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W&B Weave

What it is

W&B Weave is a lightweight toolkit for building and evaluating LLM applications, developed by Weights & Biases. It provides tools for tracing, versioning, and rigorous evaluation of AI workflows and agents.

What problem it solves

It addresses the difficulty of debugging and optimizing complex, multi-step LLM chains and agents. Weave allows developers to capture every step of an AI interaction, compare model outputs side-by-side, and run automated evaluations to improve quality, cost, and latency.

Where it fits in the stack

Category: Process & Understanding / AI Observability & Evaluation

Typical use cases

  • Agent Tracing: Visualizing the inner "thinking" steps and tool calls of autonomous agents.
  • LLM Application Debugging: Identifying where a prompt chain failed or where latency is accumulating.
  • Automated Evaluations: Running scorers (e.g., toxicity, relevance, factual accuracy) against a dataset of model outputs.
  • Prompt Engineering: Testing and versioning different prompt templates with visual comparisons.

Strengths

  • Easy Integration: Start tracing with a single line of code (weave.init).
  • Standardized Traces: Organizes logs into easy-to-navigate trace trees.
  • Agnostic: Works with any LLM, framework (LangChain, LlamaIndex), or protocol (MCP).
  • Built-in Evaluations: Includes out-of-the-box scorers and support for custom scoring functions.
  • Human-in-the-Loop: Supports collecting human feedback on model outputs.

Limitations

  • Cloud Dependency: While highly integrated, it primarily relies on the Weights & Biases cloud platform for visualization.
  • Evolving Product: As a newer addition to the W&B ecosystem, features and APIs are rapidly evolving.

Getting started

Installation

pip install weave wandb

CLI examples

# Login to Weights & Biases
wandb login

# Initialize a new W&B project (Weave uses W&B projects for storage)
wandb init --project my-weave-app

# List runs in the current project
wandb runs

API examples

Basic Tracing with Decorators

import weave
import openai

# Initialize Weave with a project name
weave.init("my-llm-app")

@weave.op()
def call_llm(prompt: str):
    client = openai.OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# This call will be automatically traced in the W&B dashboard
print(call_llm("What is AI observability?"))

Sources / references

Contribution Metadata

  • Last reviewed: 2026-05-27
  • Confidence: high