X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.
Lets you write compositional Python programs that compile into self‑improving LLM pipelines — replacing brittle prompt engineering with a declarative, programmatic approach and built‑in algorithms to optimize prompts and weights for RAG, multi‑stage pipelines, and agent loops.
Centralizes logs, metrics, traces, frontend RUM and LLM observability into one self-hostable platform, using Parquet + S3-native storage and SQL/PromQL querying to reduce long‑term storage costs and unify telemetry analysis.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.
Locally hosted frontend that connects to many text, image, and TTS backends (KoboldAI, Ooba, Tabby, OpenAI, Claude, OpenRouter, Mistral, NovelAI, Horde). Built around character cards, lorebooks, group chats, and extensions for deep prompt control.
Drives autonomous penetration testing and CTF solving via cooperating LLM sessions that track a pentest task tree. Scored 86.5% on the XBOW benchmark suite at ~$1.11 per solved task, and works with OpenAI, Claude, Gemini, and local Ollama models.
Runs large language models entirely in C/C++ with no external dependencies, using 1.5-to-8-bit integer quantization and CPU+GPU hybrid inference to fit models larger than available VRAM. Backs Ollama, LM Studio, and most local-inference tooling.
Bring-your-own-key chat client that keeps every conversation in the local browser, never a server. One UI reaches OpenAI, Claude, Gemini, DeepSeek and a dozen more providers across web, desktop and mobile, with MCP, plugins, and one-click self-hosting.
Builds production RAG systems around deep document understanding, explainable chunking, hybrid retrieval, citations, and agent workflows for messy enterprise documents.
Self-hosted AI coding assistant you run on your own hardware as an alternative to cloud Copilot. Offers context-aware completion, an in-IDE answer engine and chat, using RAG over your repositories so suggestions match your team's code.
Puts OpenAI-, Anthropic- and Ollama-compatible endpoints in front of 60+ inference backends, so existing client code runs unchanged against local models for text, vision, audio, image and embeddings. Runs CPU-only or accelerated, data stays local.
Runs open-source LLMs entirely on your own laptop or desktop — no GPU, API key, or cloud required. A cross-platform desktop app with LocalDocs, letting you chat privately over your own files; conversations never leave the machine unless you opt in.