Pydantic AI

⚡️Monty: the ultrafast Python interpreter by Agents for Agents — Samuel Colvin, Pydantic

Mar 14, 2026 · 34:02

Samuel Colvin joins Shawn Wang to introduce Monty, Pydantic's ultrafast Python interpreter built with AI for agents, positioning it as a lightweight, secure alternative to sandboxes for code execution. Monty runs inside the same process, achieving sub-microsecond latency (800 nanoseconds in a hot loop), versus over a second for cloud sandboxes. Colvin explains the inspiration came from conversations at Anthropic about type safety's critical role in AI tool calling, and how Monty fills the gap between simple tool calling and full sandboxes. Monty is not full CPython—it lacks third-party library support and some standard library modules—but covers essential built-ins, implemented largely by LLMs that Colvin calls 100x faster for tasks where internal and external APIs are well-known, unit tests are trivial, and there's no API bikeshedding. He demonstrates a Pydantic AI agent using Monty to scrape LLM pricing from websites, with type checking built into the runtime so only permitted host functions (e.g., Playwright, Beautiful Soup wrappers) can be called. Colvin also shares his workflow using Claude Code, Codex, and Gemini CLI in combination for code review and implementation, and…

Agent Engineering with Pydantic + Graphs — with Samuel Colvin, CEO of Pydantic Logfire

Feb 6, 2025 · 1:02:15

Samuel Colvin, CEO of Pydantic Logfire, argues that Pydantic AI offers a production-ready agent framework with type-safe graphs, unlike most agent frameworks that sacrifice engineering quality. He explains Pydantic's shift from a validation library to an AI tool, noting 300M monthly downloads and a 20% reduction in time-to-first-token for a major model company after upgrading to Pydantic V2. Colvin details Logfire's use of DataFusion over ClickHouse for better JSON support and control, and why Pydantic AI resists an LLM gateway, favoring OpenAI-aligned APIs. He defends graphs for complex workflows, reveals agents now use graphs internally, and shares that Pydantic.run enables browser-based demos. The episode covers OpenTelemetry's emerging GenAI semantic conventions, the need for self-hosted observability due to PII in LLM data, and Colvin's investment in Marimo as a text-based Jupyter replacement.