DSPy - Weights & Biases Documentation

Use W&B with DSPy to track and optimize your language model programs. W&B complements the Weave DSPy integration by providing:

Evaluation metrics tracking over time
W&B Tables for program signature evolution
Integration with DSPy optimizers like MIPROv2

For comprehensive observability when optimizing DSPy modules, enable the integration in both W&B and Weave.

NoteAs of wandb==0.21.2 and weave==0.52.5, Weave initializes automatically when used with W&B:

If weave is imported and then wandb.init() is called (script case)
If wandb.init() was called and then weave is imported later (notebook/Jupyter case)

No explicit weave.init(...) call is required.

Install and authenticate

Install the required libraries and authenticate with W&B:

Command Line
Python
Notebook

Install the required libraries:
pip install wandb weave dspy

Set the WANDB_API_KEY environment variable and log in:

export WANDB_API_KEY=<your_api_key>
wandb login

Install the required libraries:
pip install wandb weave dspy
In your code, log in to W&B:
import wandb wandb.login()

Install and import the required libraries, then log in to W&B:

!pip install wandb weave dspy

import wandb
wandb.login()

New to W&B? See our quickstart guide.

Track program optimization (experimental)

For DSPy optimizers that use dspy.Evaluate (such as MIPROv2), use the WandbDSPyCallback to log evaluation metrics over time and track program signature evolution in W&B Tables.

import dspy
from dspy.datasets import MATH

import weave
import wandb
from wandb.integration.dspy import WandbDSPyCallback

# Initialize W&B (importing weave is sufficient; no explicit weave.init needed)
project_name = "dspy-optimization"
with wandb.init(project=project_name) as run:
    # Add W&B callback to DSPy
    dspy.settings.callbacks.append(
        WandbDSPyCallback(run=run)
    )

    # Configure language models
    teacher_lm = dspy.LM('openai/gpt-4o', max_tokens=2000, cache=True)
    student_lm = dspy.LM('openai/gpt-4o-mini', max_tokens=2000)
    dspy.configure(lm=student_lm)

    # Load dataset and define program
    dataset = MATH(subset='algebra')
    program = dspy.ChainOfThought("question -> answer")

    # Configure and run optimizer
    optimizer = dspy.MIPROv2(
        metric=dataset.metric,
        auto="light",
        num_threads=24,
        teacher_settings=dict(lm=teacher_lm),
        prompt_model=student_lm
    )

    optimized_program = optimizer.compile(
        program,
        trainset=dataset.train,
        max_bootstrapped_demos=2,
        max_labeled_demos=2
    )

After running this code, you receive both a W&B Run URL and a Weave URL. W&B displays evaluation metrics over time, along with Tables that show the evolution of program signatures. The run’s Overview tab includes links to Weave traces for detailed inspection. If a run object is not passed to WandbDSPyCallback, the global run object is used.

For comprehensive details about Weave tracing, evaluation, and optimization with DSPy, see the Weave DSPy integration guide.

Log predictions to W&B Tables

Enable detailed prediction logging to inspect individual examples during optimization. The callback creates a W&B Tables for each evaluation step, which can help you to analyze specific successes and failures.

from wandb.integration.dspy import WandbDSPyCallback

# Enable prediction logging (enabled by default)
callback = WandbDSPyCallback(log_results=True)
dspy.settings.callbacks.append(callback)

# Run your optimization
optimized_program = optimizer.compile(program, trainset=train_data)

# Disable prediction logging if needed
# callback = WandbDSPyCallback(log_results=False)

Access prediction data

After optimization, find your prediction data in W&B:

Navigate to your run’s Overview page.
Look for Table panels named with a pattern like predictions_0, predictions_1, and so forth.
Filter by is_correct to analyze failures.
Compare tables across runs in the project workspace.

Each table includes columns for:

example: Input data
prediction: Model output
is_correct: Evaluation result

Learn more in the W&B Tables guide.

Save and version DSPy programs

To reproduce and version your best DSpy programs, save them as W&B Artifacts. Choose between saving the complete program or only the state.

from wandb.integration.dspy import WandbDSPyCallback

# Create callback instance
callback = WandbDSPyCallback()
dspy.settings.callbacks.append(callback)

# Run optimization
optimized_program = optimizer.compile(program, trainset=train_data)

# Save options:

# 1. Complete program (recommended) - includes architecture and state
callback.log_best_model(optimized_program, save_program=True)

# 2. State only as JSON - lighter weight, human-readable
callback.log_best_model(optimized_program, save_program=False, filetype="json")

# 3. State only as pickle - preserves Python objects
callback.log_best_model(optimized_program, save_program=False, filetype="pkl")

# Add custom aliases for versioning
callback.log_best_model(
    optimized_program,
    save_program=True,
    aliases=["best", "production", "v2.0"]
)

​Install and authenticate

​Track program optimization (experimental)

​Log predictions to W&B Tables

​Access prediction data

​Save and version DSPy programs

Install and authenticate

Track program optimization (experimental)

Log predictions to W&B Tables

Access prediction data

Save and version DSPy programs