Quick Start

import sentrial

# Configure with your API key (from Settings > API Keys)
sentrial.configure(api_key="sentrial_live_xxx")

# Simple API: begin/finish pattern
interaction = sentrial.begin(
    user_id="user_12345",           # Required: tracks by user
    event="support_request",        # Event type (becomes agent name)
    input="Help me reset my password",
    convo_id="conv_789"             # Optional: group related events
)

# Your agent logic runs here...
response = your_agent.run(user_input)

# Finish with outcome and metrics
interaction.finish(
    output=response,
    success=True,
    estimated_cost=0.023,           # Track costs
    custom_metrics={
        "satisfaction": 4.5,
        "resolution_time": 45
    }
)

from sentrial import SentrialClient
from sentrial.langchain import SentrialCallbackHandler
from langchain.agents import AgentExecutor

# Initialize
client = SentrialClient(api_key="sentrial_live_xxx")
session_id = client.create_session(
    name="Customer Support",
    agent_name="support-agent",
    user_id="user_123"
)

# Create callback handler - tracks everything automatically
handler = SentrialCallbackHandler(client, session_id)

# Add to your agent
result = agent_executor.invoke(
    {"input": "User's question"},
    {"callbacks": [handler]}  # Just add this!
)

# After completion, get usage stats
print(f"Cost: ${handler.total_cost:.4f}")
print(f"Tokens: {handler.total_tokens}")

# Track tool calls
client.track_tool_call(
    session_id=session_id,
    tool_name="search_knowledge_base",
    tool_input={"query": "password reset"},
    tool_output={"results": ["KB-001", "KB-002"]},
    reasoning="User needs password help"
)

# Track agent decisions
client.track_decision(
    session_id=session_id,
    reasoning="Will search KB before escalating",
    alternatives=["escalate_to_human", "ask_clarifying_question"],
    confidence=0.92
)

# Complete session with metrics
client.complete_session(
    session_id=session_id,
    success=True,
    estimated_cost=0.045,
    prompt_tokens=1500,
    completion_tokens=500
)

from sentrial import SentrialClient
from sentrial.langchain import SentrialCallbackHandler
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent

# Initialize Sentrial
client = SentrialClient(api_key="sentrial_live_xxx")

# Your agent setup
llm = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [search_kb, create_ticket, send_email]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

def handle_request(user_input: str, user_id: str) -> str:
    """Handle request with full Sentrial tracking."""

    # Create session
    session_id = client.create_session(
        name=f"Support: {user_input[:40]}...",
        agent_name="customer-support",
        user_id=user_id
    )

    # Create handler for automatic tracking
    handler = SentrialCallbackHandler(
        client,
        session_id,
        verbose=True  # Prints tracking info
    )

    try:
        result = executor.invoke(
            {"input": user_input},
            {"callbacks": [handler]}
        )

        # Complete with real metrics from handler
        client.complete_session(
            session_id=session_id,
            success=True,
            estimated_cost=handler.total_cost,
            prompt_tokens=handler.total_prompt_tokens,
            completion_tokens=handler.total_completion_tokens
        )

        return result["output"]

    except Exception as e:
        client.complete_session(
            session_id=session_id,
            success=False,
            failure_reason=str(e)
        )
        raise

# Use it
response = handle_request(
    "I can't log into my account",
    user_id="user_12345"
)