Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
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OpenAI, GPT 6.1, GPT-Image-2, Whisper API clients for Go. Zero-dependency.
- go
- openai
- golang
- streaming-api
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
- bloom
- deep-learning
- distributed-systems
- language-models
Ollama Python library
- ollama
- python
🌌 A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.
- data-structures
- full-text
- search
- typo-tolerance
All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI Tools & Agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
- python
- fine-tuning
- gguf
- huggingface
🚀 An awesome list of curated Nano Banana pro prompts and examples. Your go-to resource for mastering prompt engineering and exploring the creative potential of the Nano banana pro(Nano banana 2) AI image model.
- gemini
- nanobanana
- prompt-engineering
- prompt-guide
Sketch Material 3 Expressive screens in the browser and turn them into vibe-coding prompts.
- design-tool
- material-3-expressive
- material-design
- material3
🚀 World's largest GPT Image 2 prompt library, updated daily — 2000+ curated prompts with preview images, 16 languages. OpenAI's next-gen image model with pixel-perfect text rendering, cross-image consistency, and commercial-grade illustration. Free & open source.
- ai-image-generation
- ai-prompts
- awesome
- awesome-list
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
Locally run an Instruction-Tuned Chat-Style LLM
High-speed Large Language Model Serving for Local Deployment
Open-source inference platform for language, vision, embedding, reranking, image, audio, and video models. Deploy with multiple engines across heterogeneous hardware, from a laptop to a distributed cluster. Serve models through OpenAI-compatible APIs.
The fast, precise document-intelligence engine for every language: text, tables and metadata from 100+ formats, with OCR, transcription and code intelligence.
- text-extraction
- document-intelligence
- metadata-extraction
- pdf-extraction
~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
- academia
- arxiv
- brave
- deep-research
Open-source, high-performance AI gateway that works as your LLM gateway, MCP gateway and agent gateway. One OpenAI-compatible API for 10,000+ models, with failover, load balancing, semantic caching, virtual keys, budgets and MCP Code Mode. Works with any SDK or agent and adds under 200µs of overhead at 5k RPS.
- gateway
- llm-gateway
- guardrails
- mcp-gateway
Lightweight (7MB) terminal-first AI-native development environment
Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!
🚀 Next Gen Multi-tenant AI One-Stop Solution. Builtin Admin & Billing System. Enterprise-Grade Unified LLM Gateway Support for 200+ Models And 35+ Providers, Load Balacing w/ Priority-base Routing, Cost Management, Chat Share, Cloud Sync, Credit/Subscription Billing, All File Parsing, Web Search, Built-in Model Cache.
An AI-powered search engine with a generative UI
- generative-ai
- generative-ui
- nextjs
- react
❯ Lightweight, beautiful and user-friendly interactive prompts
- prompt
- prompts
- cli
- command-line
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.
- knowledge-graph
- large-language-model
- logical-reasoning
- multi-hop-question-answering