Sieves
Open-source Python library for zero-shot document AI with structured output.
Sieves excels for rapid prototyping and proof-of-concept work. The zero-shot ethos and Bridge abstraction save real time over labeling. However, it's Python-only and requires comfort with model selection—not a plug-and-play app. Choose it when you need speed with scarce data; skip if you need production SLAs or a no-code interface. Alternatives like Label Studio offer annotation workflows but require labeled data, while commercial document APIs charge per page.
Verified 20h ago · liveness 66/100 · cite: rightaichoice.com/tools/sieves
- Rapid prototyping of document AI pipelines without labeled data
- Developers building zero-shot NLP solutions in Python
- Data scientists needing structured output for proof-of-concept work
- AI consultants and researchers exploring document understanding tasks
- Production-grade applications requiring strict SLA guarantees
- Non-technical users without Python programming skills
- Users needing pre-trained models for very niche domains without any examples
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Skip Sieves if you need a production-ready, hosted document AI service with SLAs, or if you're a non-programmer looking for a no-code solution.
You need to bring your own API keys for model backends (e.g., OpenAI, Anthropic) and pay for inference usage—there's no free hosted compute.
Sieves is free and open-source, making it a no-cost option for prototyping compared to commercial APIs that charge per page or per token. It's ideal for startups and developers who want to experiment without upfront costs, but you'll pay for compute and model usage yourself.
In short
Sieves — Open-source Python library for zero-shot document AI with structured output. Best for Rapid prototyping of document AI pipelines without labeled data, Developers building zero-shot NLP solutions in Python, Data scientists needing structured output for proof-of-concept work. Free to use.
What people actually say about Sieves — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
26 mentions across 3 sources (Hacker News, GitHub, Lemmy) · researched Jul 3, 2026.
- +Zero-shot capability reduces need for labeled training data.
- +Modular pipeline architecture (Tasks, Docs, Bridges) is developer-friendly.
- +Plug-and-play with multiple model backends (DSPy, Outlines, Hugging Face).
- +Built-in structured generation ensures outputs follow defined schemas.
- +Bundles common utilities: parsing, chunking, NER, classification, summarization.
- −Almost no community feedback or user reviews available.
- −GitHub stars are low (126), indicating early-stage adoption.
- −No production case studies or benchmarks shared.
- −Lack of documentation for advanced customization or scaling.
- −Potential for vendor lock-in if custom tasks rely on specific backends.
- • Compute costs for running LLM backends (not included)
- • Possible consultancy fees from Mantis for custom support
Viability Score
How well maintained and how widely used is Sieves? 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: August 2026
How we score →Key Features
- Zero-shot document classification without training data
- Named entity recognition and relation extraction
- PII masking for sensitive document redaction
- Sentiment analysis and summarization
- Translation and question answering
- Document parsing via Docling and Marker
- Text chunking via Chonkie and NaiveChunker
- Chainable pipeline architecture using + operator
- Structured output generation with schema validation
- Observability and usage tracking
- Custom task creation and model configuration via Bridges
- Integration with DSPy, GLiNER, Hugging Face, LangChain, Outlines
- Task optimization and distillation
- Python library (open source)
- Maintained by Mantis AI consultancy
About Sieves
Sieves is an open-source Python library for zero-shot document AI that lets you build structured output pipelines without training data or labeling. It bundles common NLP utilities—classification, information extraction, PII masking, sentiment analysis, summarization, translation, and question answering—with document parsing (via Docling and Marker) and text chunking (via Chonkie and NaiveChunker), all organized in an observable pipeline architecture. You chain tasks in simple Python (Pipeline([...]) or use +) and get validated output conforming to your schema. A Bridge system connects tasks to multiple model backends (Outlines, DSPy, LangChain, GLiNER, Hugging Face), so you can swap models without rewriting logic. It's designed for developers, data scientists, and AI practitioners who need rapid prototyping for document understanding—especially when labeled data is scarce or expensive. Compared to heavy annotation platforms like Label Studio or commercial APIs that charge per page, Sieves offers a lightweight, zero-shot path to structured output that keeps debugging and iteration fast.
Behind the Verdict
Sieves is a developer-first library that shines when you need to extract structure from documents quickly, without the overhead of labeling. Its architecture is clean: you compose a pipeline of tasks, each backed by a model wrapper of your choice, and get validated JSON-like output. The zero-shot nature means you can go from raw PDFs to structured data in hours, not weeks. The Bridge abstraction is genuinely useful—it lets you experiment with different models (Outlines, DSPy, LangChain, GLiNER, Hugging Face) without rewriting your pipeline logic. This is a major time-saver when you're unsure which model performs best on your domain. Observability and usage tracking are built in, which helps during debugging and iteration. The main weaknesses: it's Python-only, so non-coders are locked out. Performance depends on the underlying model endpoints, so you'll need to manage that yourself. There's no hosted API or managed infrastructure—you bring your own compute and model keys. It's also not built for high-throughput production use out of the box; you'll likely need to optimize or wrap it. If you're a developer or data scientist prototyping document AI, Sieves is a strong choice. If you need a no-code tool, managed SLAs, or a fully hosted API, look elsewhere (e.g., Label Studio for annotation, or commercial document APIs).
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Real-world workflow fit
Concrete scenarios for the personas Sieves actually fits — and what changes day-one when you adopt it.
You need to extract key fields from hundreds of scanned invoices for a proof-of-concept. You install Sieves, link a Docling parser and run a Named Entity Recognition task with a GLiNER bridge, no labeled data needed. You get validated JSON output within hours.
Outcome: A working prototype that extracts vendor names, amounts, and dates from invoices, ready to present to stakeholders.
A client wants to classify support tickets into categories. You use Sieves' classification task with a DSPy backend, quickly tune the prompts, and produce a demo pipeline that categorizes tickets in near-real-time.
Outcome: A deployable classification pipeline that demonstrates value to the client, with minimal training data required.
You're comparing different models for relation extraction on scientific papers. Using Sieves' Bridge abstraction, you swap between Outlines and Hugging Face models in minutes and run the same pipeline to compare performance.
Outcome: A reproducible benchmark comparing model wrappers, saving days of manual coding.
Use Cases
- Extract named entities from legal contracts without any labeled data.
- Classify customer support emails into categories with zero training.
- Summarize long PDF reports into concise bullet points.
- Mask personally identifiable information in free-text documents.
- Translate multilingual documents while preserving structure.
- Build a question-answering system over internal knowledge bases.
Limitations
- Sieves is a zero-shot document AI library that requires no training data, but it is designed for rapid prototyping and may not be optimized for high-throughput production use.
- Performance depends on underlying model wrappers and inference endpoints.
- The library is actively developed but may have limited pre-built tasks for highly specialized domains.
as of 2026-08-27
Verification history
We have re-verified Sieves 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Sieves tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Developers and researchers who want a free, self-hosted library for rapid prototyping of document AI pipelines without labeled data.
What this tier adds
Starting tier: full library access with all pre-built tasks, document parsing, and model bridges at no cost. You only pay for your own inference.
Where the pricing makes sense
The company stage and team size where Sieves's pricing actually pencils out — and where peers do it cheaper.
Sieves is free and open-source, making it a no-cost option for prototyping compared to commercial APIs that charge per page or per token. It's ideal for startups and developers who want to experiment without upfront costs, but you'll pay for compute and model usage yourself.
Setup time & first value
How long it actually takes to get something useful out of Sieves — broken out by persona, not the marketing-page minute.
A developer familiar with Python can install Sieves via pip and run the getting-started example in under 30 minutes. Configuring custom tasks or model bridges may take a few hours. Data scientists with prior NLP experience can expect a half-day to reach first meaningful output.
Switching to or from Sieves
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Label Studio: Migrate your labeled datasets for validation, but leverage Sieves' zero-shot tasks to reduce the need for new labeling.
- →From commercial document APIs: Export your document corpora and use Sieves' parsing and extraction tasks with your existing model keys to replicate functionality without per-page charges.
- ↗To Label Studio: If you need human-in-the-loop labeling for higher accuracy, export your Sieves pipeline outputs and import them into Label Studio for annotation.
- ↗To a production-grade service like AWS Textract or Google Cloud Document AI: Wrap your Sieves pipeline logic in a service, but shift to managed APIs for scale and SLAs.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Sieves
Common stack mates teams adopt alongside Sieves, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Sieves vs Screenplayiq
If you're a screenwriter or producer needing financial script forecasts, ScreenplayIQ is the clear choice with its box office prediction and pitch deck tools. For developers building NLP pipelines without training data, Sieves offers unmatched zero-shot flexibility and is free. Choose based on your domain: film industry vs. general document AI.
Sieves vs Geologicai
Choose GeologicAI if you are a mining company needing rapid, multi-sensor core analysis with AI logging to accelerate critical minerals projects by over 400%. Choose Sieves if you are a developer prototyping zero-shot document AI pipelines with structured output and no training data requirement — it's free and open source.
Sieves vs Versatile
Versatile and Sieves are completely incomparable tools serving different ecosystems. If you are a steel erector needing real-time crane visibility without workflow changes, Versatile is your only choice. If you are a developer building zero-shot document AI pipelines with no training data, Sieves wins with its free, flexible library. There is no functional overlap.
Alternatives to Sieves
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DocLine.ai
AI-powered document processing to extract structured data from invoices, receipts, contracts, and forms.
Frequently Asked Questions
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