freesolo 0.1.6

Tracing and evaluation SDK for LLM applications.

freesolo

freesolo is a Python tracing and evaluation 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. Run traces and evaluations from the same SDK

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
• FREESOLO_BASE_URL (optional, defaults to https://freesolo.co)

export FREESOLO_API_KEY=fslo_...

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.

Evaluations

freesolo also includes a small evaluation SDK for CI jobs, GitHub bots, and eval scripts. All evaluation runs require FREESOLO_API_KEY or an explicit api_key.

Evaluation data is a list of plain dictionaries. There is no separate Example class to construct.

Define scorers by subclassing CustomScorer and returning BinaryResponse or NumericResponse. Scorers run in your process, and Freesolo uploads the final results with your API key. Pass scorer objects, not strings.

from typing import Any

from freesolo import Freesolo
from freesolo.evaluation import BinaryResponse, CustomScorer


class ExactMatch(CustomScorer[BinaryResponse]):
    async def score(self, row: dict[str, Any]) -> BinaryResponse:
        actual = str(row.get("actual_output", "")).strip()
        expected = str(row.get("expected_output", "")).strip()
        return BinaryResponse(
            value=actual == expected and bool(actual),
            reason="actual_output matched expected_output",
        )


client = Freesolo(project_name="support-agent")

results = client.evals.run(
    [
        {
            "input": "What is the capital of France?",
            "actual_output": "Paris",
            "expected_output": "Paris",
        }
    ],
    scorers=[ExactMatch()],
    eval_run_name="ci-smoke",
    assert_test=True,
)

print(results[0].success)

Custom scorer:

from typing import Any

from freesolo import Freesolo
from freesolo.evaluation import BinaryResponse, CustomScorer


class NoEmptyAnswer(CustomScorer[BinaryResponse]):
    async def score(self, row: dict[str, Any]) -> BinaryResponse:
        ok = bool(str(row.get("actual_output", "")).strip())
        return BinaryResponse(value=ok, reason="actual_output is non-empty")


results = Freesolo(project_name="support-agent").evals.run(
    [{"actual_output": "hello"}],
    scorers=[NoEmptyAnswer()],
    eval_run_name="custom-smoke",
    assert_test=True,
)

For CI, set assert_test=True to raise an AssertionError when any row fails.

Runnable evaluation examples live in:

python -m freesolo.examples.evaluation.custom

Tracing is available from the same root client:

from freesolo import Freesolo

client = Freesolo()

with client.traces.start("support-agent-run"):
    ...

You can also import namespaced tracing helpers directly:

from freesolo.tracing import start_trace, wrap