dspy-auto-gepa 0.1.7

Thin orchestration package around DSPy's GEPA optimizer

AutoGEPA

Thin orchestration package around DSPy's GEPA optimizer.
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Table of Contents

1. About
2. Quick Start
3. Usage
4. API
5. Contributing
6. License

About

AutoGEPA automates the tedious parts of setting up a DSPy optimization pipeline: converting raw data into dspy.Examples, generating a metric file with an LLM, splitting datasets, running baselines, and training with GEPA.

• Automatic field inference — Maps row columns to DSPy Signature fields automatically
• LLM-generated metrics — Drafts evaluation metrics automatically, saving them as reproducible .py files for human review
• End-to-end pipeline — Datasets → Baseline → GEPA optimization → Compare & promote
• Zero-config defaults — Sensible defaults for all hyperparameters and LMs

Requires DSPy 3.1+.

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Quick Start

Install

Install AutoGEPA with uv (recommended):

uv add dspy-auto-gepa

Or with pip:

pip install dspy-auto-gepa

Basic Usage

import dspy
from dspy_auto_gepa import AutoGEPA

# Configure models
lm = dspy.LM("openrouter/openai/gpt-oss-120b")
large_lm = dspy.LM("openrouter/moonshotai/kimi-k2.5")
dspy.configure(lm=lm)

class TicketSignature(dspy.Signature):
    """Classify support tickets."""
    message: str = dspy.InputField()
    urgency: str = dspy.OutputField()
    sentiment: str = dspy.OutputField()

program = dspy.ChainOfThought(TicketSignature)

rows = [
    {"message": "The server room AC is out and equipment is overheating.", "urgency": "high", "sentiment": "negative"},
    {"message": "Can someone clean conference room B next week?", "urgency": "low", "sentiment": "neutral"},
]

# Fields are automatically inferred from the module's signature
auto = AutoGEPA(
    rows=rows,
    module=program,
    name="TicketSignature",
    metric_lm=large_lm,
    reflection_lm=large_lm,
)

results = auto.run(force=False)  # Set True to re-run even if a saved model exists

# Check if a cached model was loaded
if results.loaded_from:
    print(f"Loaded existing model from {results.loaded_from}")
else:
    print(f"Baseline score: {results.baseline:.4f}")
    print(f"Optimized score: {results.optimized:.4f}")
    print(f"Improvement: {results.improvement:.4f}")
    print(f"Saved optimized program to {results.saved_to}")

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Usage

Automatic field inference (recommended)

When your row columns match your module's signature fields, you don't need to specify any field mappings. AutoGEPA infers input and output fields directly from the dspy.Signature attached to your module.

If your row columns don't match the module's signature fields, AutoGEPA will raise a clear error telling you which fields are missing and suggesting you use dict mappings:

ValueError: Row columns do not match module signature fields. Missing from rows: ['message', 'sentiment', 'urgency']. Extra in rows: ['msg_text', 'sent', 'urg']. Pass input_fields/output_fields to map row columns to signature fields, or ensure row columns match exactly.

With dict field mappings

When your row columns have different names than your module's signature fields:

# Row columns: msg_text, urg, sent
# Signature fields: message, urgency, sentiment

auto = AutoGEPA(
    rows=rows,
    module=program,
    name="TicketSignature",
    input_fields={"msg_text": "message"},    # row_col → sig_field
    output_fields={"urg": "urgency", "sent": "sentiment"},
    metric_lm=large_lm,
    reflection_lm=large_lm,
)

results = auto.run()

Advanced: step-by-step control

If you prefer fine-grained control over each stage, you can call the individual methods that run() orchestrates under the hood:

# Optional: generate the metric file first for human inspection
metric_file = auto.build_metric()
print(f"Metric written to {metric_file}")
# After reviewing, proceed:

ds = auto.datasets()

baseline = auto.run_baseline(datasets=ds)

optimized = auto.train(datasets=ds)

final = auto.run_baseline(module=optimized, datasets=ds)

# Or compare and promote
comparison = auto.compare(
    optimized_module=optimized,
    datasets=ds,
)
auto.promote(
    optimized_module=optimized,
    destination=auto._run_dir / "optimized_ticket_classifier.json",
)

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API

Constructor

AutoGEPA(...) accepts all configuration fields directly:

Parameter	Type	Description
rows	list[dict] | DataFrame | None	Training data. Accepts list[dict], pandas DataFrame, polars DataFrame/LazyFrame, or any object with .to_dicts() or .to_pandas()
module	dspy.Module | None	The DSPy module to optimize
name	str | None	Task name for artifact subdirectory
input_fields	list[str] | dict[str, str] | None	Input field names. List for exact match, dict for {row_column: signature_field} mapping. Inferred from signature if omitted
output_fields	list[str] | dict[str, str] | None	Output field names. Same format as input_fields. Inferred from signature if omitted
metric	Path | str | None	Path to a custom metric .py file (skips generation)
split	tuple[float, ...]	Train/val/test split ratios. Default (0.7, 0.2, 0.1)
seed	int	Random seed for reproducibility. Default 42
artifact_dir	Path | str	Root directory for artifacts. Default ".auto_gepa"
metric_lm	dspy.LM | None	LM used for metric generation. Defaults to dspy.LM("openrouter/openai/gpt-oss-120b")
reflection_lm	dspy.LM | None	LM used for GEPA reflection. Defaults to dspy.LM("openrouter/moonshotai/kimi-k2.5")
gepa_auto	Literal["light", "medium", "heavy"]	GEPA optimization intensity. Default "light"
num_threads	int	Parallel threads for evaluation. Default 16
metric_generator_signature	Type[dspy.Signature] | None	Custom signature class for metric generation
metric_generator_module	Type[dspy.Module] | None	Custom module class for metric generation

Methods

Method	Signature	Description
build_metric	(rows, module, name, metric, out_path, force=False) → Path	Generates the metric .py file explicitly. Skips if a custom metric path is provided. out_path overrides the default save location. Use force=True to overwrite an existing generated metric
run	(rows, module, name, metric, force=False) → RunResult	Orchestrates the full pipeline: datasets → baseline → train → compare → promote. If force=False and a saved model exists at .auto_gepa/<name>/optimized_<name>.json, loads it and skips training. Returns a RunResult with baseline, optimized, improvement, saved_to (or loaded_from if cached)
datasets	(rows, module, name, metric, force=False) → Datasets	Converts rows to dspy.Examples and splits into train/val/test. Uses constructor defaults if args omitted. name sets the artifact subdirectory. force=True overwrites an existing metric file
run_baseline	(module=None, datasets) → float	Evaluates the unoptimized module. Uses module from constructor if not overridden
train	(module=None, datasets) → dspy.Module	Runs GEPA optimization. Uses module from constructor if not overridden
compare	(optimized_module, datasets, baseline_module=None) → dict	Side-by-side score comparison. Uses constructor module as baseline_module if not overridden
promote	(optimized_module, destination) → Path	Saves the optimized program to the given destination
load_metric	() → callable	Lazily loads the generated metric function

Result Types

• RunResult — returned by run():
  • baseline: float — score before optimization
  • optimized: float — score after optimization
  • improvement: float — absolute difference
  • saved_to: Path \| None — where the optimized program was saved
  • loaded_from: Path \| None — if a cached model was loaded instead of training
• Datasets — returned by datasets():
  • train: list[dspy.Example]
  • val: list[dspy.Example]
  • test: list[dspy.Example]

Field Resolution Behaviour

AutoGEPA resolves fields in this order:

1. Both provided explicitly (list[str] | dict[str, str]) — uses exactly what you gave it. Lists mean exact column names. Dicts mean {row_column: signature_field} mapping.
2. Neither provided — infers both from the module's DSPy Signature. Raises a clear error if row columns don't match signature fields, listing what's missing and what's extra.
3. Only one provided — if the other can be inferred from module signature or remaining row keys, great. If not, raises an error.

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Contributing

Quick workflow:

1. Fork and branch: git checkout -b feature/name
2. Make changes
3. Commit and push
4. Open a Pull Request

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License

MIT (as declared in pyproject.toml).

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