synth-ai 0.4.2.dev0

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 verifiers 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.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"))

Zero-Shot Verifiers (SDK)

Run a built-in verifier graph with rubric criteria passed at runtime:

import asyncio
import os
from synth_ai.sdk.graphs import VerifierClient

async def run_verifier():
    client = VerifierClient(
        base_url=os.environ["SYNTH_BACKEND_BASE"],
        api_key=os.environ["SYNTH_API_KEY"],
    )
    result = await client.evaluate(
        job_id="zero_shot_verifier_single",
        session_trace={"session_id": "s", "event_history": []},
        rubric={
            "event": [{"id": "accuracy", "weight": 1.0, "description": "Correctness"}],
            "outcome": [{"id": "task_completion", "weight": 1.0, "description": "Completed task"}],
        },
        options={"event": True, "outcome": True, "model": "gpt-5-nano"},
        policy_name="my_policy",
        task_app_id="my_task",
    )
    return result

asyncio.run(run_verifier())

You can also call arbitrary graphs directly:

from synth_ai.sdk.graphs import GraphCompletionsClient

client = GraphCompletionsClient(base_url="https://api.usesynth.ai", api_key="...")
resp = await client.run(
    graph={"kind": "zero_shot", "verifier_type": "zero_shot_verifier_mapreduce"},
    input_data={"session_trace": {"session_id": "s", "event_history": []}, "rubric": {"event": [], "outcome": []}},
)

GraphGen: Train Custom Verifier and RLM Graphs

Train custom verifier and RLM graphs using GraphGen:

from synth_ai.sdk.api.train.graphgen import GraphGenJob

# Train a verifier graph (judge/scorer)
verifier_job = GraphGenJob.from_dataset(
    dataset="verifier_dataset.json",
    graph_type="verifier",
    policy_models=["gpt-4.1"],
    proposer_effort="medium",  # Use "medium" (gpt-4.1) or "high" (gpt-5.2)
    rollout_budget=200,
)
verifier_job.submit()
result = verifier_job.stream_until_complete(timeout=3600.0)

# Run inference with trained verifier
judgment = verifier_job.run_verifier(
    session_trace=my_trace,
    context={"rubric": my_rubric},
)
print(f"Score: {judgment.score}, Reasoning: {judgment.reasoning}")

# Train an RLM graph (massive context via tools)
rlm_job = GraphGenJob.from_dataset(
    dataset="rlm_dataset.json",
    graph_type="rlm",
    configured_tools=[
        {"name": "materialize_context", "kind": "rlm_materialize", "stateful": True},
        {"name": "local_grep", "kind": "rlm_local_grep", "stateful": False},
        {"name": "codex_exec", "kind": "daytona_exec", "stateful": True},
    ],
    policy_models=["gpt-4.1"],
    proposer_effort="medium",
    rollout_budget=100,
)
rlm_job.submit()
result = rlm_job.stream_until_complete(timeout=3600.0)

# Run inference with trained RLM graph
output = rlm_job.run_inference({"query": "Find relevant sections", "context": large_document})

Graph Types:

• verifier: Trains a judge/scorer that evaluates traces and returns structured rewards
• rlm: Trains a graph optimized for massive contexts (1M+ tokens) using tool-based search
• policy: Trains a standard input→output graph (default)

RLM Tools:

• materialize_context - Store input fields for fast searching (~1ms local)
• local_grep - Regex search on materialized content (~1ms)
• local_search - Substring search (~1ms)
• query_lm - Sub-LM calls for processing chunks
• codex_exec - Shell execution for complex operations

When to use RLM:

• Context exceeds ~100K tokens (too large for prompt)
• You need to search/filter large datasets
• RAG-style workflows over massive corpora