This release is a pre-release and may not be stable for production use.
Synth
Serverless Posttraining APIs for Developers
Average accuracy on LangProBe prompt optimization benchmarks.
Highlights
- 🚀 Train across sft, RL, and prompt opt by standing up a single cloudflared Fastapi wrapper around your code. No production code churn.
- ⚡️ Parallelize training and achieve 80% GPU util. via PipelineRL
- 🗂️ Train prompts and models across multiple experiments
- 🛠️ Spin up experiment queues and datastores locally for dev work
- 🔩 Run serverless training via cli or programmatically
- 🏢 Scales gpu-based model training to 64 H100s seemlessly
- 💾 Use GEPA-calibrated judges for fast, accurate rubric scoring
- 🖥️ Supports HTTP-based training across all programming languages
- 🤖 CLI utilities tuned for use with Claude Code, Codex, Opencode
Getting Started
# Use with OpenAI Codex
uvx synth-ai codex
# Use with Opencode
uvx synth-ai opencode
Synth is maintained by devs behind the MIPROv2 prompt optimizer.
Documentation
In-Process Runner (SDK)
Run GEPA/MIPRO/RL jobs against a tunneled task app without the CLI:
import asyncio
import os
from synth_ai.sdk.task import run_in_process_job
result = asyncio.run(
run_in_process_job(
job_type="prompt_learning",
config_path="configs/style_matching_gepa.toml",
task_app_path="task_apps/style_matching_task_app.py",
overrides={"prompt_learning.gepa.rollout.budget": 4},
backend_url=os.getenv("TARGET_BACKEND_BASE_URL"), # resolves envs automatically
)
)
print(result.job_id, result.status.get("status"))
1 maintainer
Metadata
1 maintainer
Release files for synth-ai 0.3.3.dev20251205
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Total release size: 1.5 MB
Release files / synth_ai-0.3.3.dev20251205.tar.gz
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