TypeLLM
TypeLLM constrains open LLMs to return JSON-Schema-conformant typed values instead of free text you parse.
TypeLLM attacks a real annoyance: coaxing schema-conformant values out of open models instead of repairing broken JSON downstream. The pitch is concrete — define a field as integer, boolean or an enum, get that type back — and the 0.2.x line adds the plumbing production code needs: shared clients across threads, per-call timeout/cancel/seed, per-field thinking, and fewer requests for mixed number-and-string calls. The 98.70% JevBench figure is the hook, but it is self-reported on the project's own benchmark and drops to 84.42% without thinking mode. Bring your own GPU and SGLang; pick hosted structured-output APIs if you want a managed endpoint.
Verified 1d ago · liveness 76/100 · cite: rightaichoice.com/tools/typellm
- Backend engineers doing structured extraction from receipts, invoices and forms
- Teams already self-hosting open autoregressive LLMs on SGLang
- Developers who want booleans, integers and enums instead of parseable text
- ML engineers building classification or decision pipelines that need probabilities
- Non-technical users wanting a hosted, no-setup UI
- Teams without GPU infrastructure or appetite to run SGLang themselves
- Projects locked into closed proprietary model APIs
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Skip TypeLLM if you don't already serve an open autoregressive model on SGLang, since it is a Python client that constrains generation at your own endpoint rather than a hosted parsing or API service.
Thinking mode is now set per field and dropping it costs accuracy — the vendor's own table shows 84.42% versus 98.70% on the same Qwen3.8-27B pairing, so a forgotten "thinking": True shows up as extraction errors rather
TypeLLM is an early-access library pointed at an SGLang endpoint you run, so the cost that scales is your own GPU serving time rather than a subscription. Against hosted structured-output APIs you trade a per-token bill for hardware you already pay for; against closed models, note the vendor's own table lists GPT-6 Astra at 100.00% on 231 JevBench tasks versus 98.70% for TypeLLM + Qwen3.8-27B with thinking.
In short
TypeLLM — TypeLLM constrains open LLMs to return JSON-Schema-conformant typed values instead of free text you parse. Best for Backend engineers doing structured extraction from receipts, invoices and forms, Teams already self-hosting open autoregressive LLMs on SGLang, Developers who want booleans, integers and enums instead of parseable text. Contact Sales pricing.
What's new in TypeLLM
Checked yesterdayAcross the latest 5 updates: 5 changelog entries.
TypeLLM 0.2.3: input token counts from a single send
last_usage.input_tokens now counts context, questions and images once per send rather than counting them separately, giving a cleaner view of what a call actually consumed.
TypeLLM 0.2.2: thinking moves to per-field, default text limit cut to 128
Client- and run-level thinking arguments were removed and thinking is now set per field, while text_max_tokens defaults to 128 instead of 512 — a breaking change for existing setups.
TypeLLM 0.2.1 cuts request count for mixed number and string fields
Calls that combine number and string fields now issue fewer requests, reducing round trips on mixed extraction tasks.
TypeLLM 0.2.0: shared client across threads, per-call timeout/cancel/seed
One client can serve many threads, and generate() accepts timeout, cancel and seed per call. Breaking: numeric_cache_dir and typellm.numeric were removed.
TypeLLM 0.1.8 adds balanced permutation averaging
permutations="auto" now averages K or 2K balanced option orderings instead of K factorial, making permutation-invariant scoring practical on wider option sets.
What people actually say about TypeLLM — is it worth it?
We scanned public community sources for TypeLLM on Sep 28, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to TypeLLM. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is TypeLLM? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- JSON Schema field definitions with plain-English per-field instructions
- Guaranteed output types: string, integer, number, boolean
- Enum fields for allowed string or numeric values
- Per-field thinking mode (set "thinking": True on a field)
- Image input with numeric decoding alongside text context
- Parallel field execution with depends_on for ordering
- Permutation-invariant decision probabilities for constrained choices
- Balanced permutation averaging via permutations="auto"
- Nullable fields and JSON answers with prefilled keys
- Shared client across threads with per-call timeout, cancel and seed
- last_usage.input_tokens counted once per send across context, questions and images
- Reduced request count for calls mixing number and string fields
- text_max_tokens default of 128 per field
- Python client for an SGLang HTTP endpoint
- Agent-assisted setup via a hosted SKILL.md instruction file
About TypeLLM
TypeLLM is a Python client and extension for open autoregressive LLMs that you serve yourself. Instead of asking a model for JSON and repairing it afterwards, you define each output field as a JSON Schema entry — a type, an optional enum, and a plain-English instruction — and TypeLLM constrains generation so the model returns values your code uses directly: string, integer, number, boolean, or one of your allowed enum choices. Thinking mode stays available per field for hard extraction, and image input with numeric decoding is supported alongside text context. Current releases (0.2.x, September 2026) run one client across many threads, accept per-call timeout, cancel and seed, set thinking per field rather than per client, and cut the number of requests needed when a call mixes number and string fields. The documented setup points a Python client at an SGLang HTTP endpoint with prefix caching enabled, using the same model ID on both sides — the docs example uses Qwen3.8-27B. Its benchmark table covers 231 public JevBench tasks, listing TypeLLM + Qwen3.8-27B at 98.70% with thinking and 84.42% without, alongside GPT-6 Astra at 100.00% and GPT-5.6 Luna at 89.18%. It fits backend and ML engineers wiring extraction, classification and tool-call pipelines where a stringly-typed response is a liability, not teams looking for a hosted drop-in API.
Behind the Verdict
TypeLLM is a narrow tool that solves a real class of bug. If you already serve an open autoregressive model on SGLang, the usual path for structured extraction is to prompt for JSON, then write a validator, then write repair logic for the cases where the model wrapped its answer in prose or emitted a string where you wanted an integer. TypeLLM removes that loop: each output field is declared as a type with an optional enum and a short instruction, and the client returns a Python dictionary with the types you declared — item as string, quantity as integer, total as number, paid as boolean, category constrained to your enum. Integers and floats stay first-class instead of everything collapsing to text. The implementation story is visible in the client surface rather than in marketing. Fields run in parallel by default and depends_on drives ordering, so a later field can build on an earlier constrained choice. Decision probabilities for constrained choices are permutation-invariant, which matters if you are scoring multiple-choice options and don't want the answer to depend on option order. Thinking mode, image input with numeric decoding, and nullable fields are all there. The September 2026 0.2.x releases moved thinking from client level to per field, added a default text_max_tokens of 128 (down from 512), cut request counts for calls mixing number and string fields, and made last_usage.input_tokens count context, questions and images once. Where it gets uncomfortable is the evidence. The headline 98.70% comes from TypeLLM + Qwen3.8-27B in thinking mode on 231 JevBench tasks — a benchmark the project publishes, with results it reports itself. The same pairing without thinking lands at 84.42%, so roughly fourteen points of accuracy hinge on a mode you must remember to set per field. The vendor's own table also shows GPT-6 Astra at 100.00%, which is a useful reminder that a hosted closed model may simply beat this local setup on the task you care about. Operationally, TypeLLM is a library pointed at an endpoint you run, not a service. Throughput, context window and cost inherit entirely from the model you serve, so the cost argument is really an argument about avoiding a second generation pass to reparse text. The docs describe one client and point at an SGLang HTTP endpoint; the Python install is pip install -U typellm. There is a hosted SKILL.md so Claude Code, Cursor or another coding agent can wire the setup for you. If you don't run GPUs or don't want to own an SGLang deployment, none of this is worth the detour.
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Real-world workflow fit
Concrete scenarios for the personas TypeLLM actually fits — and what changes day-one when you adopt it.
They stand up Qwen3.8-27B on SGLang with prefix caching, install typellm, and define merchant as string, total as number, expense_type as an enum of meal/travel/equipment, and reimbursable as boolean with a short instruction per field.
Outcome: client.generate() returns a Python dict with those exact types, so the expense record can be written straight to a database without a JSON repair step.
They define a support-ticket taxonomy as an enum field plus a confidence field constrained to 0.0, 0.25, 0.5, 0.75, 1.0, and turn on permutations="auto" so option order doesn't sway the answer.
Outcome: They get permutation-invariant decision probabilities per ticket instead of a single string label, and can route low-confidence tickets to a human.
They declare the tool's arguments as typed fields with depends_on ordering and per-call timeout and seed, reusing one client across concurrent threads.
Outcome: Tool calls come back with correct argument types on the first pass, removing the retry loop that fires when a model returns a quoted integer or an out-of-enum string.
Use Cases
- Extract merchant, total, expense type and reimbursability from receipt text into typed fields
- Classify expenses into a fixed enum taxonomy with a calibrated confidence value
- Generate strictly typed tool-call arguments for an agent without JSON repair loops
- Parse invoices into integer quantities, float totals and boolean paid flags
- Build extraction graphs where later fields depend on earlier constrained choices
- Score multiple-choice decisions using permutation-invariant probabilities
Models Under the Hood
as of 2026-09-28
Limitations
- TypeLLM is a library you point at your own SGLang endpoint, so throughput, context window and cost all inherit from the model you serve; the docs describe only a Python client against an SGLang HTTP endpoint.
- Accuracy depends heavily on thinking mode being enabled — the homepage table shows 84.42% without it versus 98.70% with it on the same Qwen3.8-27B pairing, and the 0.2.2 release moved thinking to a per-field setting you must remember to set.
- The accuracy figures are self-reported against a benchmark the project publishes, and the vendor's own table lists GPT-6 Astra at 100.00% on the same 231 JevBench tasks.
- The 0.1.x and 0.2.x release notes contain repeated breaking changes — client- and run-level thinking arguments removed, numeric_cache_dir and typellm.numeric removed, text_max_tokens default cut from 512 to 128 — so pin your version, and access is still gated behind an early-access request.
as of 2026-09-28
Where the pricing makes sense
The company stage and team size where TypeLLM's pricing actually pencils out — and where peers do it cheaper.
TypeLLM is an early-access library pointed at an SGLang endpoint you run, so the cost that scales is your own GPU serving time rather than a subscription. Against hosted structured-output APIs you trade a per-token bill for hardware you already pay for; against closed models, note the vendor's own table lists GPT-6 Astra at 100.00% on 231 JevBench tasks versus 98.70% for TypeLLM + Qwen3.8-27B with thinking.
Setup time & first value
How long it actually takes to get something useful out of TypeLLM — broken out by persona, not the marketing-page minute.
For a team already running SGLang: minutes to install (pip install -U typellm) and point a client at the endpoint, plus the time to write field definitions — the docs example covers five fields. For teams without existing GPU serving: add the full SGLang deployment for Qwen3.8-27B with prefix caching first. Coding agents like Claude Code or Cursor can follow the hosted SKILL.md to wire it up.
Switching to or from TypeLLM
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From prompt-and-parse JSON pipelines: replace your schema prompt plus validator with TypeLLM field definitions and drop the repair loop
- →From hosted structured-output APIs: point the client at your own SGLang endpoint so throughput and cost inherit from the model you serve
- →From hand-rolled grammar or enum constraints: declare the allowed values as an enum field instead of maintaining a grammar
- ↗To a hosted structured-output API: re-express your field definitions as that provider's response schema and drop the SGLang dependency
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “TypeLLM”, and we withheld 6: 6 could not be judged, because “TypeLLM” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about TypeLLM.
Official links
Tools that pair well with TypeLLM
Common stack mates teams adopt alongside TypeLLM, with the specific reason each pairing earns its keep.
RWKV Runner
Free, open-source desktop app for running and fine-tuning RWKV RNN language models locally with infinite context.
Predibase
Predibase is a managed platform for fine-tuning and serving open-source LLMs, now part of Rubrik.
Cortex.cpp
Free, open-source desktop app to run 123 HuggingFace models locally or route prompts to Claude, GPT, Gemini and DeepSeek with your own API keys
Featured Head-to-Head Comparisons
Typellm vs Unsloth
These two live at opposite ends of the self-hosted LLM stack, and you shouldn't treat them as substitutes. Unsloth is the thing you reach for when you want to train and serve a model on your own GPU — its 2x-faster/60%-less-VRAM pitch, Dynamic 3.0 quants, and no-code Desktop app target anyone with a consumer card and a privacy budget. TypeLLM is what you reach for after that, or beside it, when the model's output has to be a boolean, integer, or enum rather than free text — it constrains generation instead of parsing it. If your problem is 'I need a fine-tuned local model,' pick Unsloth. If it's 'my model returns a stringly-typed mess in a pipeline,' pick TypeLLM. Buyers shortlisting one rarely shortlist the other.
Typellm vs Klippa
These two should not be on the same shortlist. If you are a finance, HR, logistics, legal, or fintech team that needs invoices, receipts, IDs and shipping documents turned into structured data with verification, fraud detection, liveness/NFC checks and ERP push into Exact, AFAS, NetSuite, SAP or Visma, Klippa (Doxis) is purpose-built for you and TypeLLM offers you nothing. If you are a backend or ML engineer already serving open autoregressive models on SGLang and you want enums, booleans and integers back instead of parseable text — with parallel fields, depends_on ordering and per-field thinking — TypeLLM solves a problem Klippa does not touch. Choose by which problem you actually have, not by which product looks cheaper, because neither has public list pricing to compare.
Typellm vs Predibase
These are not competing products and you should not choose between them. Predibase is a managed platform where you pay to fine-tune and serve open models — its value is infrastructure removal and cheap LoRAX multi-adapter inference. TypeLLM is a self-hosted library you bolt onto an SGLang-served open model to guarantee typed outputs per field, paying with your own GPUs. A team could use both (TypeLLM on a Predibase-served model, if the endpoint is OpenAI-compatible), but that would be a stack decision, not a comparison. Buy based on the problem: training and serving at scale → Predibase; schema-guaranteed extraction on hardware you already run → TypeLLM.
Typellm vs Resistant Ai
These are not competitors and nobody should be choosing between them. Resistant AI sells a fraud-decision system to risk teams at regulated financial institutions — document forgery checks, KYB/claims/tenant vetting, and 80+ transaction-monitoring models layered on existing rules. TypeLLM is a developer tool for engineers who already run open autoregressive models on SGLang or vLLM and want typed values instead of parsed free text. If you have a fraud problem, buy Resistant AI; if you have a stringly-typed extraction pipeline, use TypeLLM. The only surface where they touch is if a fraud team builds its own extraction stack — and even then Resistant AI is a purchase, TypeLLM is a component.
Typellm vs Marvin
Pick Marvin if you want to bolt LLM intelligence onto an existing Python codebase this week — it's free, uses the OpenAI/Anthropic keys you already have, and Pydantic-style typed outputs plus agent loops, streaming and retries cover most product work. Pick TypeLLM only if you're already serving open models on SGLang and your bottleneck is guaranteed schema conformance, probabilities and compute cost — it's the more specialized tool with no list price and no recent Marvin-side news to match its rapid 0.1.x/0.2.x cadence. If you don't run GPUs or can't get a TypeLLM quote, that decision is already made for you.
Typellm vs Mirascope
These are not rival frameworks so much as different layers of the same Python stack, and the honest pick depends on one question: do you control the model? If you're calling OpenAI, Anthropic, or Google and need agents, tool loops, prompt versioning, and per-call cost tracking, pick Mirascope — it gets you to production without owning GPUs. If you self-host open autoregressive models on SGLang and your pain is stringly-typed extraction from receipts, invoices, or forms, pick TypeLLM — its whole reason to exist is guaranteeing boolean, integer, number, and enum outputs instead of parseable text. Teams with GPUs and structured-extraction workloads should seriously consider running both: Mirascope for orchestration and vendor routing, TypeLLM for the fields that must come back typed. If you have no GPU appetite and no SGLang deployment, TypeLLM isn't a realistic starting point today.
Alternatives to TypeLLM
View all
RWKV Runner
Free, open-source desktop app for running and fine-tuning RWKV RNN language models locally with infinite context.
Predibase
Predibase is a managed platform for fine-tuning and serving open-source LLMs, now part of Rubrik.
Cortex.cpp
Free, open-source desktop app to run 123 HuggingFace models locally or route prompts to Claude, GPT, Gemini and DeepSeek with your own API keys
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