# Building Modular AI Agent Features with Pydantic AI Capabilities

> Source: <https://dev.to/joxiahdev/building-modular-ai-agent-features-with-pydantic-ai-capabilities-39d5>
> Published: 2026-06-14 08:30:00+00:00

If you're building AI Agents with Pydantic AI, understanding **Capabilities** is invaluable - it's the recommended way to add modular, reusable features to your agents.

This tutorial is part of my ongoing Pydantic AI series on [YouTube](https://youtube.com/@joxiahdev), where I build a full no-code AI agent platform from scratch.

A capability in Pydantic AI is a modular unit of behavior that can be passed to an AI agent.

A capability can give your agent:

Think of it as a plug-and-play feature module - build it once, attach it to any agent.

Capabilities are created using the `Capability`

or `AbstractCapability`

class:

``` python
from pydantic_ai.capabilities import AbstractCapability
```

Using `AbstractCapability`

gives you full control over instructions, tools, and behavior. It's Pydantic AI's recommended pattern if you're building a library or platform on top of the framework.

In this tutorial, I build a **Research Capability** powered by the Tavily Search API, and an **Email Capability** powered by Resend.

Here's the research capability:

``` python
from pydantic_ai import FunctionToolset
from pydantic_ai.capabilities import AbstractCapability
from dataclasses import dataclass
from pydantic_ai.common_tools.tavily import tavily_search_tool
from settings import settings

@dataclass
class ResearchCapability(AbstractCapability):
    def get_instructions(self):
        return "You can use the Tavily search tool for research"

    def get_toolset(self):
        toolset = FunctionToolset()
        toolset.add_tool(tavily_search_tool(api_key=settings.tavily_api_key))
        return toolset
```

That's it - `get_instructions()`

tells the agent what it can do, and `get_toolset()`

gives it the tools to do it. Attach this to any agent, and it instantly gains web research abilities.

I also built a **Company Knowledge Capability** that combines:

This lets an agent answer questions from your company's documents, website content, or any knowledge base with both vector search and relationship-aware graph queries.

This post covers the concept — the full video walks through building both capabilities live, plus the RAG/GraphRAG ingestion pipeline, observability with Logfire, and wiring it all into a working agent.

🎥 [Watch on YouTube →](https://youtu.be/ILHtYme4O60)

💻 [Full source code →](https://github.com/bjoxiah/pydantic-ai-series/tree/no-code-agent)

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