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Documentation Index

Fetch the complete documentation index at: https://docs.usesynth.ai/llms.txt

Use this file to discover all available pages before exploring further.

Managed Research lets you hand a repo and research goal to hosted AI workers. Synth runs the workspace, tracks the run, and returns durable evidence: messages, tasks, logs, checkpoints, artifacts, usage, PRs, and final reports. Use it when the work should be repeatable and inspectable instead of trapped in a one-off chat.

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Responsibility split

You provideSynth handles
Repo, goal, constraints, context, credentials, eval command, and review criteriaWorker orchestration, hosted workspace execution, durable state, logs, checkpoints, artifacts, usage, PRs, and final reports

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Choose an interface

Get started (MCP + Codex / Claude)

Install the Managed Research MCP, set SYNTH_API_KEY, and verify with smr_list_projects.

MCP Quickstart

Start and steer runs from Codex, Claude Code, Cursor, or another MCP client.

Python SDK Quickstart

Start runs from scripts, CI jobs, notebooks, and other Python workflows.

Run Configuration

Learn the launch fields for runbooks, work modes, harnesses, models, providers, and budgets.

Runs and Evidence

Inspect messages, task state, actor state, artifacts, checkpoints, usage, and reports.

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Public contract

Managed Research is MCP-first, with a supported Python SDK for scripted workflows. The UI remains supported for interactive review. Direct /smr REST usage is internal and unstable. Use MCP or the managed-research Python package for public integrations.

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Run lifecycle

Most workflows follow the same loop:
  1. Create or select a project.
  2. Attach repo, context, credentials, data, and constraints.
  3. Preflight launch configuration before spending runtime.
  4. Start a one-off or project-scoped run.
  5. Steer the run with messages when needed.
  6. Inspect state, logs, tasks, artifacts, checkpoints, usage, and final report.