json-repair

JSON Repair is a toolkit designed to address JSON anomalies that can arise from Large Language Models (LLMs). It offers a comprehensive solution for repairing JSON strings, ensuring accuracy and reliability in your data processing. With its user-friendly interface and extensive capabilities, JSON Repair empowers developers to seamlessly integrate JSON repair into their workflows.

SwarmZero SDK is a library that simplifies the creation and execution of AI Agents and Swarms of Agents. It supports various LLM Providers such as OpenAI, Azure OpenAI, Anthropic, MistralAI, Gemini, Nebius, and Ollama. Users can easily install the library using pip or poetry, set up the environment and configuration, create and run Agents, collaborate with Swarms, add tools for complex tasks, and utilize retriever tools for semantic information retrieval. Sample prompts are provided to help users explore the capabilities of the agents and swarms. The SDK also includes detailed examples and documentation for reference.

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

Semgrep MCP Server is a beta server under active development for using Semgrep to scan code for security vulnerabilities. It provides a Model Context Protocol (MCP) for various coding tools to get specialized help in tasks. Users can connect to Semgrep AppSec Platform, scan code for vulnerabilities, customize Semgrep rules, analyze and filter scan results, and compare results. The tool is published on PyPI as semgrep-mcp and can be installed using pip, pipx, uv, poetry, or other methods. It supports CLI and Docker environments for running the server. Integration with VS Code is also available for quick installation. The project welcomes contributions and is inspired by core technologies like Semgrep and MCP, as well as related community projects and tools.

Langchain.rb is a Ruby library that makes it easy to build LLM-powered applications. It provides a unified interface to a variety of LLMs, vector search databases, and other tools, making it easy to build and deploy RAG (Retrieval Augmented Generation) systems and assistants. Langchain.rb is open source and available under the MIT License.

Lumos is a Chrome extension powered by a local LLM co-pilot for browsing the web. It allows users to summarize long threads, news articles, and technical documentation. Users can ask questions about reviews and product pages. The tool requires a local Ollama server for LLM inference and embedding database. Lumos supports multimodal models and file attachments for processing text and image content. It also provides options to customize models, hosts, and content parsers. The extension can be easily accessed through keyboard shortcuts and offers tools for automatic invocation based on prompts.

The Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with structured and unstructured data in Redis. The server supports natural language queries, seamless MCP integration, full Redis support for various data types, search and filtering capabilities, scalability, and lightweight design. It provides tools for managing data stored in Redis, such as string, hash, list, set, sorted set, pub/sub, streams, JSON, query engine, and server management. Installation can be done from PyPI or GitHub, with options for testing, development, and Docker deployment. Configuration can be via command line arguments or environment variables. Integrations include OpenAI Agents SDK, Augment, Claude Desktop, and VS Code with GitHub Copilot. Use cases include AI assistants, chatbots, data search & analytics, and event processing. Contributions are welcome under the MIT License.

Gmail AutoAuth MCP Server is a Model Context Protocol (MCP) server designed for Gmail integration in Claude Desktop. It supports auto authentication and enables AI assistants to manage Gmail through natural language interactions. The server provides comprehensive features for sending emails, reading messages, managing labels, searching emails, and batch operations. It offers full support for international characters, email attachments, and Gmail API integration. Users can install and authenticate the server via Smithery or manually with Google Cloud Project credentials. The server supports both Desktop and Web application credentials, with global credential storage for convenience. It also includes Docker support and instructions for cloud server authentication.

OpenAI is a Swift community-maintained implementation over OpenAI public API. It is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth, and other public benefits. The repository provides functionalities for text completions, chats, image generation, audio processing, edits, embeddings, models, moderations, utilities, and Combine extensions.

DeepSearcher is a tool that combines reasoning LLMs and Vector Databases to perform search, evaluation, and reasoning based on private data. It is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. The tool maximizes the utilization of enterprise internal data while ensuring data security, supports multiple embedding models, and provides support for multiple LLMs for intelligent Q&A and content generation. It also includes features like private data search, vector database management, and document loading with web crawling capabilities under development.

Sparkle is a tool that streamlines the process of building AI-driven features in applications using Large Language Models (LLMs). It guides users through creating and managing agents, defining tools, and interacting with LLM providers like OpenAI. Sparkle allows customization of LLM provider settings, model configurations, and provides a seamless integration with Sparkle Server for exposing agents via an OpenAI-compatible chat API endpoint.

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

Pipecat Flows is a framework designed for building structured conversations in AI applications. It allows users to create both predefined conversation paths and dynamically generated flows, handling state management and LLM interactions. The framework includes a Python module for building conversation flows and a visual editor for designing and exporting flow configurations. Pipecat Flows is suitable for scenarios such as customer service scripts, intake forms, personalized experiences, and complex decision trees.

AIAvatarKit is a tool for building AI-based conversational avatars quickly. It supports various platforms like VRChat and cluster, along with real-world devices. The tool is extensible, allowing unlimited capabilities based on user needs. It requires VOICEVOX API, Google or Azure Speech Services API keys, and Python 3.10. Users can start conversations out of the box and enjoy seamless interactions with the avatars.

AgentPress is a collection of simple but powerful utilities that serve as building blocks for creating AI agents. It includes core components for managing threads, registering tools, processing responses, state management, and utilizing LLMs. The tool provides a modular architecture for handling messages, LLM API calls, response processing, tool execution, and results management. Users can easily set up the environment, create custom tools with OpenAPI or XML schema, and manage conversation threads with real-time interaction. AgentPress aims to be agnostic, simple, and flexible, allowing users to customize and extend functionalities as needed.

LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. It offers features such as easy deployment of LangChain models and pipelines, ready-to-use authentication functionality, high-performance FastAPI framework for serving requests, scalability and robustness for language processing applications, support for custom pipelines and processing, well-documented RESTful API endpoints, and asynchronous processing for faster response times.

JSON Repair is a toolkit designed to address JSON anomalies that can arise from Large Language Models (LLMs). It offers a comprehensive solution for repairing JSON strings, ensuring accuracy and reliability in your data processing. With its user-friendly interface and extensive capabilities, JSON Repair empowers developers to seamlessly integrate JSON repair into their workflows.

LLM Document OCR is a Node.js tool that utilizes GPT4 and Claude3 for OCR and data extraction. It converts PDFs into PNGs, crops white-space, cleans up JSON strings, and supports various image formats. Users can customize prompts for data extraction. The tool is sponsored by Mercoa, offering API for BillPay and Invoicing.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=