Adoption guides for host platforms. Each recipe gives concrete file paths, concrete config, and verification steps for wiring vis into a specific host. Unlike the conduct modules (which say what to do) and the engines (which say what to compute), recipes say where to put it — per-host specifics that would be wrong to bake into the portable layers.
| Recipe | What it covers |
|---|---|
| claude-code.md | Wiring conduct modules and hooks into Claude Code via CLAUDE.md and .claude/settings.json |
| openai-agents.md | Integrating the framework with the OpenAI Agents SDK: tool descriptors, handoffs, and guardrails |
| cursor.md | Dropping conduct modules into Cursor via .cursor/rules/ and .mdc frontmatter |
| langchain.md | Adopting the framework inside a LangChain agent pipeline: chain structure and callback hooks |
| pydantic-ai.md | Wiring conduct and engines into a Pydantic AI agent: validators, deps, and result types |
| baml.md | Using BAML for structured output extraction in agent pipelines: schema authoring and BamlError handling |
| system-prompt.md | Loading conduct modules via a system prompt when no framework-native integration is available |
| eval-harnesses.md | Connecting vis to an eval harness: failure-code tagging, axis aggregation, and regression gates |
- Match your host — find the recipe for the platform or SDK you're adopting into.
- Read the recipe end-to-end — each recipe is self-contained; it names the files to create, the config to set, and the check to run to confirm the integration is live.
- Follow cross-references — recipes link into
conduct/,engines/, andtaxonomy/for the rules and math that back the wiring. Read those docs for the why; the recipe gives the how.