A Python framework for building AI agents—programs that use language models to perform tasks through instructions and tools. It supports structured results, different model providers, testing agent behavior, shared dependencies, and multi-step workflows.

Use it to build Python agents, connect models from providers such as OpenAI, Anthropic, or Gemini, define typed tools, run evaluations, and connect MCP servers.

It helps keep agent inputs, outputs, and tool calls clearly defined while giving developers ways to test and observe how agents behave.

pydantic-ai — Skill by itechmeat

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/itechmeat/llm-code/pydantic-ai

Any agent

npx skills add itechmeat/llm-code --skill pydantic-ai

Clone the repo

git clone --depth 1 https://github.com/itechmeat/llm-code

Made for: Claude Code, Codex.

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,878 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00080	$0.03878
Opus 5	$0.00040	$0.01939
Sonnet 5	$0.00016	$0.00776
Haiku 4.5	$0.00008	$0.00388

Measured 2d ago against content hash 31394a21933f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pydantic-ai scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/pydantic-ai/SKILL.md · 303 lines

How it starts

The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Pydantic AI

Python agent framework for building production-grade GenAI applications with the "FastAPI feeling".

Topic	Reference
Agents	agents.md
Capabilities	agents.md
Tools	tools.md
Models	models.md
Embeddings	embeddings.md
Evals	evals.md
Integrations	integrations.md
Graphs	graphs.md
UI Streams	ui.md
Installation	installation.md

When to Use

Building AI agents with structured output
Need type-safe, IDE-friendly agent development
Require dependency injection for tools
Multi-model support (OpenAI, Anthropic, Gemini, etc.)
Production observability with Logfire
Complex workflows with graphs

Installation

See references/installation.md for full/slim install options and optional dependency groups. Requires Python 3.10+.

Release Highlights (2.13.0 -> 2.22.0)

Durability capabilities replace wrapper agents: TemporalDurability, DBOSDurability, and PrefectDurability (2.14.0) attach to a regular Agent via capabilities=[...], replacing the deprecated TemporalAgent / DBOSAgent / PrefectAgent wrapper classes (removed in v3). Existing wrapper-based workflows keep replaying correctly after switching. See integrations.md for the updated Temporal/DBOS/Prefect examples.
New models/providers: Claude Opus 5 (2.20.0), gemini-3.6-flash/gemini-3.5-flash-lite (2.16.0), Mistral reasoning_effort (2.14.0) and mistral_prompt_cache_key (2.16.0), OpenAI explicit prompt caching for gpt-5.6 (2.15.0), BedrockMantleProvider (2.18.0), and the AdvisorTool builtin tool for Anthropic/OpenRouter (2.18.0).
Usage & limits: cache_hit_ratio on RequestUsage/RunUsage (2.13.0), ToolFailed for model-visible failures that don't consume a retry (2.16.0), optional run_id= on runs (2.16.0), tool-retry budget overrides at run/iter/override time (2.15.0), and per_request_input_tokens_limit on UsageLimits (2.21.0).
Error handling & instrumentation: ModelHTTPError now carries headers and a parsed retry_after from every provider SDK (2.19.0); RaiseContentFilterError capability and include_model_request_parameters instrumentation setting (2.13.0); per-message OTel serialization is cached to avoid O(n^2) cost (2.17.0).
Dependencies: the fastmcp optional group now constrains fastmcp<4 (2.19.0).

Read the full file on GitHub · 303 lines

Files

What ships with it

9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 303 lines · 80 tokens per session scan A 31394a21933f

Subscribe to this mod's changes

pydantic-ai is a skill published in the GitHub repository itechmeat/llm-code (22 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 3,878 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI Frontend Testing

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Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

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pydantic-ai