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Pydantic AI: TypeSafe (Jev) Model

Run a Pydantic AI agent on Jev: each field of the output type becomes one question, the prompt is the state, and confidence comes back per field.

Category
Tools & Integrations
Published by
Community
Author
Added
2026-09-17
Tagscommunitypydanticpythonintegration

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LangChain's langchain-typesafe integration exposes Jev as a TypeSafeClassifier plus experimental model-routing and auto-mode middleware that screens risky tool calls before execution.
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@jev.fn compiles a Python function definition (parameters, docstring, return annotation) into a state plus typed questions, then validates the answers back into Pydantic models.
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A Python asyncio runtime that puts Jev in a supervision loop above Codex or OpenCode workers: it judges completion, test sufficiency, and stuck loops, then steers, retries, or stops the job.
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From the community

Posts from builders shipping with Jev right now.

The case against Jev-scored compaction

This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the Show more

tamara
tamara
@tamarajtran

found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant

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Classifying rows in DuckDB