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schemafit is a local-first, MIT-licensed CLI tool that statically lints JSON schemas against the unique constraints of top LLM providers like OpenAI and Anthropic, preventing costly runtime 400 errors. Ideal for developers integrating multiple AI providers, it offers precise error localization, offline operation, and automated schema repair—all without requiring API keys or calls.
Description
SchemaFit is an MIT-licensed CI linter for LLM structured-output schemas. It checks JSON Schema, tool definitions, and response_format schemas against provider-specific constraints before runtime. It catches unsupported keywords, nesting issues, required/optional mismatches, and portability problems across OpenAI, Anthropic, Gemini, Mistral, and Cohere, so teams can fail PRs instead of production calls.
Detailed Description
schemafit is a specialized command-line interface tool designed to statically lint JSON Schema, structured-output specifications, or tool definitions against the documented constraint surfaces of leading large language model (LLM) providers such as OpenAI, Anthropic, Gemini, Mistral, and Cohere. Its core purpose is to prevent runtime errors—specifically HTTP 400 errors—that occur when schemas violate provider-specific constraints. By encoding each provider's constraints as versioned, declarative rule packs, schemafit performs offline, static analysis of schemas, identifying exact points of failure before deployment. This proactive approach enables development teams to catch schema issues early in the continuous integration (CI) pipeline, thereby reducing costly debugging and runtime failures in production environments. Key features of schemafit include detailed linting capabilities that pinpoint the exact JSON-Pointer path, the specific keyword causing the failure, and the reason behind it. This granularity helps developers quickly understand and resolve schema violations. The tool also includes a repair pass that attempts to emit provider-valid schema variants, facilitating schema correction and compatibility across multiple LLM providers. schemafit operates fully offline with zero runtime dependencies, requiring no API calls or authentication keys, which enhances security and reliability. It supports multiple operating systems including Linux, macOS, and Windows, running on Python 3. Additionally, schemafit outputs results in SARIF format, making it easy to integrate with existing CI/CD workflows and static analysis tools. schemafit is best suited for developers, AI engineers, and teams building applications that leverage structured outputs from multiple LLM providers. It is particularly valuable for those who need to maintain compatibility across different providers or who want to enforce schema correctness as part of their CI pipelines. Use cases include validating JSON schemas for AI tool definitions, structured prompts, or API response formats to ensure they meet provider-specific constraints before deployment. This reduces the trial-and-error debugging process typically required when schemas cause runtime errors. The tool is open source and MIT-licensed, making it freely accessible to individuals and organizations without licensing costs. There are no paid plans or subscriptions, and users can download and use schemafit without any financial commitment. This open-source nature encourages community contributions and transparency. Compared to alternative schema validation tools, schemafit stands out by focusing specifically on LLM provider constraints and offering a local-first, static linting approach that requires no network connectivity or API keys. Many other tools either lack provider-specific constraint awareness or require live API calls for validation, which can introduce latency, security concerns, or dependency on external services. schemafit’s repair pass and detailed error reporting further differentiate it by providing actionable feedback and automated schema correction suggestions. Notable limitations include its current focus on a specific set of LLM providers, which means schemas targeting other or emerging providers may not be supported out of the box. Additionally, while schemafit excels at static linting, it does not perform runtime validation or dynamic schema adaptation based on live API responses. Users must also be comfortable working with command-line tools and integrating linting results into their CI workflows. Despite these considerations, schemafit offers a robust, offline-first solution for ensuring schema correctness and preventing costly production errors in AI-driven applications.
Tool Features
- Lints JSON Schema and structured-output specs against multiple LLM providers' constraints
- Identifies exact JSON-Pointer path, keyword, and reason for schema failures
- Fails CI builds before schemas cause 400 errors in production
- Includes a repair pass to emit provider-valid schema variants
- Operates fully offline with zero runtime dependencies
- Requires no API calls or API keys
- Supports OpenAI, Anthropic, Gemini, Mistral, and Cohere providers
Description
✦
schemafit is a local-first, MIT-licensed CLI tool that statically lints JSON schemas against the unique constraints of top LLM providers like OpenAI and Anthropic, preventing costly runtime 400 errors. Ideal for developers integrating multiple AI providers, it offers precise error localization, offline operation, and automated schema repair—all without requiring API keys or calls.
SchemaFit is an MIT-licensed CI linter for LLM structured-output schemas. It checks JSON Schema, tool definitions, and response_format schemas against provider-specific constraints before runtime. It catches unsupported keywords, nesting issues, required/optional mismatches, and portability problems across OpenAI, Anthropic, Gemini, Mistral, and Cohere, so teams can fail PRs instead of production calls.
Detailed Description
schemafit is a specialized command-line interface tool designed to statically lint JSON Schema, structured-output specifications, or tool definitions against the documented constraint surfaces of leading large language model (LLM) providers such as OpenAI, Anthropic, Gemini, Mistral, and Cohere. Its core purpose is to prevent runtime errors—specifically HTTP 400 errors—that occur when schemas violate provider-specific constraints. By encoding each provider's constraints as versioned, declarative rule packs, schemafit performs offline, static analysis of schemas, identifying exact points of failure before deployment. This proactive approach enables development teams to catch schema issues early in the continuous integration (CI) pipeline, thereby reducing costly debugging and runtime failures in production environments. Key features of schemafit include detailed linting capabilities that pinpoint the exact JSON-Pointer path, the specific keyword causing the failure, and the reason behind it. This granularity helps developers quickly understand and resolve schema violations. The tool also includes a repair pass that attempts to emit provider-valid schema variants, facilitating schema correction and compatibility across multiple LLM providers. schemafit operates fully offline with zero runtime dependencies, requiring no API calls or authentication keys, which enhances security and reliability. It supports multiple operating systems including Linux, macOS, and Windows, running on Python 3. Additionally, schemafit outputs results in SARIF format, making it easy to integrate with existing CI/CD workflows and static analysis tools. schemafit is best suited for developers, AI engineers, and teams building applications that leverage structured outputs from multiple LLM providers. It is particularly valuable for those who need to maintain compatibility across different providers or who want to enforce schema correctness as part of their CI pipelines. Use cases include validating JSON schemas for AI tool definitions, structured prompts, or API response formats to ensure they meet provider-specific constraints before deployment. This reduces the trial-and-error debugging process typically required when schemas cause runtime errors. The tool is open source and MIT-licensed, making it freely accessible to individuals and organizations without licensing costs. There are no paid plans or subscriptions, and users can download and use schemafit without any financial commitment. This open-source nature encourages community contributions and transparency. Compared to alternative schema validation tools, schemafit stands out by focusing specifically on LLM provider constraints and offering a local-first, static linting approach that requires no network connectivity or API keys. Many other tools either lack provider-specific constraint awareness or require live API calls for validation, which can introduce latency, security concerns, or dependency on external services. schemafit’s repair pass and detailed error reporting further differentiate it by providing actionable feedback and automated schema correction suggestions. Notable limitations include its current focus on a specific set of LLM providers, which means schemas targeting other or emerging providers may not be supported out of the box. Additionally, while schemafit excels at static linting, it does not perform runtime validation or dynamic schema adaptation based on live API responses. Users must also be comfortable working with command-line tools and integrating linting results into their CI workflows. Despite these considerations, schemafit offers a robust, offline-first solution for ensuring schema correctness and preventing costly production errors in AI-driven applications.
Frequently Asked Questions
What is schemafit?
schemafit is a command-line interface tool that statically lints JSON Schema and structured-output specifications against the documented constraints of major LLM providers such as OpenAI, Anthropic, Gemini, Mistral, and Cohere. It identifies schema errors before deployment, helping prevent runtime 400 errors.
How much does schemafit cost?
schemafit is an open-source tool licensed under the MIT license and is available for free. There are no paid plans or subscription fees.
Who is schemafit best for?
schemafit is best suited for developers, AI engineers, and teams who build applications using multiple LLM providers and want to ensure their JSON schemas and structured outputs comply with each provider's constraints before deployment.
What are the main features of schemafit?
Key features include static linting of JSON schemas against multiple LLM providers’ constraints, pinpointing exact JSON-Pointer paths and keywords causing failures, a repair pass to emit provider-valid schema variants, offline operation with zero runtime dependencies, no need for API calls or keys, and support for OpenAI, Anthropic, Gemini, Mistral, and Cohere.
Does schemafit offer a free trial?
Since schemafit is an open-source and free tool, there is no need for a trial period. Users can download and use it immediately without cost.
What integrations does schemafit support?
schemafit integrates easily into continuous integration (CI) pipelines by providing non-zero exit codes on schema failures and outputs results in SARIF format, which is compatible with many static analysis and developer tooling platforms.
How does schemafit work?
schemafit works by encoding each LLM provider’s documented schema constraints into versioned, declarative rule packs. It then statically analyzes your JSON Schema or structured-output specifications offline, identifying exact points of failure and reasons without making any API calls. It can also attempt to repair schemas to produce provider-compliant variants.
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