synth-optimizers 0.2.0.20260604

Public Synth optimizer tooling with a Rust GEPA core.

synth-optimizers

A shared optimizer platform for AI applications — starting with open-source GEPA on a language-agnostic task contract.

PyPI · Containers · Cookbooks

synth-optimizers provides a shared Rust optimizer core for running search algorithms against any task exposed through the synth-containers HTTP contract.

• Shared platform core — reusable Rust machinery for container I/O, workspaces, cache profiles, budgets, telemetry, failure handling, and replayable evidence.
• Algorithm layer — GEPA is the first public optimizer; future algorithms can plug into the same platform contract.
• GEPA runs today — configure GEPA with TOML or GepaConfig; it proposes prompt changes, rolls them out, scores them, keeps a Pareto frontier, and emits inspectable run evidence.

Supported algorithms

Algorithm	Status	In this repo	Paper & docs
GEPA — reflective prompt evolution	Supported	rust/crates/synth_gepa/ (Rust engine + service), src/synth_optimizers/gepa.py (Python API), skills/gepa/SKILL.md (agent runbook)	Paper · gepa-ai docs

The shared synth_optimizer_platform crate is the substrate for optimizer implementations; GEPA is the first algorithm built on top of it.

Install

pip install synth-optimizers
# or
uv add synth-optimizers

Latest daily dev build:

pip install --pre \
  synth-containers==0.2.0.dev202605312141 \
  synth-optimizers==0.2.0.dev202606010003

uv add --prerelease allow \
  synth-containers==0.2.0.dev202605312141 \
  synth-optimizers==0.2.0.dev202606010003

Install uv for local development and editable installs.

Local development

Pair this repo with synth-containers at matching dev versions, then sync and install editable:

cd optimizers
uv sync --group dev
uv pip install -e ../containers
uv pip install -e .
uv run maturin develop --manifest-path rust/crates/synth_optimizers_py/Cargo.toml

Quickstart

A run is defined by TOML (or GepaConfig): which container to talk to, which prompt modules to optimize, and how to score them.

[container]
url = "http://127.0.0.1:8765"
command = ["uv", "run", "python", "banking77_container/synth_service_app.py", "--port", "8765"]

[candidate]
target_modules = ["stage2_system"]

[seed_candidate]
stage2_system = "Classify the query into exactly one Banking77 intent. Return only the label."

[dataset]
train_seeds = [0, 1, 2, 3, 4, 5, 6, 7]
heldout_seeds = [100, 101, 102, 103]

from synth_containers import Container
from synth_optimizers import GepaConfig, GepaRun, GepaTaskPools, OptimizerRun, TasksetSelection

container = Container("my-task")

with container.serve() as handle:
    result = OptimizerRun(
        GepaConfig(
            container=handle.connection(),
            taskset=TasksetSelection(train_ids=["train:0", "train:1"], heldout_ids=["test:100"]),
            task_pools=GepaTaskPools(
                pareto=["train:0"],
                minibatch=["train:0"],
                reflection=["train:0", "train:1"],
                heldout=["test:100"],
            ),
            program=None,
            objectives=None,
            policy=None,
        )
    ).execute()

print(result.best_candidate)
print(f"cost: ${result.cost_usd:.2f}")

Or load TOML directly: GepaRun.from_toml("gepa.toml").execute().

CLI:

synth-optimizers gepa run --config gepa.toml
synth-optimizers gepa service --db service.sqlite
synth-optimizers events compare --left a.jsonl --right b.jsonl

Runnable task examples: GEPA cookbooks (Banking77, HotpotQA, MiniGrid, TBLite, Crafter).

Authentication and models

Policy models run inside your task container; the reflective proposer runs Codex on the host (or in Docker). Rollout requests never carry proposer keys.

Default OpenAI API key setup:

export OPENAI_API_KEY="sk-..."
export SYNTH_OPTIMIZERS_TERMINAL=1   # optional: live usage in the terminal

[policy]
provider = "openai"
model = "gpt-4.1-nano"
api_key_env = "OPENAI_API_KEY"

[proposer]
backend = "codex_app_server"
runtime_substrate = "local"
provider = "openai"
auth_mode = "api_key"
api_key_env = "OPENAI_API_KEY"
copy_host_auth = false
model = "gpt-5.4-nano"
sandbox_mode = "workspace-write"
approval_policy = "never"
timeout_seconds = 900

OpenRouter proposer (provider = "openrouter", api_key_env = "OPENROUTER_API_KEY") — policy can stay on OpenAI. See skills/gepa/SKILL.md for full TOML.

Features

• OpenAI API key proposer — run-local Codex home; does not use your host ~/.codex login.
• OpenRouter proposer — provider-aware Codex config and base URL; OpenRouter works for policy rollouts too.
• ChatGPT subscription proposer — auth_mode = "chatgpt" with required codex_home (OAuth via Codex CLI or opencode-openai-codex-auth); models include gpt-5.4-mini, gpt-5.3-codex, gpt-5.3-codex-spark, gpt-5.5; proposer usage is $0, policy rollouts still bill normally.
• Live usage — SYNTH_OPTIMIZERS_TERMINAL=1 prints running token and cost splits (usage total=… policy=… proposer=…).
• Docker proposer — runtime_substrate = "docker" with [proposer.docker].image; workspaces stage under ~/.cache/synth-gepa-docker-workspaces/, sync back, then cleanup; image: docker/codex-gepa-proposer/Dockerfile.
• Gemini and other policy providers — supported on the policy side via [policy].provider, base_url, and container env keys; proposer stays Codex.
• DeepSeek direct — provider = "deepseek" with backend = "deepseek_chat" runs the proposer through DeepSeek Chat Completions; OpenRouter DeepSeek slugs remain supported through provider = "openrouter".
• Preflight validation — missing keys, missing codex_home/auth.json, or disallowed ChatGPT models fail before rollouts start.

Agent docs: skills/gepa/SKILL.md.

Links

• GEPA docs (gepa-ai) — algorithm overview, case studies, and adapter guides
• GEPA paper — GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• Cookbooks — runnable GEPA examples
• synth-containers — the task contract
• uv — Python package and project manager
• GEPA service OpenAPI

License

Apache-2.0