cmpnd

The cmpnd package traces DSPy programs and deploys and optimizes them on the cmpnd platform. One call turns on tracing, and one more deploys a program.

Installation

uv add cmpnd      # or: pip install cmpnd

Authentication

The cmpnd library works with the cmpnd service at platform.cmpnd.ai. Create an API key on the platform under Settings → API keys, and pass it as api_key= to cmpnd.configure() or cmpnd.Client(). For a self-hosted cmpnd install, also pass the install's URL as endpoint=.

Quick start

cmpnd.auto_instrument() records a trace of every DSPy call in the process. Your code needs no other change. Use it to sketch a DSPy program and see what it does.

import cmpnd
import dspy

cmpnd.configure(api_key="ck_xxx", project_tags="my-project")
cmpnd.auto_instrument()

dspy.configure(lm=dspy.LM("openai/gpt-6-luna"))

cot = dspy.ChainOfThought("question -> answer")
result = cot(question="What is DSPy?")   # the trace goes to cmpnd

cmpnd.trace_id(result)  # "a1b2c3d4-...", or None outside instrumentation

cmpnd.trace_id() links a result to its platform trace, for example to attach feedback later. Traces recorded this way have no program name. The platform groups them by the structure of the program's modules. The API reference below lists the other options of cmpnd.configure().

Program names

When you’re ready to name programs, evolve them, and share them with your team, switch to cmpnd.Client. A client replaces configure() and auto_instrument() and offers more features. A client records only the programs you run through it, and each of those programs has a program name. A client takes most of the same arguments as configure().

A program name is the name your organization gives a program, such as question-answerer. Traces, deployments and optimization runs under one program name go to one program. The platform makes the program the first time your organization uses the program name.

@cmpnd.program(name) above a dspy.Module class gives the class a program name. Building the class takes its own arguments and a required lm=. The lm= argument is the dspy.LM that the program runs on:

import cmpnd
import dspy

class Answer(dspy.Signature):
    """Answer the question in one short sentence."""
    question: str = dspy.InputField()
    answer: str = dspy.OutputField()

@cmpnd.program("question-answerer")
class QA(dspy.Predict):
    def __init__(self):
        super().__init__(Answer)

qa1 = QA(lm=dspy.LM("openai/gpt-6-luna"))

A client runs the program and records its trace under the program name:

gepa = dspy.GEPA(metric=metric, reflection_lm=dspy.LM("openai/gpt-6-luna"), auto="light")

with cmpnd.Client(api_key="ck_xxx", project_tags="my-project") as sdk:
    # the trace will go to the program "question-answerer"
    result = sdk.run_program(qa1, question="What is DSPy?")
    qa2 = sdk.run_optimization(gepa, qa1, trainset=train, valset=val)

A client is active from construction. The with form flushes the client's traces and closes it on exit. Without with, the client stays active until you call close() or the process exits, and its traces go out at exit:

sdk = cmpnd.Client(api_key="ck_xxx", project_tags="my-project")
sdk.run_program(qa2, question="What is DSPy?")

# optional: flushes the traces now, so a later read sees them
sdk.close()

The cmpnd.Program constructor gives a program name to a module you already have, such as a dspy.Predict. It makes the same kind of program as the decorator:

qa3 = cmpnd.Program(
    "question-answerer", module=dspy.Predict("question -> answer"), lm=dspy.LM("openai/gpt-6-luna")
)

• A program name starts with an ASCII letter or digit, then has letters, digits, ., _ or -, at most 64 characters. It does not start with p_. A bad name raises ValueError. Declaring a program makes no network call.
• run_optimization returns a copy of the program that holds the optimized module. The copy keeps the program name and the LM.
• Calling a cmpnd.Program directly while auto_instrument() is on raises a TypeError that points to cmpnd.Client.

Deployed programs

Every deployment belongs to a program, and each program has a program name. Give a DSPy module a program name with cmpnd.Program, then deploy it for server-side execution:

import cmpnd
import dspy

cmpnd.configure(api_key="ck_xxx")

qa4 = cmpnd.Program(
    "question-answerer", module=dspy.Predict("question -> answer"), lm=dspy.LM("openai/gpt-6-luna")
)
deployed = cmpnd.deploy(qa4)

result = deployed(question="What is DSPy?")  # runs server-side
print(result.answer)

deploy() packages the module's source, sends it to the platform and waits until the platform has read it. It then returns a DeployedProgram. Call the program to execute it server-side. str(deployed) and deployed.deployment_id return the deployment ID. deployed.artifact_metadata holds the typed artifact record when the platform has inspected it. An unknown interface leaves input_fields and output_fields as None. A known interface with no fields returns empty lists.

The platform makes the program the first time your organization uses the program name. Later deploys under the same program name go to the same program, and so do the traces of a client that runs it.

Options

cmpnd.deploy(
    program,           # required: a cmpnd.Program
    metric=None,       # optional: a metric callable, packaged for optimization
    timeout=300,       # optional: seconds to wait for the deployment to be ready
    metadata=None,     # optional: a JSON-serializable dict, at most 64 KiB, stored with the deployment
)

How it works

1. The SDK packages the module's source, including its LM configuration, signature and types.
2. The platform reads the program, the model string and the sampling settings from that source.
3. deploy() returns the DeployedProgram when the deployment is ready.

Optimization on the platform

cmpnd.optimize() optimizes a program through the platform. Like deploy(), it returns a DeployedProgram that you call directly, and the program's score is the best score found.

import cmpnd
import dspy

cmpnd.configure(api_key="ck_xxx")

program = cmpnd.Program(
    "haiku", module=dspy.ChainOfThought("topic -> haiku"), lm=dspy.LM("openai/gpt-6-luna")
)

# GEPA needs a five-argument feedback metric that returns dspy.Prediction(score=, feedback=)
def metric(example, pred, trace=None, pred_name=None, pred_trace=None):
    ok = example.season.lower() not in pred.haiku.lower()
    return dspy.Prediction(
        score=float(ok), feedback=None if ok else "Don't name the season."
    )

best = cmpnd.optimize(
    program,
    optimizer="gepa",
    trainset=train,
    valset=val,
    metric=metric,
    reflection_lm=dspy.LM("openai/gpt-6-luna"),
    auto="light",
)

print(best.score)
print(best(topic="late autumn").haiku)

reflection_lm is the model that proposes each new instruction. The search is only as good as this model, so it is usually a strong model and not the one being optimized. A dspy.LM passes its sampling settings to the provider. A bare model name gives the same model with no sampling settings. The platform supplies the credential and the endpoint, so the SDK refuses an api_key, api_base or base_url on the LM before any network call. On a hosted org, both live on the provider credential (Account → Model Providers), keyed by the model's protocol. One openai credential serves every openai/* model.

The SDK checks the metric before any network call. GEPA writes each new instruction from the textual feedback the metric returns, so it needs a five-argument feedback metric. The SDK refuses a metric with fewer than five positional arguments, because such a run would report success and learn nothing. A *args metric cannot be checked, and the SDK accepts it with a warning.

gepa is the only optimizer the platform runs. optimize() raises ValueError for the retired bootstrap_fewshot and random_search before any network call, and the platform answers them with a 404 that names gepa.

A metric with its own model

A metric reaches a model the same way the program's predictors do. A metric that builds a dspy.Predict runs on the program's own model. A metric that needs a different model, such as a stronger judge, declares it in the optimize call's lms argument and selects it by name through a sixth parameter, lms:

def faithfulness(gold, pred, trace, pred_name, pred_trace, lms=None):
    """Does the summary say only what the document says?"""
    verdict = dspy.Predict("document, summary -> score: float, feedback: str")(
        document=gold.document,
        summary=pred.summary,
        lm=lms["critic"],
    )
    return dspy.Prediction(score=verdict.score, feedback=verdict.feedback)


best = cmpnd.optimize(
    program, optimizer="gepa", trainset=train, valset=val, metric=faithfulness,
    reflection_lm=dspy.LM("openai/gpt-6-luna"),
    lms={"critic": dspy.LM("openai/gpt-6-luna")},
    max_metric_calls=300,
)

lms declares any model that is not one of the program's predictors. The rules:

• A name the run did not declare raises at the metric and lists the declared names.
• "default-lm" is reserved for the program's own model, which comes from the deployment. Declaring it raises an error that names it.
• The platform refuses a model your org holds no credential for when it dispatches the run, before the run starts. These LMs, like reflection_lm, must not set api_key, api_base or base_url.

The lms parameter does not change the arity check. Five or more positional arguments make a feedback metric, and a metric without lms never receives it.

Reusing a deployment

Pass the deployment_id of a ready deployment to skip the initial deploy:

best = cmpnd.optimize(program, optimizer="gepa",
                      trainset=train, valset=val, metric=metric,
                      reflection_lm="openai/gpt-6-luna", auto="light",
                      deployment_id=deployed.deployment_id)

Options

cmpnd.optimize(
    program,                 # required: the DSPy module to optimize
    optimizer="gepa",        # required: the only optimizer the platform runs
    trainset=train,          # required: training examples
    valset=val,              # required: validation examples
    metric=metric,           # required: the metric, arity-checked
    config=None,             # optional: optimizer config, allow-listed by the platform
    deployment_id=None,      # optional: a ready deployment to start from
    reflection_lm=None,      # required: a dspy.LM or a bare model name
    lms=None,                # optional: name -> dspy.LM, the run's named models
    max_metric_calls=None,   # budget: the metric-call ceiling for one step
    auto=None,               # budget: "light", "medium" or "heavy"
    may_author_code=None,    # optional: False keeps each dspy.Flex at its deployed source
    max_steps=10,            # optional: the most optimize steps
    patience=2,              # optional: stop after this many steps without improvement
    seed=0,                  # optional: the starting seed, advanced each step
    poll_interval=3.0,       # optional: seconds between status checks
    timeout=600.0,           # optional: seconds to wait for each step
)

Pass exactly one budget: max_metric_calls, auto or config["max_full_evals"]. The SDK raises before any network call when the budget is missing.

optimize() raises cmpnd.OptimizeError when the platform answers a step's initiation with a 4xx, such as a 409 for missing data or a metric mismatch. It raises cmpnd.OptimizeFailed when a step reaches failed.

Command line

The package installs the cmpnd command. cmpnd login saves your API key for the command line. cmpnd -h lists the commands, and cmpnd COMMAND -h describes one:

usage: cmpnd [-h] [--version] [--api-key API_KEY] [--endpoint ENDPOINT]
             {auth,login,logout,org,credentials,tool-servers,optimizations,programs,signatures,traces,evals,datasets,deployments,ps,status,logs,inspect,schema,sample,run,deploy,optimize}
             ...

CMPND platform CLI — deploy, invoke, and inspect DSPy programs.

options:
  -h, --help           show this help message and exit
  --version            show program's version number and exit
  --api-key API_KEY    API key (or set CMPND_API_KEY)
  --endpoint ENDPOINT  API endpoint (or set CMPND_ENDPOINT)

Deploy a source file under a program name, or optimize the program a file declares:

cmpnd deploy program.py question-answerer
cmpnd optimize gepa task.py haiku --reflection-model openai/gpt-6-luna --auto light

cmpnd deploy uploads the file as written. cmpnd optimize reads program, trainset, valset and metric from the file, deploys the program under the program name and prints the best deployment ID.

Custom spans

Trace non-DSPy code with the decorator:

from cmpnd import trace, SpanType

@trace(name="fetch_documents", span_type=SpanType.RETRIEVE)
def fetch_documents(query: str) -> list[str]:
    return documents

Or with the context manager:

from cmpnd import start_span, SpanType

def run_pipeline(query: str):
    with start_span("vector_search", span_type=SpanType.RETRIEVE) as span:
        span.set_attribute("index", "my-faiss-index")
        docs = search(query)
        span.set_outputs({"doc_count": len(docs)})

    return generate(query, docs)

What gets traced

• Modules: the module type, signature name and instructions, input and output field names, and demo count.
• LM calls: the model name and provider, token usage and the request and response content.
• Adapters: format and parse operations.
• Tools: the tool name and description and each invocation's inputs and outputs.
• Evaluations: scores and the program under evaluation.

Span types

SpanType has these members: UNKNOWN, MODULE, PREDICT, CHAIN_OF_THOUGHT, REACT, RLM, RETRIEVE, LM_CALL, ADAPTER_FORMAT, ADAPTER_PARSE, TOOL, INTERPRETER_EXECUTE, SIGNATURE_VALIDATE, DEMO_SELECT, OPTIMIZATION and EVALUATION.

API reference

cmpnd.configure()

Sets the global API key, endpoint and options that auto_instrument(), deploy(), optimize() and execute() use:

cmpnd.configure(
    api_key="ck_xxx",                      # required, or CMPND_API_KEY
    endpoint="https://platform.cmpnd.ai",  # optional, or CMPND_ENDPOINT
    project_tags="my-project",             # optional: one tag or a list of tags
    batch_size=100,                        # optional: spans per export batch
    flush_interval_seconds=5.0,            # optional: seconds between flushes
    capture_inputs=True,                   # optional: record span inputs
    capture_outputs=True,                  # optional: record span outputs
    default_tags={"env": "prod"},          # optional: tags on every trace
)

api_key and endpoint fall back to the CMPND_API_KEY and CMPND_ENDPOINT environment variables. project is a deprecated alias for project_tags. Passing both raises ValueError. CMPND_MAX_INPUT_SIZE and CMPND_MAX_OUTPUT_SIZE set the captured input and output size limits, 65,536 bytes each by default.

cmpnd.auto_instrument()

Registers the callback with DSPy, so every DSPy call records a trace.

cmpnd.Client()

Takes most of the same arguments as cmpnd.configure() and keeps them apart from the global config. Use it as a context manager or as a plain object.

• run_program(program, **inputs) runs a cmpnd.Program and records its trace under the program name.
• run_optimization(optimizer, program, trainset=..., valset=...) compiles a cmpnd.Program with a DSPy optimizer, records every rollout, and returns a copy of the program that holds the optimized module. For dspy.GEPA, it also records the optimization run.
• list_traces(**filters) and get_trace(trace_id) read traces with the client's credentials. They flush the client's traces first.
• close() flushes the client's traces and closes the client. A closed client raises RuntimeError on every method.

cmpnd.program()

@cmpnd.program(name) above a dspy.Module class returns a cmpnd.Program class with the same class name. Building it takes the class's own arguments and a required lm= keyword. The decorator refuses a class whose constructor already takes lm. Give an instance of it to cmpnd.Program instead.

cmpnd.Program()

cmpnd.Program(name, *, module, lm) gives a DSPy module a program name and the dspy.LM it runs on. @cmpnd.program(name) builds the same thing from a dspy.Module class.

cmpnd.CmpndCallback

The callback class, for manual registration:

import dspy
import cmpnd

cmpnd.configure(api_key="ck_xxx")
dspy.configure(callbacks=[cmpnd.CmpndCallback()])

cmpnd.deploy()

Deploys a cmpnd.Program under its program name and returns a callable DeployedProgram.

cmpnd.optimize()

Optimizes a program through the platform. It returns the best DeployedProgram, whose .score is the best score found. It raises cmpnd.OptimizeError or cmpnd.OptimizeFailed on failure.

cmpnd.execute()

Submits one durable run by deployment_id, waits for the result within the local timeout and returns a dspy.Prediction. A DeployedProgram call goes through this function. A fast result returns at once, and the SDK never submits one run twice.

timeout is the whole submit-and-wait budget in seconds, 30 by default, or None for no local limit. program_name and lm select a module and model and travel beside the inputs. metadata and tags take dict[str, str] values that the platform applies to the run's root trace. Names the platform sets, such as deployment_run_id raise TypeError before anything is sent. The errors:

• A refused request raises httpx.HTTPStatusError.
• A lost or failed submission reply raises RunSubmissionUncertain.
• A spent budget raises RunWaitTimeout, which holds the run ID.
• An answer that expired before it was read raises RunResultExpired.

cmpnd.deployment_run_id(result) returns the run ID of a result, or of an exception when the platform had assigned a run.

cmpnd.submit_deployment_run(id, inputs) returns the accepted handle without waiting. cmpnd.list_deployment_runs(), get_deployment_run(id), get_deployment_run_result(id) and wait_deployment_run(id) find, inspect and resume runs. list_deployment_runs(deployment_id=...) limits the list to one deployment. RunSubmissionUncertain names the list command to check before you send new work.

cmpnd.DeployedProgram

The callable program that deploy() and optimize() return. Call it with the deployed artifact's input fields. str() and .deployment_id return its ID. .score is its score when it came from optimize(), and None otherwise. .deployment_signature holds the typed deployment response, and .artifact_metadata holds its metadata when available.

cmpnd.trace()

A decorator that traces a function.

cmpnd.start_span()

A context manager that opens a span.

cmpnd.get_current_trace()

Returns the current trace, or None.

cmpnd.get_current_span()

Returns the current span, or None.

cmpnd.trace_id()

Returns the platform trace ID stamped on a prediction from an instrumented program, or None when the prediction came from outside instrumentation. Use it to attach feedback through POST /api/v1/traces/{cmpnd.trace_id(pred)}/metadata.

cmpnd.flush_exporter()

Publishes everything the background exporter has queued and leaves the exporter running. Call it before a reader fetches a trace the program just produced. It returns True when the drain finished within the timeout, 5 seconds by default, and False when it timed out or no exporter is running.

cmpnd.shutdown_exporter()

Flushes and stops the background exporter.