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Pydantic AI v2.0.0 went stable on June 23, 2026, fourteen days ago. If you’re building Python AI agents and still on V1, this is the upgrade you’ve been waiting — and probably delaying — to evaluate. Here’s what actually changed.

What Is Pydantic AI

Pydantic AI is the AI agent framework from the Pydantic team — the same people who built the type-validation library that underpins most of the Python data ecosystem. The framework wraps model provider APIs into a type-safe, structured-output-first agent loop, with native support for Anthropic, OpenAI, and Google models. If you already use Pydantic for data validation, the programming model will feel familiar: declare your expectations explicitly, get errors early, avoid surprises at runtime.

V1 has been in production use since 2025. V2 is not a rewrite — it’s a structural upgrade built on lessons from V1 adoption.

The Core Change: The Capability Primitive

V2’s central idea is the Capability — a composable unit that bundles an agent’s instructions, tools, lifecycle hooks, and model settings into a single object.

In V1, wiring up a memory system, a guardrail, or a code execution environment meant touching multiple disconnected parts of the agent: adding tools in one place, instructions in another, hooks in another. You had to understand the full internals to add a meaningful extension without breaking something else.

In V2, all of that is expressed as one Capability:

from pydantic_ai import Agent
from pydantic_ai.capabilities import Capability, Thinking, WebSearch, ToolSearch
from pydantic_ai.mcp import MCPToolset
from pydantic_ai_harness import CodeMode

agent = Agent(
    'anthropic:claude-sonnet-5',
    instructions='Research thoroughly and cite your sources.',
    capabilities=[
        Thinking(effort='high'),      # extended thinking (model settings)
        CodeMode(),                    # sandboxed code execution (from Harness)
        WebSearch(),                   # native or local fallback
        ToolSearch(),                  # on-demand tool discovery
        Capability(
            id='github',
            description='Look up GitHub issues, PRs, and code.',
            instructions='Use GitHub tools for repository questions.',
            toolset=MCPToolset('https://mcp.example.com/github'),
            defer_loading=True,        # loads only when the model requests it
        ),
    ],
)

Capabilities are:

  • Composable — stack them in a list, each one only affects what it manages
  • Serializable — agents can be defined in spec files and loaded dynamically
  • Lazy — defer_loading=True avoids bloating the prompt with tools the model rarely needs
  • Layered — some capabilities manage only model settings (like Thinking), others wrap native tools with fallback implementations (like WebSearch), others use hooks to modify context mid-execution

The Harness

Pydantic AI V2 splits itself into two layers:

Core stays small and stable. It ships the agent loop, provider adapters, the Capability and hooks API, and only capabilities that need deep provider support or are fundamental to every agent.

The Harness (pydantic_ai_harness) is the fast-moving batteries layer: memory systems, guardrails, context management, file system access, CodeMode, and a growing catalog from third-party contributors. It installs separately from core, so you only pull in what you use.

uv add pydantic-ai             # core only
uv add pydantic_ai_harness     # capabilities batteries

The split matters for stability. Core commits to zero breaking changes within a major version and a three-month breaking-changes window between major versions. The Harness moves faster — new capabilities ship there first and graduate to core only if they prove broadly essential.

Four Breaking Changes from V1

Before upgrading, there are four behavior changes to know — these go beyond what the V1 deprecation warnings catch:

1. OpenAI now uses the Responses API by default

openai: model names now route to the Responses API. If you need Chat Completions (for older integration compatibility, vendor routing, or structured output formats that differ), use openai-chat: as the provider prefix.

# V1 and V2 (Chat Completions)
agent = Agent('openai-chat:gpt-4.1')

# V2 only — Responses API
agent = Agent('openai:gpt-4.1')

2. WebSearch and WebFetch are native by default

These capabilities now use native provider implementations (when available) instead of MCP tools. MCP(url=...) runs locally by default rather than connecting to a remote server. If your V1 setup relied on specific MCP server endpoints for web access, check your URL routing before upgrading.

3. Instrumentation defaults to version 5

OpenTelemetry instrumentation now defaults to version 5 with aggregated token-usage attributes. If you’re running dashboards or alerting on the prior token-usage attribute format, your metrics pipeline needs updating before you upgrade.

4. Function tools now run alongside successful output tools

Previously, with end_strategy='graceful', function tools requested alongside a successful output tool were skipped. In V2 they execute. If your agent logic depended on them being skipped in that scenario — for cost control, side-effect prevention, or flow control — you need an explicit check before upgrading.

Migration Path

The Pydantic team’s recommended sequence:

Step 1 — Clear V1 deprecations first. Update to the latest V1 release and eliminate every deprecation warning. The automated tooling in V1’s final version catches the majority of what needs to change. Do not skip this — it covers most of the migration.

Step 2 — Review the four breaking changes. The deprecation warnings cannot catch the behavior changes listed above. Read each one and assess whether it applies to your codebase before upgrading.

Step 3 — Upgrade.

uv add pydantic-ai

Step 4 — Migrate to Capabilities. The V1 tools=[], instructions=, and hook registration APIs still work in V2. You don’t have to refactor everything to Capabilities immediately. Refactor incrementally when it makes your code clearer, not as a prerequisite to running V2.

Provider Changes

Default providers (included in the base install): Anthropic, OpenAI, Google.

Optional providers (install separately): AWS Bedrock, Groq, Mistral, others. Long-tail providers were moved out of the base install to reduce dependency weight for projects that don’t use them.

uv add pydantic-ai[bedrock]    # adds Bedrock support
uv add pydantic-ai[groq]       # adds Groq support
uv add pydantic-ai[mistral]    # adds Mistral support

Who Should Upgrade Now

Upgrade now if you’re building new agents from scratch, or if you’re maintaining V1 agents that have already been cleaned up to pass all deprecation warnings. V2 is stable, the migration path is well-defined, and the Capabilities model will make extensions easier to reason about as your agents grow.

Wait a sprint if you have V1 agents in production with any of the four breaking-change scenarios above. Test in a staging environment first — especially the OpenAI Responses API switch and the function tool execution change.

Ignore V2 for now if you’re on a vendor platform that pins the Pydantic AI version for you, or if your agents are simple enough that the Capabilities model adds complexity rather than clarity. V1 remains supported through the three-month overlap window ending around September 2026.

This article is based on Pydantic AI’s official v2 announcement, the upgrade guide at pydantic.dev, and the v2.0.0 changelog. ChatForest did not test this framework hands-on.