TypeSafe AI

How to use TypeSafe's System One API

• Introduction: Jev is TypeSafe's flagship model and the first System One model. Send state and typed questions; get structured answers your code can use directly.
• Quick start: Prefer to just dive in? Here's everything you need to get started immediately.
• System One: System One models make fast, structured decisions for software. Jev is TypeSafe's flagship model and the first System One model.
• State: What state is, how to structure it, and how to give a System One model the context it needs.
• Primitives (Questions): The three TypeSafe question types (Choice, Score, Noul), the typed answers they return, how to choose between them, and how to ask several at once.
• Choice: A Choice is a System One question type for selecting one option from a defined set. The answer includes the selected option, a probability for each option, and confidence.
• Score: A Score is a System One question type for rating content against ordered, descriptive levels. The answer includes a score, a probability for each level, and confidence.
• Noul: A Noul question asks the model to evaluate a yes/no question and return the probability that the answer is yes.
• Advanced: structure: Instructions, Choice options, Score levels, and Noul criteria all accept JSON structure.
• AI primer: Why TypeSafe trains decision models with calibrated probabilities instead of optimizing for generated text.
• Confidence: How TypeSafe reports certainty, how it differs from probability, and how to use it to control system behavior.
• How to build with TypeSafe: Design AI-powered software by keeping code in control and giving System One narrow, structured decisions.
• Example use cases: Explore TypeSafe use cases by industry and turn promising ideas into software workflows.
• Patterns: Architectural patterns for building systems with TypeSafe.
• Speculative fan-out: Send many questions in a single call, including speculative ones, and let your code decide what's relevant.
• Confidence-gated routing: Use confidence as a second axis. The answer tells you what; confidence tells you whether to act.
• Composite scoring: Break a complex judgment into atomic scores, combine with weights you control in code.
• Intent routing: Classify incoming requests and route each to the optimal handler: deterministic logic, a specialist LLM, or a human.
• Demos: Interactive examples showing what's possible with TypeSafe.
• Smart home assistant demo: Demo code: a smart home assistant that uses TypeSafe to evaluate user requests.
• Client SDKs: Install a TypeSafe client SDK and use typed questions and answers in your application.
• TypeSafe Python SDK: Install the TypeSafe Python SDK and get started with asynchronous or synchronous API calls.
• Usage: Guides and patterns for working with the TypeSafe Python SDK.
• Changelog: Python clients for the TypeSafe AI API
• API reference: Python clients for the TypeSafe AI API
• Async client: Use AsyncTypeSafeClient to ask questions, list models, and configure asynchronous TypeSafe API requests.
• Sync client: Use TypeSafeClient to ask questions, list models, and configure synchronous TypeSafe API requests.
• Questions: Provide state and ask yes/no, choice, and score questions using objects or dictionaries.
• Answers and responses: Read answers, confidence scores, token usage, and available models returned by the TypeSafe API.
• Retries: Configure retries with RetryPolicy — attempt count, retryable statuses, backoff, and retry headers handling.
• Common types: Common types for TypeSafe API SDK.
• Exceptions: Handle TypeSafe API errors, rate limits, connection failures, and timeouts.
• Constants: Default settings and environment variable names for the TypeSafe Python SDK.
• JavaScript SDK: API reference for JavaScript SDK.
• Function: choice(): Function to ask choice questions.
• Function: noul(): Function to ask yes/no questions.
• Function: score(): Function to ask score questions.
• Models: Overview of models available in TypeSafe.
• Agent skill: Drop-in skill for Claude Code, Codex, and other agent environments.
• Legal: Legal documents and policies for TypeSafe.
• Jev 1.13 jaggedness: Known issues and jagged edges with Jev 1.13.
• Self-consistency: nouls: Routing uncertain probabilities to human review while keeping underlying noul values visible.
• Self-consistency: choices: Adding uncertain outcomes to moderation decisions.
• Parallel questions: Efficient questioning methods in TypeSafe calls.
• Re-ranking: Method for improving accuracy in legal queries.
• Line-by-line search: Implementing semantic search in legal documents.
• Structure recovery: Techniques for reconstructing Markdown formatting.
• Function calling: Mapping natural-language trading requests to TypeSafe questions.
• Skill suggestion: Optimal skill selection for agent tasks.
• Knowledge graph entity alignment: Aligning product details across catalogs.
• Classifying RAG passages: Scoring and evaluating retrieved passages.
• Double-checking citations: Verifying citations against source documents.
• Guardrails for LLMs: Screening messages in LLM apps.
• SDE cascade: Structured-data-extraction methodology.
• Date extraction: Techniques for extracting date information from documents.
• Pre-parsed value extraction: Extracting contact information using regexes.
• Hierarchical classification: Document classification through taxonomies.
• Autoresearch feature discovery: Automating feature discovery in TypeSafe.