Pydantic AI Development Services

Ship LLM agents with typed, validated outputs — not parsed strings.

Vstorm builds production AI applications with Pydantic AI — the type-safe agent framework from the team behind the validation layer that powers OpenAI, Anthropic, LangChain, and LlamaIndex.

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Read Clutch reviews ★ ★ ★ ★ ★ 4.9 / 5
The Framework

Why production teams build on Pydantic AI

Pydantic AI is an agent framework for building production-grade LLM applications. It was built by the Pydantic team — the same team behind the validation library that powers the OpenAI SDK, Anthropic SDK, Google ADK, LangChain, and LlamaIndex. Where the Pydantic library handles data validation across general Python development, Pydantic AI brings that same discipline into the agent runtime: typed inputs, typed outputs, validated tool calls, and automatic retries when the model returns something malformed.

The goal is to make LLM calls behave like typed Python functions, catching failures during development rather than in production.

17%

Wrong field values inside structurally valid JSON — from the best of 21 models

The Structured Output Benchmark evaluated 21 frontier and open-weight models on extraction tasks. Schema compliance was near-perfect across the board, yet the best model's exact value accuracy reached only 83.0% on text and 67.2% on document images. Valid JSON with wrong data fails silently downstream — which is why validation at the boundary matters.

SOB: The Structured Output Benchmark (arXiv, 2026)

160

Lines for equivalent agents — vs 280 in LangGraph, 420 in CrewAI

Benchmarks comparing equivalent agent implementations show Pydantic AI requiring roughly 160 lines of code against 280 for LangGraph and 420 for CrewAI. Fewer lines mean fewer surfaces for bugs, shorter onboarding, and faster iteration.

Pydantic AI documentation

16,500+

GitHub stars and 443 contributors since stable v1.0

Pydantic AI hit stable v1.0 with an API stability commitment in September 2025. It is now at v1.94, with weekly releases, durable execution via Temporal and DBOS, native MCP interoperation, and structured observability via Logfire and OpenTelemetry.

pydantic/pydantic-ai on GitHub

Case Studies

Production agents with validated outputs

All case studies
STCC zero-hallucination clinical triage system

0 hallucinations across 329+ validation scenarios

“Beyond saving engineering teams hundreds of valuable hours each quarter, Synera aims for the easiest to use AI agent platform…”

2 hrs → 3 min

to generate a workflow

Andrew Sartorelli

Head of Product Management · Synera

Mixam multi-agent product advisor

95.4% success rate in workflow results

Read case study
Next step
Map your first Pydantic AI workflow

A 30-minute call with an engineer — one workflow, the data behind it, and a realistic path to production. No pitch deck.

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What we do

Five engagement phases — from feasibility to production

We support Pydantic AI projects at every stage. Engagements start where you are today.

Proof of Value

Validate feasibility of Pydantic AI agent patterns against your real LLM workloads.

  • Working agent prototype built for your target task
  • Schema enforcement testing on real and adversarial inputs
  • Latency and cost measurement against your throughput requirements
  • Feasibility report: validation gains, framework fit, ROI vs. current approach

Consultation

Assess where Pydantic AI fits in your broader Python and AI architecture.

  • Codebase audit: unvalidated outputs and LLM integration patterns surfaced across services
  • Framework selection: Pydantic AI vs. LangGraph, or custom orchestration — chosen on your real constraints
  • Validation strategy: where to enforce schemas, when to retry, when to fail loudly
  • Integration roadmap aligned with your FastAPI services, data pipelines, or existing agent infrastructure

Solution architecture

Translate requirements into a production-ready Pydantic AI blueprint.

  • Schema design: Pydantic models for every agent boundary, versioned and discriminated where needed
  • Agent and graph topology: single-agent, multi-agent, or Pydantic Graph — matched to task complexity
  • Reliability layer: retry policies, fallback models, durable execution, and human-approval gates
  • Complete technical specification with timelines, dependencies, and resourcing

Development and delivery

Build, integrate, and deploy a fully operational Pydantic AI solution in your environment.

  • Typed agents, validated tool calls, dependency injection, and streaming outputs built to spec
  • Connected to your FastAPI services, async workers, message queues, and existing data models
  • Logfire observability: real-time tracing, cost tracking, eval pipelines, and OTel export
  • Eval suites, adversarial testing, security review, and full technical documentation

Optimization and support

Maintain and improve performance as your models, schemas, and use cases evolve.

  • Continuous evals with alerts when validation rates or accuracy drift in production
  • Cost and latency tuning: model routing, prompt caching, structured-output settings
  • Versioned schema evolution and Pydantic AI upgrades managed without breaking downstream consumers
  • Dedicated support with quarterly performance reviews
Our process
From use case to production — the TriStorm methodology
We do not start by building. We start by finding the right thing to build. TriStorm is our three-phase framework for taking mid-market organizations from agentic AI assessment to deployed, observable production systems, without gaps between strategy and engineering.
Learn more about our process
Stage 1
Strategic alignment and planning
Before any architecture or code, we work with your operations and technical leadership to identify where Pydantic AI creates the highest operational leverage. Most teams have brittle string-parsing in places they have not yet quantified. We surface the failure rates, scope the validation gains, and produce a prioritized roadmap with an ROI model per use case.
Stage 2
Proof of Value
Before committing to a full build, we prove the approach on your real workloads. We stand up a working typed-agent prototype, test schema enforcement on real and adversarial inputs, and measure validation gains, latency, and framework fit — so the go/no-go decision rests on evidence, not a slide.
Stage 3
Process augmentation
With the approach proven, we architect and build the production system — schema layer, agent topology, reliability architecture, and Logfire observability wired from day one. We embed with your team and run structured knowledge transfer as we go, so production means a system your engineers own and can extend without us. No vendor lock-in.
Why Vstorm?

Four reasons mid-market and enterprise teams work with us

We have been contributing to Pydantic AI since beta. Our 25 AI specialists have delivered typed agent systems across healthcare, print-on-demand, professional services, and e-commerce.

01

Experience — 30+ production deployments on the Pydantic AI stack

Our 25 AI specialists have shipped typed agent systems across healthcare, print-on-demand, professional services, and e-commerce — each integrated with real operational infrastructure. We know which architecture decisions matter at design time, and which ones surface six months into production.

02

Stack — Specialized, production-ready tooling

We pair Pydantic AI with Logfire for observability, OpenTelemetry for tracing, Pydantic Evals for performance, and durable execution via Temporal and DBOS. It is not assembled per engagement — it is the same stack we have refined across 30+ deployments, with known failure modes and configuration patterns.

03

Support — End-to-end ownership

We carry full technical accountability from the first scoping call through deployment and monitoring. As Pydantic AI releases update, we handle versioning, breaking-change assessment, and migration — your team does not manage upgrades alone.

04

Partnership — Knowledge transfer built into delivery

We work embedded with your team. Architectural decisions are explained as they are made — not documented after the fact. The goal is capability transfer, not dependency.

Leaders, not just users

A Pydantic AI development partner that contributes to the framework

Vstorm is an official Pydantic implementation partner. We have contributed to Pydantic AI since beta — filing issues, shipping fixes, and working with the core team on the production edge cases client work surfaces. So you are not paying us to read the changelog: we already know what changed and why.

Need data-contract validation without the agent runtime? See our Pydantic development services.

443 +
Pydantic AI framework contributors
16,500 +
GitHub stars on Pydantic AI
30 +
Production deployments since 2017
25
AI specialists on the team
"Vstorm haven't just adopted Pydantic AI, they've helped shape it — contributing extensions, pushing on the rough edges, and showing us where real-world agent systems break. That kind of collaboration is gold for an open source project."
Samuel Colvin
Samuel Colvin, Founder & CEO, Pydantic
Common Questions

Frequently asked questions

What is Pydantic AI? +
Pydantic AI is a Python agent framework for building production-grade LLM applications. It was built by the Pydantic team — the same team behind the validation library that powers the OpenAI SDK, Anthropic SDK, Google ADK, LangChain, and LlamaIndex. The goal: bring the ergonomic, type-safe developer experience of FastAPI to AI agent development.
How is Pydantic AI different from the Pydantic library? +
They are distinct products. The Pydantic library is a data validation tool used across general Python development — FastAPI, ETL pipelines, configuration management, and more. Pydantic AI is a separate agent framework built on top of it. It adds typed agents, validated tool calls, dependency injection, automatic retries on malformed LLM outputs, Pydantic Graph for complex workflows, and native Logfire observability.
Is Pydantic AI production-ready? +
Yes. Pydantic AI hit a stable v1.0 in September 2025 with an API stability commitment. It is now at v1.94, with weekly releases and 16,500+ GitHub stars. It supports durable execution, human-approval gates on sensitive tool calls, MCP integration, and structured logging via Logfire and OpenTelemetry.
What does Pydantic AI cost? +
Pydantic AI is open-source and free. Logfire has a free tier and usage-based paid plans; many teams export OTel data to their existing observability stack instead. Your primary cost is LLM and embedding API usage. Retries on validation failure add a small token overhead — typically a few percent.
How does Pydantic AI compare to LangChain or LangGraph? +
All three help build agents. Pydantic AI prioritizes type safety and minimal dependencies — every input, output, and tool call is validated. LangChain ships a larger pre-built tool ecosystem but with looser typing. LangGraph focuses on explicit, graph-based state machines. Benchmarks show Pydantic AI implementations run roughly 160 lines versus 280 in LangGraph and 420 in CrewAI for equivalent tasks. Teams that want LLM calls to behave like typed Python functions tend to choose Pydantic AI.
How does Pydantic AI compare to CrewAI? +
CrewAI uses high-level abstractions for multi-agent crews, tasks, and collaboration. Pydantic AI is lower-level — you compose agents yourself, with full control over types, retries, tools, and boundaries. CrewAI optimizes for fast scaffolding. Pydantic AI optimizes for long-term maintainability and engineering rigor.
How does Pydantic AI handle invalid LLM outputs? +
When the model returns output that fails schema validation, Pydantic AI feeds the validation errors back to the model and retries automatically, up to a configurable limit. Tool calls with malformed arguments are handled the same way. If retries are exhausted, the failure surfaces as a structured exception your code can catch — a loud error, not silent bad data flowing downstream.
Does Pydantic AI support multi-agent workflows? +
Pydantic AI supports multi-agent workflows through agent delegation, programmatic hand-offs, and graph-based control flow with pydantic-graph for complex stateful workflows defined with Python type hints. Checkpointing and resumability are available when using Pydantic AI's durable-execution integrations, such as Temporal, DBOS, Prefect, or Restate.
Does Pydantic AI support streaming? +
Pydantic AI supports streaming text and structured outputs. For structured output, it can validate partial results as they arrive, so applications can render incremental, schema-aware updates rather than waiting for the full response. Final validation still matters, and early abort behavior depends on your application logic. It works naturally with async frameworks such as FastAPI for interactive streaming UIs.
Does Pydantic AI work with any LLM provider? +
Yes. Pydantic AI is model-agnostic and supports OpenAI, Anthropic, Google, Groq, Mistral, Cohere, xAI, Cerebras, Hugging Face, AWS Bedrock, OpenRouter, and any OpenAI-compatible API, including locally hosted models. You can swap providers without rewriting agent logic.
What is Pydantic Graph? +
Pydantic Graph is Pydantic AI's module for defining complex, stateful agent workflows as typed graphs using Python type hints. Graphs are checkpointable and resumable — making them suitable for long-running, asynchronous, and human-in-the-loop workflows.
Does Pydantic AI support durable execution? +
Yes. Pydantic AI agents can preserve progress across transient API failures and application restarts. It integrates natively with Temporal, DBOS, and Prefect for production-grade durable execution in long-running or human-in-the-loop workflows.
What is Pydantic Logfire? +
Logfire is the Pydantic team's OpenTelemetry-based observability platform for Python and AI workloads. It provides real-time LLM call traces, cost tracking, eval-based monitoring, and tight Pydantic AI integration. It exports OTel, so you can send the same data to Datadog, Grafana, or Honeycomb instead of running Logfire as a standalone backend.
What are the main use cases for Pydantic AI? +
Use Pydantic AI when you are building production agents, not just chat interfaces: agents that extract structured data from documents and emails, classify and route tickets or leads, moderate content, call internal tools, automate business workflows, run multi-step research and analysis, power validated RAG pipelines, and return typed outputs that downstream systems can trust.
Not sure where Pydantic AI fits in your stack? +
A 30-minute call is enough to scope your use case and recommend the right entry point. Book a call → vstorm.co/schedule-a-meeting
Get started

Ready to build your Pydantic AI solution?

Whether you are validating a use case, retrofitting validation onto an existing LLM pipeline, or replacing brittle string-parsing with typed agents — our team can take you from concept to production.

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