weave

What it is and what it does

Weave is a tracing and evaluation framework for generative AI development built by Weights & Biases. It lets you decorate Python functions with @weave.op to automatically capture their inputs, outputs, and execution flow, creating a queryable trace tree. This is useful for debugging LLM pipelines, understanding how data flows through your application, and building systematic evaluations of model behavior. The toolkit integrates with OpenTelemetry for observability and requires a Weights & Biases account to store and visualize traces.

The package depends on 15 runtime libraries including pydantic for schema validation, opentelemetry for instrumentation, sentry-sdk for error tracking, and tenacity for retry logic. It is actively maintained, supports current Python versions (3.10+), and carries no known security vulnerabilities. The Apache 2.0 license permits commercial use with standard attribution requirements.

Use it for:

• Instrument LLM API calls (OpenAI, Anthropic, etc.) and downstream processing to debug multi-step generative workflows.
• Build reproducible evaluations comparing model outputs across different prompts, parameters, or model versions.
• Log and organize experimental runs during development to track which configurations and data transformations produced which results.
• Monitor production LLM applications by capturing request traces and flagging anomalies or errors in real time.
• Validate data transformations and intermediate outputs in complex AI pipelines to catch silent failures early.

Worth the install?

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

Weave is a toolkit for tracing, logging, and evaluating generative AI applications, letting you instrument functions to capture inputs, outputs, and execution traces for debugging and analysis.

Yes, if you are actively developing or operating generative AI applications and want structured tracing without manual instrumentation. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. The requirement for a Weights & Biases account and Python 3.10+ are minor barriers; the 15 runtime dependencies are a consideration only if you have strict dependency budgets.

Install

weave on PyPI

pip install weave

uv add weave

poetry add weave

Installing weave

Before you install

Low install friction with a pure-Python wheel. Active maintenance with a release 1 day old and 1116 repository stars. Requires Python 3.10 or higher and a Weights & Biases account (free tier available) to function.

License in practice

Apache License 2.0 is permissive; you can use, modify, and distribute Weave freely in commercial and private projects provided you include license and attribution notices.

Quickstart

pip install weave

import weave
weave.init("my-project")

@weave.op
def my_function(x: int) -> int:
    return x + 1

my_function(5)

Requires Python 3.10 or higher and a Weights & Biases account (free tier available) to initialize and log traces.

Verify before relying

• Whether the 15 runtime dependencies (including opentelemetry, pydantic, sentry-sdk) add significant overhead or complexity to projects with strict dependency constraints.
• Performance characteristics and latency impact of tracing on function execution, especially for high-frequency or latency-sensitive workloads.
• Whether Weave's trace storage and retrieval scale well for long-running production applications generating high volumes of traces.

Package facts

Evidence: weave-0.53.6-py3-none-any.whl

Tags

More Application Frameworks packages

Further reading