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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.
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.
Developer framework for building AI agents that autonomously trade on Polymarket prediction markets. Bundles the Polymarket and Gamma APIs, a Chroma RAG layer that pulls in news, and a CLI to query markets, reason with an LLM, and execute trades.
Stores agent memory as human- and agent-readable Markdown files with wikilinks instead of an opaque vector DB. Auto Memory/Resource/Dream jobs distill conversations into long-term notes, and hybrid wikilink + BM25 + embedding search retrieves them.
Provides pre-parsed Parquet snapshots of English and French Wikipedia articles with structured fields (sections, infoboxes, tables, references, images) and credibility signals — optimized for large-scale analysis, retrieval-augmented generation, and model development.
Converts PDFs, Office files, HTML, images and audio into one structured DoclingDocument, with deep PDF layout, reading order, table-structure and formula recognition, OCR, and native LangChain/LlamaIndex/Haystack integrations for RAG pipelines.
Parses, generates, and filters training data from noisy sources like PDFs and weak QA, then feeds it into LLM pre-training, SFT, RL, or RAG cleaning. Ships 100+ operators and ready-made pipelines for text, reasoning, Text2SQL, and agentic data.
Self-hostable alternative to Google NotebookLM: organize PDFs, videos, audio, web pages, and Office docs, then chat over them, take AI-assisted notes, and search via full-text and vector. Routes to 18+ model providers and generates 1-4 speaker podcasts.
Framework for building and orchestrating multi-agent LLM systems, with agent types, tool integration, and human-in-the-loop workflows. Supports multi-agent conversation patterns, multiple LLM providers, and RAG-style tooling for research and prototyping agentic workflows.
Provides a local-first Markdown knowledge graph that LLMs and humans can both read and write via the Model Context Protocol (MCP). Features two-way, editable notes, semantic search (embeddings + hybrid ranking), and optional cloud sync and team workspaces.
Companion resources for Chip Huyen's AI Engineering book: chapter summaries, study notes, prompt examples, case studies, and a few analysis scripts. Focuses on engineering practices for adapting foundation models to production rather than step-by-step code tutorials.
A 100-line LLM framework built on one graph abstraction of nodes and flows, with zero dependencies and no vendor wrappers. The tiny core composes agents, workflows, and RAG, and is small enough for a coding agent to read and extend on its own.