raindrop-ai

What it is and what it does

Raindrop AI is a Python SDK for tracking events and tracing in LLM applications. It provides both a module-level API and instance-based clients, allowing you to capture AI interactions (with `track_ai`), plain events (with `track`), and multi-step workflows (with `begin`/`finish`). The SDK integrates with OpenTelemetry for automatic span creation and can instrument functions via decorators. It batches events locally and ships them to the Raindrop backend, with support for multiple projects, conversation grouping, and custom properties and attachments.

The package handles text field truncation (capped at 1,000,000 characters by default as of version 0.0.52), routes spans per project via headers, and supports concurrent multi-project setups within a single process. It depends on httpx, requests, pydantic, protobuf, opentelemetry-sdk, and traceloop-sdk for its core functionality. Manual events always route correctly; tracing is a process singleton shared across instances, with per-span routing via project attributes.

Use it for:

• Track AI chat completions and LLM interactions with input, output, and conversation context for post-hoc analysis.
• Instrument multi-step agent workflows with begin/finish to capture tool calls, property updates, and final outputs.
• Route telemetry from multiple AI agents or services to separate Raindrop projects within a single process.
• Collect user signals (thumbs up/down, feedback) and attach them to specific events or interactions.
• Automatically span-trace LLM library calls and custom functions via decorators for distributed tracing.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Instrument LLM applications with event tracking and OpenTelemetry-based tracing, capturing AI interactions, user signals, and tool calls for observability and analysis.

Yes. The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive MIT license. It is well-suited for teams building LLM applications who need event tracking and OpenTelemetry tracing. The main constraint is the Python 3.10+ requirement; verify that your observability backend and LLM framework integrations align with your architecture before committing.

Install

raindrop-ai on PyPI

pip install raindrop-ai

uv add raindrop-ai

poetry add raindrop-ai

Installing raindrop-ai

Before you install

Low install friction with a pure-Python wheel. Active maintenance with a release 11 days ago. Requires Python 3.10 or later and six runtime dependencies including httpx, requests, pydantic, and OpenTelemetry components.

License in practice

MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.

Quickstart

pip install raindrop-ai

import raindrop.analytics as raindrop

raindrop.init(api_key="your-api-key", tracing_enabled=True)
raindrop.track_ai(
    user_id="user-123",
    event="chat-completion",
    model="gpt-4",
    input="What is the weather?",
    output="It's sunny and 72°F.",
    convo_id="conv-456",
)

Requires Python 3.10 or later; requires a valid Raindrop API key.

Verify before relying

• Whether the package's OpenTelemetry integration works seamlessly with all major LLM frameworks and libraries.
• Performance overhead and latency impact of tracing on typical LLM application request paths.
• Behavior and guarantees when network connectivity is lost during event batching or export.

Package facts

Evidence: raindrop_ai-0.0.66-py3-none-any.whl

Tags

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