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cendor-sdk for AI coding assistants

You told an assistant “build me a governed agent” and it reached for cendor-sdk. The SDK gets the same AI-assistant treatment as the libraries — the correct call-shapes are taught inline and on demand, so your assistant writes Agent, run, budget, and guard right the first time. This page is a short pointer; the canonical call-shape reference lives with the libraries (the SDK re-exports their objects, so the shapes are the same).

Four ways to make your assistant fluent

  • Type Teach ships inside the package. cendor-sdk (Python) and @cendor/sdk (TypeScript) carry an inline @example and a correct-shape signature on every public symbol — Agent, tool, run, budget, guard, Policy, AuditLog. Your editor’s language server (and any agent-mode assistant that reads diagnostics) is handed the right shape as you type, and the wrong shape is a compile error whose message states the right one. No setup — it’s in the install.
  • Rules files — paste a short cheatsheet into your repo so your assistant reads the correct shapes on every edit. The SDK row (Agent(name=…, model=…, guardrails=[…], max_usd=0.5); run(agent, "hi")) is already in every block. See Rules files.
  • MCP server (agent mode) — connect the read-only Cendor MCP server and your assistant can look up SDK pages live, e.g. get_page("sdk/agents") or get_page("sdk/governance"), plus the shared get_api / example call-shape tools. Remote mcp.cendor.ai or local npx @cendor/mcp / uvx cendor-mcp.
  • One command — npx @cendor/init / uvx cendor-init detects the SDK in your project and wires the rules files (and, with --mcp, the connect config); its doctor static-checks your wiring for CI. See init CLI & doctor.

The call-shape reference is shared

The SDK’s primitives are the library objects, re-exported — so the canonical trap table and CI-typechecked examples live on one page for both doors: For AI assistants. Don’t duplicate it here; point your assistant at that URL (or the full docs bundle, which includes every SDK page).

For the SDK-specific surfaces, the docs themselves are the reference: Agents & the loop, Governance, Guardrails, and the FAQ.

Honest limits

  • These aids teach call shapes, never performance numbers — every benchmark-backed claim lives in the libraries’ Benchmarks; acttrace (the SDK’s audit layer) produces evidence, not a compliance guarantee.
  • Type Teach and the rules files are only as current as your installed version / the day you pasted. For a live lookup use the MCP server (agent mode); if a shape disagrees with your editor’s hover, trust the editor — it’s reading the version you actually have.
  • Parity is documented, not version-coupled. Where the TypeScript SDK differs from Python (e.g. it ships OpenAI + Anthropic first-class), the Languages & parity matrix is the source of truth.