Search Results for "json schema editor"
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Showing 14 open source projects for "json schema editor"
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JSON_REPAIR
A python module to repair invalid JSON from LLMs
...The repair process can also be combined with optional JSON Schema validation to enforce structural constraints and ensure the output conforms to expected data types and formats. Developers can integrate the library into applications as a drop-in replacement for standard JSON parsing functions, allowing systems to tolerate imperfect structured data without crashing. -
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Nexa SDK
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML
...It supports text generation, image generation, vision-language models (VLM), and speech-to-text (ASR), and text-to-speech (TTS) capabilities. Additionally, it offers an OpenAI-compatible API server with JSON schema mode for function calling and streaming support, and a user-friendly Streamlit UI. Users can run Nexa SDK in any device with Python environment, and GPU acceleration is supported, including CUDA, Metal, and ROCm. An executable version is also available. -
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Claude SEO
Universal SEO skill for Claude Code
Claude SEO is an open-source Claude Code skill suite for running structured SEO analysis through AI-assisted workflows. It combines 25 sub-skills and 18 specialist agents across technical SEO, content quality, Schema.org markup, GEO and AEO, local SEO, e-commerce, international SEO, backlinks, semantic clustering, and Google API workflows. The system is designed to produce prioritized action plans instead of generic audit notes. Recommendations include first-principle observations,... -
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Outlines
Structured Outputs
Outlines is a Python library for making language models generate predictable, structured outputs. Instead of repairing malformed responses afterward, it constrains generation to match a requested type or format. Developers can require predefined choices, primitive Python types, Pydantic models, JSON schemas, regular expressions, function signatures, or custom grammars. The same interface works with local models, inference servers, and hosted APIs from several providers. Supported... -
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Unstract
No-code LLM Platform to launch APIs and ETL Pipelines
Unstract is a powerful open-source, no-code platform built to automate the extraction and structuring of unstructured documents using large language models and flexible workflows, enabling developers and data teams to turn messy files into organized JSON content without complex coding. It integrates a visual Prompt Studio environment where users can iteratively design extraction schemas, compare outputs from different models, and monitor costs and accuracy side by side, making it easier to... -
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mcpo
A simple, secure MCP-to-OpenAPI proxy server
mcpo is a minimal bridge that exposes any MCP tool as an OpenAPI-compatible HTTP server. Instead of writing glue code, you point mcpo at an MCP server command and it generates REST endpoints and an OpenAPI spec that other systems (or LLM agent frameworks) can call immediately. This design lets you reuse a growing library of MCP servers with platforms that only understand HTTP+OpenAPI, unifying tool access across ecosystems. The project emphasizes “dead-simple” setup and pairs with Open WebUI... -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation... -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
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Skyvern
Automate browser-based workflows with LLMs and Computer Vision
...Skyvern understands how to solve CAPTCHAs to complete complicated workflows. Support for authenticating into user accounts, including support for 2FA/TOTP. Extract data from workflows in any schema of your choice including CSV or JSON. Automate procurement pipelines, breeze through government forms, and complete workflows in any language. -
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Universal Tool Calling Protocol (UTCP)
Official python implementation of UTCP. UTCP is an open standard
The python-utcp repository is the official Python SDK implementation of the Universal Tool Calling Protocol (UTCP). UTCP is an open, modern standard designed to let AI agents call any tool or API directly—over HTTP, CLI, WebSocket, gRPC, and more—without the overhead of extra wrapper layers or middleware. It leverages a modular, plugin-based architecture built around Pydantic models and separates the core functionality into a lightweight client and extensible protocol plugins, enabling... -
11
LangChain Extract
Did you say you like data?
LangChain Extract is an open-source reference application designed to demonstrate how large language models can be used to extract structured data from unstructured text and document files. The project implements a lightweight web service that allows developers to define extraction schemas and apply them to various sources such as plain text, HTML, or PDF documents. Built using FastAPI and the LangChain framework, the application exposes a REST API that can process documents and return... -
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AIConfig
AIConfig is a config-based framework to build generative AI apps
AIConfig is an open-source framework designed to simplify the development and management of generative AI applications by separating AI logic from application code. The framework allows prompts, model configurations, and parameters to be stored as structured configuration files that can be version controlled and managed independently from the rest of the software system. This approach improves collaboration between developers, prompt engineers, and machine learning practitioners by turning... -
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Functionary
Chat language model that can use tools and interpret the results
...The model extends traditional chat-based language models by enabling them to determine when external functions should be called and how to extract the necessary parameters from natural language input. Function definitions are typically provided in JSON schema format, allowing the model to generate structured function calls compatible with modern tool-calling interfaces used in AI applications. Functionary can decide whether to execute tools sequentially or in parallel and can analyze the outputs of those tools to produce context-aware responses. This capability allows AI systems to interact with external services, APIs, or computation engines rather than relying solely on knowledge embedded in the model. -
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Opyrator
Turns your machine learning code into microservices with web API
...Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python function. An Opyrator-compatible function is required to have an input parameter and return value based on Pydantic models. The input and output models are specified via type hints. ...
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