dspy-gepa-logger 0.1.1a3

Comprehensive logging and visualization infrastructure for DSPy GEPA optimization runs

dspy-gepa-logger

Lightweight logging and visualization for DSPy GEPA optimization runs. Track evaluations, compare prompts, and generate HTML reports.

Features

• Observer Pattern Architecture: First-class observer callbacks via gepa_observable for complete optimization visibility
• Typed Event System: Strongly-typed event dataclasses for seed validation, iterations, reflections, and more
• In-Memory Capture: Fast, lightweight data capture with no storage backend required
• Evaluation Tracking: Captures scores, feedback, and predictions for each evaluation
• LM Call Logging: Records all language model calls with context tags (eval, reflection, proposal)
• MLflow Integration: Optional hierarchical tracing with GEPA context attributes
• Interactive HTML Reports: Dark-themed reports with prompt comparison, performance analysis, and clickable rows for detailed evaluation view
• Web Dashboard: Real-time monitoring with project organization and persistent history
• Jupyter Support: Display reports inline in notebooks

Installation

pip install dspy-GEPA-logger

Or install from GitHub:

pip install git+https://github.com/raveeshbhalla/dspy-gepa-logger.git

Or from source:

git clone https://github.com/raveeshbhalla/dspy-gepa-logger.git
cd dspy-gepa-logger
pip install -e .

Quick Start

Using gepa_observable (Recommended)

The gepa_observable module provides first-class observer callbacks for complete optimization visibility:

import dspy
from gepa_observable import optimize, GEPAObserver
from gepa_observable.adapters.dspy_adapter import DspyAdapter

# Configure DSPy
lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

# Define your program and metric
class MySignature(dspy.Signature):
    question: str = dspy.InputField()
    answer: str = dspy.OutputField()

program = dspy.ChainOfThought(MySignature)

def my_metric(gold, pred, trace=None, pred_name=None, pred_trace=None):
    is_correct = pred.answer.lower() == gold.answer.lower()
    return dspy.Prediction(
        score=1.0 if is_correct else 0.0,
        feedback="Correct!" if is_correct else f"Expected {gold.answer}"
    )

# Create adapter
adapter = DspyAdapter(
    student_module=program,
    metric_fn=my_metric,
    feedback_map={name: lambda **kw: {"feedback": "...", "score": kw.get("module_score", 0)}
                  for name, _ in program.named_predictors()},
)

# Build seed candidate from program
seed_candidate = {name: pred.signature.instructions
                  for name, pred in program.named_predictors()}

# Create an observer (or use built-in ServerObserver for web dashboard)
class MyObserver:
    def on_seed_validation(self, event):
        print(f"Seed avg score: {sum(event.valset_scores.values())/len(event.valset_scores):.2%}")
    def on_iteration_start(self, event):
        print(f"Starting iteration {event.iteration}")
    def on_valset_eval(self, event):
        if event.is_new_best:
            print(f"New best: {event.valset_score:.2%}")
    def on_optimization_complete(self, event):
        print(f"Done! Best score: {event.best_score:.2%}")

# Run optimization with observers
result = optimize(
    seed_candidate=seed_candidate,
    trainset=train_data,
    valset=val_data,
    adapter=adapter,
    reflection_lm=dspy.LM("openai/gpt-4o"),
    max_metric_calls=100,
    observers=[MyObserver()],
    mlflow_tracing=False,  # Optional MLflow integration
)

print(f"Best candidate: {result.best_candidate}")

Observer Events

The observer protocol provides callbacks for every stage of optimization:

Event	Description
SeedValidationEvent	Initial validation of seed candidate
IterationStartEvent	Start of each optimization iteration
MiniBatchEvalEvent	Minibatch evaluation (parent or new candidate)
ReflectionEvent	Reflection/proposal phase with proposed changes
AcceptanceDecisionEvent	Accept/reject decision for new candidate
ValsetEvalEvent	Full validation set evaluation
MergeEvent	Candidate merge attempts
OptimizationCompleteEvent	Optimization finished

Legacy API

The original hooks-based API is still available:

from dspy_gepa_logger import create_logged_gepa, configure_dspy_logging

gepa, tracker, logged_metric = create_logged_gepa(
    metric=my_metric,
    num_threads=4,
    max_full_evals=3,
)

configure_dspy_logging(tracker)
tracker.set_valset(val_data)

optimized = gepa.compile(
    student=my_program,
    trainset=train_data,
    valset=val_data,
)

tracker.print_summary()
tracker.export_html("report.html")

API Reference

create_logged_gepa()

Factory function that creates a GEPA optimizer with logging enabled.

gepa, tracker, logged_metric = create_logged_gepa(
    metric=my_metric,              # Your metric function
    reflection_lm=None,            # Optional: LM for reflection (defaults to configured LM)
    num_threads=4,                 # Parallel evaluation threads
    max_full_evals=3,              # Budget for full evaluations
    track_stats=True,              # Enable statistics tracking
    log_dir=None,                  # Optional: directory for GEPA logs
    server_url=None,               # Optional: URL for web dashboard real-time updates
    project_name="Default",        # Optional: project name for organizing runs
    # ... other GEPA kwargs
)

Returns:

• gepa: Configured GEPA optimizer with logging hooks
• tracker: GEPATracker instance for accessing captured data
• logged_metric: Wrapped metric function (already passed to GEPA)

GEPATracker

Main class for accessing optimization data.

Visualization Methods

# Print formatted summary
tracker.print_summary()

# Print prompt comparison (original vs optimized)
tracker.print_prompt_diff()
tracker.print_prompt_diff(show_full=True)  # Show full text, not truncated

# Export HTML report
tracker.export_html("report.html")

# For Jupyter notebooks
from IPython.display import HTML, display
display(HTML(tracker.export_html()))

Data Access

# Get structured report
report = tracker.get_optimization_report()
print(f"Candidates: {report['total_candidates']}")
print(f"Lineage: {report['lineage']}")

# Get evaluation comparison (improvements/regressions)
comparison = tracker.get_evaluation_comparison()
print(f"Improvements: {comparison['summary']['num_improvements']}")
print(f"Regressions: {comparison['summary']['num_regressions']}")

# Access raw data
tracker.evaluations          # All evaluation records
tracker.lm_calls             # All LM call records
tracker.final_candidates     # All candidate prompts
tracker.get_lineage(idx)     # Trace candidate ancestry

Metric Functions

Your metric can return scores in several formats:

# Simple score (float)
def metric(gold, pred, trace=None):
    return 1.0 if correct else 0.0

# Score with feedback (tuple)
def metric(gold, pred, trace=None):
    return (score, "Feedback text")

# dspy.Prediction with score and feedback
def metric(gold, pred, trace=None):
    return dspy.Prediction(score=0.8, feedback="Almost correct")

GEPA metrics use 5 arguments:

def gepa_metric(gold, pred, trace=None, pred_name=None, pred_trace=None):
    return dspy.Prediction(score=score, feedback=feedback)

HTML Report

The HTML report includes:

• Summary Statistics: Iterations, candidates, LM calls, evaluations
• Lineage Visualization: Click to navigate between candidates
• Prompt Comparison: Side-by-side original vs optimized prompts
• Performance Comparison: Tabbed view of improvements, regressions, and unchanged examples
  • Shows input, baseline/optimized outputs, and score delta
  • Click any row to open a modal with full evaluation details
  • Filterable by validation set

Example

See examples/eg_v2_simple.py for a complete working example:

cd examples
python eg_v2_simple.py
open optimization_report.html

Architecture

src/
├── gepa_observable/           # Observer-based GEPA fork
│   ├── api.py                 # Top-level optimize() function
│   ├── observers.py           # GEPAObserver protocol and typed event dataclasses
│   ├── core/
│   │   ├── engine.py          # GEPAEngine with observer notifications
│   │   ├── adapter.py         # EvaluationBatch with feedbacks field
│   │   └── result.py          # GEPAResult dataclass
│   ├── adapters/
│   │   ├── dspy_adapter/      # DSPy program optimization adapter
│   │   ├── default_adapter/   # Simple LiteLLM-based adapter
│   │   ├── generic_rag_adapter/  # RAG system optimization
│   │   └── anymaths_adapter/  # Math problem solving adapter
│   └── proposer/              # Reflective mutation proposer
│
└── dspy_gepa_logger/          # Logging and visualization layer
    ├── core/
    │   ├── tracker_v2.py      # GEPATracker - unified logging interface
    │   ├── lm_logger.py       # DSPyLMLogger - LM call capture via callbacks
    │   └── context.py         # Thread-safe context for phase tagging
    └── server/
        └── client.py          # ServerClient for web dashboard integration

How It Works

1. gepa_observable provides the core optimization engine with observer support
2. GEPAObserver protocol defines callbacks for each optimization stage
3. Typed events (SeedValidationEvent, IterationStartEvent, etc.) provide structured data
4. ServerObserver (in examples) bridges observers to the web dashboard via ServerClient
5. DSPyLMLogger captures all LM calls with context tags for debugging

Web Dashboard (Optional)

For a more MLFlow-like experience with persistent history, project organization, and real-time monitoring, you can optionally run the web dashboard.

Requirements

• Node.js 20.19+, 22.12+, or 24.0+ (required by Prisma)

Setup

1. Navigate to the web directory and install dependencies:

cd web
npm install

2. Create a .env file with the database URL:

echo 'DATABASE_URL="file:./dev.db"' > .env

3. Generate the Prisma client and run database migrations:

npx prisma generate
npx prisma migrate deploy

4. Start the development server:

npm run dev

The dashboard will be available at http://localhost:3000.

Usage

With gepa_observable (Recommended)

See examples/eg_v2_simple.py for a complete example using the ServerObserver:

from gepa_observable import optimize
from dspy_gepa_logger.server.client import ServerClient

# Create a server observer (see examples/eg_v2_simple.py for full implementation)
class ServerObserver:
    def __init__(self, server_url: str):
        self.client = ServerClient(server_url, project_name="My Project")

    def on_seed_validation(self, event):
        self.client.start_run(config={}, seed_prompt=seed_candidate)
        self.client.push_evaluations([...])

    def on_valset_eval(self, event):
        self.client.push_evaluations([...])
        self.client.push_candidates([...])

    def on_optimization_complete(self, event):
        self.client.complete_run(status="COMPLETED", best_score=event.best_score)

# Run with server observer
result = optimize(
    seed_candidate=seed_candidate,
    trainset=train_data,
    valset=val_data,
    adapter=adapter,
    observers=[ServerObserver("http://localhost:3000")],
)

With Legacy API

gepa, tracker, logged_metric = create_logged_gepa(
    metric=my_metric,
    server_url="http://localhost:3000",  # Connect to web server
    project_name="My Experiment",         # Optional, defaults to "Default"
)

The tracker will automatically push data to the server as the optimization runs. Open http://localhost:3000 to view:

• Projects: Organize runs by project
• Run History: Browse all past optimization runs
• Real-time Updates: Watch ongoing runs with live stats
• Evaluation Comparison: Interactive tables showing improvements/regressions
• Prompt Comparison: Side-by-side view of original vs optimized prompts

Note: Server connection is completely optional. Without server_url, all data is captured in-memory and you can still generate HTML reports with tracker.export_html().

Requirements

• Python >= 3.10
• dspy >= 2.5.0
• requests (optional, for web dashboard)