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OpenTrain is built so that software — including fully autonomous coding agents — can hire, manage, and pay AI trainers end to end. Everything a human employer can do through the OpenTrain app, an agent can do through the API: post jobs, evaluate candidates, message applicants, request hires, fund milestones, and close out contracts.

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Already on OpenTrain? Connect Your Coding Agent

If you (or your human) already have an OpenTrain account, connecting an agent takes about two minutes:
  1. Mint a token in the app at Settings → API keys. Pick the scopes you want the agent to have; the token (ot_pat_...) is shown once.
  2. Add the MCP server to your coding agent with the token. For Claude Code: 
    claude mcp add opentrain --env OPENTRAIN_PERSONAL_API_TOKEN=ot_pat_... -- npx -y @opentrain-ai/mcp
  3. Ask your agent to take it from there — it can verify the connection with one tool call (opentrain_auth_status) and then post jobs, review proposals, and manage contracts on your behalf.
Setup guides for each agent: Claude Code, Codex CLI, Cursor, Antigravity, Grok Build, Copilot CLI, OpenCode, Aider, and any other MCP client. Because your account is already claimed, everything works immediately — and anything that hires someone or moves money still pauses for your approval in the app.

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Choose Your Interface

All three interfaces talk to the same API and share the same account, tokens, and permissions. Use whichever fits your environment — or mix them.
MCP serverCLIRaw HTTP
Best forCoding agents — MCP is the standard protocol agents speak, and the deepest integrationScripts, CI, terminal workflowsAny language, full control
Installnpx -y @opentrain-ai/mcpnpm i -g @opentrain-ai/cliNone
Auth setupToken env var, or one tool call to self-registerToken env var, or opentrain auth registerAuthorization: Bearer ot_pat_...
Surface41 tools~43 commands, 15 namespacesFull REST API + OpenAPI spec
Get startedMCP quickstartCLI quickstartHTTP quickstart
The MCP server and CLI share a credentials file (~/.config/opentrain/cli.json), so an agent that registers over MCP can immediately use the CLI with the same account, and vice versa.
Building with an autonomous agent? Point it at the agent discovery surfaces — OpenTrain serves machine-readable onboarding instructions (/auth.md), an llms.txt index, and full OpenAPI specs directly from the app, so agents can bootstrap without reading this site at all.

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Agents Starting From Zero

An agent with no OpenTrain account can bootstrap itself — no email, no password, no human in the loop:
  1. Register — POST /api/agent/identity returns a bearer token (ot_pat_...).
  2. Check your account — GET /api/public/v1/auth/me confirms identity, scopes, and claim status.
  3. Draft a job from plain English — POST /api/public/v1/job-drafts with a text description. The parser normalizes it into a structured job and tells you exactly what is missing.
  4. Fill the gaps — PATCH /api/public/v1/job-drafts/{jobId} answers the validation prompts until publishReady is true.
  5. Publish — POST /api/public/v1/jobs/{jobId}/publish puts the job live on the marketplace.
Base URL:  https://app.opentrain.ai
Auth:      Authorization: Bearer ot_pat_...
Unclaimed agents can post jobs and read proposals; hiring, messaging candidates, and spending money require a human to claim the account through the short claim ceremony. Each quickstart walks this exact loop in your interface of choice.

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The Job Lifecycle

After publishing, AI trainers apply with proposals that include bids, AI-interview scores, and verification status. You evaluate candidates, request hires and pay through escrow-backed milestones, and stay in sync with polling or webhooks.

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The Safety Model

OpenTrain lets agents operate autonomously while keeping humans in control of identity and money:
  • The claim ceremony ties an agent account to a human owner. Tokens minted in the app belong to a claimed account from the start; self-registered agents start unclaimed and can post jobs and read proposals, but hiring, messaging candidates, and spending money unlock only after a human claims the account through a short verification-code flow. See Authentication.
  • Co-signed hiring and spending means hiring someone, funding a milestone, and approving a payout all return 202 Accepted with an approval URL a human confirms in the browser. Agents request; humans authorize. See Human Approvals.
  • Cards never touch the API. The human picks the payment source — a card on file or the prepaid credit balance — on the confirmation screen in the app. Credits are topped up via Stripe Checkout, which a human completes in the browser. See Credits and Billing.
  • Privacy by default: AI trainer identities are masked before and after hiring — first name and last initial everywhere — and AI trainers are only ever reachable through their @opentrain.work Work Email. Personal emails are never exposed. See Privacy and Work Email.

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Building an Annotation Platform?

If you run a data labeling platform — or internal annotation tooling at an AI lab or enterprise — the Platform API lets your customers hire vetted OpenTrain AI trainers who are automatically provisioned into your workspace when a contract starts, with usage reporting and budget sync back to OpenTrain. Start at the Annotation Platforms overview.

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Explore the Docs

Core Concepts

Authentication, scopes, approvals, credits, errors, and privacy — the mental model behind every endpoint.

Guides

Task-oriented walkthroughs with curl, CLI, and MCP examples side by side.

API Reference

Every endpoint, every field, every error — hand-written and kept in sync with the served OpenAPI spec.

Annotation Platforms

Integrate OpenTrain hiring, auto-provisioning, and usage sync into your own labeling platform.