Pydantic AI
Open-source Python agent framework with strong tooling, model flexibility, and observability via Logfire.
Pydantic AI is an open-source Python agent framework that fits the framework tier alongside LangChain and CrewAI, with strong tooling, model flexibility, and observability via Logfire. It's free and open source.
Vendor details
Canonical URL
https://ai.pydantic.dev
Category
Agent infrastructure
Subcategory
Agent framework (OSS)
Use cases & customers
Primary use cases
Target customers
Deployment options
In practice
You're building an agent in Python and want a framework that feels native to the language. Pydantic AI is an open-source Python agent framework with strong tooling and model flexibility.
You don't want to be locked to one model provider in your agent stack. Pydantic AI is model-flexible, so you can switch the underlying model as needs change.
An agent in production is a black box until something breaks. Pydantic AI offers observability through Logfire, so you can see what your agent is actually doing.
Agentic Index coverage score
12.5 / 14 capabilities · 89%
| Integrations & Tool CallingFirst class Model Context Protocol support plus Agent2Agent and UI event stream standards, with built in capabilities for web search, web fetch, image generation, tool search, code execution and file access, and a composable capability system bundling tools, hooks, instructions and model settings into reusable units that can be built in house or installed as third party capability packages, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
|---|---|
| Workflow OrchestrationAgent graphs are defined through Python type hints rather than config layers, supporting multi agent systems and sub agent orchestration from the Harness library, with DURABLE EXECUTION so long running asynchronous workflows preserve state and resume after crashes, process restarts or transient API failures; official integrations with Temporal, Restate and other durable systems use only the public interface, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
| Knowledge Grounding & RAGBuilt in web search and web fetch capabilities let agents ground on retrieved material, and structured output validation constrains what the model may return, but no knowledge base ingestion, vector store abstraction or retrieval architecture ships as a first class primitive; grounding beyond web retrieval is left to the developer, Pydantic AI documentation 2026-07-30 | Partial |
| Human Oversight & GuardrailsTHE MOST GRANULAR HUMAN OVERSIGHT PRIMITIVE FOUND IN THIS PASS: Human in the Loop Tool Approval lets a developer flag that specific tool calls require approval before they proceed, and the condition can depend on THE TOOL CALL ARGUMENTS THEMSELVES, on conversation history, or on user preferences, so approval is conditional on context rather than a blanket setting. The official Harness capability library additionally ships guardrails as a reusable capability, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
| Security, Identity & GovernanceSCOPING MATTERS HERE AND CERTIFICATION DOES NOT TRANSFER. The framework itself is an MIT licensed Python library with no hosted surface, so it carries no attestation; what it does provide is runtime type validation of all structured inputs and outputs, a guardrails capability in the Harness library, conditional tool call approval, and 100% test coverage. The SOC 2 Type II with no exceptions, HIPAA compliance with BAAs, GDPR with an EU Data Region, and SSO through Dex supporting Okta, Azure AD, Auth0, Google Workspace, LDAP/AD and generic OIDC or SAML all belong to PYDANTIC LOGFIRE, the separate commercial observability product, and cover Logfire rather than agents a customer builds on the framework, Pydantic AI documentation and Logfire compliance documentation 2026-07-30 | Partial |
| Observability & AuditabilityAmong the strongest observability positions in the index and notable for being VENDOR NEUTRAL: instrumentation is built in, OpenTelemetry native and semantic convention compliant, so traces flow into any existing OTel stack rather than only the vendor's own. Paired with Pydantic Logfire it traces every LLM call and tool invocation with token usage and cost, offers conversation panels and tool call inspection, spans the whole application stack rather than only the LLM layer, and exposes all observability data through SQL with PostgreSQL compatible syntax, Pydantic AI and Logfire documentation 2026-07-30 | Full |
| Memory & State PersistenceDurable execution persists agent state across process restarts and API failures so long running workflows resume where they stopped, and conversation history is a first class object the framework carries and exposes, to the point that human approval rules can be conditioned on it, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
| Deployment & Data ResidencyAs an MIT licensed library the agent runtime runs wherever the customer runs Python, with no vendor infrastructure in the path and therefore no residency question at the agent layer. The companion observability platform offers Enterprise Cloud, Enterprise Dedicated and Enterprise Self Hosted, the last delivered as an official Helm chart running the same product in the customer's own Kubernetes cluster against their own Postgres, object storage and identity provider, plus an EU Data Region on the hosted tier, Pydantic AI licensing and Logfire self hosting documentation 2026-07-30 | Full |
| Prebuilt Agents, Templates & PacksPydantic AI Harness is an official library of ready made capabilities including code execution, file access, guardrails and sub agent orchestration that developers pick and compose to assemble coding agents, research assistants and similar, with third party capability packages installable alongside, and agents can be defined ENTIRELY IN YAML OR JSON with no code, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
| Triggers & Channel CoverageEvent driven workflows and UI event stream standards are supported with token by token streaming for interactive applications, but as a library the triggering surface and channel coverage are the host application's responsibility; no scheduler, webhook framework or channel matrix ships with the framework, Pydantic AI documentation 2026-07-30 | Partial |
| Model Flexibility & RoutingAmong the strongest model positions in the index: compatible with virtually every LLM provider and model with a documented path for implementing custom models, and Pydantic AI Gateway adds built in cost control and MODEL ROUTING with no stated performance overhead, supporting bring your own key or built in providers for single key access across models, Pydantic AI product and documentation pages 2026-07-30 | Full |
| APIs, SDKs & MCP ExtensibilityMIT licensed and developed in the open on GitHub, so the entire surface is inspectable and forkable, with first class Model Context Protocol support, Agent2Agent, UI event stream standards, a composable capability system for building and publishing your own capabilities, installable third party capability packages, and a documented public interface stable enough that the official durable execution integrations are built on it as a reference for others; companion SDKs cover Python, TypeScript/JavaScript and Rust plus a VS Code extension, Pydantic AI GitHub repository and documentation 2026-07-30 | Full |
| Testing, Debugging & OptimizationPydantic Evals is a shipped, customer facing evaluation product rather than internal vendor tooling: create datasets, run evaluations, systematically test the performance and accuracy of the agentic systems you build, and track model performance over time from the CLI or visualised in Logfire, with the evaluation workflow integrated into the same OpenTelemetry trace as production runs, Pydantic AI and Logfire documentation 2026-07-30 | Full |
| Browser & Computer UseThe official Harness capability library ships CODE EXECUTION and FILE ACCESS as composable capabilities alongside web fetch, so agents can run code and operate on a filesystem as first class primitives; this is computer use as a library primitive the developer wires up and sandboxes, not a hosted sandbox environment supplied by the vendor, Pydantic AI documentation and GitHub repository 2026-07-30 | Full |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Recent platform changes
Pydantic AI released version 2.20.0, introducing native support for the Claude Opus 5 and GPT-5.6 models. The update adds explicit prompt caching for GPT-5.6, reasoning context support for the broader GPT-5.x family, and enables DynamicCapability toolsets in durable execution workflows via DBOS and Prefect.
Bears on: Agent capability
View sourcePydantic AI released version 2.14.0, introducing TemporalDurability, DBOSDurability, and PrefectDurability capabilities. These native durable execution layers replace the framework's deprecated wrapper agents. The release also adds support for Mistral's reasoning_effort parameter via thinking settings.
Bears on: Workflow orchestration
View sourcePydantic AI released version 2.6.0, adding file support to its CodeExecutionTool for Anthropic and OpenAI models. The update also introduces time-to-first-token tracking for streaming model requests and adds Amazon Bedrock model profiles for Writer, Z.AI, and Moonshot AI.
Bears on: Agent capability
View sourcePricing
Free / OSS
usage
Included quota
Framework: none (BYO key, you fund model tokens). Logfire (the paid product) free Personal tier: 10M spans/records per month, 1 seat, 3 projects, 30-day retention, no card.
What is public
Pydantic AI is a free, open-source Python agent framework (MIT, by the Pydantic team / Samuel Colvin), model-agnostic and BYO key — the framework itself has no price. The team's commercial product is the adjacent Pydantic Logfire observability platform (restructured Jan 1 2026): Personal $0 (10M spans/mo, 1 seat, 30-day retention), Team $49/mo (5 seats, $2/M overage), Growth $249/mo (unlimited seats/projects), and Enterprise (contact; self-host, SSO, custom retention, SOC 2 Type II / HIPAA / GDPR).
Billing mechanics
The framework is free OSS — cost is your own model-provider token spend (BYO key across virtually every provider). The paid layer is Logfire, billed on a mix of seat count, project count, and telemetry volume (spans/records): 10M records/mo free, then $2 per million records overage, with a configurable spend cap on every paid plan. Pydantic AI Gateway adds multi-provider model routing with cost limits/failover.
Cost watchouts
The framework is free but you fund all model tokens (BYO key); the real recurring cost is Logfire if you adopt it (seats + telemetry volume + $2/M-record overage); self-hosting Logfire is Enterprise-only
Variable cost rationale
Framework cost is pure pass-through model tokens (zero floor); Logfire adds seat + volume-based fees with a configurable spend cap, so exposure is bounded if you set the cap
Additional watchouts
Don't conflate the free framework with the paid Logfire platform; the framework has no vendor cost — only your model API spend
Overage / add-ons
Framework: none (you pay your model provider directly). Logfire: $2 per million records beyond the included volume, with a configurable spend cap to avoid surprises.
Sales call required
No, self serve available
Free / trial
Free (OSS self-host)
Commercial notes
Pydantic Validation underlies the OpenAI/Anthropic/Google SDKs, LangChain, and more; the framework is MIT OSS with SDKs for Python/TS/Rust; Logfire is OpenTelemetry-based (SQL-queryable, MCP server), restructured Jan 1 2026 (Personal $0 / Team $49 / Growth $249 / Enterprise); Pydantic AI Gateway handles model routing + budget control
Key ambiguities
Pydantic AI (framework) and Pydantic Logfire (paid observability) are distinct products — the 'price' depends entirely on whether you adopt Logfire and on your model-token spend
Cancellation / refund
Framework: nothing to cancel (OSS). Logfire: self-serve Personal/Team/Growth with configurable spend caps; Enterprise on custom terms (SLA, self-host)
Support SLA / resale
Framework: community (GitHub/Slack). Logfire: paid-plan support; Enterprise adds SSO, custom retention, EU data region, SLA, SOC 2 Type II, HIPAA BAA, GDPR
Missing data
Not applicable for the framework (free OSS); Logfire Enterprise pricing is custom (contact sales)
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Alternatives to Pydantic AI
The closest documented capability profiles to Pydantic AI among agent infrastructure platforms tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.
- LangChain11.5 / 14Fuller documented coverage on Knowledge Grounding & RAGPydantic AI vs LangChain →
- SmythOS10.5 / 14A lighter documented profile than Pydantic AI
- Inworld AI Agent Runtime11.0 / 14Fuller documented coverage on Triggers & Channel Coverage
- Kestra12.0 / 14Fuller documented coverage on Security, Identity & Governance and Triggers & Channel Coverage
- Mastra11.0 / 14Fuller documented coverage on Security, Identity & Governance
- Agno12.5 / 14Fuller documented coverage on Knowledge Grounding & RAG and Security, Identity & GovernancePydantic AI vs Agno →
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded