Do I need to host a dashboard server?

Yes, to use the visual features, you provide a 'server_url' in the GEPA constructor which points to the gepa-observable dashboard instance.

What kind of events can I track with this skill?

The system tracks 8 lifecycle events including SeedValidation, IterationStart, Reflections (proposed changes), AcceptanceDecisions, and OptimizationComplete.

Will migrating break my existing DSPy code?

No, gepa-observable is designed to be 100% API compatible with the original DSPy GEPA implementation. You only need to change your import statement and add the optional observability parameters.

Can I capture raw LM calls?

Yes, by setting 'capture_lm_calls=True' in the optimizer, all interactions with the language model during the optimization process are logged for review.

DSPy GEPA Observability Migration

Name: DSPy GEPA Observability Migration
Author: raveeshbhalla

byraveeshbhalla

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分析与监控

Migrates standard DSPy GEPA usage to an observable framework for real-time prompt optimization monitoring and lineage tracking.

This skill facilitates the transition from standard DSPy Generalized Evolutionary Prompt Adaptation (GEPA) to the gepa-observable library, providing developers with deep insights into how their prompts evolve. It introduces an integrated observability system that includes a web dashboard, custom observer callbacks, and automated LM call logging. By capturing eight specific lifecycle events—from seed validation to final optimization—it allows teams to review every iteration and understand the lineage of their prompt engineering process while maintaining 100% API compatibility with existing DSPy workflows.

主要功能

01Real-time web dashboard integration for monitoring optimization progress

02Support for custom metrics and complex evaluation datasets

03Custom observer protocol for hooking into 8 distinct lifecycle events

049 GitHub stars

05Seamless migration from dspy.teleprompt.GEPA to gepa_observable

06Comprehensive LM call and console output capture

使用场景

01Visualizing the evolution of prompts during long-running DSPy optimization jobs

02Logging every LM interaction during training for auditability and lineage tracking

03Debugging LLM prompt reflections and acceptance decisions in real-time

主要功能

01Real-time web dashboard integration for monitoring optimization progress

02Support for custom metrics and complex evaluation datasets

03Custom observer protocol for hooking into 8 distinct lifecycle events

049 GitHub stars

05Seamless migration from dspy.teleprompt.GEPA to gepa_observable

06Comprehensive LM call and console output capture

使用场景

01Visualizing the evolution of prompts during long-running DSPy optimization jobs

02Logging every LM interaction during training for auditability and lineage tracking

03Debugging LLM prompt reflections and acceptance decisions in real-time