freesolo 0.1.4

OpenAI, Anthropic, and Gemini tracing package for LLM applications.

freesolo

freesolo is a Python tracing package for LLM apps.

For the Node/npm package, see npm/.

It is built for the lowest-friction integration possible:

1. Install the package
2. Set FREESOLO_API_KEY
3. Wrap your OpenAI, Anthropic, Gemini, or OpenAI-compatible client
4. Call the client normally

Current provider support

freesolo currently supports automatic client instrumentation for:

• OpenAI
• Anthropic
• Gemini
• OpenAI-compatible clients via wrap(...) / wrap_provider(...)

Install

Install the package plus the provider SDK you use:

pip install freesolo openai

or

pip install freesolo anthropic

or

pip install freesolo google-genai

Environment

• FREESOLO_API_KEY

Quickstart

from openai import OpenAI
from freesolo import wrap

client = wrap(OpenAI())

result = client.responses.create(
    model="gpt-4.1-mini",
    instructions="Reply in plain text.",
    input=[
        {
            "role": "user",
            "content": [{"type": "input_text", "text": "How do I reset my password?"}],
        }
    ],
)

print(result.output_text or "")

OpenRouter Quickstart

from openai import OpenAI
from freesolo import wrap

client = wrap(
    OpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key="YOUR_OPENROUTER_API_KEY",
    )
)

response = client.chat.completions.create(
    model="openai/gpt-4.1-mini",
    messages=[
        {"role": "system", "content": "Reply in plain text."},
        {"role": "user", "content": "Write a one-sentence launch blurb."},
    ],
    max_tokens=120,
)

print(response.choices[0].message.content or "")

Gemini Quickstart

from google import genai
from freesolo import instrument_gemini

client = instrument_gemini(genai.Client())

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="Write a one-sentence release note for traced Gemini support.",
)

print(response.text or "")

Group Multiple Model Calls

For agentic or long-horizon tasks, strongly prefer wrapping the whole task in start_trace(...) so all of the model calls land in one trace.

For a single one-off OpenAI, Anthropic, or Gemini request, you can skip it.

from anthropic import Anthropic
from freesolo import instrument_anthropic, start_trace

client = instrument_anthropic(Anthropic())

with start_trace("support-agent-run"):
    first = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=64,
        messages=[{"role": "user", "content": "Say hello"}],
    )
    second = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=64,
        messages=[{"role": "user", "content": "Say goodbye"}],
    )

What Gets Stored

• Trace title if you explicitly pass it to start_trace("...")
• Trace metadata if you explicitly pass it to start_trace(..., metadata=...)
• Input payloads with system_prompt, user_prompt, and images
• Output payloads as plain text
• Token usage when available
• Image inputs with inline previews for the trace UI

Notes

• You do not need @trace() for ordinary LLM tracing.
• A single instrumented OpenAI, Anthropic, or Gemini request creates a trace automatically.
• For OpenAI-compatible providers like OpenRouter, prefer wrap(...) instead of provider-specific helpers.
• For agentic or long-horizon workflows, strongly recommend start_trace("descriptive-title") so planning, retries, and follow-up calls stay grouped.
• Delivery is best-effort by default. Trace ingestion failures do not break your app.