gepa-adk
Evolutionary optimization for Google ADK agents.
What is this?
gepa-adk evolves AI agent instructions automatically. Give it an agent and training examples, and it finds better prompts through iterative improvement using genetic algorithms and Pareto frontier selection.
Supports single-agent evolution, multi-agent co-evolution, workflow optimization (Sequential, Loop, Parallel agents), output schema evolution, generation config tuning, and multimodal inputs including video.
Requirements
- Python 3.12 or 3.13 (
>=3.12,<3.14) - Ollama with a local model (recommended for development), or any model supported by LiteLLM
Installation
pip install gepa-adk
For database-backed ADK sessions (DatabaseSessionService, used by
examples/app_runner_integration.py), install the db extra:
pip install "gepa-adk[db]"
The extra forwards to google-adk[db]. On ADK 1.x, which has no db extra,
pip prints a harmless unknown-extra warning and installs normally.
# For local models (recommended)
export OLLAMA_API_BASE=http://localhost:11434
Quick Start
Evolve a greeting agent to produce formal, Dickens-style greetings:
from google.adk.agents import LlmAgent
from google.adk.models.lite_llm import LiteLlm
from gepa_adk import evolve, run_sync, EvolutionConfig, SimpleCriticOutput
model = LiteLlm(model="ollama_chat/llama3.2:latest")
agent = LlmAgent(
name="greeter",
model=model,
instruction="Greet the user appropriately.",
)
critic = LlmAgent(
name="critic",
model=model,
instruction="Score for formal, Dickens-style greetings. 0.0-1.0.",
output_schema=SimpleCriticOutput,
)
trainset = [
{"input": "I am His Majesty, the King."},
{"input": "I am your mother."},
{"input": "I am a close friend."},
]
config = EvolutionConfig(
max_iterations=5,
patience=1,
reflection_model="ollama_chat/llama3.2:latest",
)
result = run_sync(evolve(agent, trainset, critic=critic, config=config))
print(f"Score: {result.original_score:.2f} -> {result.final_score:.2f}")
print(result.evolved_components["instruction"])
When you have labelled examples, score with plain code instead of a critic
agent. LabelAgreementScorer scores 1.0 when the output (or one JSON field
of it) equals the row's expected label after stripping whitespace, else
0.0. Any object with score and async_score methods (the
Scorer protocol, from gepa_adk import Scorer) also works as scorer=. critic= and
scorer= are mutually exclusive:
from gepa_adk import LabelAgreementScorer
trainset = [{"input": "I am your mother.", "expected": "Dearest Mother, how fare you?"}]
# For JSON output, LabelAgreementScorer(field="label") compares that one field
# instead of the whole output.
result = run_sync(evolve(agent, trainset, scorer=LabelAgreementScorer(), config=config))
Examples
Getting started:
- basic_evolution.py — Single agent with critic
- basic_evolution_adk_reflection.py — Single agent evolution with an ADK reflection agent
- critic_agent.py — Story generation with dedicated critic
- custom_reflection_prompt.py — Custom reflection prompts
Multi-agent & workflows:
- multi_agent.py — Multi-agent co-evolution
- loop_agent_evolution.py — LoopAgent workflow evolution
- parallel_agent_evolution.py — ParallelAgent workflow evolution
- nested_workflow_evolution.py — Nested workflow evolution
- multi_agent_component_demo.py — Per-agent component evolution in a multi-agent pipeline
- sandwich_evolution.py — Nested workflow evolution that preserves the workflow structure
Advanced:
- schema_evolution_example.py — Output schema evolution
- schema_evolution_critic.py — Output schema evolution via critic feedback
- schema_reflection_demo.py — Schema reflection with validation
- config_evolution_demo.py — Generation config evolution
- video_transcription_evolution.py — Video input evolution
- app_runner_integration.py — ADK App/Runner integration
Documentation
Getting Started · Guides · API Reference
Credits
Based on GEPA (source). Built on Google ADK (source).
License
1 maintainer
Metadata
1 maintainer
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