synth-ai 0.15.3.dev0

Python-only SDK for Synth containers, tunnels, pools, and Research

Synth AI SDK

Python SDK and CLI for Managed Research, Research Factory, and the infrastructure surfaces that support them.

Documentation: https://docs.usesynth.ai/sdk/overview

Installation

uv add synth-ai

Authenticate

Set SYNTH_API_KEY before using the SDK or CLI:

export SYNTH_API_KEY="sk_..."

Local Workspaces

For local multi-repo development, synth-ai treats workspace resolution as a read-only overlay. .env and Synth home config may provide defaults and secrets; selecting a worktree must not rewrite those defaults.

Use SYNTH_WORKSPACE_MANIFEST or SYNTH_WORKSPACE_ROOT for command-scoped worktree resolution. The resolver in synth_ai.core.utils.workspace returns repo paths and a scoped env mapping for subprocesses without mutating .env.

Pass base_url when you need to pin a production, local, staging, or private backend explicitly:

from synth_ai import SynthClient

client = SynthClient(base_url="http://127.0.0.1:8000")

The CLI also reads SYNTH_BACKEND_URL and accepts --backend-url.

Quickstart

from synth_ai import SynthClient

client = SynthClient()

print(client.research.limits.get_typed().plan)

Managed Research (hero SDK)

Install the research extra when you need hosted runs, projects, Factory Tag, or MCP:

uv add "synth-ai[research]"

Hero entrypoint — SynthClient().research only (no standalone control client in new code):

import os

from synth_ai import SynthClient
from synth_ai.research import ResearchTagSessionCreateRequest, ResearchWorkMode

client = SynthClient()
research = client.research
factory_id = os.environ["SYNTH_FACTORY_ID"]
effort_id = os.environ["SYNTH_FACTORY_EFFORT_ID"]
project_id = os.environ["SYNTH_RESEARCH_PROJECT_ID"]  # An existing, prepared project.

# Org limits
limits = research.limits.get_typed()
print(limits.plan)

# Authoritative economics reads; the client does not recompute allowances or discounts.
plan = research.economics.plan()
catalog = research.economics.catalog()
entitlements = research.economics.entitlements()

# Async Research Factory: inspect the experiment floor before launching work
factory = research.factories.get(factory_id)
floor = research.factories.status(factory.factory_id)
preview = research.factories.preview_wake(factory.factory_id)
# After reviewing preview.efforts, the SDK replays the resolved request_contract
# with its opaque preview_token; callers do not reconstruct the write request.
if preview.confirmation_required:
    receipt = research.factories.wake_due(
        factory.factory_id,
        preview=preview,
    )

# Factory Tag loop
session = research.factories.tag.sessions.create(
    ResearchTagSessionCreateRequest(
        request="Improve rollout throughput",
        factory_id=factory_id,
        effort_id=effort_id,
    )
)
research.factories.tag.sessions.messages.send(session.session_id, "Status update")
scope = research.factories.tag.scopes.get_default()

# Launch against the explicitly selected pre-existing project.
work_mode = ResearchWorkMode.DIRECTED_EFFORT
preflight = research.runs.check_preflight(project_id, work_mode=work_mode)
session = research.runs.create(
    project_id,
    objective="Produce a bounded repository assessment and a readable report.",
    work_mode=work_mode,
)

# Run readouts (nested namespaces — never ``manderqueue`` on hero)
session.snapshots.get(detail="control")
progress = session.progress.get_typed()
usage = session.usage.get()
work_products = session.work_products.list()
artifacts = session.artifacts.list()
if work_products:
    report = session.work_products.content.get(work_products[0].work_product_id)
session.message_queue.messages.list()
research.projects.objectives.list(project_id, run_id=session.run_id)

CLI smoke:

synth-ai research limits get
synth-ai research tag smoke
synth-ai research smoke

CLI

synth-ai --help
synth-ai containers list
synth-ai tunnels health
synth-ai pools list

Public Surface

Use SynthClient as the front door:

Surface	Client namespace	Use it for
Managed Research / Factory	client.research	Hosted research runs, projects, Factory Tag, limits, MCP (synth-ai[research]).
Containers	client.containers	Hosted container records and lifecycle operations.
Tunnels	client.tunnels	Managed tunnel records, leases, health, and rotation.
Pools	client.pools	Container pools, tasks, rollouts, artifacts, usage, and events.
CLI	synth-ai	Terminal access to containers, tunnels, and pools.

Use Managed Research when you want hosted research workers, repo runs, evidence, checkpoints, MCP, or final reports.

Managed Research Billing

Standalone SMR and Managed Factory draw from the same org-level allowance and flex-credit wallet. Free, Standard ($20/month), and Max ($200/month) expose premium and value usage windows with reset times, then use explicit flex credits after included usage is exhausted. Premium models consume allowance faster; value models stretch the same allowance further. Promo, make-good, banked, and override grants are manual audit events rather than automatic resets.

The canonical backend surfaces are GET /smr/billing/catalog, GET /smr/billing/plan, GET /smr/billing/runs/{run_id}/drawdown, and GET /smr/billing/factory-efforts/{factory_effort_id}/drawdown. In the Python SDK, use client.research.economics.entitlements() for the organization snapshot and client.research.economics.plan(), .catalog(), .run_drawdown(run_id), or .factory_effort_drawdown(factory_effort_id) for canonical billing reads. Use .project(project_id) for project usage, budgets, and entitlements. Do not infer allowance from legacy Autumn balances or local spend summaries, and do not recompute discounts in the client.

Links

• Install and authenticate
• SynthClient guide
• Tunnels
• Pools
• Containers
• SDK reference
• OpenAPI contracts

Local Development

Use uv run for Python tools:

uv sync --group dev
uv run ruff format --check .
uv run ruff check .
uv run ty check
make docs-gen   # generate Mintlify SDK reference into docs/
make docs-dev   # preview at http://localhost:3000/overview

Optional: install Lefthook and run lefthook install to run formatting, linting, and type checks on staged Python files.

SMR Handoff X thread — hand agent tasks to Managed Research from Cursor, Codex, or Claude Code (repo).