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Tags: LLM tools
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1–24 of 188 · Most stars

Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

  • ocr
  • chineseocr
  • pdf2markdown
  • pp-ocr

TranslatedProvides a practical interactive interface for large language models (LLMs) such as GPT/GLM, with special optimization for the paper reading/polishing/writing experience. Modular design, supports custom shortcut buttons & function plugins, supports project analysis & self-translation for Python, C++ and other projects, PDF/LaTeX paper translation & summarization, supports querying multiple LLM models in parallel, and supports local models such as chatglm3. Integrates Tongyi Qianwen (Qwen), deepseekcoder, iFlytek Spark, ERNIE Bot (Wenxin Yiyan), llama2, rwkv, claude2, moss, and more.

The official gpt4free repository | various collection of powerful language models | opus 4.6 gpt 5.3 kimi 2.5 deepseek v3.2 gemini 3

The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.