs
curl https://ai-identity-gateway.onrender.com/v1/agents/ag_abc123/usage \
  -H "Authorization: Bearer YOUR_API_KEY"

# Organization-level operations
curl https://ai-identity-gateway.onrender.com/v1/agents \
  -H "Authorization: Bearer aid_org_your_org_key"

# Agent-level LLM requests
curl -X POST https://ai-identity-gateway.onrender.com/v1/chat/completions \
  -H "Authorization: Bearer aid_sk_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"model": "claude-sonnet-4-20250514", "messages": [{"role": "user", "content": "Hi"}]}'

# Agents
GET    /v1/agents                    # List all agents
POST   /v1/agents                    # Create a new agent
GET    /v1/agents/:id                # Get agent details
PATCH  /v1/agents/:id                # Update an agent
DELETE /v1/agents/:id                # Delete an agent

# Keys
POST   /v1/agents/:id/keys           # Create an agent key
GET    /v1/agents/:id/keys           # List agent keys
DELETE /v1/agents/:id/keys/:key_id   # Revoke a key

# Policies
GET    /v1/policies                  # List policies
POST   /v1/policies                  # Create a policy
PATCH  /v1/policies/:id              # Update a policy

# Forensics / Logs
GET    /v1/agents/:id/logs           # Get agent request logs
GET    /v1/agents/:id/usage          # Get usage summary

# Gateway (OpenAI-compatible)
POST   /v1/chat/completions          # Proxied chat completion
POST   /v1/embeddings                # Proxied embeddings