Documentation
Everything you need to instrument, monitor, and debug your AI agents
Getting Started
Install AgentTrace and instrument your first agent
Python SDK
Comprehensive guide to the Python SDK
TypeScript SDK
Use AgentTrace with TypeScript and JavaScript
API Reference
Complete REST API documentation
Getting Started
1. Install the SDK
Choose your preferred language and install the SDK:
Python
pip install agenttrace-sdkTypeScript/JavaScript
# Not yet published - install from source npm install github:agenttrace/agenttrace#packages/sdk-typescript2. Set Your API Key
Get your API key from the dashboard and set it as an environment variable:
export AGENTTRACE_API_KEY="your-api-key-here"3. Instrument Your Agent
Add tracing to your agent with a simple decorator:
from agenttrace import trace_agent
@trace_agent(project="my-agent")
async def my_agent(query: str):
# Your agent logic here
response = await llm.generate(query)
return responsePython SDK
Basic Usage
from agenttrace import AgentTrace
# Initialize the client
client = AgentTrace(
api_key="your-api-key",
project="my-agent"
)
# Create a trace
with client.trace("user-query") as trace:
# Add steps
trace.step("thinking", metadata={"input": "Hello"})
# Add LLM calls
trace.llm_call(
model="gpt-4",
prompt="Hello",
response="Hi there!",
tokens=15,
cost=0.002
)
trace.step("responding", metadata={"output": "Hi there!"})Advanced Features
- Automatic token counting
- Cost calculation for major LLM providers
- Async support
- Error tracking and stack traces
- Custom metadata and tags
Configuration
client = AgentTrace(
api_key="your-api-key",
project="my-agent",
api_url="https://api.agenttrace.io", # Optional: custom API URL
enable_local_logging=True, # Optional: log locally
batch_size=10, # Optional: batch size
flush_interval=5 # Optional: flush interval (seconds)
)TypeScript SDK
Basic Usage
import { AgentTrace } from '@agenttrace/sdk';
// Initialize the client
const client = new AgentTrace({
apiKey: 'your-api-key',
project: 'my-agent'
});
// Create a trace
const trace = await client.startTrace('user-query');
// Add steps
await trace.addStep('thinking', { input: 'Hello' });
// Add LLM calls
await trace.addLLMCall({
model: 'gpt-4',
prompt: 'Hello',
response: 'Hi there!',
tokens: 15,
cost: 0.002
});
// End the trace
await trace.end();Using Decorators
import { traceAgent } from '@agenttrace/sdk';
class MyAgent {
@traceAgent({ project: 'my-agent' })
async processQuery(query: string) {
// Your agent logic here
return response;
}
}API Reference
AgentTrace provides a RESTful API for all operations. Base URL: https://api.agenttrace.io
Authentication
All API requests require an API key in the Authorization header:
curl -H "Authorization: Bearer YOUR_API_KEY" \
https://api.agenttrace.io/v1/tracesEndpoints
POST /v1/traces
Create a new trace
GET /v1/traces
List all traces for a project
GET /v1/traces/:id
Get a specific trace by ID
POST /v1/traces/:id/steps
Add a step to an existing trace
Integrations
AgentTrace integrates seamlessly with popular AI frameworks
Need Help?
Join our community or reach out to our support team