raindrop-dspy

Raindrop observability integration for DSPy. Wraps DSPy modules (Predict, ChainOfThought, etc.) to automatically capture input fields, output fields, model name, and token usage.

Installation

pip install raindrop-dspy dspy

Quick Start

import dspy
from raindrop_dspy import RaindropDSPy

raindrop = RaindropDSPy(api_key="rk_...", user_id="user-123")

lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

predict = dspy.Predict("question -> answer")
wrapped = raindrop.wrap(predict)

result = wrapped(question="What is the capital of France?")
print(result.answer)

raindrop.shutdown()

Projects

Route events to a specific project by passing its slug as project_id:

raindrop = RaindropDSPy(
    api_key="rk_...",
    project_id="support-prod",
)

project_id sets the X-Raindrop-Project-Id header on every event. Omit it (or pass "default") to use your org's default Production project, which is the existing behavior. The same option is accepted by the create_raindrop_dspy(...) factory. Invalid slugs are ignored with a warning and no header is sent.

Debug Mode

Enable verbose logging to troubleshoot issues:

raindrop = RaindropDSPy(api_key="rk_...", debug=True)

Factory Function (Legacy)

The create_raindrop_dspy factory function is still available for backwards compatibility:

from raindrop_dspy import create_raindrop_dspy

raindrop = create_raindrop_dspy(
    api_key="rk_...",
    user_id="user-123",
    tracing_enabled=True,
    bypass_otel_for_tools=True,
)
wrapped = raindrop.wrap(predict)

What Gets Captured

API Reference

RaindropDSPy(api_key=None, user_id=None, convo_id=None, project_id=None, tracing_enabled=True, bypass_otel_for_tools=True, disable_auto_instrument=True, debug=False)

Create a new Raindrop DSPy integration instance.

Library auto-instrumentation is opt-in

As of 0.0.4, disable_auto_instrument defaults to True: the integration no longer lets Traceloop monkey-patch every LLM client library it recognizes in your process (including the OpenAI client DSPy drives through LiteLLM). The wrapper captures input/output, token usage, and model name directly from the wrapped module's forward() calls and DSPy LM history, so no library patching is needed for full dashboards.

If you specifically want LLM-call-level spans from library instrumentation and have verified compatibility in your environment, opt back in with disable_auto_instrument=False.

Methods

Identifying Users

raindrop.identify("user-123", traits={"name": "Alice", "plan": "pro"})

Tracking Signals

raindrop.track_signal(
    event_id="evt-abc",
    name="thumbs_up",
    signal_type="feedback",
    sentiment="POSITIVE",
    comment="Great response!",
)

Flushing and Shutdown

raindrop.flush()     # flush pending data
raindrop.shutdown()  # flush + release resources

Async Support

DSPy modules with async forward methods are automatically detected and wrapped:

import dspy
from raindrop_dspy import RaindropDSPy

raindrop = RaindropDSPy(api_key="rk_...")

class AsyncPredict(dspy.Module):
    async def forward(self, question: str) -> dspy.Prediction:
        ...

wrapped = raindrop.wrap(AsyncPredict())
result = await wrapped(question="Hello")

Double-Wrap Protection

Calling wrap() on an already-wrapped module is a no-op — the module is returned unchanged.

Development

cd packages/dspy-python
pip install -e ".[dev]"
python -m pytest tests/ -v

License

MIT

Full documentation