Tracify Python SDK

Observability and tracing for AI agents. Gain full visibility into your agent's decision-making process, tool usage, and LLM costs.

Installation

pip install tracify-sdk

Quickstart

1. Get your API Key from the Tracify Dashboard.
2. Set it as an environment variable:

export TRACIFY_API_KEY=your_sk_live_...

3. Instrument your agent:

from tracify import trace_agent, llm_call, tool_call

@trace_agent(environment="development", release="quickstart")
async def my_agent(query: str):
    # LLM Call
    llm_call(
        input_data=query,
        output_data="Thinking...",
        model_id="gpt-4o",
        cost_usd=0.002,
        latency_ms=800
    )
    
    # Tool Use
    tool_call(
        tool_name="web_search",
        input_data={"q": query},
        output_data="Found results...",
        latency_ms=1200
    )
    
    return "Final Answer"

Steps

Inside a @trace_agent function, every step you record is linked to the step it ran in, so the trace graph shows which step called which and how loops repeat. Use span for a block and observe for a function:

import tracify

@tracify.observe(type="tool")
def search_docs(query):
    return search(query)

@tracify.trace_agent()
def answer(question):
    for turn in range(5):
        with tracify.span("think", type="agent"):
            with tracify.span("chat", type="llm", input=question) as step:
                reply = call_model(question)
                step.set_output(reply.text)
                step.set(model="claude-sonnet-5-5", input_tokens=reply.usage.input, output_tokens=reply.usage.output)
            if reply.done:
                return reply.text
            search_docs(reply.query)

type is one of agent, llm, tool, retrieval or span. Errors are recorded with their type and message. Concurrent runs (threads or asyncio tasks) stay in separate traces. Inputs and outputs have obvious secrets (bearer tokens, API keys, JWTs, password= values) removed and are capped at 10,000 characters; avoid sending anything else you would not want stored.

Features

• @trace_agent Decorator: Automatically captures run lifecycle and errors.
• Granular Spans: Manually record LLMs, tools, and custom decisions.
• Cost Tracking: Monitor your infrastructure spend in real-time.
• Asynchronous & Synchronous Support: Works with async/await or standard functions.
• Session context: Attach session_id, end_user_id, environment, release, and tags to connect related traces.

Feedback and evaluation scores

Pass project_id when constructing the client to attach end-user feedback and typed scores to traces:

client = TracifyClient(project_id="your-project-id")
client.feedback(run_id, True, kind="thumb", comment="Helpful answer")
client.score(run_id, "answer_quality", 0.92, data_type="numeric")

Feedback and scores use the same API key as ingestion and do not interrupt the agent when delivery fails.

Prompt deployment

Resolve a deployed prompt without shipping a new application build:

deployed = client.get_prompt("support-agent", "production")
client.ingest(spanType="llm", input=question, output=answer, promptVersionId=deployed["version"]["id"])

Prompt responses are cached for 60 seconds by default. Use cache_ttl_seconds=0 or provide a fallback for development and guaranteed-availability behavior.

License

MIT