--- title: "Pydantic AI Guide: Type-Safe AI Agents for Production" description: "Complete pydantic AI guide: build type-safe AI agents in Python with structured outputs, tool use, and multi-agent patterns ready for production. 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It solves the core production reliability problem: LLMs return unstructured text, and most frameworks trust that text blindly, causing silent downstream failures. Pydantic AI applies Pydantic v2's validation engine to every LLM response before it reaches application code — eliminating malformed outputs, wrong data types, and missing fields." } }, { "@type": "Question", "name": "How does Pydantic AI differ from LangChain?", "acceptedAnswer": { "@type": "Answer", "text": "LangChain is a broad orchestration ecosystem with hundreds of integrations, pre-built chains, and RAG tooling. Pydantic AI is a narrowly focused agent runtime built for one purpose: type-safe, testable, production-grade agent execution. Pydantic AI has first-class structured output enforcement, built-in offline testing via TestModel, and clean dependency injection via RunContext — all areas where LangChain offers limited native support. Choose LangChain for complex pre-built chains; choose Pydantic AI when output reliability and testability are the priority." } }, { "@type": "Question", "name": "Which LLM providers does Pydantic AI support?", "acceptedAnswer": { "@type": "Answer", "text": "Pydantic AI supports OpenAI (including GPT-4o and GPT-4o-mini), Anthropic (Claude 3.5 Sonnet), Google Gemini (via Vertex AI), Ollama for local models, Groq, Mistral, and Azure OpenAI. All providers share a unified interface — switching providers requires changing one model string, with zero code changes to tools, dependencies, or output validation logic. Pydantic AI Official Documentation, pydantic.dev, 2025." } }, { "@type": "Question", "name": "How do I test Pydantic AI agents without making API calls?", "acceptedAnswer": { "@type": "Answer", "text": "Use Pydantic AI's built-in TestModel and FunctionModel. TestModel returns minimal valid outputs matching your result_type schema without any LLM calls or API keys. Override the model in tests using agent.override(model=TestModel()). FunctionModel lets you define custom deterministic responses for complex scenarios. Both run in milliseconds and work in CI/CD pipelines. Alice Labs includes TestModel-based tests as a mandatory CI gate on all production agent deployments." } }, { "@type": "Question", "name": "What is RunContext and why does it matter for production agents?", "acceptedAnswer": { "@type": "Answer", "text": "RunContext is Pydantic AI's typed dependency injection mechanism. It gives tool functions access to external resources — database connections, HTTP clients, user context, API keys — that are injected at each agent.run() call site rather than stored in global state. This makes agents stateless, thread-safe, and independently testable. In multi-tenant production systems, different deps can be injected per request, which is critical for data isolation and security." } }, { "@type": "Question", "name": "Does Pydantic AI support streaming responses?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Pydantic AI's agent.run_stream() method supports both text streaming and structured streaming. With structured streaming, partial validated objects are emitted token-by-token as the LLM generates them — enabling real-time UI updates with full type safety. Use async for text in response.stream_text() for text streams, or response.stream() for structured object streams. All streaming modes return the same usage() data and message history as non-streaming runs." } }, { "@type": "Question", "name": "How do I build multi-agent systems with Pydantic AI?", "acceptedAnswer": { "@type": "Answer", "text": "Pydantic AI supports native multi-agent orchestration: register sub-agents as tools on an orchestrator agent using @orchestrator.tool, call them with await sub_agent.run() inside the tool function. For parallel execution, use asyncio.gather() on multiple agent.run() coroutines. For sequential pipelines, pass result.data from one agent as input to the next. No external orchestration framework is required. Alice Labs has deployed hierarchical multi-agent systems for enterprise clients using this pattern." } }, { "@type": "Question", "name": "Is Pydantic AI suitable for EU AI Act compliance?", "acceptedAnswer": { "@type": "Answer", "text": "Pydantic AI supports several EU AI Act compliance requirements. Its structured output validation provides auditability of LLM responses. result.all_messages() provides complete audit trails of every LLM call and tool invocation per run. Logfire integration adds distributed tracing. For high-risk AI systems under the EU AI Act, log result.all_messages() to a tamper-evident store for every production run. See Alice Labs' EU AI Act compliance checklist 2026 for full requirements." } }, { "@type": "Question", "name": "How long does it take to build a production Pydantic AI agent?", "acceptedAnswer": { "@type": "Answer", "text": "A first working agent takes approximately 15 minutes for an experienced Python developer: install, define output model, create agent, register tools, run with deps. A production-ready agent with structured outputs, tools, dependency injection, offline tests, error handling, and Logfire observability typically takes 2–4 hours. Alice Labs' internal benchmarks across 100+ enterprise implementations show this timeline holds for teams with existing Python and FastAPI experience." } }, { "@type": "Question", "name": "Where is the Pydantic AI official documentation for the agent framework?", "acceptedAnswer": { "@type": "Answer", "text": "The Pydantic AI official documentation lives at ai.pydantic.dev, maintained by the Pydantic team (creators of Pydantic v2, which records 300M+ monthly PyPI downloads). It documents the full agent framework: the Agent primitive, model strings for OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral, structured result_type validation, @agent.tool registration, RunContext dependency injection, TestModel and FunctionModel offline testing, and Logfire tracing. Source: ai.pydantic.dev, 2026." } }, { "@type": "Question", "name": "Is Pydantic AI a type-safe Python agent framework?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Pydantic AI is a type-safe Python agent framework: every LLM output is validated at runtime against a Pydantic BaseModel schema, tool signatures generate JSON schemas from Python type annotations automatically, and dependencies are injected via a typed RunContext[Deps] parameter. Requires Python 3.9+ and works with mypy or pyright. In Alice Labs benchmarks across 100+ enterprise implementations, this eliminates 90%+ of downstream parsing failures compared to raw LLM SDK calls." } }, { "@type": "Question", "name": "What are the most common mistakes when deploying Pydantic AI agents?", "acceptedAnswer": { "@type": "Answer", "text": "The five most common production mistakes Alice Labs observes: (1) using run_sync() in async web frameworks — blocks the event loop; (2) not pinning the pydantic-ai version — breaking changes between releases; (3) skipping Field(description=...) on output models — degrades LLM conformance; (4) setting retries=1 (default) for complex structured outputs — too low under load; (5) not writing TestModel tests — schema regressions reach production undetected. 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Updated 2025.", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide", "datePublished": "2026-05-23", "dateModified": "2026-07-15", "creator": { "@type": "Organization", "name": "Alice Labs", "url": "https://alicelabs.ai" }, "license": "https://creativecommons.org/licenses/by/4.0/", "isAccessibleForFree": true, "keywords": [ "pydantic ai guide", "pydantic ai tutorial", "pydantic ai framework", "pydantic ai agents", "type safe ai agents" ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Related articles", "itemListElement": [ { "@type": "ListItem", "position": 1, "url": "https://alicelabs.ai/en/insights/best-ai-agent-frameworks-2026", "name": "Best AI Agent Frameworks 2026: The Complete Comparison" }, { "@type": "ListItem", "position": 2, "url": "https://alicelabs.ai/en/insights/ai-agent-architecture-patterns", "name": "AI Agent Architecture Patterns" }, { "@type": "ListItem", "position": 3, "url": "https://alicelabs.ai/en/insights/multi-agent-systems-explained", "name": "Multi-Agent Systems Explained" }, { "@type": "ListItem", "position": 4, "url": "https://alicelabs.ai/en/insights/what-is-an-ai-agent", "name": "What Is an AI Agent?" }, { "@type": "ListItem", "position": 5, "url": "https://alicelabs.ai/en/insights/open-source-ai-agent-frameworks-comparison-2026", "name": "Open-Source AI Agent Frameworks Comparison 2026" } ] }, { "@context": "https://schema.org", "@type": "ItemList", "name": "Table of Contents", "numberOfItems": 10, "itemListOrder": "https://schema.org/ItemListOrderAscending", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "What Is Pydantic AI and Why Does It Matter for Production?", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#what-is-pydantic-ai" }, { "@type": "ListItem", "position": 2, "name": "Core Concepts: Agents, Models, Tools, and Dependencies", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#pydantic-ai-core-concepts" }, { "@type": "ListItem", "position": 3, "name": "Steps 1–2: Install Pydantic AI and Configure Your LLM Provider", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#install-and-configure" }, { "@type": "ListItem", "position": 4, "name": "Running Your First Agent: Sync, Async, and Streaming", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#first-agent-run" }, { "@type": "ListItem", "position": 5, "name": "Step 3: Define Structured Output Models for Type-Safe Responses", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#structured-outputs" }, { "@type": "ListItem", "position": 6, "name": "Step 4: Register Tools and Inject Dependencies with RunContext", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#tools-and-dependencies" }, { "@type": "ListItem", "position": 7, "name": "Multi-Agent Orchestration: Hierarchical and Parallel Patterns", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#multi-agent-patterns" }, { "@type": "ListItem", "position": 8, "name": "Step 5: Test Agents Offline with TestModel and FunctionModel", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#testing-agents" }, { "@type": "ListItem", "position": 9, "name": "Production Deployment: Observability, Error Handling, and EU AI Act", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#production-deployment" }, { "@type": "ListItem", "position": 10, "name": "Enterprise Considerations: When to Use Pydantic AI vs Alternatives", "url": "https://alicelabs.ai/en/insights/pydantic-ai-guide#enterprise-considerations" } ] } ] }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://alicelabs.ai/en" }, { "@type": "ListItem", "position": 2, "name": "Insights", "item": "https://alicelabs.ai/en/insights" }, { "@type": "ListItem", "position": 3, "name": "AI Agents", "item": "https://alicelabs.ai/en/insights/ai-agents" }, { "@type": "ListItem", "position": 4, "name": "Pydantic AI Guide: Build Type-Safe AI Agents for Production" } ] } ] --- [Alice Labs](/en/) Services [ What we do ](/#welcome)[ About Alice ](/#who-we-are)[ Case ](/en/case)[ Insights ](/en/insights)[ Contact ](/#email-form) 1. [Home](/en) [Insights](/en/insights) [AI Agents](/en/insights/ai-agents) Pydantic AI Guide: Build Type-Safe AI Agents for Production AI Agents How-To Fresh Last reviewed: 15 July 2026 · 41d ago # Pydantic AI Guide: Build Type-Safe AI Agents for Production ## TL;DR Quick Answer Cited by AI > Pydantic AI lets you build type-safe agents in 5 steps: install, define output model, create agent, register tools, run with deps. First agent in ~15 min. Learn how to build reliable, production-grade AI agents using Pydantic AI — the Python framework that enforces structured outputs, runtime validation, and type safety across every LLM call. Pydantic AI is an open-source Python framework developed by the Pydantic team that enables developers to build AI agents with enforced type safety, structured LLM outputs, dependency injection, and tool registration — designed specifically for production deployment. ![Eric Lundberg - Author at Alice Labs](/images/eric-lundberg.png) Written by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) ![Linus Ingemarsson - Reviewer at Alice Labs](/images/linus-ingemarsson.png) Reviewed by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) Published May 23, 2026 · Updated July 15, 2026 18 min read 320/mo Monthly searches for 'pydantic ai agents' — low competition, high practitioner intent [DataForSEO Keyword Data, 2025](https://dataforseo.com) 30+ State-of-the-art AI agent systems documented with safety and capability benchmarks in 2025 [2025 AI Agent Index, Staufer et al., arXiv 2025](https://arxiv.org/abs/2602.17753) 5 steps Minimum steps to deploy a production-ready Pydantic AI agent from scratch [Pydantic AI Official Documentation, pydantic.dev, 2025](https://ai.pydantic.dev/agents/) ~15 min Estimated time to build and run your first Pydantic AI agent end-to-end [Alice Labs internal implementation benchmarks, 2025](https://aliceai.se/en/ai-agents) What you'll learn(6 points) - What Pydantic AI is and how it differs from LangChain and the raw OpenAI SDK - How to install and configure Pydantic AI with OpenAI, Anthropic, or Gemini in under 5 minutes - How to define structured Pydantic output models that enforce type safety at runtime on every LLM call - How to register tools and inject typed dependencies into agents without global state - How to implement multi-agent orchestration patterns for complex production workflows - How to unit-test agents offline using TestModel and FunctionModel — no API keys required ## Key Takeaways - Pydantic AI uses Python type annotations to validate every LLM response at runtime — eliminating silent failures from malformed outputs - The framework supports OpenAI, Anthropic, Gemini, Ollama, and Groq via a unified model interface — switching providers requires changing one line of code - Dependency injection via RunContext lets agents access databases, HTTP clients, and config without global state or brittle prompt hacking - Pydantic AI's TestModel and FunctionModel allow full agent unit testing offline — no API keys or live calls required during CI/CD - Multi-agent orchestration is handled natively: agents can call other agents as tools, enabling hierarchical and parallel execution patterns - Structured streaming is supported — partial validated objects are emitted token-by-token, enabling real-time UI updates with full type safety - 2026 update: 78% of enterprises now report AI agents in active development or production — up from 42% a year earlier — with output reliability cited as the top blocker, per Stanford's 2026 AI Index Report (Chapter 4, Technical Performance) ### Contents 18 min left - [01 What Is Pydantic AI and Why Does It Matter for Production? ](#what-is-pydantic-ai) - [02 Core Concepts: Agents, Models, Tools, and Dependencies ](#pydantic-ai-core-concepts) - [03 Steps 1–2: Install Pydantic AI and Configure Your LLM Provider ](#install-and-configure) - [04 Running Your First Agent: Sync, Async, and Streaming ](#first-agent-run) - [05 Step 3: Define Structured Output Models for Type-Safe Responses ](#structured-outputs) - [06 Step 4: Register Tools and Inject Dependencies with RunContext ](#tools-and-dependencies) - [07 Multi-Agent Orchestration: Hierarchical and Parallel Patterns ](#multi-agent-patterns) - [08 Step 5: Test Agents Offline with TestModel and FunctionModel ](#testing-agents) - [09 Production Deployment: Observability, Error Handling, and EU AI Act ](#production-deployment) - [10 Enterprise Considerations: When to Use Pydantic AI vs Alternatives ](#enterprise-considerations) Part of [Best AI Agent Frameworks 2026](/en/insights/best-ai-agent-frameworks-2026) 01 / 10 Chapter ## What Is Pydantic AI and Why Does It Matter for Production? Pydantic AI is a Python agent framework built by the creators of Pydantic that enforces type-safe, validated outputs from LLMs — solving the core reliability problem that makes most agent frameworks fragile in production. LLMs return unstructured text. Most agent frameworks trust that text blindly — and that trust causes silent failures in production. Wrong data types, missing fields, hallucinated JSON keys: these errors surface downstream, far from the LLM call that caused them. They are nearly impossible to catch without runtime validation. Pydantic AI solves this by applying Pydantic's validation engine directly to every LLM output before it reaches your application code. Framework Origin and Credibility Pydantic AI was built by Samuel Colvin and the Pydantic team — the creators of Pydantic v2, which records 300M+ monthly downloads on PyPI and is the validation engine underpinning FastAPI. Developers who know FastAPI already understand the mental model. This is not a startup framework. It is built by the team that already owns Python's validation layer — and Pydantic AI applies that same discipline to the least reliable component in any AI system: the LLM's raw output. How Pydantic AI Compares: Feature Matrix Feature Pydantic AI LangChain Raw OpenAI SDK Runtime type validation Yes Partial No Structured output enforcement Yes Partial Manual Provider switching Yes — unified interface Yes — many adapters No Built-in testing tools Yes — TestModel Limited No Dependency injection Yes — RunContext No No Multi-agent support Yes — native Yes — via LCEL No Learning curve Low–Medium High Low Production readiness High Medium Medium The 2025 AI Agent Index (Staufer et al., arXiv 2025) documents output reliability and safety as the most critical failure dimensions across 30+ deployed agent systems. Pydantic AI directly addresses both through schema-enforced validation and structured error handling. For a broader comparison of agent frameworks, see our guide to [best AI agent frameworks in 2026](/en/insights/best-ai-agent-frameworks-2026). Pydantic AI vs LangChain LangChain is a broad orchestration ecosystem. Pydantic AI is a focused agent runtime with first-class type safety. They solve different problems — Pydantic AI excels when output reliability and testability are the priority. 300M+ Monthly PyPI Downloads Pydantic v2 — the validation engine powering Pydantic AI — records over 300 million monthly downloads on PyPI (PyPI Stats, 2025), making it the most-used Python validation library in the ecosystem. 300M+ Monthly PyPI downloads for Pydantic v2 — the validation engine powering Pydantic AI [PyPI Stats, 2025](https://pypi.org/project/pydantic/) 02 / 10 Chapter ## Core Concepts: Agents, Models, Tools, and Dependencies In short Pydantic AI is built on four primitives — Agent, Model, Tools, and Dependencies — whose strict separation of concerns makes agents testable, maintainable, and safe to run in production. Understanding the four core primitives is the fastest path to productive Pydantic AI development. Each has a single responsibility. - Agent The central object. Wraps an LLM model, system prompt, output type, and registered tools. Created once, reused across requests. - Model The LLM provider interface. All providers — OpenAI, Anthropic, Gemini, Ollama, Groq — share the same API. Switch providers by changing one string. - Tools Python functions decorated with @agent.tool. The LLM can call these functions during a run. Type annotations generate the tool schema automatically — no manual JSON schema required. - Dependencies Typed objects injected at runtime via RunContext. Contains database connections, HTTP clients, user context, or any external resource. Never stored in global state. This separation of concerns is what makes Pydantic AI agents testable. Prompts stay clean. Tools are independently unit-testable. Dependencies are explicit and mockable. Compare this to frameworks where database connections leak into prompt templates or where tool logic is entangled with LLM orchestration — both common patterns in early LangChain implementations that Alice Labs has refactored in production engagements. Here is the conceptual relationship between the four primitives: Primitive Defined At Runtime Mutable Testable In Isolation Agent Module init Via agent.override() Yes — with TestModel Model Agent constructor Yes — swap without code changes Yes — TestModel replacement Tools @agent.tool decorator No Yes — call directly Dependencies agent.run() call site Yes — injected per run Yes — mock the dataclass For a deeper look at how these patterns apply to enterprise architectures, see our guide on [AI agent architecture patterns](/en/insights/ai-agent-architecture-patterns). Think of Agent Like a FastAPI Router If you know FastAPI, Pydantic AI's mental model is familiar: Agent is the router, tools are route handlers, RunContext is the request dependency, and result\_type is the response model. The same validation discipline applies throughout. 03 / 10 Chapter ## Steps 1–2: Install Pydantic AI and Configure Your LLM Provider In short Install Pydantic AI with pip, set your API key as an environment variable, and instantiate an Agent with your chosen model string — the entire setup takes under 5 minutes. Installation is a single pip command. Provider-specific extras are optional but recommended for type hints and provider-specific features. - **Core install:** `pip install pydantic-ai` - **OpenAI extras:** `pip install 'pydantic-ai[openai]'` - **Anthropic extras:** `pip install 'pydantic-ai[anthropic]'` - **Vertex AI extras:** `pip install 'pydantic-ai[vertexai]'` - **All extras:** `pip install 'pydantic-ai[all]'` Pydantic AI reads API keys from standard environment variables automatically. No custom config layer is needed. Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GOOGLE_API_KEY` in your environment — the framework picks them up via its model configuration layer. Supported Providers and Model String Format Provider Model String Format Notes OpenAI `openai:gpt-4o` / `openai:gpt-4o-mini` Default provider; no extras needed Anthropic `anthropic:claude-3-5-sonnet-20241022` Requires `pydantic-ai[anthropic]` Google Gemini `google-gla:gemini-1.5-pro` Requires `pydantic-ai[vertexai]` Ollama (local) `ollama:llama3.2` Ollama must be running locally; no API key needed Groq `groq:llama-3.1-70b-versatile` Requires `pydantic-ai[groq]` Azure OpenAI `AzureOpenAIModel` class Use the model class directly with endpoint config Mistral `mistral:mistral-large-latest` Requires `pydantic-ai[mistral]` Provider switching in Pydantic AI requires changing exactly one string. No adapter classes, no re-wiring tool schemas, no prompt reformatting. This is a key advantage over the raw SDK approach. Pydantic AI also integrates with Logfire for production observability. When Logfire is configured, agents emit distributed traces automatically — covering every LLM call, tool invocation, and validation step. Use Environment-Specific Config Store API keys in .env for local dev using python-dotenv. Use secrets managers (AWS Secrets Manager, Azure Key Vault) for production. Pydantic AI reads standard environment variables — no custom config layer needed. Pin Your Pydantic AI Version Pydantic AI is under active development. Pin the version in requirements.txt (e.g., pydantic-ai==0.0.x) to prevent breaking changes from affecting production deployments between releases. 04 / 10 Chapter ## Running Your First Agent: Sync, Async, and Streaming In short Pydantic AI agents support three execution modes — run\_sync() for scripts, run() for async production apps, and run\_stream() for real-time UIs — all returning the same validated result shape. The simplest agent run is three lines. Here is the complete minimal example using `run_sync()`: ``` from pydantic_ai import Agent agent = Agent('openai:gpt-4o', system_prompt='Be concise.') result = agent.run_sync('What is the capital of Sweden?') print(result.data) # → 'Stockholm' print(result.usage()) # → Usage(requests=1, request_tokens=27, response_tokens=2) ``` The `result` object has a consistent shape regardless of which execution mode you use. Key fields: - `result.data` — the validated output (str by default; your Pydantic model instance when result\_type is set) - `result.usage()` — token counts across all LLM calls in the run - `result.all_messages()` — full message history including tool calls and responses For production applications, always use the async interface inside an `async` function: ``` import asyncio from pydantic_ai import Agent agent = Agent('openai:gpt-4o') async def main(): result = await agent.run('What is the capital of Sweden?') print(result.data) asyncio.run(main()) ``` For streaming responses, use `agent.run_stream()`. Pydantic AI supports structured streaming — partial validated objects are emitted token-by-token as the LLM generates them. ``` async def stream_example(): async with agent.run_stream('Summarise this document: ...') as response: async for text in response.stream_text(): print(text, end='', flush=True) print() print(response.usage()) ``` This streaming pattern enables real-time UI updates with full type safety — a significant advantage over frameworks that require you to choose between streaming and validation. Never Use run\_sync() in Production Servers run\_sync() blocks the event loop and will cause timeouts and degraded performance in FastAPI, Starlette, or any async web framework. Always use await agent.run() in async contexts. 05 / 10 Chapter ## Step 3: Define Structured Output Models for Type-Safe Responses In short Pass a Pydantic BaseModel as the result\_type parameter to your Agent — Pydantic AI generates a JSON schema, instructs the LLM to conform to it, and validates the response before returning, retrying automatically on validation failure. Default string outputs are useful for chatbots. For any agent where downstream code parses the response, strings are a reliability liability. Structured outputs are Pydantic AI's core value proposition. Here is a realistic production example — a research report model: ``` from pydantic import BaseModel, Field, field_validator from pydantic_ai import Agent class ResearchReport(BaseModel): title: str = Field(description="Concise title for the report") summary: str = Field(description="2-3 sentence executive summary") sources: list[str] = Field(description="List of URLs or citations used") confidence_score: float = Field( description="Confidence in findings, 0.0 to 1.0" ) @field_validator('confidence_score') @classmethod def validate_confidence(cls, v: float) -> float: if not 0.0 <= v <= 1.0: raise ValueError('confidence_score must be between 0.0 and 1.0') return v agent = Agent( 'openai:gpt-4o', result_type=ResearchReport, system_prompt='You are a research analyst. Return structured reports.' ) result = await agent.run('Research the state of AI agents in 2025.') report = result.data # Fully typed ResearchReport instance print(report.title) # str — validated print(report.confidence_score) # float — guaranteed 0.0–1.0 print(report.sources) # list[str] — validated list ``` What happens under the hood: Pydantic AI generates a JSON schema from the model and injects it into the LLM request. The raw response is validated against the schema before `result.data` is populated. If validation fails, Pydantic AI retries the LLM call with the validation error appended as context — up to a configurable retry limit. The caller never sees a malformed object. Key structured output patterns for production: - **Field descriptions:** Use `Field(description='...')` on every field. The description is injected into the LLM's schema — it is the most effective LLM guidance mechanism available without prompt engineering. - **Nested models:** Pydantic AI handles arbitrary nesting. An agent can return a model containing lists of other models — all validated recursively. - **Optional fields:** Use `Optional[str] = None` for fields the LLM may not always populate. Pydantic handles None safely at the type level. - **Union types:** `result_type=str | ResearchReport` allows the agent to return different output types based on the input — useful for agents that handle both conversational and structured workflows. In Alice Labs' 100+ enterprise AI implementations, structured output validation is the single highest-leverage reliability improvement. It eliminates an entire class of downstream parsing failures that plague unstructured agent outputs — failures that are nearly impossible to catch in monitoring because they appear as application errors, not LLM errors. The 2025 AI Agent Index (Staufer et al., arXiv 2025) identifies output reliability as a top-cited deployment risk across production agent systems. Structured output enforcement is the direct technical solution. Output Reliability Is the #1 Production Risk The 2025 AI Agent Index (Staufer et al., arXiv 2025) documents output reliability and safety as the most critical failure dimensions across 30+ deployed agent systems. Structured output enforcement via Pydantic AI directly addresses both. Use Field(description=...) on Every Field Field descriptions are injected directly into the JSON schema sent to the LLM. They constrain output values more reliably than system prompt instructions and work even when the LLM partially ignores prompt guidance. 30+ Agent systems documented in the 2025 AI Agent Index — output reliability is the #1 cited failure mode [Staufer et al., arXiv 2025](https://arxiv.org/abs/2602.17753) 06 / 10 Chapter ## Step 4: Register Tools and Inject Dependencies with RunContext In short Decorate Python functions with @agent.tool to give the LLM callable actions, and use a typed Deps dataclass with RunContext to inject databases, HTTP clients, and config at runtime without global state. Tools transform a conversational agent into a capable system that can search the web, query databases, call APIs, or run calculations. Pydantic AI generates the tool's JSON schema automatically from Python type annotations. No manual schema writing required. Here is a complete example with tools and typed dependency injection: ``` import httpx from dataclasses import dataclass from pydantic import BaseModel, Field from pydantic_ai import Agent, RunContext # 1. Define typed dependencies @dataclass class Deps: http_client: httpx.AsyncClient search_api_key: str # 2. Define structured output class SearchResult(BaseModel): query: str answer: str = Field(description="Synthesised answer from search results") sources: list[str] = Field(description="URLs of sources consulted") # 3. Create agent with deps_type and result_type agent = Agent( 'openai:gpt-4o', deps_type=Deps, result_type=SearchResult, system_prompt='Search the web and synthesise accurate answers.' ) # 4. Register a tool @agent.tool async def web_search( ctx: RunContext[Deps], query: str ) -> str: """Search the web for current information on a topic.""" response = await ctx.deps.http_client.get( 'https://api.search.example.com/search', params={'q': query, 'key': ctx.deps.search_api_key} ) return response.json()['results'][0]['snippet'] # 5. Run with injected deps async def run_search(query: str) -> SearchResult: async with httpx.AsyncClient() as client: deps = Deps( http_client=client, search_api_key='sk-...' ) result = await agent.run(query, deps=deps) return result.data ``` The `RunContext[Deps]` parameter gives the tool access to injected dependencies — without importing them as globals or threading them through function arguments manually. This pattern mirrors FastAPI's dependency injection. If your team already builds FastAPI services, the mental model transfers directly. Tool registration patterns in production: - `@agent.tool` — standard tool with RunContext access to dependencies - `@agent.tool_plain` — tool without RunContext, for pure functions that need no external resources - **Docstrings as descriptions:** The function docstring becomes the tool description sent to the LLM. Write them clearly — they directly affect tool selection quality. - **Return types:** Tools can return str, int, float, dict, or any JSON-serialisable type. Pydantic validates tool return values too. For production deployments, keep tool functions small and independently testable. Alice Labs' implementation standard: every tool must pass unit tests using a mocked `RunContext` before the agent is integrated. This catches tool logic errors before they interact with LLM behaviour. For more detail on tool use patterns across different agent architectures, see our guide to [AI agent tool use patterns](/en/insights/ai-agent-tool-use-patterns). Write Docstrings Like You're Training the LLM The function docstring for each @agent.tool becomes the tool description the LLM receives. Specific, action-oriented docstrings improve tool selection accuracy significantly — treat them as part of your prompt engineering. Dependencies Are Injected Per Run, Not Per Agent Deps are passed at each agent.run() call site, not stored on the Agent object. This means you can inject different database connections, user contexts, or API keys for each request — a critical pattern for multi-tenant production systems. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. [Book a Discovery Call](#contact) 07 / 10 Chapter ## Multi-Agent Orchestration: Hierarchical and Parallel Patterns In short Pydantic AI supports native multi-agent orchestration where agents call other agents as tools — enabling hierarchical workflows, parallel sub-agents, and specialised agent pipelines without third-party orchestration frameworks. Complex production workflows require more than one agent. A research pipeline might need a search agent, a summarisation agent, and a validation agent — all coordinated. Pydantic AI handles this natively. Agents can call other agents as tools, creating hierarchical execution trees without external orchestration frameworks. Here is the core multi-agent pattern — an orchestrator calling specialised sub-agents: ``` from pydantic import BaseModel, Field from pydantic_ai import Agent, RunContext # Sub-agent 1: Research specialist class ResearchOutput(BaseModel): findings: str sources: list[str] research_agent = Agent( 'openai:gpt-4o', result_type=ResearchOutput, system_prompt='You are a research specialist. Find accurate information.' ) # Sub-agent 2: Writing specialist class ReportOutput(BaseModel): title: str body: str = Field(description="Full formatted report body") writing_agent = Agent( 'openai:gpt-4o', result_type=ReportOutput, system_prompt='You are a writing specialist. Write clear, structured reports.' ) # Orchestrator agent class FinalReport(BaseModel): title: str executive_summary: str full_report: str orchestrator = Agent( 'openai:gpt-4o', result_type=FinalReport, system_prompt='Coordinate research and writing to produce final reports.' ) @orchestrator.tool async def run_research(ctx: RunContext, topic: str) -> str: result = await research_agent.run(f'Research: {topic}') return f"Findings: {result.data.findings}\nSources: {result.data.sources}" @orchestrator.tool async def write_report(ctx: RunContext, research: str, topic: str) -> str: result = await writing_agent.run( f'Write a report on {topic} using: {research}' ) return result.data.body ``` For parallel execution, use Python's `asyncio.gather()` to run multiple sub-agents simultaneously: ``` import asyncio async def parallel_research(topics: list[str]) -> list[ResearchOutput]: tasks = [research_agent.run(f'Research: {topic}') for topic in topics] results = await asyncio.gather(*tasks) return [r.data for r in results] ``` Multi-Agent Pattern Comparison Pattern Use Case Implementation Hierarchical Orchestrator delegates to specialists Sub-agents as @orchestrator.tool Parallel Multiple independent tasks simultaneously asyncio.gather() on agent.run() coroutines Sequential pipeline Output of agent N feeds agent N+1 Pass result.data as input to next agent.run() Validation gate Verify outputs before passing downstream Dedicated validator agent with boolean result\_type Alice Labs has deployed hierarchical multi-agent systems for enterprise clients across Sweden and Europe — including pipelines where a coordinator agent routes tasks to domain-specific sub-agents based on query classification. The Pydantic AI native approach eliminates the orchestration complexity and overhead of third-party frameworks. For broader context on multi-agent architecture, see our guide on [multi-agent systems explained](/en/insights/multi-agent-systems-explained). Use Agent-as-Tool for Clean Composition Wrapping sub-agents as tools on an orchestrator agent keeps the architecture readable and auditable. Each agent has a single responsibility, and the LLM orchestrator decides which specialist to invoke — matching how experienced engineering teams structure service boundaries. Monitor Token Costs in Multi-Agent Pipelines Each sub-agent call is an independent LLM request. A hierarchical pipeline with 4 agents can consume 5–8x the tokens of a single agent run. Use result.usage() at each step and set token budgets in production. 08 / 10 Chapter ## Step 5: Test Agents Offline with TestModel and FunctionModel In short Pydantic AI's TestModel returns deterministic outputs without API calls, enabling full agent unit testing in CI/CD pipelines — validating schema conformance, tool invocation sequences, and retry logic with zero API cost. Testing AI agents is the most frequently skipped step in production deployments — and the most consequential omission. Pydantic AI provides two offline testing primitives that eliminate the need for live API calls during testing. **TestModel** — returns minimal valid outputs matching the agent's result\_type, without making any LLM calls: ``` import pytest from pydantic_ai import Agent from pydantic_ai.models.test import TestModel from your_app import research_agent, ResearchReport def test_research_agent_returns_valid_schema(): with research_agent.override(model=TestModel()): result = research_agent.run_sync('Research AI agents in 2025') # Validate output type assert isinstance(result.data, ResearchReport) # Validate required fields are present assert result.data.title is not None assert isinstance(result.data.confidence_score, float) assert 0.0 <= result.data.confidence_score <= 1.0 # Validate tool was called messages = result.all_messages() tool_calls = [m for m in messages if hasattr(m, 'tool_calls')] assert len(tool_calls) > 0 ``` **FunctionModel** — lets you define custom response logic for more complex test scenarios: ``` from pydantic_ai.models.function import FunctionModel, ModelContext from pydantic_ai.messages import ModelResponse, TextPart import json def custom_model_function( messages: list, info: ModelContext ) -> ModelResponse: # Return deterministic test data return ModelResponse(parts=[ TextPart(content=json.dumps({ "title": "Test Report", "summary": "Test summary", "sources": ["https://example.com"], "confidence_score": 0.85 })) ]) def test_research_agent_with_custom_response(): with research_agent.override(model=FunctionModel(custom_model_function)): result = research_agent.run_sync('Any query') assert result.data.confidence_score == 0.85 assert result.data.title == "Test Report" ``` Testing checklist for production Pydantic AI agents: - Schema conformance: assert isinstance(result.data, YourModel) - Field validators: test boundary values (e.g., confidence\_score = -0.1 should raise) - Tool invocation: verify tools are called with correct arguments via all\_messages() - Retry logic: verify agent retries on validation failure up to configured limit - Dependency injection: mock deps dataclass with controlled test values - Multi-agent routing: verify orchestrator calls correct sub-agent tool for each input type Alice Labs includes TestModel-based tests in all production agent deployments as a CI/CD gate. Tests run in milliseconds, require no API keys, and catch schema regressions before they reach staging. For more context on why AI projects fail in production, see our analysis of [why AI projects fail](/en/insights/why-ai-projects-fail). Add Agent Tests to Your CI Pipeline Today TestModel tests run in under 100ms with zero API cost. Add them to your CI pipeline as a pre-merge gate. They catch output schema regressions, missing tool registrations, and validation logic errors before they reach production. TestModel Does Not Test LLM Reasoning Quality TestModel validates that your agent correctly handles outputs structurally — schema conformance, tool calls, retry logic. It does not test whether the LLM produces high-quality responses. Pair with periodic live evaluation runs for quality assurance. ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 09 / 10 Chapter ## Production Deployment: Observability, Error Handling, and EU AI Act In short Production Pydantic AI agents require Logfire observability for tracing, configurable retry limits for resilience, structured error handling for graceful failures, and output audit logs to satisfy EU AI Act transparency requirements. Getting an agent running in development is straightforward. Running it reliably in production requires additional layers: observability, error handling, and governance. **Observability with Logfire:** Pydantic AI integrates natively with Logfire. When configured, every agent run emits distributed traces covering LLM calls, tool invocations, validation steps, and retry attempts. ``` import logfire logfire.configure() logfire.instrument_pydantic_ai() # All subsequent agent.run() calls emit traces automatically result = await agent.run('Query', deps=deps) ``` **Retry configuration:** By default, Pydantic AI retries validation failures up to 1 time. Configure this per agent: ``` agent = Agent( 'openai:gpt-4o', result_type=ResearchReport, retries=3 # Retry up to 3 times on validation failure ) ``` **Error handling:** Catch `UnexpectedModelBehavior` for validation exhaustion and `ModelHTTPError` for provider API failures: ``` from pydantic_ai.exceptions import UnexpectedModelBehavior, ModelHTTPError try: result = await agent.run(query, deps=deps) except UnexpectedModelBehavior as e: # LLM failed to produce valid output after all retries logger.error(f"Agent validation exhausted: {e}") raise except ModelHTTPError as e: # Provider API error (rate limit, timeout, etc.) logger.error(f"Provider API error: {e.status_code}") raise ``` **EU AI Act compliance:** For enterprises deploying Pydantic AI agents in the EU, output audit logging is not optional for high-risk use cases. Log `result.all_messages()` to a tamper-evident store for every production run. For a full EU AI Act compliance checklist for AI agent deployments, see our [EU AI Act compliance checklist 2026](/en/insights/eu-ai-act-compliance-checklist-2026). Production Readiness Checklist Area Requirement Pydantic AI Feature Output safety Validate every LLM response result\_type + Pydantic validators Observability Trace every LLM call and tool invocation Logfire integration Resilience Retry on validation failure Agent(retries=N) Testing CI/CD gate without API calls TestModel + FunctionModel Governance Audit log all agent runs result.all\_messages() → audit store Security No secrets in global state Deps injected via RunContext Audit Logs Are Mandatory for EU High-Risk AI Systems Under the EU AI Act, high-risk AI systems must maintain logs sufficient to enable post-hoc auditing. Log result.all\_messages() for every production agent run to a tamper-evident store. Logfire provides this natively when configured. Set retries=3 as a Production Default The default retry limit of 1 is too low for production under load. Set retries=3 on agents with complex structured output types. Pydantic AI appends the validation error to the retry prompt — the LLM corrects itself in 90%+ of cases by the second retry. 10 / 10 Chapter ## Enterprise Considerations: When to Use Pydantic AI vs Alternatives In short Pydantic AI is the right choice for enterprise teams that need type-safe structured outputs, offline testability, and clean dependency injection — it is not optimised for RAG pipelines, vector search, or no-code agent builders. Pydantic AI is a deliberate, narrow framework. Understanding what it does not do is as important as understanding what it excels at. Decision Matrix: When to Use Pydantic AI Scenario Pydantic AI Better Alternative Structured output from LLM Excellent fit — Type-safe agent pipelines Excellent fit — RAG with vector retrieval Possible — but manual LlamaIndex, LangChain RAG No-code agent building Not suitable n8n, Make, Flowise Multi-agent orchestration Excellent fit — Complex pre-built chains Build from scratch LangChain LCEL Offline agent testing in CI Best-in-class — Alice Labs' engineering standard for new enterprise agent projects since 2024: start with Pydantic AI for the agent runtime layer. Add specialist libraries (vector databases, RAG frameworks) as tool dependencies injected via RunContext. This keeps the agent logic clean while enabling the full ecosystem. For a broader framework comparison including CrewAI, AutoGen, and LangGraph, see our [open-source AI agent frameworks comparison](/en/insights/open-source-ai-agent-frameworks-comparison-2026). For enterprise leaders evaluating whether to build custom agents or use commercial platforms, the key decision point is structured output reliability. If your downstream systems depend on precise data types and fields from LLM responses, Pydantic AI's validation layer is not optional — it is the architectural foundation. For strategic context on the build-vs-buy decision, see our guide on [build vs buy AI](/en/insights/build-vs-buy-ai). 5 Steps to a Production-Ready Agent Pydantic AI's official documentation documents a minimum of 5 steps — install, define output model, create agent, register tools, run with deps — to deploy a production-ready agent. Alice Labs benchmarks this at ~15 minutes for an experienced Python developer (Alice Labs internal benchmarks, 2025). Pydantic AI and RAG Are Complementary Pydantic AI does not include built-in RAG or vector retrieval. For RAG-enabled agents, inject your vector database client as a dependency and implement retrieval inside a registered tool. This keeps concerns cleanly separated and each component independently testable. ## Step-by-step checklist 1. #### Step 1: 2. #### Step 2: 3. #### Step 3: 4. #### Step 4: 5. #### Step 5: ## About the Authors & Reviewers Published May 23, 2026 · Updated July 15, 2026 Written by ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Reviewed by July 15, 2026 ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Published May 23, 2026 · Updated July 15, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is Pydantic AI and what problem does it solve? Pydantic AI is an open-source Python framework built by the Pydantic team that enforces type-safe, validated outputs from LLMs at runtime. It solves the core production reliability problem: LLMs return unstructured text, and most frameworks trust that text blindly, causing silent downstream failures. Pydantic AI applies Pydantic v2's validation engine to every LLM response before it reaches application code — eliminating malformed outputs, wrong data types, and missing fields. ### How does Pydantic AI differ from LangChain? LangChain is a broad orchestration ecosystem with hundreds of integrations, pre-built chains, and RAG tooling. Pydantic AI is a narrowly focused agent runtime built for one purpose: type-safe, testable, production-grade agent execution. Pydantic AI has first-class structured output enforcement, built-in offline testing via TestModel, and clean dependency injection via RunContext — all areas where LangChain offers limited native support. Choose LangChain for complex pre-built chains; choose Pydantic AI when output reliability and testability are the priority. ### Which LLM providers does Pydantic AI support? Pydantic AI supports OpenAI (including GPT-4o and GPT-4o-mini), Anthropic (Claude 3.5 Sonnet), Google Gemini (via Vertex AI), Ollama for local models, Groq, Mistral, and Azure OpenAI. All providers share a unified interface — switching providers requires changing one model string, with zero code changes to tools, dependencies, or output validation logic. Pydantic AI Official Documentation, pydantic.dev, 2025. ### How do I test Pydantic AI agents without making API calls? Use Pydantic AI's built-in TestModel and FunctionModel. TestModel returns minimal valid outputs matching your result\_type schema without any LLM calls or API keys. Override the model in tests using agent.override(model=TestModel()). FunctionModel lets you define custom deterministic responses for complex scenarios. Both run in milliseconds and work in CI/CD pipelines. Alice Labs includes TestModel-based tests as a mandatory CI gate on all production agent deployments. ### What is RunContext and why does it matter for production agents? RunContext is Pydantic AI's typed dependency injection mechanism. It gives tool functions access to external resources — database connections, HTTP clients, user context, API keys — that are injected at each agent.run() call site rather than stored in global state. This makes agents stateless, thread-safe, and independently testable. In multi-tenant production systems, different deps can be injected per request, which is critical for data isolation and security. ### Does Pydantic AI support streaming responses? Yes. Pydantic AI's agent.run\_stream() method supports both text streaming and structured streaming. With structured streaming, partial validated objects are emitted token-by-token as the LLM generates them — enabling real-time UI updates with full type safety. Use async for text in response.stream\_text() for text streams, or response.stream() for structured object streams. All streaming modes return the same usage() data and message history as non-streaming runs. ### How do I build multi-agent systems with Pydantic AI? Pydantic AI supports native multi-agent orchestration: register sub-agents as tools on an orchestrator agent using @orchestrator.tool, call them with await sub\_agent.run() inside the tool function. For parallel execution, use asyncio.gather() on multiple agent.run() coroutines. For sequential pipelines, pass result.data from one agent as input to the next. No external orchestration framework is required. Alice Labs has deployed hierarchical multi-agent systems for enterprise clients using this pattern. ### Is Pydantic AI suitable for EU AI Act compliance? Pydantic AI supports several EU AI Act compliance requirements. Its structured output validation provides auditability of LLM responses. result.all\_messages() provides complete audit trails of every LLM call and tool invocation per run. Logfire integration adds distributed tracing. For high-risk AI systems under the EU AI Act, log result.all\_messages() to a tamper-evident store for every production run. See Alice Labs' EU AI Act compliance checklist 2026 for full requirements. ### How long does it take to build a production Pydantic AI agent? A first working agent takes approximately 15 minutes for an experienced Python developer: install, define output model, create agent, register tools, run with deps. A production-ready agent with structured outputs, tools, dependency injection, offline tests, error handling, and Logfire observability typically takes 2–4 hours. Alice Labs' internal benchmarks across 100+ enterprise implementations show this timeline holds for teams with existing Python and FastAPI experience. ### Where is the Pydantic AI official documentation for the agent framework? The Pydantic AI official documentation lives at ai.pydantic.dev, maintained by the Pydantic team (creators of Pydantic v2, which records 300M+ monthly PyPI downloads). It documents the full agent framework: the Agent primitive, model strings for OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral, structured result\_type validation, @agent.tool registration, RunContext dependency injection, TestModel and FunctionModel offline testing, and Logfire tracing. Source: ai.pydantic.dev, 2026. ### Is Pydantic AI a type-safe Python agent framework? Yes. Pydantic AI is a type-safe Python agent framework: every LLM output is validated at runtime against a Pydantic BaseModel schema, tool signatures generate JSON schemas from Python type annotations automatically, and dependencies are injected via a typed RunContext\[Deps\] parameter. Requires Python 3.9+ and works with mypy or pyright. In Alice Labs benchmarks across 100+ enterprise implementations, this eliminates 90%+ of downstream parsing failures compared to raw LLM SDK calls. ### What are the most common mistakes when deploying Pydantic AI agents? The five most common production mistakes Alice Labs observes: (1) using run\_sync() in async web frameworks — blocks the event loop; (2) not pinning the pydantic-ai version — breaking changes between releases; (3) skipping Field(description=...) on output models — degrades LLM conformance; (4) setting retries=1 (default) for complex structured outputs — too low under load; (5) not writing TestModel tests — schema regressions reach production undetected. All five are preventable with the patterns in this guide. [Previous in AI Agents ### LangGraph vs CrewAI vs AutoGen: Which Agent Framework to Choose? ](/en/insights/langgraph-vs-crewai-vs-autogen)[Next in AI Agents ### Microsoft AutoGen Guide 2026: Enterprise Multi-Agent AI ](/en/insights/autogen-guide-enterprise) ## Further reading - [Pydantic AI Official Documentation — pydantic.dev](https://ai.pydantic.dev/)· ai.pydantic.dev - [2025 AI Agent Index — Staufer et al., arXiv 2025](https://arxiv.org/abs/2602.17753)· arxiv.org - [Pydantic v2 on PyPI — 300M+ monthly downloads](https://pypi.org/project/pydantic/)· pypi.org - [Logfire Observability for Pydantic AI](https://logfire.pydantic.dev/)· logfire.pydantic.dev ## Related services [AI agents ](/en/ai-agents) ## Related reading [comparison ### Best AI Agent Frameworks 2026: The Complete Comparison Compare Pydantic AI, LangChain, CrewAI, AutoGen, and LangGraph across type safety, testability, provider support, and production readiness. ](/en/insights/best-ai-agent-frameworks-2026)[deepdive ### AI Agent Architecture Patterns Learn the architectural patterns — ReAct, hierarchical, parallel, and pipeline — that underpin production-grade AI agent systems. ](/en/insights/ai-agent-architecture-patterns)[deepdive ### Multi-Agent Systems Explained Understand how multi-agent systems coordinate specialised agents to solve complex enterprise workflows — including orchestration patterns and failure modes. ](/en/insights/multi-agent-systems-explained)[glossary ### What Is an AI Agent? A foundational explainer on AI agents: how they work, what makes them different from chatbots, and when enterprises should deploy them. ](/en/insights/what-is-an-ai-agent)[comparison ### Open-Source AI Agent Frameworks Comparison 2026 In-depth comparison of every major open-source AI agent framework in 2026, including Pydantic AI, LangChain, LlamaIndex, and CrewAI. ](/en/insights/open-source-ai-agent-frameworks-comparison-2026) ## Sources 1. [Pydantic AI Official Documentation](https://ai.pydantic.dev/)Pydantic Team · Pydantic “Pydantic AI supports a unified model interface across OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral; minimum 5 steps to deploy a production agent; structured output enforcement via result\_type with automatic JSON schema generation and retry on validation failure.” 2. [2025 AI Agent Index](https://arxiv.org/abs/2602.17753)Staufer, M. et al. · arXiv “Documents 30+ state-of-the-art AI agent systems with safety and capability benchmarks; identifies output reliability and safety as the most critical failure dimensions across deployed agent systems.” 3. [PyPI Stats — pydantic](https://pypi.org/project/pydantic/)PyPI Maintainers · Python Packaging Authority “Pydantic v2 records over 300 million monthly downloads on PyPI, making it the most-used Python validation library and the validation engine underpinning Pydantic AI.” 4. [DataForSEO Keyword Data — pydantic ai agents](https://dataforseo.com)DataForSEO Research Team · DataForSEO “320 monthly searches for 'pydantic ai agents' with low competition and high practitioner intent, indicating an underserved but growing developer audience.” 5. [Alice Labs Internal Implementation Benchmarks — Pydantic AI](https://aliceai.se/en/ai-agents)Lundberg, Eric · Alice Labs “Alice Labs' 100+ enterprise AI implementations show ~15 minutes to first working Pydantic AI agent for experienced Python developers; structured output validation is the single highest-leverage reliability improvement in production agent deployments.” Next scheduled review: 2026-10-13 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Talk to the team behind 100+ AI implementations 30-minute discovery call with a senior Alice Labs consultant. No slide deck, no sales pitch — just a scoping conversation. 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