Open-source alternatives to Document Chat System

The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework

Weaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query. The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result

Nexent is an enterprise AI control plane and LLM agent orchestration platform. It provides a zero-code environment for designing, deploying, and managing production AI agents through a multi-agent collaboration framework that coordinates specialized autonomous agents using standardized messaging protocols. The platform integrates the Model Context Protocol to connect agents with external tools, plugins, and services via a universal communication interface. It further distinguishes itself with a dedicated RAG knowledge base manager that imports unstructured documents and utilizes hybrid search

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

pgai is a PostgreSQL AI toolkit and framework designed to integrate large language models and vector embeddings directly into a database. It serves as a bridge for executing machine learning model requests and performing text-to-SQL translations within standard database queries. The project provides an automated vector embedding pipeline that handles the loading, parsing, and chunking of text from tables and unstructured documents. This system utilizes a background worker to synchronize embeddings automatically as source data changes and includes specialized tools for building retrieval-augme

This project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age

Hyper-Extract is a framework designed for automated knowledge extraction, graph construction, and retrieval-augmented generation. It functions as a command-line tool that transforms unstructured text into structured knowledge graphs and hypergraphs, enabling users to build interconnected, searchable, and machine-readable data repositories from their documents. The system distinguishes itself through its focus on personal knowledge management and incremental processing. It allows users to update existing knowledge bases by processing only new document deltas, avoiding redundant computation. Th

Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

This project is a reference implementation and application template for Retrieval-Augmented Generation (RAG). It integrates Azure OpenAI with Azure AI Search to enable conversational chat interfaces that provide grounded responses based on private enterprise data. The system is distinguished by its multimodal AI interface, allowing it to process and reason over combined text, image, and PDF content. It employs a hybrid search architecture that combines vector and keyword retrieval with semantic reranking to prioritize the most relevant documents for prompt augmentation. The project covers a

Context-Engineering is a prompt engineering framework and cognitive architecture for large language models. It provides a set of patterns and methodologies for designing structured prompts and modular reasoning flows that decompose complex tasks into specialized, step-by-step problem solving templates. The project distinguishes itself through stateful prompt management and context window optimization. It maintains persistent memory across multiple interaction turns by compressing conversation history into compact internal state cells and employs techniques to maximize information density per

LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr

QChatGPT is an LLM bot orchestration platform and multi-platform chatbot gateway. It serves as a bridge that routes messages between various instant messaging services and AI models using a unified codebase, functioning as an agentic workflow manager to handle complex multi-turn dialogues. The platform distinguishes itself through an extensible plugin framework that allows for the addition of custom logic and event-driven features via standardized protocols. It also includes a web-based bot controller, providing a browser interface to manage bot behavior and monitor real-time performance with

This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de

Dynamiq is an agent development platform designed for building, orchestrating, and monitoring autonomous agents. It provides a framework for constructing complex, multi-step workflows using a graph-based engine that supports conditional branching, feedback loops, and iterative task execution. The platform distinguishes itself through its focus on secure, private infrastructure, allowing for the deployment of language models and orchestration services within virtual private clouds to maintain data sovereignty. It integrates retrieval-augmented generation pipelines to ground model responses in

This project is a web-based user interface and multi-model API gateway for interacting with various large language model providers and local inference services. It functions as a retrieval-augmented generation chatbot for private document questioning, a manager for model fine-tuning, and an autonomous agent framework. The system distinguishes itself by integrating an autonomous assistant mode that uses web search and external tools to solve complex, multi-step tasks without manual prompting. It also features an API gateway capable of rotating multiple authentication keys to balance usage and

This repository is a collection of frameworks and guides for Llama models, functioning as a fine-tuning framework, an inference pipeline, and an AI workflow orchestrator. It provides tools for adapting large language models to specific datasets and domains. The project includes a parameter-efficient fine-tuning toolkit that utilizes techniques like low-rank adaptation to reduce memory and compute requirements. It also serves as an implementation guide for retrieval-augmented generation, combining model inference with external data retrieval to improve response accuracy. The capability surfac

Verba is a retrieval-augmented generation interface and chatbot that uses Weaviate to provide factual answers based on private datasets. It functions as a vector database knowledge base, combining a hybrid search engine with an orchestration interface to connect various large language model providers and embedding services. The system differentiates itself through a RAG pipeline manager for adjusting text chunking rules and retrieval settings, alongside a 3D vector space visualization tool for analyzing the spatial organization and clustering of high-dimensional embeddings. It employs a modul

Orama is a search engine and vector database that provides full-text indexing, geospatial calculations, and semantic vector storage. It functions as an LLM retrieval engine designed to provide grounded context to language models for conversational interfaces. The project implements hybrid search by combining dense vector embeddings with inverted keyword indices to retrieve documents based on both semantic meaning and exact text matches. It utilizes a WebAssembly module to execute search logic across different JavaScript environments and platforms. The system covers a broad range of retrieval

The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

This project is a conversational assistant and retrieval-augmented generation system designed to provide technical answers from official documentation and support knowledge bases. It implements a retrieval architecture that routes queries through specialized tools and utilizes a model abstraction layer to switch between different chat and embedding providers without modifying core integration code. The system employs a graph-based state machine for durable agent execution, enabling state persistence and human-in-the-loop interactions. It features an agentic middleware framework that allows fo

LangChainJS is an AI agent orchestrator and application framework designed for building autonomous systems that use large language models to plan and execute tasks. It serves as an integration library that connects language models with tools, memory, and external data sources to create context-aware logic and complex workflows. The project provides a provider-agnostic interface and model provider abstraction, allowing applications to switch between different language model providers without rewriting core logic. It includes a toolkit for retrieval augmented generation, utilizing retrievers to

Everywhere is a desktop AI assistant that understands whatever is on your screen and can act across applications without requiring screenshots or manual context switching. It reads structured UI data through accessibility and automation APIs to perceive the active application and visible content, then provides context-aware help, summaries, translations, and answers to natural language questions about what you are viewing. The tool distinguishes itself by combining on-screen content analysis with a multi-LLM agent platform that routes requests to providers like OpenAI, Anthropic, and local mo