Discover the Best AI Resources
Curated essentials, no noise — just what matters
Multi-tenant agent harness that makes enterprise knowledge retrievable, graph-reasonable, and deliverable by LLM-powered agents. Integrates RAG + a Milvus-based knowledge graph, LangGraph orchestration, and document parsing for citation-backed answers and graph reasoning; deployable via Docker (requires a compatible LLM API).
Converts PDFs, images, and Office documents into Markdown or JSON for retrieval, extraction, and agent workflows, with OCR, layout analysis, formula handling, and multiple runtime modes.
Local-first runtime for autonomous AI agents that run on-device and stay model-agnostic across OpenAI, Anthropic, Gemini, Grok, and local models. A plugin system adds chat platforms (Discord, Telegram, X), voice, browser automation, RAG, and wallets.
Edits a codebase from natural-language prompts in the terminal, coordinating specialized sub-agents — file picker, planner, editor, reviewer — instead of one model. Beats Claude Code 61% vs 53% on its own evals; agents scriptable in TypeScript.
Packs a Git repository into a single AI-friendly file for easy ingestion by LLMs. Offers per-file and total token counts, optional Tree-sitter compression, secret scanning, and multiple interfaces (CLI, web, browser extension, Docker, MCP) for AI-driven code review and analysis.
Connects LLM agents to 1,000+ apps (Gmail, Slack, GitHub, Notion, Stripe) with managed OAuth, just-in-time tool selection by intent, and sandboxed Python 3.11 execution. Agents authenticate and act on a user's behalf without bespoke integration code.
Routes each user query to the most suitable agent via a classifier that weighs agent profiles and conversation history, keeping context shared across handoffs. Python and TypeScript, with a SupervisorAgent that runs sub-agents in parallel.
Drives UI automation from screenshots alone: describe steps in natural language and a vision model acts on what it sees, no DOM selectors. One API spans web, Android, iOS, HarmonyOS and desktop; plugs into Playwright/Vitest or runs autonomously.
Open-source platform for autonomous coding agents that work like developers: editing files, running shell commands, browsing the web, and calling APIs in an isolated sandbox. Model-agnostic, with GitHub, Slack, and CI/CD integration.
Offers OpenAI- and Anthropic-compatible access to DeepSeek models, including chat, reasoning, tool calls, JSON output, long-context variants, pricing, rate limits, and agent-tool integration guides.
Runs huge mixture-of-experts LLMs like DeepSeek-R1/V3 on a single 24GB GPU plus CPU DRAM by keeping attention on the GPU and offloading expert weights to CPU. Reports 3-28x speedups via Intel AMX/AVX512 kernels and fits 139K context in 24GB VRAM.
Connects any LLM to internal knowledge sources and lets teams chat with cited, RAG-style answers. Notable for broad connectors (Drive, Notion, GitHub, YouTube), universal LLM/embedding support, and self-hostable Docker deployment — aimed at teams that need private, searchable LLM-backed knowledge.