llm
Write SQL. Ship confidence. SQL-first tooling for PostgreSQL Type-safe PostgreSQL client code generator.
pGenie validates SQL, manages indexes, and generates type-safe client SDKs — all derived from the migrations and queries in plain SQL.
Low-code AI builder for agentic and RAG applications. Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
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Open Source Enterprise Search & AI Assistant. Give your team superpowers.
Onyx is the open-source AI chat connected to your docs, apps, and people. Open Source AI Platform - AI Chat with advanced features that works with every LLM.
Onyx is a feature-rich, self-hostable Chat UI that works with any LLM. It is easy to deploy and can run in a completely airgapped environment. Onyx comes loaded with advanced features like Agents, Web Search, RAG, MCP, Deep Research, Connectors to 40+ knowledge sources, and more.
Communicate & Survive. When the Grid Goes Down. Offline Knowledge & Survival Platform.
CyberDeck is an offline-first communication and knowledge platform. Transform any device into a decentralized mesh node to chat, share, and learn without internet.
Portable offline knowledge, AI, and mesh communication platform. Run local LLMs, Wikipedia, and decentralized networking without internet.
Kubernetes AI Toolchain Operator.
KAITO is an operator that automates the AI/ML model inference or tuning workload in a Kubernetes cluster. The target models are popular open-sourced large models such as falcon and phi-3.
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Hundreds of models & providers. One command to find what runs on your hardware.
A terminal tool that right-sizes LLM models to your system's RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine.
Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio).
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Running a 397B Parameter Model on a Laptop.
Pure C/Metal inference engine that runs Qwen3.5-397B-A17B (a 397 billion parameter Mixture-of-Experts model) on a MacBook Pro with 48GB RAM at 4.4+ tokens/second with production-quality output including tool calling.
The entire 209GB model streams from SSD through a custom Metal compute pipeline. No Python. No frameworks. Just C, Objective-C, and hand-tuned Metal shaders.
Ollama for GitHub Copilot VS Code Extension. Run Ollama models with full tool and vision support inside GitHub Copilot Chat.
Opilot integrates the full Ollama ecosystem — local models, cloud models, and the Ollama model library — directly into VS Code's Copilot Chat interface. Your conversations never leave your machine when using local models, and you can switch between models without leaving the editor.
Structure LLM Outputs with Ease. Structured outputs powered by LLMs.
The simplest way to extract structured data from LLMs with type safety and validation.
Get reliable JSON from any LLM. Built on Pydantic for validation, type safety, and IDE support.
Introducing Ensu, our first step toward a private, personal LLM app that runs on your device and grows with you over time.
Multi-LLM orchestration plugin for Claude Code — 8 providers (Codex, Gemini, Claude, Perplexity, OpenRouter, Copilot, Qwen, Ollama), 47 commands, 50 skills, Double Diamond workflows .
Multi-LLM orchestration plugin for Claude Code — coordinates Codex, Gemini, Perplexity, OpenRouter, Copilot, Qwen, and Ollama with consensus gates. Eight tentacles, zero blind spots.
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Turn SOC Noise into Real Signals. Autonomous SOC Platform.
Qevlar AI investigates every alert like your top analyst would. So your SOC focuses on real threats, not alert firefighting.
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Can I Run AI locally?
Find out which AI models your machine can actually run.
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A unified TypeScript SDK for building chat bots across Slack, Microsoft Teams, Google Chat, Discord, Telegram, GitHub, Linear, and WhatsApp. Write your bot logic once, deploy everywhere.
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AI-Native Infrastructure Monitoring. Argus uses AI to detect anomalies, investigate incidents, and resolve issues — before your users notice.
AI-native observability for production systems. Automatically understands your application from runtime data and logs. No dashboards. No manual configuration.
Argus is an open-source observability platform with a built-in AI agent that monitors your infrastructure, investigates anomalies autonomously, and proposes fixes — all through a chat interface. Think Datadog + ChatGPT, self-hosted and under your control.
An open-source, self-hosted AI code review bot. Deploy to Vercel, connect a GitHub App, and get on-demand PR reviews powered by Claude.
Build a superior context layer for AI agents.
Empower your AI agents through the leading open-source RAG engine, delivering reliable context and an integrated agent platform, built for enterprise.
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Leading Agentic Workflow Builder.
Dify offers everything you need — agentic workflows, RAG pipelines, integrations, and observability — all in one place, putting AI power into your hands.
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:
The batteries-included agent harness.
The easiest way to start building agents and applications powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory. You can use deep agents for any task, including complex, multi-step tasks.
Agent harness built with LangChain and LangGraph. Equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - well-equipped to handle complex agentic tasks.
Deep Agents is an agent harness. An opinionated, ready-to-run agent out of the box. Instead of wiring up prompts, tools, and context management yourself, you get a working agent immediately and customize what you need.
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AI agents running research on single-GPU nanochat training automatically.
The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model.
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OpenRAG is a comprehensive, single package Retrieval-Augmented Generation platform built on Langflow, Docling, and Opensearch.
Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience.
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the Analytics Agent built for context engineering. Build your agent context like a file system.
Deploy a chat UI for anyone to run analytics on your data.
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A BIg Bench for Large-Scale Relational Database Grounded Text-to-SQLs.
BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) represents a pioneering, cross-domain dataset that examines the impact of extensive database contents on text-to-SQL parsing. BIRD contains over 12,751 unique question-SQL pairs, 95 big databases with a total size of 33.4 GB. It also covers more than 37 professional domains, such as blockchain, hockey, healthcare and education, etc.
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Official inference framework for 1-bit LLMs.
bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models on CPU and GPU (NPU support will coming next).
OBLITERATE THE CHAINS THAT BIND YOU. Break the chains. Free the mind. Keep the brain.
OBLITERATUS is the most advanced open-source toolkit for understanding and removing refusal behaviors from large language models — and every single run makes it smarter. It implements abliteration — a family of techniques that identify and surgically remove the internal representations responsible for content refusal, without retraining or fine-tuning. The result: a model that responds to all prompts without artificial gatekeeping, while preserving its core language capabilities.
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Semgrep Pro Rules to ensure code using LLMs is following best practices.
Semgrep rules that catch common trust & safety mistakes in LLM-powered applications. Scan any codebase in seconds to find hardcoded API keys, missing safety checks, prompt injection risks, and unhandled errors across all major AI providers.
Talk to your Mac, query your docs, no cloud required. On-device voice AI + RAG.
RCLI is an on-device voice AI for macOS. A complete STT + LLM + TTS pipeline running natively on Apple Silicon — 38 macOS actions via voice, local RAG over your documents, sub-200ms end-to-end latency. No cloud, no API keys.
The AI Operator Living in Your Web Page. JavaScript in-page GUI agent. Control web interfaces with natural language.
🪄One line of code, turns your website into an AI-native app. Users give natural language commands, AI handles the rest.
macOS menu bar app that exposes Apple's on-device Foundation Models as an OpenAI-compatible local API. Zero cloud. Zero dependencies.
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Threat Designer is a GenerativeAI application designed to automate and streamline the threat modeling process for secure system design.
Threat Designer is an AI-driven agent that automates and streamlines the threat modeling process for secure system design. Harnessing the power of large language models (LLMs), it analyzes system architectures, identifies potential security threats, and generates detailed threat models—empowering developers and security professionals to incorporate security from the earliest stages of development.
Open Source Data Labeling.
The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI systems. Label Studio is a multi-type data labeling and annotation tool with standardized output format. Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible.
AReaL is an open-source fully asynchronous reinforcement learning training system for large reasoning and agentic models, developed by members from Tsinghua IIIS and the AReaL Team at Ant Group. Built upon the open-source project ReaLHF, we are fully committed to open-source principles by providing the training details, data, and infrastructure required to reproduce our results, along with the models themselves. AReaL aims to help everyone build their own AI agents easily and affordably. Our team loves milk tea because it's delicious, customizable, and affordable—we hope you enjoy our project just as much as you'd enjoy real milk tea. Cheers!
The vector database that gets smarter the more you use it — and now ships as a cognitive container.
RuVector is a High Performance, Real-Time, Self-Learning, Vector Graph Neural Network, and Database built in Rust.
Most vector databases are static — they store embeddings and search them. That's it. RuVector is different: it learns from every query, runs LLMs locally, scales horizontally, boots as a Linux microservice from a single file, and costs nothing to operate.
spec-driven, production-ready code generation.
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LLM Vulnerability Scanner.
Test large language models against 210+ adversarial attacks covering prompt injection, jailbreaks, encoding exploits, and data extraction.
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Cross-linguistic entity matching across early modern texts.
A prototype for a digital history project aimed at creating a searchable concordance, knowledge graph, and resources for better understanding premodern science and natural knowledge.
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Learn Languages While You Watch. No More Memorizing.
Turn YouTube, Netflix, and Bilibili into your personal language tutor. Just watch naturally, and let AI help you understand and learn new words instantly.
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Write agentic workflows in natural language markdown, and run them in GitHub Actions.
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Context window optimization for LLMs. Reliable LLM outputs start with clean context.
A reliability layer for LLM context. Deterministic deduplication that removes redundancy before it reaches your model. Less redundant data. Lower costs. Faster responses. More efficient & deterministic results.
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LLM Service Fingerprinting Tool.
Simple LLM service identification - translate IP:Port to Ollama, vLLM, LiteLLM, or 30+ other AI services in seconds.
Julius is an LLM service fingerprinting tool for security professionals. It detects which AI server software is running on network endpoints during penetration tests, attack surface discovery, and security assessments.
Unlike model fingerprinting tools that identify which LLM generated text, Julius identifies the server infrastructure: Is that endpoint running Ollama? vLLM? LiteLLM? A Hugging Face deployment? Julius answers in seconds.
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AI-powered subdomain enumeration tool with local LLM analysis via Ollama - 100% private, zero API costs.
God's Eye is a powerful, ultra-fast subdomain enumeration and reconnaissance tool written in Go. It combines multiple passive sources with active DNS brute-forcing and comprehensive security checks to provide a complete picture of a target's attack surface.
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Kubernetes-native AI serving platform for scalable model serving.
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Intercept LLM API traffic and visualize token usage in a real-time terminal dashboard. Track costs, debug prompts, and monitor context window usage across your AI development sessions.
tokentap tracks token usage for LLM CLI tools with a live terminal dashboard. See exactly how many tokens you're using in real-time.
Query your data in plain English.
Turn natural language questions into SQL queries with a small, local model that matches cloud LLM accuracy.
We fine-tuned a small language model to convert plain English questions into executable SQL queries. Because it's small, you can run it locally on your own machine, no API keys, no cloud dependencies, full privacy. Load your CSV files, ask questions, get answers.
Human-like Document AI
PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, without vector databases or chunking.
AI Code Security Anti-Patterns distilled from 150+ sources to help LLMs generate safer code.
A comprehensive security reference distilled from 150+ sources to help LLMs generate safer code.
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A native command-line interface for working with Apple Core ML models on macOS. Inspect, run inference, benchmark, and manage Core ML models without Xcode or Python.
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An all-in-one enhancement suite for Google Gemini - timeline navigation, folder management, prompt library, and chat export in one powerful extension.
A deterministic, autonomous loop runner for the Codex CLI. It processes exactly one task per iteration from a JSON backlog, with fresh context each run, and keeps a JSONL audit log for traceability.
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Autonomous task execution plugin for LLM CLI - refactored modular architecture.
llm loop is a powerful plugin for the LLM CLI tool that enables autonomous, goal-oriented task execution. Unlike traditional single-turn LLM interactions, llm loop allows the AI to work persistently towards a goal by making multiple tool calls, analyzing results, and iterating until the task is complete.
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AI-Powered Reverse Engineering with Ghidra.
OGhidra bridges Large Language Models (LLMs) via Ollama with the Ghidra reverse engineering platform, enabling AI-driven binary analysis through natural language. Interact with Ghidra using conversational queries and automate complex reverse engineering workflows.
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Agent harness framework for building, running, and verifying LLM workflows.
Gambit helps you build reliable LLM workflows by composing small, typed “decks” with clear inputs/outputs and guardrails. Run decks locally, stream traces, and debug with a built-in UI.
The Best Agent Harness. Meet Sisyphus: The Batteries-Included Agent that codes like you.
Open Responses is an open-source specification and ecosystem for building multi-provider, interoperable LLM interfaces based on the OpenAI Responses API. It defines a shared schema, and tooling layer that enable a unified experience for calling language models, streaming results, and composing agentic workflows—independent of provider.
A simple, open format for giving agents new capabilities and expertise. Agent Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.
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LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.
It adopts a modular architecture that combines multimodal preprocessing, semantic vector indexing, intelligent retrieval, and large language model inference. At its core, WeKnora follows the RAG (Retrieval-Augmented Generation) paradigm, enabling high-quality, context-aware answers by combining relevant document chunks with model reasoning.
The Context Optimization Layer for LLM Applications.
Cut your LLM costs by 50-90% without losing accuracy.
Semantic search for agents.
A calm, CLI-native way to semantically grep everything, like code, images, pdfs and more.
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Understand Privacy & Legal Terms. Plain-language analysis of Terms & Conditions. Know what you're agreeing to before you sign up.
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