Open-source alternatives to Conivel

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

An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition

Langchain-Chatchat is a system for building retrieval-augmented generation applications and autonomous AI agents. It integrates a knowledge base management system and an agent framework to enable language models to interact with private documents and execute multi-step tasks through external tools. The platform supports local deployment of language models on private infrastructure to operate without an internet connection. It includes a multimodal AI platform that combines vision models for image analysis with text-to-image generation capabilities. The system provides a web-based conversatio

This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.

This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models.

Official code for ACL 2024 paper: VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models.

DB-GPT is an agentic data analysis platform and business intelligence AI that functions as a large language model data assistant. It provides a text-to-SQL interface and a sandboxed code execution environment to translate natural language into executable database queries and Python scripts. The platform utilizes iterative agentic reasoning to plan and execute multi-step data analysis workflows through tool calls. It features a modular skill-based extension system that allows domain knowledge and analysis workflows to be packaged into reusable functional components. The system integrates data

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全局指针统一处理嵌套与非嵌套NER。

nano-graphrag is a retrieval system that uses knowledge graphs to provide structured context for large language model responses. It functions as a knowledge graph indexer that transforms unstructured text into a network of entities and relationships, as well as a hybrid graph retrieval system. The project differentiates itself by combining local neighborhood searches with global community summaries to answer complex natural language questions. It includes a knowledge graph visualizer that generates HTML representations of entities and their relationships to map indexed knowledge. The framewo

Fast-GraphRAG is a system for generating and querying knowledge graphs from domain data. It uses a GraphRAG retrieval workflow to traverse structured data and isolate precise evidence for answering complex questions. The project utilizes an agent-driven retrieval framework to coordinate the querying of knowledge graphs and the synthesis of final answers. It supports incremental data synchronization, allowing structured knowledge bases to be updated in real time as source information evolves. The system integrates with API-compatible language models and embedding providers to power its data p

The code for EMNLP24 paper "Double-Checker: Large Language Model as a Checker for Few-shot Named Entity Recognition"

LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific

ACL2026 "MiniRAG: Making RAG Simpler with Small and Open-Sourced Language Models"

RAG-Anything is a retrieval-augmented generation framework designed to index diverse document formats and perform semantic search using local machine learning models. It functions as a local multimodal data processor, extracting and organizing information from various file types into a unified knowledge base to facilitate private document analysis. The system distinguishes itself through its high-throughput ingestion engine, which processes large batches of documents into searchable vector embeddings. By executing machine learning models directly on local hardware, the framework ensures that

This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated know

Please save your dataset in data folder. Note that CoNLL2003 and WNUT2017 are open-source datasets, ACE2004 and ACE2005 are not free. We keep our CoNLL2003 and WNUT2017 train and test JSON files in data folder.

Ragas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented generation pipelines. It functions as an application optimizer to identify bottlenecks in language model workflows using automated metrics and model-based scoring. The framework includes a system for generating synthetic datasets that mimic production scenarios and edge cases to create realistic test cases. It enables reference-free assessment, allowing the evaluation of response quality by analyzing grounding in the provided context without requiring gold-standard labels. The s

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

This is a meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.

FastGPT is a comprehensive platform for building, deploying, and managing context-aware artificial intelligence applications. It provides a unified environment that integrates custom data sources with language models, utilizing a retrieval-augmented generation engine to ground responses in accurate, domain-specific information. The system is designed for enterprise-scale use, featuring multi-tenant architecture, administrative controls, and secure authentication protocols including OAuth 2.0 and custom single sign-on integration. The platform distinguishes itself through a visual, node-based

Dify is an open-source platform for building, orchestrating, and deploying generative AI applications and autonomous agents. It provides a visual development environment that allows users to design complex, multi-step logic chains and conversational flows, which can then be published as APIs, web interfaces, or embedded widgets. The platform acts as a centralized infrastructure layer, managing model connections, prompt templates, and knowledge retrieval to support scalable AI-powered services. What distinguishes the platform is its focus on stateful application design and workflow orchestrati