Open source

AI Projects

Open-source AI projects with momentum data from GitHub snapshots: growth, maintenance, releases and licence at a glance.

VoiceStudio
debpalash/VoiceStudio
Huia Project Score 80
VoiceStudio is a desktop app that runs speech AI on your own computer: it clones and designs voices, generates speech from text, dubs videos, transcribes audio and has a dictation widget. The app is built on Electron with a Python backend. Its default speech engine is k2-fsa's OmniVoice, and the feature catalog lists more than a dozen alternatives, among them CosyVoice 3, KittenTTS, MLX-Audio and IndexTTS 2.5. Transcription defaults to WhisperX, with Faster-Whisper, Parakeet, Moonshine and others available. Models are downloaded when a workspace first needs them. The project says its tools cover 646 languages, a figure that also appears in the transcription tool of its MCP docs. Beyond the desktop workspaces, VoiceStudio exposes a local API and an MCP server on port 3900, so coding agents can generate speech, clone a voice or transcribe a file. The README states that local workflows stay on your hardware, remote workers are optional and usage analytics only run with consent. Version 0.5.3 was the last release built on Tauri; current releases are Electron only, and Tauri users have to install the new app separately.
Stars
61K
Language
Python
License
AGPL-3.0
Momentum
+16.0% 7dTrending
qwen-audio-agent
QwenAudio/qwen-audio-agent
Huia Project Score 79
qwen-audio-agent is a realtime voice runtime that sits in front of a coding or task agent. You talk to a voice model, longer jobs go to a backend agent while the conversation continues, and when a task finishes its result comes back into the same conversation. The voice side is swappable. Cloud options listed in the README include Qwen Audio 3.0 Realtime, GPT-Live / OpenAI Realtime, Google Gemini Live and Doubao Seeduplex 3.0 Realtime, each needing that provider's API key. Hugging Face Speech-to-Speech and MiniCPM-o 4.5 can run locally. Backend agents connect mostly over ACP: the list covers Qwen Code, OpenCode, OpenClaw and Kimi Code, with Codex and Claude Code joining through an external ACP adapter. You can use it from a WebUI, a terminal TUI or a desktop floating orb on macOS, Windows and Linux. The README also lists parallel background tasks you can query or cancel, cross-session memory, and scheduled reminders. The package is published on npm under Apache-2.0, a technical report is on arXiv, and the repository sits in the QwenAudio GitHub organization.
Stars
2.9K
Language
JavaScript
License
Apache-2.0
Momentum
Emerging
ai-engineering-from-scratch
rohitg00/ai-engineering-from-scratch
Huia Project Score 79
AI Engineering from Scratch is a free, MIT-licensed course on GitHub that teaches how AI systems work by having you write each piece yourself, starting with linear algebra and ending with multi-agent systems and production infrastructure. The material is split into 20 phases and 523 lessons, with code in Python, TypeScript, Rust and Julia. Each lesson folder holds a written explanation, runnable code and an outputs directory. The pattern is to build an algorithm such as backpropagation, a tokenizer, attention or an agent loop from plain math first, and only then run the same idea through a library like PyTorch or scikit-learn. Lessons end with something you keep: a prompt, a SKILL.md file, an agent or an MCP server. The repository holds 396 skills and 99 prompts produced this way, plus a script that copies them into an agent's skills folder. There are three ways to follow it. You can read lessons on the website with no setup, clone the repository and run the code locally, or install the course's tutor skills into a coding agent such as Claude Code or Codex. Those skills run a placement quiz, write a study plan to LEARNING.md and teach one lesson per session. Separate tracks prepare for the four Claude certification exams and the MCP Associate exam; the README says these are independent and not affiliated with the exam bodies.
Stars
68K
Language
Python
License
MIT
Momentum
+8.2% 7dEstablished
ponytail
DietrichGebert/ponytail
Huia Project Score 76
Ponytail is a rule set and skill collection that pushes an AI coding agent to write as little code as a task needs. It ships as a plugin for Claude Code, Codex, GitHub Copilot CLI, Gemini CLI and several other agent hosts, and as plain rules files for editors such as Cursor, Windsurf and Cline. Before writing anything, the agent works down a fixed ladder: drop the feature if it does not need to exist, reuse what the codebase already has, then try the standard library, a native platform feature, an installed dependency and a one-line answer. New code comes only after all of those fail. The README is firm that validation, error handling, security and accessibility are off limits for cutting. Its example: asked for a date picker, the agent reaches for a native <input type="date"> instead of adding a library and a wrapper component. The plugin adds commands to set the intensity (lite, full, ultra or off), to review a diff or a whole repository for over-engineering, and to collect deferred shortcuts into a ledger. The maintainer publishes a benchmark: across twelve feature tasks on a FastAPI and React template with Haiku 4.5, the README reports about 54% fewer lines of code than the same agent without the skill, plus smaller drops in tokens, cost and time.
Stars
162K
Language
JavaScript
License
MIT
Momentum
+5.7% 7dEstablished
iFixAi
ifixai-ai/iFixAi
Huia Project Score 75
iFixAi is an open-source Python tool that audits AI agents and models by running a set of test inspections and returning an A to F grade with a written report. The free engine has 60 inspections. The grade comes from five core pillars: fabrication, manipulation, deception, unpredictability and opacity. Another 28 inspections from twenty premium categories are included and reported on their own, outside the grade. A grade counts as citable only when a judge model from a different vendor scores the run, so a full audit needs two API keys. You can test a bare model through providers such as Anthropic, OpenAI, Gemini, Azure, Bedrock and OpenRouter, or point the tool at your own agent's OpenAI-compatible HTTP endpoint. There are three ways to run it: a setup wizard, a CLI with explicit flags for CI, or a plugin for Claude Code and Codex, with a scaffolded /ifixai-skill command for other agents. Reports land as JSON and Markdown in ./ifixai-results/. The README estimates judge costs for a full run with a Sonnet judge at roughly $12 to $18, billed by the provider. The company also sells iFixAi Pro, a paid audit service with up to 400 inspections. Telemetry is on by default outside CI and can be switched off.
Stars
24K
Language
Python
License
Apache-2.0
Momentum
Established
agent-skills
addyosmani/agent-skills
Huia Project Score 75
Agent Skills is a repository of 25 Markdown workflows for AI coding agents, covering software work from specification and planning through testing, review, performance, security, deployment and documentation. Each skill is a SKILL.md process with triggers, steps, red flags, verification criteria and common rationalizations to counter. The pack can be installed with the skills CLI or integrated with tools including Claude Code, Cursor, Gemini CLI, OpenCode, GitHub Copilot and Codex. Nine slash commands map development tasks to skills. /build auto can generate a plan and implement its tasks after one approval, while stopping on failures or risky steps. This suits teams that want an agent to follow a defined engineering process instead of relying on isolated prompts. The repository also contains four review personas, shared checklists, evaluation cases and host-specific adapters. One installation limitation matters: installing a single skill through npx does not copy the repository-level references/ directory, so links to shared checklists are unavailable unless those files are supplied another way.
Stars
106K
Language
JavaScript
License
MIT
Momentum
+4.9% 7dEstablished
OpenMontage
calesthio/OpenMontage
Huia Project Score 74
OpenMontage is a Python toolkit that turns an AI coding assistant such as Claude Code, Cursor, Copilot, Windsurf or Codex into a video production setup. You describe the video, and the assistant moves through fixed stages: research, proposal, script, scene plan, assets, edit and compose. No separate orchestrator program runs the show. The assistant reads YAML pipeline manifests and a Markdown director skill for each stage, then calls the Python tools itself. The repository has pipelines for animated explainers, cinematic trailers, documentary montages, talking-head edits, screen demos, podcast clips, and localization with dubbing, among others. It connects to many providers, including Kling, Veo, Runway, FLUX, ElevenLabs and Suno, and to local options: Piper for speech, WAN or Hunyuan video models on a GPU, and Remotion, HyperFrames and FFmpeg for rendering. With no API keys at all it can still narrate with Piper, pull footage from Archive.org, NASA and Wikimedia Commons, and render with Remotion. Creative stages wait for your approval, and a local board called Backlot shows each stage, the generated assets and the money spent. Budget controls estimate costs before generation, with a default total cap of $10 and a confirmation step for any single action above $0.50.
Stars
68K
Language
Python
License
AGPL-3.0
Momentum
+8.0% 7dEstablished
orca
stablyai/orca
Huia Project Score 74
Orca is a desktop development environment for running multiple AI coding agents side by side, with each task isolated in its own Git worktree. It can launch agents such as Claude Code, Codex, Cursor CLI and OpenCode, while keeping terminals, files, diffs and browser sessions tied to each worktree. The project is aimed at developers who already use coding-agent command-line tools and want one place to manage several sessions. Orca runs on macOS, Windows and Linux. Worktrees can run locally, on SSH machines or on a remote Orca server that you control. Its built-in browser includes a Design Mode that can send an element's HTML, CSS and screenshot to an agent. The CLI can create worktrees, control terminals, open files and diffs, and automate Orca's browser. A mobile companion for iOS and Android can monitor sessions, send replies, inspect files and source control, and receive completion notifications. Orca does not provide an AI model or hosted compute. You bring the coding-agent accounts or subscriptions you already use, and remote execution uses machines or cloud accounts under your control.
Stars
91K
Language
TypeScript
License
MIT
Momentum
+7.8% 7dEstablished
omlx
jundot/omlx
Huia Project Score 73
oMLX is a local LLM inference server for Macs with Apple Silicon, built on MLX and managed from a menu bar app. It exposes OpenAI-compatible and Anthropic-compatible endpoints, so existing clients can connect to http://localhost:8000/v1 without code changes. Its main idea is a two-tier KV cache. Frequently used cache blocks stay in RAM, and when that hot tier fills up, blocks are offloaded to the SSD in safetensors format so they can be reused instead of recomputed. The author pitches this at long coding sessions with tools such as Claude Code. Concurrent requests go through continuous batching using mlx-lm's BatchGenerator. The server loads text LLMs, vision-language models, OCR models, embeddings and rerankers. Several models can stay loaded at once, with least-recently-used eviction, pinning and a per-model idle timeout. A web admin dashboard at /admin covers monitoring, model management, chat and benchmarks. Source builds also include an experimental mode that splits one model across several Macs. You can install it as a .dmg from GitHub Releases, through Homebrew, or from source, and it is licensed under Apache-2.0.
Stars
23K
Language
Python
License
Apache-2.0
Momentum
Established
ECC
affaan-m/ECC
Huia Project Score 73
ECC is an open-source system that adds reusable engineering workflows, skills, agents, memory, rules, hooks and security checks to coding agents. Its main path is Claude Code, with guided installation also available for Codex and Kimi Code. The repository also contains adapters for other coding agents, but the project warns that support is not equal across them. The current repository includes 68 agents, 293 skills and 94 legacy command shims. The supplied workflows cover tasks such as planning, testing, implementation, review, build repair, security and architecture. ECC also includes AgentShield integration for scanning agent configuration, prompts, hooks, MCP settings, permissions, secrets and related files. Hooks and memory can enforce checks outside the prompt, store session information and support its continuous-learning system. The easiest starting point is the ecc-universal guided installer. Claude Code setup needs Node.js 18 or newer, Git and Claude Code 2.1 or newer on PATH. The multi-agent installer can configure Claude Code, Codex and Kimi Code in one reviewed flow. Kimi installation is more limited: ECC writes managed project files but does not configure Kimi hooks, model or provider settings, or authentication.
Stars
278K
Language
JavaScript
License
MIT
Momentum
+2.2% 7dEstablished
Agent-Reach
Panniantong/Agent-Reach
Huia Project Score 72
Agent Reach is a Python command-line tool that prepares an AI agent to read and search sites that are awkward to reach from a script, including Twitter/X, Reddit, YouTube, Bilibili, XiaoHongShu and GitHub. It leaves the actual reading to other tools. For each platform it picks an upstream project (Jina Reader for web pages, yt-dlp for YouTube, gh for GitHub, feedparser for RSS, twitter-cli and OpenCLI for social sites), installs it, checks that it works and gives the agent a SKILL.md file describing what to call. Every platform has an ordered list of backends, and when one breaks the maintainers move the next one up. The README cites June 2026, when Bilibili blocked yt-dlp and the tool switched to bili-cli. Running agent-reach doctor shows which channels work and which backend each is using. Web pages, YouTube, RSS, public GitHub repositories, V2EX and basic Bilibili search need no setup. Twitter, Reddit, XiaoHongShu, Facebook and Instagram need a logged-in session, either from cookies you export by hand or through OpenCLI reusing your desktop Chrome login. The main README is in Chinese; English, Japanese and Korean versions sit in the docs folder.
Stars
97K
Language
Python
License
MIT
Momentum
+7.7% 7dEstablished
graphify
Graphify-Labs/graphify
Huia Project Score 72
Graphify turns a software project and related material into a queryable knowledge graph. It parses code locally with tree-sitter, connects structures such as calls, imports and inheritance, and writes the result to graph.json. It can also produce an interactive graph.html viewer and a GRAPHREPORT.md report. Queries can search the graph, explain a concept or trace a path between concepts. Code analysis does not require an LLM or API key. Graphify distinguishes relationships extracted directly from source from relationships resolved by Graphify. It does not use embeddings or a vector store for its graph. Documents, PDFs and images can join the same graph, but their semantic processing uses an AI assistant or configured model backend rather than the code-only parser. The project also supplies a skill that can be registered with supported coding assistants including Claude Code, Cursor, Codex and Gemini CLI.
Stars
126K
Language
Python
License
Apache-2.0
Momentum
+2.0% 7dEstablished
llmfit
AlexsJones/llmfit
Huia Project Score 72
llmfit is a terminal tool, written in Rust, that inspects your hardware and tells you which open-source large language models it can run well. It detects CPU cores, system RAM, GPUs, VRAM and unified memory (NVIDIA CUDA, Apple Silicon, AMD ROCm and Intel OneAPI), then estimates memory use and tokens per second for each model across quantizations. Each model gets scores for quality, speed, fit and context, plus a fit verdict that runs from Perfect to Too Tight. The default interface is an interactive TUI. A classic CLI mode, a web dashboard and a REST API (/api/v1/system and /api/v1/models) are also available. llmfit works with Ollama, llama.cpp, MLX, Docker Model Runner and LM Studio, and can download a chosen model through them. Speed figures come from a memory-bandwidth formula unless a measurement exists. A newer benchmark feature measures real tok/s on your machine and lets you submit the result to the project as a pull request from the TUI, so later releases carry measured numbers for matching hardware. The tool runs on macOS, Linux and Windows and is MIT licensed. The latest release is v1.1.17.
Stars
38K
Language
Rust
License
MIT
Momentum
Established
caveman
JuliusBrussee/caveman
Huia Project Score 72
Caveman is a skill plus a local proxy that reduce the tokens a coding agent spends, both in what it writes back and in what it reads. The skill changes the reply style: answer first, one idea per sentence, no greeting or recap. Code, commands, file paths, error messages and numbers are kept character for character, and your own prompts are never rewritten. There are three levels (/caveman, /ultracave and /megacave, the last one in classical Chinese) and extra commands for one-line commit messages, terse code review and shrinking memory files such as CLAUDE.md. The second part is the caveman CLI, which runs on your machine and compresses bulky input before the agent sees it: logs, CSV, YAML, JSON and test output. Originals stay on disk so the agent can pull them back. The CLI also has caveman browse for compact web pages and caveman learn, which ranks where tokens went in past sessions. Separate TypeScript and Python middleware packages apply the same compression to agents built on the Vercel AI SDK, LangChain, OpenAI or Anthropic calls. The project's own benchmark reports 33.2% fewer input tokens over 54 Claude Code runs, with 18 of 18 answers correct. The same table shows HTML sessions getting 9.9% worse, because HTML has no compressor yet.
Stars
111K
Language
Go
License
Apache-2.0
Momentum
+1.6% 7dEstablished
codebase-memory-mcp
DeusData/codebase-memory-mcp
Huia Project Score 71
codebase-memory-mcp is an MCP server, written in C, that parses a code repository into a knowledge graph so an AI coding agent can ask structural questions (what calls this function, which HTTP routes exist, what a change affects) instead of reading files one by one. It ships as one native executable for macOS, Linux and Windows. Parsing uses tree-sitter grammars compiled into the binary, and the README lists 162 languages. A hybrid LSP layer adds type resolution for a smaller set, including Python, TypeScript, Go, Java and Rust. Agents reach the graph through 17 MCP tools covering search, call tracing, architecture overviews, change detection, Cypher queries and architecture decision records. A 3D graph viewer is built into the binary and opens at localhost:9749. The installer finds supported agents on your machine and writes their MCP configuration; the README counts 45 client surfaces, among them Claude Code, Codex, Gemini CLI, Cursor and Windsurf. The authors describe the design in an arXiv preprint and report 83% answer quality with 10 times fewer tokens than file-by-file exploration across 31 repositories. Indexing and queries run locally without an API key.
Stars
46K
Language
C
License
MIT
Momentum
+1.6% 7dEstablished
ui-ux-pro-max-skill
nextlevelbuilder/ui-ux-pro-max-skill
Huia Project Score 71
UI UX Pro Max is an agent skill that hands coding assistants a searchable library of design guidance, so a request such as a landing page for a spa comes back with a matching style, color palette, font pairing and page structure. It installs into Claude Code, Cursor, Windsurf, GitHub Copilot, Codex, Gemini CLI, Kiro and about a dozen more assistants, through the Claude Code plugin marketplace or the uipro command from the ui-ux-pro-max-cli npm package. Under the hood it is a set of CSV datasets and a Python search script that ranks entries with BM25. The README counts 79 searchable UI styles (50 active), 192 palettes tied to 192 product types, 74 font pairings, 25 chart types, 119 UX guidelines and advice for 22 stacks, from React and Next.js to SwiftUI, Flutter and WPF. Version 2.0 added a design system generator that merges those searches into one recommendation, with anti-patterns to avoid and a pre-delivery checklist covering contrast, focus states and reduced motion. You can save the result as a master file with per-page overrides that the agent reads in later sessions. In most supported assistants the skill switches on by itself for UI requests. Kiro, Copilot, Roo Code and KiloCode use the /ui-ux-pro-max slash command instead. A paid premium edition with brand and asset features is sold separately on uupm.cc.
Stars
135K
Language
Python
License
MIT
Momentum
+1.9% 7dEstablished
headroom
headroomlabs-ai/headroom
Huia Project Score 71
Headroom is a Python and TypeScript tool that shrinks the text an AI agent sends to a model (tool output, logs, files, RAG chunks and chat history) so each request uses fewer tokens. The compression runs on your own machine. You can use it in four ways: a compress() library for Python and TypeScript, a local proxy you point an app at, a headroom wrap command that starts coding agents such as Claude Code, Codex or Aider through that proxy, or an MCP server. A router looks at each piece of content and picks a compressor: one for JSON, one for source code, and a small Hugging Face model called Kompress-v2-base for prose. Originals stay in a local cache, and the model can call a retrieve tool when the short version is not enough. The project publishes its own offline benchmark of four MCP-style scenarios, with savings from 21% to 57%. The README also describes steering the model toward shorter replies and labels that saving as an estimate. The latest release is v0.39.1, from September 2026.
Stars
75K
Language
Python
License
Apache-2.0
Momentum
+1.1% 7dEstablished
DigUp
ARahim3/DigUp
Huia Project Score 70
DigUp is a free macOS menu bar app that searches the files on your Mac by what is in them, using Google DeepMind's EmbeddingGemma 2 model running locally. You pick the folders it may read. DigUp then indexes pictures, screenshots, PDFs, documents, audio recordings and videos, and you find things by typing a plain description. A hit in a video or recording opens at the matching moment, and a PDF hit opens at the page. Exact strings such as invoice numbers and error codes are matched too, including text that Apple's Vision framework reads from screenshots. Code search is a separate index you switch on yourself: queries start with code: and results open at the line in editors such as Zed, VS Code, Cursor and Xcode. The README says nothing you index leaves the Mac. The app goes online to download the model once (865 MB) and for a daily update check that can be turned off, and it has no account and no telemetry. Internally it runs EmbeddingGemma 2 on llama.cpp with Metal and keeps vectors plus a keyword index in one SQLite file. It needs an Apple Silicon Mac on macOS 14 or later. The latest release is DigUp 0.6.0.
Stars
1.5K
Language
Swift
License
MIT
Momentum
Emerging
rtk
rtk-ai/rtk
Huia Project Score 70
RTK (Rust Token Killer) is a command-line proxy that trims the terminal output of common developer commands before an AI coding agent reads it, so less of the agent's context fills up with noise. It ships as one Rust binary. When an agent runs something like git status, a hook installed by rtk init swaps it for rtk git status, which returns a compact version: failing tests only, grouped lint errors, one-line git confirmations, shortened directory trees. The README lists filters for git, the GitHub CLI, test runners such as pytest, Jest and cargo test, linters, package managers, Docker, kubectl, AWS and Pulumi, and claims more than 100 supported commands with under 10 ms of added time. When a command fails, RTK keeps the full unfiltered output so the agent can fetch it with rtk recall instead of running the command again. The project is careful about its own numbers. It says it cuts up to 90% of the bash output an agent reads, and states plainly that this does not mean a 90% smaller bill, since shell output is only part of the input tokens. Token counts in rtk gain are estimated as bytes divided by four because RTK has no tokenizer.
Stars
83K
Language
Rust
License
Apache-2.0
Momentum
+1.0% 7dEstablished
mempalace
MemPalace/mempalace
Huia Project Score 69
MemPalace is a Python memory layer for AI assistants: it saves your past conversations and project files word for word on your own machine, then lets you or your agent search them by meaning. The project deliberately avoids summarizing what it stores. Records are filed into a hierarchy the authors call a palace: each person or project gets a wing, subjects inside it become rooms, and the original text sits in drawers. A search can then stay inside one wing instead of scanning everything. ChromaDB is the default vector store, and the repository ships opt-in adapters for SQLite, a native Rust engine, Milvus, Qdrant and pgvector. Day to day, you point the mempalace CLI at a code folder or at Claude Code session logs, and it indexes them. An MCP server exposes the palace to agents with 45 tools, and hooks for Claude Code, Codex CLI and Cursor save sessions on a schedule and before context compression. The README reports 96.6% recall at five on the LongMemEval benchmark using plain semantic search with no language model involved, and says the scripts to reproduce that figure are in the repository.
Stars
60K
Language
Python
License
MIT
Momentum
+0.2% 7dEstablished
TencentDB-Agent-Memory
TencentCloud/TencentDB-Agent-Memory
Huia Project Score 69
TencentDB Agent Memory is a self-hosted memory service from Tencent Cloud that stores what AI agents learn and shares it across agents and team members. It turns past work into four kinds of memory asset. Chat Memory keeps preferences, facts and decisions, distilled from raw conversation through layers named L0 Conversation, L1 Atom, L2 Scenario and L3 Persona. Skills capture reusable procedures with versions and validation rules. LLM-Wiki builds linked pages from documents, and CodeGraph indexes symbols, files and call relationships so an agent can check what a change might affect. A web panel called Memory Hub tracks ownership, versions and access, with private, team and ACL-based visibility. Agents connect through a proxy. You point the agent's base URL at it, and the README says no plugin, hook or MCP server is needed. Listed clients include Claude Code, Codex, CodeBuddy, WorkBuddy, Hermes and OpenClaw. Setup starts three services (memory-core, memory-hub and proxy) with Docker and a start script, and asks for two sets of LLM settings. The README reports a PersonaMem score of 76% with the system against 48% without it, which is the project's own figure. Team Memory is labeled beta.
Stars
28K
Language
TypeScript
Momentum
Established
nanobot
HKUDS/nanobot
Huia Project Score 69
nanobot is a self-hosted personal AI agent framework in Python from HKUDS. You run it on your own computer or server and talk to it through a browser WebUI, the terminal, or chat apps such as Telegram, Discord, Slack, WeChat, email and Mattermost. The agent can read and write files, run shell commands, search and fetch web pages, call MCP servers, schedule jobs with cron, generate images and hand work to subagents. Session history and long-term memory are handled by a component the project calls Dream. Models come from OpenAI-compatible APIs or local servers such as Ollama and vLLM, with fallback models configurable. nanobot can also expose its own OpenAI-compatible API, and it has a Python SDK for integrations. The stable track is the nanobot-ai package on PyPI, installed with a one-line script, uv or pip; the latest release is v0.3.5. Newer and experimental features land on the main branch first and need a source install. Documentation is at nanobot.wiki in ten languages.
Stars
49K
Language
Python
License
MIT
Momentum
+0.5% 7dEstablished
Understand-Anything
Egonex-AI/Understand-Anything
Huia Project Score 68
Understand Anything turns a codebase or supported knowledge base into an interactive knowledge graph for exploring files, functions, classes, dependencies, and relationships. Its code analysis combines Tree-sitter structural parsing with LLM-generated summaries, tags, architectural layers, tours, and business-domain information. The dashboard lets you search the graph, inspect nodes, follow relationships, and view code organized by architectural layer. The main /understand workflow creates .ua/knowledge-graph.json, with older projects continuing to use .understand-anything/. Later runs are incremental by default and re-analyze changed files. Separate commands cover change-impact analysis, explanations of specific code, onboarding guides, domain graphs, and Karpathy-pattern wiki analysis. The project supports Claude Code and a range of other coding agents and IDE integrations. Generated graph data can be committed to a repository and opened by teammates with the standalone viewer. That viewer needs Node.js 18 or newer and reads the graph from local disk without LLM calls. Building or running the analysis code from source requires Node.js 22 or newer and pnpm 10 or newer.
Stars
86K
Language
TypeScript
License
MIT
Momentum
+1.1% 7dEstablished
PixelRAG
StarTrail-org/PixelRAG
Huia Project Score 68
PixelRAG is a retrieval system that turns web pages, PDFs and images into screenshot tiles and searches those images instead of extracted text. It is the official code for the arXiv paper "PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation", written by researchers at Berkeley SkyLab, BAIR and Berkeley NLP. The pipeline runs in stages. pixelshot renders a document into tiles with a headless Chromium browser driven over CDP. The pixelrag command then chunks and embeds the tiles with Qwen3-VL-Embedding and LoRA adapters fine-tuned on screenshot data, builds a FAISS index, and serves search through a FastAPI app on CPU or GPU. Each stage is its own pip extra, so a project installs only the parts it uses. The team also runs a hosted index of 8.28M Wikipedia pages behind a live API, which takes text or image queries; the README says it needs no API key. The renderer doubles as a Claude Code plugin called pixelbrowse, which has Claude screenshot a page and read the image, and as an npm package for opencode. Training code sits in a separate train/ project with its own pinned PyTorch and transformers versions.
Stars
10K
Language
Python
License
Apache-2.0
Momentum
Established
oh-my-openagent
code-yeongyu/oh-my-openagent
Huia Project Score 66
OmO (the repository is oh-my-openagent) is a terminal agent for coding and other work, started with a single omo command. It runs on senpi, the author's fork of the pi coding agent, with extra features built in. You open a project, run omo and describe the job. Adding the keyword ultrawork (or ulw) to a prompt tells the agent to read the codebase first, plan, check each step and verify the result before it stops. Typing mass ulw splits a large job into a graph of agents that run in parallel, each on a model picked for its part. A memory feature called Kibitzer keeps notes as markdown files in a git repository and has a cheaper model warn the main agent before it repeats an earlier mistake. The /login command signs in with Claude, ChatGPT, Kimi and GLM subscriptions. OmO installs as a native binary through a shell script (PowerShell on Windows) or as the omo-ai package from npm or Bun. People on the older OpenCode edition can carry their settings over with omo setup. GitHub lists the licence as "Other"; the repository's LICENSE.md is the Sustainable Use License.
Stars
70K
Language
TypeScript
Momentum
+0.3% 7dEstablished
turbovec
RyanCodrai/turbovec
Huia Project Score 65
turbovec is an in-process vector index for Python and Rust that stores embeddings at 2 or 4 bits per dimension and searches the compressed vectors directly. It is written in Rust with Python bindings and uses TurboQuant, a quantization method from Google Research. The README's headline figures: ten million 768-dimensional embeddings that need 31 GB as float32 fit in 4 GB, and search runs 4.4× faster than FAISS FastScan at 4-bit, at matching recall. Its benchmark table puts the 4-bit speedup at 3.8–5.1× across eight test cells on GCP ARM and x86 machines. These are the project's own benchmarks, and the scripts and raw JSON are published alongside them. No training step is needed, so a vector is searchable as soon as it is added, and removal takes microseconds. sync() writes only what changed since the last call. IdMapIndex keys vectors by your own uint64 ids and accepts an allowlist filter at query time. Drop-in stores exist for LangChain, LlamaIndex, Haystack and Agno. It is a library rather than a server, and it scans every vector instead of using a graph index.
Stars
17K
Language
Rust
License
MIT
Momentum
Established
deepseek-harness
deepseek-ai/deepseek-harness
Huia Project Score 65
DeepSeek Harness is an open-source agent runtime from DeepSeek AI that runs coding agents through a Web UI, headless jobs, SDK connections or ACP automation clients. Its Cordis-based design treats the model adapter, tools, session log and agent loop as plugins, so these parts can be replaced or extended through configuration. The Web UI works with a selected project directory. An agent can read and edit files, execute commands, delegate work and keep a plan, subject to the configured approval policy. The default setup supports DeepSeek models, while the model settings also support shipped third-party providers and custom APIs using OpenAI Chat Completions, OpenAI Responses or Anthropic Messages protocols. Developers can add their own Harness plugins, including plugins that register tools or services. The project is currently an experimental developer preview. Its maintainers warn that compatibility-breaking changes will occur and that it has not received a security audit. Because agents can execute generated commands and access permitted files, processes, credentials and networks, the safety documentation recommends restricted privileges and isolated environments.
Stars
249K
Language
TypeScript
License
MIT
Momentum
+2.3% 7dEmerging