{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Instrument LLM applications with event tracking and OpenTelemetry-based tracing, capturing AI interactions, user signals, and tool calls for observability and analysis.","skillfed_tags":["llm-observability","opentelemetry","event-tracking"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"raindrop-ai","links":{"html":"https://skillfed.io/packages/raindrop-ai","md":"https://skillfed.io/packages/raindrop-ai.md","pypi":"https://pypi.org/project/raindrop-ai/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-03","license_spdx":null,"license_treatment":"permissive","name":"raindrop-ai","python_support":"supports_current","summary":"Raindrop AI (Python SDK)"},"popularity":{"monthly_downloads":490449,"position":6369,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.66"}