DSPy

DSPy is a framework for programming language model pipelines. Braintrust instruments DSPy runs so you can inspect module spans, adapter formatting and parsing steps, underlying LLM calls, and tool usage in a single trace.

Setup

Install Braintrust and DSPy:

# uv
uv add braintrust dspy
# pip
pip install braintrust dspy

Set your API keys before you run your app:

.env

BRAINTRUST_API_KEY=<your-braintrust-api-key>
OPENAI_API_KEY=<your-openai-api-key>

Instrument DSPy

Python

Braintrust’s DSPy integration requires dspy>=2.6.0.

Automatic Instrumentation

braintrust.auto_instrument() patches dspy.configure() so Braintrust’s DSPy callback is added automatically when you configure DSPy.

trace-dspy-auto.py

import os

import braintrust

braintrust.auto_instrument()
braintrust.init_logger(
    api_key=os.environ["BRAINTRUST_API_KEY"],
    project="dspy-example",  # Replace with your project name
)

import dspy

lm = dspy.LM("openai/gpt-5-mini")
dspy.configure(lm=lm)

cot = dspy.ChainOfThought("question -> answer")
result = cot(question="What is the capital of France?")
print(result.answer)

If you only want to patch DSPy instead of enabling all supported Python integrations, use patch_dspy().

trace-dspy-patch-only.py

import os

from braintrust import init_logger
from braintrust.integrations.dspy import patch_dspy

init_logger(
    api_key=os.environ["BRAINTRUST_API_KEY"],
    project="dspy-example",  # Replace with your project name
)
patch_dspy()

import dspy

lm = dspy.LM("openai/gpt-5-mini")
dspy.configure(lm=lm)

predict = dspy.Predict("question -> answer")
result = predict(question="What is 2 + 2?")
print(result.answer)

Manual callback setup

Use BraintrustDSpyCallback() when you want to attach the callback explicitly. If you also want detailed LiteLLM token and cost spans, patch LiteLLM before importing DSPy.

trace-dspy-manual.py

import os

from braintrust import init_logger
from braintrust.integrations.dspy import BraintrustDSpyCallback
from braintrust.integrations.litellm import patch_litellm

patch_litellm()

import dspy

init_logger(
    api_key=os.environ["BRAINTRUST_API_KEY"],
    project="dspy-example",  # Replace with your project name
)

# Disable DSPy's disk cache if you want every LiteLLM call to be traced.
dspy.configure_cache(enable_disk_cache=False, enable_memory_cache=True)

lm = dspy.LM("openai/gpt-5-mini")
dspy.configure(lm=lm, callbacks=[BraintrustDSpyCallback()])

cot = dspy.ChainOfThought("question -> answer")
result = cot(question="What is the capital of France?")
print(result.answer)

Python resources:

Examples

In Braintrust, a DSPy execution typically appears as a parent DSPy module span with child spans for adapter work and model calls. Example trace shape:

trace-dspy-auto
└── dspy.module.ChainOfThought
    ├── dspy.adapter.format
    ├── dspy.lm
    └── dspy.adapter.parse

Braintrust captures:

DSPy module spans such as Predict, ChainOfThought, and other module executions
Adapter formatting and parsing spans
LLM call inputs, outputs, and latency on dspy.lm spans
Tool execution when your DSPy program invokes tools
Extra LiteLLM token and cost spans when you patch LiteLLM

AI providers

SDKs

Developer tools

Setup

Instrument DSPy

Automatic Instrumentation

Manual callback setup

Examples

Resources

​Setup

​Instrument DSPy

​Automatic Instrumentation

​Manual callback setup

​Examples

​Resources

Setup

Instrument DSPy

Automatic Instrumentation

Manual callback setup

Examples

Resources