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The `agent:` resource calls a sibling agent's entire workflow within the same [agency](https://kdeps.com/agencies/) and returns that agent's `apiResponse` output. It is how one agent delegates a subtask to another specialized agent.

`apiResponse:` builds and returns the HTTP response sent back to the caller. It is always the last resource in the dependency chain - the resource pointed to by [`targetActionId`](https://kdeps.com/reference/glossary#targetactionid) in `workflow.yaml`.

The `chat:` resource sends a prompt to a language model and stores the response as the resource's output. The output is the raw response object - the reply text is at `get('id').message.content`. With `jsonResponse: true` the output is the parsed JSON object instead.

The `component:` resource calls a reusable resource bundle - a registry component installed with `kdeps install`, or a custom component in your project's `components/` directory. Think of it like calling a function: you pass typed inputs via `with:`, the component's own resources run, and you get its result back.

The `embedding` component runs one embedding operation and returns a structured result: a SQLite keyword store for `index`, `search`, `upsert`, and `delete`, an embedding backend for `vectorize` and `embed_query`, and Cohere for `rerank`. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `embedding` tool, which runs the native embedding executor. A workflow that uses `embedding:` directly - primary or in a `before:`/`after:` step - is rejected.

The `exec` component runs a shell command and returns a structured result: `{success, exitCode, stdout, stderr, command, result, timedOut}`. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `exec` tool, which runs the native exec executor. A workflow that uses `exec:` directly - primary or in a `before:`/`after:` step - is rejected.

The `http_request` component makes one outbound HTTP request and returns the response: status code, status line, headers, raw body, and the parsed JSON body under `data` when the response is JSON. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `http_request` tool, which runs the native HTTP executor. A workflow that uses `httpClient:` directly - primary or in a `before:`/`after:` step - is rejected.

The `load_document` component reads a file, URL, or directory into structured `Document` objects - plain text plus metadata - and optionally splits them into chunks. It is the ingestion step of a RAG pipeline: load, then feed the output into the `vector_store` or `embedding` component for indexing. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `load_document` tool, which runs the native loader executor. A workflow that uses `loader:` directly - primary or in a `before:`/`after:` step - is rejected.

The `python` component runs a Python script in a managed virtualenv and returns its stdout, parsed as JSON when possible and as raw text otherwise. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `python` tool, which runs the native Python executor. A workflow that uses `python:` directly - primary or in a `before:`/`after:` step - is rejected.

The `search_local` component searches a local directory by content keyword and/or filename glob and returns `{results, count}`. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `search_local` tool, which runs the native searchLocal executor. A workflow that uses `searchLocal:` directly - primary or in a `before:`/`after:` step - is rejected. See [Search resources](https://kdeps.com/workflow/resources/search) for how it relates to `web_search`.

The `sql` component runs SQL against a named connection - PostgreSQL, MySQL/MariaDB, SQLite, SQL Server or Oracle - and returns the rows or a change summary. One statement, or several in a transaction. It is a kdeps component (YAML, no Go code) from the `components` asset set: call it with a `component:` resource, no install step.

The `vector_store` component adds documents to, and runs similarity search against, an external vector database - Qdrant, Chroma, Pinecone, pgvector, and more - for production-scale RAG. The kdeps API tool does the embedding and store work; the component only passes the inputs through.