gepa

Build calibrated AI Functions from human feedback using Jev and GEPA.

A research framework for principled agent self-improvement under frozen evaluators and declared mutation boundaries, recording verifiable lineage to make it reproducible and auditable.

A plugin for your agentic framework that optimizes code using the GEPA algorithm (Genetic-Pareto LLM-driven search).

Synth Python SDK for Managed Research, Research Factory, and GEPA/GELO optimizer workflows.

Claude Code for DSPy: Comprehensive CLI to Optimize Your DSPy Code. our AI-Powered DSPy Development Assistant

Optimize any AI agent’s skills, tools/MCP, and prompts against your own evals.

Allen is an open-source AI engineering team that runs on your repos — specialist agents, deterministic orchestration through workflows, and approval gates you control. Drive it from chat, Slack, or Linear.

CLI text optimizer built on GEPA. Uses Agentic Coding CLI's as mutator and observer -- no api keys required

GEPAzilla: open-source GEPA prompt optimizer with datasets, scorers, and telemetry.

Local code search for AI agents: cheaper, faster and sweeter. Because maybe grep isn't all you need... 🍬

Evolving agent harnesses: a research program on how far N orchestrated calls of a small model can rival a frontier model. We evolve the harness (structure + prompts) with reflective optimizers + a verified-acceptance gate.

GEPA and GELO optimizer runbooks, SDKs, and hosted optimizer surfaces for Synth.

Which host should serve your open model? Run your workload across pinned, verified providers and get one table: success, cost, latency, cache hits.

Self-evolve Gemini CLI instructions, commands, and skills via the gemini CLI itself — GA + GEPA/DSPy, with hard gates before apply.

Self-improvement feedback loop for AI agent skills — analyzes past sessions, drafts structured improvement proposals, and gates auto-apply behind an evaluation framework. Host-agnostic (Hermes, Claude Code, and any HostAdapter).

Production-ready boilerplate for building and automatically optimizing LangChain RAG applications. Implements three-layer architecture: Build (LangChain) → Measure (MLflow) → Optimize (GEPA + MEGA).

Local, private prompt optimization with DSPy and Ollama. Rewrites prompts, measures them against your own data, and evolves them from feedback with GEPA.

Prompt optimisation with GEPA: mine a compliance rubric from labelled decisions. 30% more violations caught, starting from a one-line prompt.

DSPy/GEPA-powered self-improvement plugin for Hermes Agent skills, memory, and evaluator prompts.

Open-source LLM eval workbench: generate verifiable prompts from parametric templates, compare any model on text or image, settle quality with blind preference, evolve with GEPA, and serve self-hosted vLLM on your GPU.