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.
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Get your documents ready for gen AI
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private.
TranslatedChatGLM-6B: An Open Bilingual Dialogue Language Model
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
- knowledge-graph
- large-language-models
- retrieval-augmented-generation
- genai
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
- semantic-search
- emacs
- obsidian-md
- chat
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.
- machine-learning
- ai
- ml
- mlflow
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.
- semantic-search
- information-retrieval
- ai
- python
Toolkit for linearizing PDFs for LLM datasets/training
TranslatedProduction-grade platform for building agentic IM bots - Agent, knowledge base orchestration, plugin system / Bots for Discord / Slack / LINE / Telegram / WeChat(企业微信 (Qiye Weixin, "Enterprise WeChat"), 企微智能机器人 (Qiwei Zhineng Jiqiren, "Enterprise WeChat smart bot"), 公众号 (Gongzhonghao, "official account")) / 飞书 (Feishu, "flying book") / 钉钉 (Dingding, "nail nail") / QQ / Matrix e.g. Integrated with ChatGPT(GPT), DeepSeek, Dify, n8n, Langflow, Coze, Claude, Gemini, GLM, Ollama, SiliconFlow, Kimi, openclaw / hermes agent, deerflow
Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
- agentneo
- agents
- ai-performance-optimization
- llm-testing
Structured Outputs
- generative-ai
- llms
- prompt-engineering
- symbolic-ai
TranslatedChatGLM2-6B: An Open Bilingual Chat LLM
- chatglm
- chatglm-6b
- large-language-models
- llm
Unified framework for building enterprise RAG pipelines with small, specialized models
- parsing
- retrieval-augmented-generation
- agents
- generative-ai-tools
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
- attention-mechanism
- deep-learning
- gpt
- gpt-2
A very simple framework for state-of-the-art Natural Language Processing (NLP)
[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
TranslatedChatGLM3 series: Open Bilingual Chat LLMs
Access large language models from the command-line
A command-line productivity tool powered by AI large language models like GPT-5, will help you accomplish your tasks faster and more efficiently.
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
- bloom
- deep-learning
- distributed-systems
- language-models
TranslatedMedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. Training medical large models, implementing incremental pre-training (PT), supervised fine-tuning (SFT), RLHF, DPO, ORPO, and GRPO.
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
- kv-cache
- llm-inference
- local-llm
- quantization