ContextGem 1.0
Claim this toolExtract structured data from documents with LLMs and decision models
From $4/user/mo, billed monthly (free plan available)Freemium Writing & Content
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Tracked since 2026
The Bottom Line
- Price
- From $4/user/mo, billed monthly (free plan available). Compare plans
Key facts
- Open-source framework for extracting structured data from documents using LLMs and decision models
- Supports aspect extraction (sections) and concept extraction (typed data) with reusable pipelines
- Includes decision models for fast, probabilistic answers to closed questions and agentic workflow integration
Pros
- Free and open-source with no licensing costs
- Minimal code required to set up extraction pipelines
- Combines LLMs and decision models for flexible, cost-effective extraction
Cons
- Requires Python programming knowledge to implement and customize
- May need API keys for cloud LLM providers, adding potential usage costs
What is ContextGem 1.0?
ContextGem is a free, open-source Python framework designed to simplify structured data extraction from documents. It leverages large language models (LLMs) and decision models to identify and extract specific sections (aspects) and infer structured data like JSON objects, strings, numbers, dates, booleans, ratings, and labels (concepts) with minimal code. The framework supports multiple file formats including PDF, DOCX, PPTX, XLSX, and HTML through integration with Docling, preserving document structure and page references.
Key capabilities include aspect extraction for locating clauses, chapters, or terms; concept extraction for converting unstructured text into typed data; and reusable extraction pipelines that combine both for consistent analysis across multiple files. ContextGem also introduces decision models that answer closed questions (yes/no, labels, scale levels) with probability distributions, offering a fast and low-cost alternative to LLM-based extraction for certain tasks. The framework supports agentic workflows, allowing integration at document ingestion, as a tool for AI agents, or at decision points in automated processes.
ContextGem is designed for developers and data scientists who need to extract structured information from documents programmatically. It provides optimization guidance for balancing accuracy, cost, and performance, and includes serialization support for storing and transferring extraction results. As an open-source project, it offers full transparency and customization without licensing fees.
Data compiled by Toolradar Pricing checked · How we evaluate
Source contextgem.shcherbak.ai Key Features
- Aspect extraction to identify and extract specific document sections like clauses, chapters, or terms
- Concept extraction to infer structured data (JSON, strings, numbers, dates, booleans, ratings, labels)
- Extraction pipelines that combine aspects and concepts for consistent multi-file analysis
- Decision models for answering yes/no, label, and rating questions with probability outputs
- Agentic workflow support for ingestion, tool calling, and decision points
- Serialization and deserialization of extraction objects and results for storage and transfer
Pricing Plans
Free TrialPricing checked Oct 30, 2026. Source: github.com/pricing, read Oct 30, 2026
| Plan | Price | Details |
|---|---|---|
| Free | Free |
+3 more Show less
|
| Team | $4 / user/month |
+5 more Show less
|
| Enterprise | $21 / user/month |
+6 more Show less
|
Is ContextGem 1.0 worth the price?
Good value
The pricing is generous at the entry point: a genuinely usable Free tier with unlimited private repositories and 2,000 Actions minutes, then $4/user/month for Team and $21/user/month for Enterprise.
The jump to Enterprise is steep at roughly 5x Team, but it buys compliance and identity features (SAML SSO, SCIM, audit APIs, FedRAMP Tailored ATO) that are typically enterprise-only line items elsewhere. It is best for development teams already living in the GitHub ecosystem, from solo developers up to regulated enterprises.
Hidden Costs & Gotchas
Enterprise tier capped at 50,000 GitHub Actions minutes/month
Free tier limited to 500MB of Packages storage
Team tier limited to 2GB of Packages storage
ContextGem 1.0 by the numbers
Toolradar data: ContextGem 1.0's entry paid plan ($4/user/mo, billed monthly) ranks #3 of 20 (joint) for price among the Writing & Content tools with a verified entry price per user, where the median is $7.25/user/mo. As of Oct 2026.
| Metric | ContextGem 1.0 | Peer median | Position (1 is best) |
|---|---|---|---|
| Entry paid plan | $4/user/mo, billed monthly | $7.25/user/mo | #3 of 20 (joint) tools priced per user, verified price |
| Free plan | Yes | 65% have one | n/a |
Each position is counted among the tools that have data for that row, as noted under it. A lower entry price ranks higher; prices are read on vendor pages and compared only with tools priced on the same basis. All Writing & Content data · How we compute this. Advertising and placement fees are not inputs to any position.
Cite this: Toolradar, “ContextGem 1.0 by the numbers”, Oct 2026.
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ContextGem 1.0 FAQ
What is the difference between aspects and concepts in ContextGem?
Aspects refer to specific sections of a document, such as clauses, chapters, or terms, that you want to locate and extract. Concepts are structured data points like JSON objects, strings, numbers, dates, booleans, ratings, or labels that you infer from the document content. Aspects identify where something is, while concepts extract what the content means.
How do decision models work in ContextGem?
Decision models answer closed questions (yes/no, one of your labels, a level of a scale) by returning the probability of each possible answer. They are fast and low-cost compared to LLM-based extraction, making them suitable for classification tasks where you need confidence scores rather than generated text.
Can ContextGem extract data from PDF files?
Yes, ContextGem can extract data from PDF files by converting them with Docling, which preserves the document structure and page references. It also supports DOCX, PPTX, XLSX, and HTML formats through the same conversion pipeline.
Does ContextGem support local LLMs?
Yes, ContextGem supports both cloud LLM providers and local models. You can configure and use local LLMs for extraction, which is useful for offline processing or when data privacy requires keeping everything on-premises.
How can I use ContextGem in an agentic workflow?
ContextGem can be integrated into agentic workflows at three points: during document ingestion to preprocess and extract initial data, as a tool that an AI agent calls to extract information on demand, and at decision points within a workflow to determine next steps based on extracted content.
What is the purpose of extraction pipelines?
Extraction pipelines allow you to create reusable workflows that combine aspect extraction and concept extraction. They enable consistent document analysis across multiple files by defining a sequence of extraction steps that can be applied repeatedly without rewriting code.
How does ContextGem handle serialization of extraction results?
ContextGem provides serialization and deserialization methods for all its objects and extraction results. This allows you to save the state of your extraction pipelines and the extracted data to disk or transfer them between systems, facilitating storage, caching, and integration with other tools.
Is ContextGem suitable for large-scale document processing?
ContextGem is designed to be scalable, with optimization guidance for accuracy, cost, and performance. You can choose appropriate LLMs or decision models based on your throughput and budget requirements, and the pipeline architecture supports batch processing of many documents.
How does ContextGem 1.0 pricing compare with other Writing & Content tools?
ContextGem 1.0's entry paid plan ($4/user/mo, billed monthly) ranks #3 of 20 (joint) for price among the Writing & Content tools with a verified entry price per user, where the median is $7.25/user/mo. Prices are read on each vendor's own pricing page and compared only with tools priced on the same basis. Source: Toolradar, Oct 2026.
Source: contextgem.shcherbak.ai