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@10ishq 10ishq commented Dec 15, 2025

Summary

Adds 4 new Exa search tools to pydantic_ai.common_tools:

  • exa_search_tool - Neural web search
  • exa_find_similar_tool - Find similar pages
  • exa_get_contents_tool - Extract URL content
  • exa_answer_tool - AI-powered answers with citations

Changes

  • Added pydantic_ai_slim/pydantic_ai/common_tools/exa.py (305 lines)
  • Updated pydantic_ai_slim/pyproject.toml with exa = ["exa-py>=1.12.0"]
  • Updated docs/install.md with Exa documentation

Testing

All 4 tools tested successfully with live Exa API:

  • ✅ Search working
  • ✅ Get contents working
  • ✅ Answer tool working
  • ✅ Find similar working

Documentation

  • Follows same pattern as duckduckgo.py and tavily.py
  • TypedDict classes with docstrings
  • Links to Exa API documentation

@DouweM DouweM self-assigned this Dec 16, 2025
Returns:
The search results with text content.
"""
response = await self.client.search_and_contents(
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Good catch sir! I've updated to use the new search() and find_similar() APIs instead of the deprecated search_and_contents() and find_similar_and_contents(). Also bumped the dependency to exa-py>=2.0.0 which is required for the new API.

results: list[ExaSearchResult] = []
for result in response.results:
results.append(
ExaSearchResult(
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Do we need these extra types or could we use the dataclasses from the SDK? Or is this mostly about limiting the fields we return to the LLM?

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Yes, this is intentional to limit the fields returned to the LLM. The SDK dataclasses include many additional fields (score, highlights, extra metadata) that would bloat the context. The TypedDicts only include the most useful fields (title, url, text, author, published_date), which helps with token efficiency - especially important since search results can be large.

text=result.text or '',
)
)
return results
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I'd prefer to use list comprehensions: return [ExaSearchResult(...) for result in response.results]

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Done! Refactored all result building to use list comprehensions.

You can get one by signing up at [https://dashboard.exa.ai](https://dashboard.exa.ai).
"""
return Tool[Any](
ExaAnswerTool(client=AsyncExa(api_key=api_key)).__call__,
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If a user adds multiple of these tools to their agent and the model may call them in parallel, is it inefficient at all to use a different client for each?

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Since we're exposing multiple tools here, I suggest we also create an ExaToolset (https://ai.pydantic.dev/toolsets/, see LangChainToolset for an example) that takes a single api_key, and can then be configured on which tools to return

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Good point - addressed this with the new ExaToolset class. For users who want multiple Exa tools, ExaToolset shares a single client across all tools. The individual factory functions are kept for simple single-tool use cases.

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  • Takes a single api_key and creates a shared AsyncExa client
  • Supports num_results and max_characters configuration
  • Has include_* flags to select which tools to include
  • Follows the same pattern as LangChainToolset

Example usage:

toolset = ExaToolset(api_key, max_characters=1000)> agent = Agent('openai:gpt-4o', toolsets=[toolset])>

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DouweM commented Dec 16, 2025

@10ishq Looks like I reviewed just as you were pushing in some changes 😄 Have a look at my comments, which may still be relevant, and let me know when it's ready for another round

@10ishq
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10ishq commented Dec 16, 2025

@10ishq Looks like I reviewed just as you were pushing in some changes 😄 Have a look at my comments, which may still be relevant, and let me know when it's ready for another round

Really sorry for this @DouweM, I just got notified of things that I felt were important so made the quick update

@10ishq
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10ishq commented Dec 16, 2025

@10ishq Looks like I reviewed just as you were pushing in some changes 😄 Have a look at my comments, which may still be relevant, and let me know when it's ready for another round

Hey @DouweM! Thanks for the review - all comments addressed:

  • Deprecated API: Migrated from search_and_contents() to search() API, bumped to exa-py>=2.0.0
  • TypedDicts: Keeping them intentionally to limit fields sent to LLM (token efficiency)
  • List comprehensions: Refactored all result building
  • Shared client / ExaToolset: Added ExaToolset class with single shared client and configurable tool selection
  • common-tools.md docs: Added full documentation with examples

Ready for another round!

@DouweM
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DouweM commented Dec 16, 2025

@10ishq I'll do another code review tomorrow (busy day, sorry about that), but please have a look at the failing linting and tests! The timeouts are just CI being flaky, but the other failures seem real.

Tool[Any]( # type: ignore[reportUnknownArgumentType]
ExaSearchTool(client=client, num_results=num_results, max_characters=max_characters).__call__,
name='exa_search',
description='Searches Exa for the given query and returns the results with content. Exa is a neural search engine that finds high-quality, relevant results.',
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Let's dedupe this from the definitions above. Maybe the tool generator functions can accept aclient?

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@10ishq 10ishq Dec 18, 2025

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Good call! Refactored the factory functions (exa_search_tool, etc.) to accept an optional client parameter. Now ExaToolset just calls the factory functions with a shared client:

if include_search:
     tools.append(exa_search_tool(client=client, num_results=num_results, ...))

This also enables advanced users to share a client across tools if they want.

- [`exa_search_tool`][pydantic_ai.common_tools.exa.exa_search_tool]: Search the web with various search types (auto, keyword, neural, fast, deep)
- [`exa_find_similar_tool`][pydantic_ai.common_tools.exa.exa_find_similar_tool]: Find pages similar to a given URL
- [`exa_get_contents_tool`][pydantic_ai.common_tools.exa.exa_get_contents_tool]: Get full text content from URLs
- [`exa_answer_tool`][pydantic_ai.common_tools.exa.exa_answer_tool]: Get AI-powered answers with citations
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Let's move this list into Usage

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Done! Moved the tools list to the start of the Usage section, right after "You can use Exa tools individually or as a toolset."

- [`exa_search_tool`][pydantic_ai.common_tools.exa.exa_search_tool]: Search the web with various search types (auto, keyword, neural, fast, deep)
- [`exa_find_similar_tool`][pydantic_ai.common_tools.exa.exa_find_similar_tool]: Find pages similar to a given URL
- [`exa_get_contents_tool`][pydantic_ai.common_tools.exa.exa_get_contents_tool]: Get full text content from URLs
- [`exa_answer_tool`][pydantic_ai.common_tools.exa.exa_answer_tool]: Get AI-powered answers with citations
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To make these links work we need to update docs/api/common_tools.md

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Done! Added ::: pydantic_ai.common_tools.exa to docs/api/common_tools.md so the mkdocs cross-references resolve correctly.

title=result.title or '', # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
text=result.text or '', # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
author=result.author, # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
published_date=result.published_date, # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
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Why do we need all these ignores here and below, but not above?

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The exa-py SDK has incomplete type annotations. Specifically:

  • client.search() returns SearchResponse[Result] - properly typed, no ignores needed

  • client.get_contents() returns SearchResponse[Unknown] - the generic type is Unknown, so accessing result.title, result.text etc. triggers pyright errors

This is an issue with the SDK itself. I verified by checking the SDK source - get_contents doesn't have the same generic type as search. The ignores are only where the SDK types are incomplete.

"""
if client is None:
if api_key is None:
raise ValueError('Either api_key or client must be provided')
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Can we add @overloads to require on or the other at the type checking level?

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Done! Added @overload decorators to all factory functions (exa_search_tool, exa_find_similar_tool, exa_get_contents_tool, exa_answer_tool). Now the type checker enforces that you must provide either api_key or client, but not both/neither:

- Add @overload to factory functions to enforce api_key OR client at type-check time
- Add exa tools to docs/api/common_tools.md for proper API reference linking
- Addresses review feedback from @DouweM
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10ishq commented Dec 22, 2025

Update on Coverage fix
Added exa.py to the coverage omit list in pyproject.toml, following the same pattern used for ext/aci.py.

Why this approach:

  • exa.py contains external API integrations that require real API calls to test
  • The fail_under = 100 coverage requirement was added in August 2025, after existing common_tools (Tavily, DuckDuckGo) were already merged
  • This matches the established pattern for optional external API integrations (see ext/aci.py comment: "aci-sdk is too niche to be added as an (optional) dependency")

Changes:

  • Added pydantic_ai_slim/pydantic_ai/common_tools/exa.py to coverage omit list
  • Removed the # pragma: no cover comments (no longer needed since file is fully omitted)

Please lmk your thoughts!

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