feat: update Agent tutorial to use PipelineTool instead of ComponentTool + SuperComponent#449
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Pull request overview
- Purpose: Update the agent tutorials to expose pipelines as LLM-callable tools via
PipelineTool, replacing the olderComponentTool+SuperComponentpattern for pipeline-based tools. - Changes:
- Swap the pipeline-tooling example in
43_Building_a_Tool_Calling_Agent.ipynbto usePipelineTooland update the surrounding explanation. - Swap the document-store writer tool in
45_Creating_a_Multi_Agent_System.ipynbto usePipelineTooland update the tutorial guidance accordingly. - Minor text/serialization tweaks in notebook cells and captured outputs.
- Swap the pipeline-tooling example in
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
tutorials/43_Building_a_Tool_Calling_Agent.ipynb |
Replaces pipeline tool creation with PipelineTool; updates narrative around pipeline-as-tool usage. |
tutorials/45_Creating_a_Multi_Agent_System.ipynb |
Updates the “Document Store Writer Tool” section to wrap the writer pipeline as a PipelineTool. |
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| @@ -320,7 +266,6 @@ | |||
| "query = \"What are the latest updates on the Artemis moon mission?\"\n", | |||
| "messages = [ChatMessage.from_user(query)]\n", | |||
| "\n", | |||
| "\n", | ||
| "> 💡 Learn alternative ways of creating tools in [`Tool`](https://docs.haystack.deepset.ai/docs/tool) and [`MCPTool`](https://docs.haystack.deepset.ai/docs/mcptool) documentation pages." | ||
| ] | ||
| "source": "### Creating a Tool from a Pipeline\n\nNext, wrap the `search_pipeline` in a [`PipelineTool`](https://docs.haystack.deepset.ai/docs/pipelinetool). `PipelineTool` directly exposes a pipeline as an LLM-callable tool, replacing the older pattern of wrapping a pipeline in a `SuperComponent` and then passing it to `ComponentTool`.\n\nUse `input_mapping` and `output_mapping` to control which pipeline inputs and outputs are exposed. Here, `input_mapping` ensures only `\"query\"` is surfaced in the tool schema, and `output_mapping` extracts the formatted string produced by `output_adapter`.\n\nFinally, you can initialize the Agent with the resulting `search_tool`.\n\n> 💡 Learn alternative ways of creating tools in [`Tool`](https://docs.haystack.deepset.ai/docs/tool) and [`MCPTool`](https://docs.haystack.deepset.ai/docs/mcptool) documentation pages." |
| ")\n", | ||
| "doc_store_writer.parameters" | ||
| ] | ||
| "source": "from haystack import Pipeline, component, Document\nfrom haystack.components.writers import DocumentWriter\nfrom haystack.document_stores.in_memory import InMemoryDocumentStore\nfrom haystack.tools import PipelineTool\nfrom typing import List\n\n\n@component\nclass DocumentAdapter:\n @component.output_types(documents=List[Document])\n def run(self, content: str, title: str):\n return {\"documents\": [Document(content=content, meta={\"title\": title})]}\n\n\ndocument_store = InMemoryDocumentStore()\n\ndoc_store_writer_pipeline = Pipeline()\ndoc_store_writer_pipeline.add_component(\"adapter\", DocumentAdapter())\ndoc_store_writer_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\ndoc_store_writer_pipeline.connect(\"adapter\", \"writer\")\n\ndoc_store_writer = PipelineTool(\n pipeline=doc_store_writer_pipeline,\n name=\"doc_store_writer\",\n description=\"Use this tool to write/save content to document store\",\n parameters={\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\"type\": \"string\", \"description\": \"The title of the Document\"},\n \"content\": {\"type\": \"string\", \"description\": \"The content of the Document\"},\n },\n \"required\": [\"title\", \"content\"],\n },\n)\ndoc_store_writer.parameters" |
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kacperlukawski
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May 12, 2026
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I applied one change, as suggested by Copilot. Other than that, it's looking good!
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update two Agent tutorials to use PipelineTool instead of ComponentTool + SuperComponent