---
title: Weave Integration
description: Learn how to use Weights & Biases (W&B) Weave to track, experiment with, evaluate, and improve your CrewAI applications.
icon: radar
mode: "wide"
---

# Weave Overview

[Weights & Biases (W&B) Weave](https://weave-docs.wandb.ai/) is a framework for tracking, experimenting with, evaluating, deploying, and improving LLM-based applications. 

![Overview of W&B Weave CrewAI tracing usage](/images/weave-tracing.gif)

Weave provides comprehensive support for every stage of your CrewAI application development:

- **Tracing & Monitoring**: Automatically track LLM calls and application logic to debug and analyze production systems
- **Systematic Iteration**: Refine and iterate on prompts, datasets, and models
- **Evaluation**: Use custom or pre-built scorers to systematically assess and enhance agent performance
- **Guardrails**: Protect your agents with pre- and post-safeguards for content moderation and prompt safety

Weave automatically captures traces for your CrewAI applications, enabling you to monitor and analyze your agents' performance, interactions, and execution flow. This helps you build better evaluation datasets and optimize your agent workflows.

## Setup Instructions

<Steps>
    <Step title="Install required packages">
      ```shell
      pip install crewai weave
      ```
    </Step>
    <Step title="Set up W&B Account">
      Sign up for a [Weights & Biases account](https://wandb.ai) if you haven't already. You'll need this to view your traces and metrics.
    </Step>
    <Step title="Initialize Weave in Your Application">
      Add the following code to your application:

      ```python
      import weave

      # Initialize Weave with your project name
      weave.init(project_name="crewai_demo")
      ```
      
      After initialization, Weave will provide a URL where you can view your traces and metrics.
    </Step>
    <Step title="Create your Crews/Flows">
      ```python
      from crewai import Agent, Task, Crew, LLM, Process

      # Create an LLM with a temperature of 0 to ensure deterministic outputs
      llm = LLM(model="gpt-4o", temperature=0)

      # Create agents
      researcher = Agent(
          role='Research Analyst',
          goal='Find and analyze the best investment opportunities',
          backstory='Expert in financial analysis and market research',
          llm=llm,
          verbose=True,
          allow_delegation=False,
      )

      writer = Agent(
          role='Report Writer',
          goal='Write clear and concise investment reports',
          backstory='Experienced in creating detailed financial reports',
          llm=llm,
          verbose=True,
          allow_delegation=False,
      )

      # Create tasks
      research_task = Task(
          description='Deep research on the {topic}',
          expected_output='Comprehensive market data including key players, market size, and growth trends.',
          agent=researcher
      )

      writing_task = Task(
          description='Write a detailed report based on the research',
          expected_output='The report should be easy to read and understand. Use bullet points where applicable.',
          agent=writer
      )

      # Create a crew
      crew = Crew(
          agents=[researcher, writer],
          tasks=[research_task, writing_task],
          verbose=True,
          process=Process.sequential,
      )

      # Run the crew
      result = crew.kickoff(inputs={"topic": "AI in material science"})
      print(result)
      ```
    </Step>
    <Step title="View Traces in Weave">
      After running your CrewAI application, visit the Weave URL provided during initialization to view:
      - LLM calls and their metadata
      - Agent interactions and task execution flow
      - Performance metrics like latency and token usage
      - Any errors or issues that occurred during execution

      <Frame caption="Weave Tracing Dashboard">
        <img src="/images/weave-tracing.png" alt="Weave tracing example with CrewAI" />
      </Frame>
    </Step>
</Steps>

## Features

- Weave automatically captures all CrewAI operations: agent interactions and task executions; LLM calls with metadata and token usage; tool usage and results.
- The integration supports all CrewAI execution methods: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
- Automatic tracing of all [crewAI-tools](https://github.com/crewAIInc/crewAI-tools).
- Flow feature support with decorator patching (`@start`, `@listen`, `@router`, `@or_`, `@and_`).
- Track custom guardrails passed to CrewAI `Task` with `@weave.op()`.

For detailed information on what's supported, visit the [Weave CrewAI documentation](https://weave-docs.wandb.ai/guides/integrations/crewai/#getting-started-with-flow).

## Resources

- [📘 Weave Documentation](https://weave-docs.wandb.ai)
- [📊 Example Weave x CrewAI dashboard](https://wandb.ai/ayut/crewai_demo/weave/traces?cols=%7B%22wb_run_id%22%3Afalse%2C%22attributes.weave.client_version%22%3Afalse%2C%22attributes.weave.os_name%22%3Afalse%2C%22attributes.weave.os_release%22%3Afalse%2C%22attributes.weave.os_version%22%3Afalse%2C%22attributes.weave.source%22%3Afalse%2C%22attributes.weave.sys_version%22%3Afalse%7D&peekPath=%2Fayut%2Fcrewai_demo%2Fcalls%2F0195c838-38cb-71a2-8a15-651ecddf9d89)
- [🐦 X](https://x.com/weave_wb)
