TypeLLM
TypeLLM extends existing autoregressive large language models with type-safe generation: developers describe outputs with JSON Schema and receive schema-guaranteed strings, numbers, booleans, and enum values. It is designed to preserve model reasoning and free-form generation while making structured outputs directly usable by software.
At a Glance
- Developers and software teams building structured LLM applications
- Teams operating open autoregressive models on their own GPU infrastructure
- Applications needing reliable typed extraction, classification, routing, or workflow decisions
- Vision-language and multimodal application developers
AI Tools by TypeLLM
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TypeLLM
Type Safe LLM Output Library
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Latest News
TypeLLM v0.1.5 released with image input for vision-language models and more parallel batch/DAG execution
Repository update documented image-input support, tested with Qwen3.8-27B
TypeLLM v0.1.4 released with permutation averaging for reducing option-order bias
Published the Can Jev roll a die? experiment comparing Jev and TypeLLM + Qwen and describing permutation averaging
Products & Services
Apache-2.0-licensed open-source Python package (version 0.1.5 as of 2026-09-24) that provides TypeLLMClient and run_schema for type-safe decoding from compatible autoregressive models served through SGLang.
A hosted API is announced as forthcoming; the website invites users to request early access and describe their intended use case.
Market Position
TypeLLM positions itself as an open-source type-safe decoding layer for existing autoregressive LLMs rather than a new model or proprietary API. Compared with Jev-style systems and related open implementations such as openjev-sglang, system-one-open, and OpenJev DeBERTa, its documented differentiators include direct integer/decimal and free-text types, optional thinking, image input, batch/sequential/DAG execution, dependency graphs, shared-prefix reuse, and permutation averaging; it requires compatible models served through SGLang.
Founding Story
TypeLLM was created to bring a typed-decision interface to open autoregressive models without changing their architecture or weights, retraining them, or relying on proprietary model APIs and fragile parsers. Its design was inspired by TypeSafe AI's Jev, but pursues a different goal: extending models developers already run with SGLang so they can think freely and then emit constrained outputs.
Business Model
Revenue Model
A hosted API is planned and accepting early-access requests; the currently documented TypeLLM package is open source and self-hosted through a user's SGLang endpoint.
Target Markets
- Developers and software teams building structured LLM applications
- Teams operating open autoregressive models on their own GPU infrastructure
- Applications needing reliable typed extraction, classification, routing, or workflow decisions
- Vision-language and multimodal application developers
- Structured extraction from receipts and other documents
- Expense classification and reimbursement decisions
- Support-ticket classification, urgency assessment, routing, and follow-up recommendations
- Typed classification, routing, scoring, ranking, verification, and guardrails for LLM workflows
- Multimodal extraction from images such as receipts
- Local or self-hosted inference with open models served via SGLang