synth-ai 0.3.1.dev3

Serverless Posttraining for Agents - Core AI functionality and tracing

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

Docs available at docs.usesynth.ai.

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"))

Env priority for the backend URL: TARGET_BACKEND_BASE_URL → BACKEND_OVERRIDE → SYNTH_BACKEND_URL → BACKEND_BASE_URL → NEXT_PUBLIC_API_URL → fallback to get_backend_from_env(). Required keys: SYNTH_API_KEY, ENVIRONMENT_API_KEY, plus any model keys used by your task app.