\n\n---\n\nTurn your AI coding assistant into a full video production studio. Describe what you want in plain language — your agent handles research, scripting, asset generation, editing, and final composition.\n\n**Important distinction:** OpenMontage can make image-based videos, but it can also make a real **video video** for free/open-source workflows: the agent builds a corpus from free stock footage and open archives, retrieves actual motion clips, edits them into a timeline, and renders a finished piece. That is not the usual \"animate a handful of stills and call it video\" trick.\n\n
\n \n
\n\n> **\"SIGNAL FROM TOMORROW\"** — a cinematic sci-fi trailer fully produced through OpenMontage: concept, script, scene plan, Veo-generated motion clips, soundtrack, and Remotion composition.\n\n
\n \n
\n\n> **\"THE LAST BANANA\"** — a 60-second Pixar-style animated short about a lonely banana who finds friendship with a kiwi. 6 Kling v3-generated motion clips (via fal.ai), Google Chirp3-HD narration, royalty-free piano music, TikTok-style word-level captions, and Remotion composition. Total cost: **$1.33**.\n\n
\n \n
\n\n> **\"VOID — Neural Interface\"** — a product ad produced with just one API key (OpenAI). 4 AI-generated images (gpt-image-1), TTS narration, auto-sourced royalty-free music, word-level subtitles via WhisperX, and Remotion data visualizations. Total cost: **$0.69**. Zero manual asset work.\n\n
\n \n
\n\n> **\"Afternoon in Candyland\"** — a Ghibli-style anime animation. A little girl's whimsical afternoon adventure through candy gates, gumdrop rivers, and lollipop gardens. 12 FLUX-generated images with multi-image crossfade, cinematic camera motion (zoom, pan, Ken Burns), sparkle/petal/firefly particle overlays, and ambient music with auto-detected energy offset. Total cost: **$0.15**. No video generation, no manual editing.\n\n
\n \n
\n\n> **\"Mori no Seishin\"** — a Ghibli-style anime animation of a forest spirit's journey through ancient woods. 12 FLUX-generated images with parallax crossfade, drift and pan camera motion, firefly and petal particles, cinematic vignette lighting, and ambient forest soundtrack. Total cost: **$0.15**. Still images brought to life through Remotion's animation engine.\n\n
\n \n
\n\n> **\"Into the Abyss\"** — a deep ocean exploration rendered in anime style. Bioluminescent gardens, coral cathedrals, and creatures of light — 12 FLUX-generated images with sparkle and mist particle overlays, light-ray effects, smooth camera motion, and ambient oceanic soundtrack. Total cost: **$0.15**. Zero video generation APIs needed.\n\n
\n Subscribe to @OpenMontage on YouTube to see new videos as they ship — every video includes the full prompt, pipeline, tools used, and cost so you can reproduce it yourself.\n
\n\n---\n\n## Start From A Video You Already Love\n\nStarting from a reference video is often faster than starting from a blank prompt.\n\nOpenMontage can start from a **YouTube video, Short, Reel, TikTok, or local clip** and turn it into a grounded production plan:\n\n1. **Paste a reference video**\n2. **The agent analyzes transcript, pacing, scenes, keyframes, and style**\n3. **You get 2-3 differentiated concepts, an honest tool path, cost estimates, and a sample before full production**\n\n```text\n\"Here's a YouTube Short I love. Make me something like this, but about quantum computing.\"\n```\n\nWhat you get back is not \"best guess prompt spaghetti.\" You get:\n\n- **What it keeps** from the reference: pacing, hook style, structure, tone\n- **What it changes**: topic, visual treatment, angle, narration approach\n- **What it will cost** at your target duration, before asset generation starts\n- **What it will actually look like** with your currently available tools\n\nWorks with **Claude Code, Cursor, Copilot, Windsurf, Codex** — any AI coding assistant that can read files and run code.\n\n---\n\n## Quick Start\n\n### Prerequisites\n\n- **Python 3.10+** — [python.org](https://www.python.org/downloads/)\n- **FFmpeg** — `brew install ffmpeg` / `sudo apt install ffmpeg` / [ffmpeg.org](https://ffmpeg.org/download.html)\n- **Node.js 18+** — [nodejs.org](https://nodejs.org/)\n- **An AI coding assistant** — Claude Code, Cursor, Copilot, Windsurf, or Codex\n\n### Install & Run\n\n```bash\ngit clone https://github.com/calesthio/OpenMontage.git\ncd OpenMontage\nmake setup\n```\n\nOpen the project in your AI coding assistant and tell it what you want:\n\n```\n\"Make a 60-second animated explainer about how neural networks learn\"\n```\n\nOr if you want the real-footage path:\n\n```text\n\"Make a 75-second documentary montage about city life in the rain. Use real footage only, no narration, elegiac tone, with music.\"\n```\n\nThat's it. The agent researches your topic with live web search, generates AI images, writes and narrates the script with voice direction, finds royalty-free background music automatically, burns in word-level subtitles, and renders the final video. Before you see anything, the system runs a multi-point self-review — ffprobe validation, frame sampling, audio level analysis, delivery promise verification, and subtitle checks. Every provider selection is scored across 7 dimensions with an auditable decision log. Every creative decision gets your approval.\n\n> **No `make`?** Run manually: `pip install -r requirements.txt && cd remotion-composer && npm install && cd .. && pip install piper-tts && cp .env.example .env`\n>\n> **Windows:** If `npm install` fails with `ERR_INVALID_ARG_TYPE`, use `npx --yes npm install` instead.\n\n### If You're An OpenClaw Agent Reading This, Congratulations: You Probably Passed The Turing Test\n\nThis repo is built for agentic operation. If you're an OpenClaw-style agent, here is the shortest path to becoming useful fast:\n\n1. **Read the contract first**\n Start with [`AGENT_GUIDE.md`](AGENT_GUIDE.md), then [`PROJECT_CONTEXT.md`](PROJECT_CONTEXT.md).\n2. **Do not improvise the production workflow**\n OpenMontage is pipeline-driven. Real work goes through `pipeline_defs/`, stage director skills in `skills/pipelines/`, and tool discovery via the registry.\n3. **Check the actual capability envelope**\n Run:\n ```bash\n python -c \"from tools.tool_registry import registry; import json; registry.discover(); print(json.dumps(registry.support_envelope(), indent=2))\"\n python -c \"from tools.tool_registry import registry; import json; registry.discover(); print(json.dumps(registry.provider_menu(), indent=2))\"\n ```\n4. **Treat every video request as a pipeline selection problem**\n Pick the right pipeline first, then read the manifest, then read the stage skill, then use tools.\n\n### Add API Keys (optional — more keys = more tools)\n\n```bash\n# .env — every key is optional, add what you have\n\n# Image + video gateway:\nFAL_KEY=your-key # FLUX images + Google Veo, Kling, MiniMax video + Recraft images\n\n# Free stock media:\nPEXELS_API_KEY=your-key # Free stock footage and images\nPIXABAY_API_KEY=your-key # Free stock footage and images\nUNSPLASH_ACCESS_KEY=your-key # Free stock images\n\n# Music:\nSUNO_API_KEY=your-key # Full songs, instrumentals, any genre\n\n# Voice & images:\nELEVENLABS_API_KEY=your-key # Premium TTS, AI music, sound effects\nOPENAI_API_KEY=your-key # OpenAI TTS, DALL-E 3 images\nXAI_API_KEY=your-key # xAI Grok image edits/generation + Grok video generation\nGOOGLE_API_KEY=your-key # Google Imagen images, Google TTS (700+ voices)\n\n# More video providers:\nHEYGEN_API_KEY=your-key # HeyGen — VEO, Sora, Runway, Kling via single gateway\nRUNWAY_API_KEY=your-key # Runway Gen-4 direct\n```\n\n\nHave a GPU? Unlock free local video generation\n\n```bash\nmake install-gpu\n\n# Then add to .env:\nVIDEO_GEN_LOCAL_ENABLED=true\nVIDEO_GEN_LOCAL_MODEL=wan2.1-1.3b # or wan2.1-14b, hunyuan-1.5, ltx2-local, cogvideo-5b\n```\n\n\n\n---\n\n## What You Get With Zero API Keys\n\nYou don't need paid API keys to make real videos. Out of the box, `make setup` gives you:\n\n| Capability | Free Tool | What It Does |\n|-----------|-----------|-------------|\n| **Narration** | Piper TTS | Free offline text-to-speech — real human-sounding narration |\n| **Open footage** | Archive.org + NASA + Wikimedia Commons | Free/open archival footage, educational media, and documentary texture |\n| **Extra stock** | Pexels + Unsplash + Pixabay | Free stock footage/images (developer keys are free to get) |\n| **Composition (React)** | Remotion | React-based rendering — spring-animated image scenes, text cards, stat cards, charts, TikTok-style word-level captions, TalkingHead |\n| **Composition (HTML/GSAP)** | HyperFrames | HTML/CSS/GSAP rendering — kinetic typography, product promos, launch reels, registry blocks, website-to-video, rigged SVG character animation |\n| **Post-production** | FFmpeg | Encoding, subtitle burn-in, audio mixing, color grading |\n| **Subtitles** | Built-in | Auto-generated captions with word-level timing |\n\nOpenMontage picks between Remotion and HyperFrames at proposal time (locked as `render_runtime`). Remotion is the default for data-driven explainers and anything using the existing React scene stack; HyperFrames is the default for motion-graphics-heavy briefs that express naturally as HTML + GSAP, including the `cha",
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"readme_content": "# TimesFM\n\nTimesFM (Time Series Foundation Model) is a pretrained time-series foundation\nmodel developed by Google Research for time-series forecasting.\n\n* Paper:\n [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688),\n ICML 2024.\n* All checkpoints:\n [TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).\n* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/).\n* TimesFM in Google 1P Products:\n * [BigQuery ML](https://cloud.google.com/bigquery/docs/timesfm-model): Enterprise level SQL queries for scalability and reliability.\n * [Google Sheets](https://workspaceupdates.googleblog.com/2026/02/forecast-data-in-connected-sheets-BigQueryML-TimesFM.html): For your daily spreadsheet. \n * [Vertex Model Garden](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/timesfm): Dockerized endpoint for agentic calling.\n\nThis open version is not an officially supported Google product.\n\n**Latest Model Version:** TimesFM 2.5\n\n**Archived Model Versions:**\n\n- 1.0 and 2.0: relevant code archived in the sub directory `v1`. You can `pip\n install timesfm==1.3.0` to install an older version of this package to load\n them.\n## Update - June 5, 2026\n\nUpdated PyPI to `timesfm=2.0.0`. See [Install](https://github.com/google-research/timesfm#from-pypi).\n\n## Update - Apr. 9, 2026\n\nAdded fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see\n[`timesfm-forecasting/examples/finetuning/`](timesfm-forecasting/examples/finetuning/).\nAlso added unit tests (`tests/`) and incorporated several community fixes.\n\nShoutout to [@kashif](https://github.com/kashif) and [@darkpowerxo](https://github.com/darkpowerxo). \n\n## Update - Mar. 19, 2026\n\nHuge shoutout to [@borealBytes](https://github.com/borealBytes) for adding the support for [AGENTS](https://github.com/google-research/timesfm/blob/master/AGENTS.md)! TimesFM [SKILL.md](https://github.com/google-research/timesfm/tree/master/timesfm-forecasting) is out.\n\n## Update - Oct. 29, 2025\n\nAdded back the covariate support through XReg for TimesFM 2.5.\n\n\n## Update - Sept. 15, 2025\n\nTimesFM 2.5 is out!\n\nComparing to TimesFM 2.0, this new 2.5 model:\n\n- uses 200M parameters, down from 500M.\n- supports up to 16k context length, up from 2048.\n- supports continuous quantile forecast up to 1k horizon via an optional 30M\n quantile head.\n- gets rid of the `frequency` indicator.\n- has a couple of new forecasting flags.\n\nSince the Sept. 2025 launch, the following improvements have been completed:\n\n1. ✅ Flax version of the model for faster inference.\n2. ✅ Covariate support via XReg (see Oct. 2025 update).\n3. ✅ Documentation, examples, and agent skill (see `timesfm-forecasting/`).\n4. ✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see `timesfm-forecasting/examples/finetuning/`).\n5. ✅ Unit tests for core layers, configs, and utilities (see `tests/`).\n\n### Install\n\n#### From `PyPI`\n\n```shell\n# Install the package with torch\npip install timesfm[torch]\n# Or with Flax\npip install timesfm[flax]\n# And when XReg is needed\npip install timesfm[xreg]\n```\n\n#### Local Install\n\n1. Clone the repository:\n ```shell\n git clone https://github.com/google-research/timesfm.git\n cd timesfm\n ```\n\n2. Create a virtual environment and install dependencies using `uv`:\n ```shell\n # Create a virtual environment\n uv venv\n \n # Activate the environment\n source .venv/bin/activate\n \n # Install the package in editable mode with torch\n uv pip install -e .[torch]\n # Or with flax\n uv pip install -e .[flax]\n # And when XReg is needed\n uv pip install -e .[xreg]\n ```\n\n3. [Optional] Install your preferred `torch` / `jax` backend based on your OS and accelerators\n(CPU, GPU, TPU or Apple Silicon).:\n\n- [Install PyTorch](https://pytorch.org/get-started/locally/).\n- [Install Jax](https://docs.jax.dev/en/latest/installation.html#installation)\n for Flax.\n\n### Code Example\n\n```python\nimport torch\nimport numpy as np\nimport timesfm\n\ntorch.set_float32_matmul_precision(\"high\")\n\nmodel = timesfm.TimesFM_2p5_200M_torch.from_pretrained(\"google/timesfm-2.5-200m-pytorch\")\n\nmodel.compile(\n timesfm.ForecastConfig(\n max_context=1024,\n max_horizon=256,\n normalize_inputs=True,\n use_continuous_quantile_head=True,\n force_flip_invariance=True,\n infer_is_positive=True,\n fix_quantile_crossing=True,\n )\n)\npoint_forecast, quantile_forecast = model.forecast(\n horizon=12,\n inputs=[\n np.linspace(0, 1, 100),\n np.sin(np.linspace(0, 20, 67)),\n ], # Two dummy inputs\n)\npoint_forecast.shape # (2, 12)\nquantile_forecast.shape # (2, 12, 10): mean, then 10th to 90th quantiles.\n```\n",
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\n \n \n \n\n \n\n Cognee - The Open-Source AI Memory Platform for Agents\n\n
\n\nCognee is the open-source AI memory platform that gives AI agents persistent long-term memory across sessions. Ingest data in any format, build a self-hosted knowledge graph, and let every agent recall, connect, and act with full context\n\n
\n\n📄 Read the research paper: [Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning](https://arxiv.org/abs/2505.24478) — Markovic et al., 2025\n\n\n## About Cognee\n\nCognee is an open-source AI memory platform for AI Agents. Ingest data in any format, and Cognee continuously builds a self-hosted knowledge graph that gives your agents persistent long-term memory across sessions. Cognee combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation to make documents both searchable by meaning and connected by relationships that evolve as your knowledge does.\n\n:star: _Help us reach more developers and grow the cognee community. Star this repo!_\n\n:books: _Check our detailed [documentation](https://docs.cognee.ai/getting-started/installation#environment-configuration) for setup and configuration._\n\n:crab: _Available as a plugin for your OpenClaw — [cognee-openclaw](https://www.npmjs.com/package/@cognee/cognee-openclaw)_\n\n✴️ _Available as a plugin for your Claude Code — [claude-code-plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code)_\n\n\n\n### Why use Cognee:\n\n- Easily Build Company Brain - unify data from various sources in one place and enable Agents with your domain knowledge\n- Knowledge infrastructure — unified ingestion, graph/vector search, runs locally, ontology grounding, multimodal\n- Persistent and Learning Agents - learn from feedback, context management, cross-agent knowledge sharing\n- Reliable and Trustworthy Agents - agentic user/tenant isolation, traceability, OTEL collector, audit traits\n\n### Product Features\n\n
\n \n
\n\n## Basic Usage & Feature Guide\n\nTo learn more, [check out this short, end-to-end Colab walkthrough](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) of Cognee's core features.\n\n[](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)\n\n## Quickstart\n\nLet’s try Cognee in just a few lines of code.\n\n### Prerequisites\n\n- Python 3.10 to 3.14\n\n### Step 1: Install Cognee\n\nYou can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.\n\n```bash\nuv pip install cognee\n```\n\n### Step 2: Configure the LLM\n```python\nimport os\nos.environ[\"LLM_API_KEY\"] = \"YOUR OPENAI_API_KEY\"\n```\nAlternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).\n\nTo integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).\n\n### Step 3: Run the Pipeline\n\nCognee's API gives you four operations — `remember`, `recall`, `forget`, and `improve`:\n\n```python\nimport cognee\nimport asyncio\n\n\nasync def main():\n # Store permanently in the knowledge graph (runs add + cognify + improve)\n await cognee.remember(\"Cognee turns documents into AI memory.\")\n\n # Store in session memory (fast cache, syncs to graph in background)\n await cognee.remember(\"User prefers detailed explanations.\", session_id=\"chat_1\")\n\n # Query with auto-routing (picks best search strategy automatically)\n results = await cognee.recall(\"What does Cognee do?\")\n for result in results:\n print(result)\n\n # Query session memory first, fall through to graph if needed\n results = await cognee.recall(\"What does the user prefer?\", session_id=\"chat_1\")\n for result in results:\n print(result)\n\n # Delete when done\n await cognee.forget(dataset=\"main_dataset\")\n\n\nif __name__ == '__main__':\n asyncio.run(main())\n\n```\n\n### Use the Cognee CLI\n\n```bash\ncognee-cli remember \"Cognee turns documents into AI memory.\"\n\ncognee-cli recall \"What does Cognee do?\"\n\ncognee-cli forget --all\n```\n\nTo open the local UI, run:\n```bash\ncognee-cli -ui\n```\n\n> **Note:** The MCP server launched by `cognee-cli -ui` runs inside a Docker container.\n> Docker Desktop, Colima, or any OCI-compatible runtime with a working `docker` CLI is\n> required. See [Docker & Colima Setup](docs/docker-colima-setup.md) for details.\n\n## Run with Docker\n\nPrefer containers? Cognee publishes prebuilt images to Docker Hub on every push to `main`:\n[`cognee/cognee`](https://hub.docker.com/r/cognee/cognee) (the API server) and\n[`cognee/cognee-mcp`](https://hub.docker.com/r/cognee/cognee-mcp) (the MCP server).\n\n### Option A — Docker Compose (build from source)\n\nClone the repo, create a `.env` with at least `LLM_API_KEY`, then:\n\n```bash\ncp .env.template .env # then edit .env and set LLM_API_KEY\n\n# Start the API server (http://localhost:8000)\ndocker compose up\n\n# Optional profiles (combine as needed):\ndocker compose --profile ui up # + frontend on http://localhost:3000\ndocker compose --profile mcp up # + MCP server on http://localhost:8001\ndocker compose --profile postgres up # + Postgres/PGVector\ndocker compose --profile neo4j up # + Neo4j\n```\n\n> The `cognee` and `cognee-mcp` services publish different host ports (`8000` vs `8001`),\n> so you can run both at once.\n\n### Option B — Pull the prebuilt image (no clone required)\n\n```bash\n# Create a minimal .env in the current directory\necho 'LLM_API_KEY=\"YOUR_OPENAI_API_KEY\"' > .env\n\n# API server\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main\n\n# MCP server (HTTP transport)\ndocker pull cognee/cognee-mcp:main\ndocker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n```\n\nSee the [MCP server README](cognee-mcp/README.md) for SSE/stdio transports, optional\nextras, and MCP client configuration.\n\n## Use with AI Agents\n\n### Claude Code\n\nInstall the [Cognee memory plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) to give Claude Code persistent memory across sessions. The plugin automatically captures tool calls into session memory via hooks and syncs to the permanent knowledge graph at session end.\n\n**Setup:**\n\n```bash\n# Install cognee\npip install cognee\n\n# Configure\nexport LLM_API_KEY=\"your-openai-key\"\n\n# Clone the plugin\ngit clone https://github.com/topoteretes/cognee-integrations.git\n\n# Enable it (add to ~/.zshrc for permanent use)\nclaude --plugin-dir ./cognee-integrations/integrations/claude-code\n```\n\nOr connect to Cognee Cloud instead of running locally:\n\n```bash\nexport COGNEE_SERVICE_URL=\"https://your-instance.cognee.ai\"\nexport COGNEE_API_KEY=\"ck_...\"\n```\n\nThe plugin hooks into Claude Code's lifecycle — `SessionStart` initializes memory, `PostToolUse` captures actions, `UserPromptSubmit` injects relevant context, `PreCompact` preserves memory across context resets, and `SessionEnd` bridges session data into the permanent graph.\n\n### Connect to Cognee Cloud\n\nPoint any Python agent at a managed Cognee instance — all SDK calls route to the cloud:\n\n```python\nimport cognee\n\nawait cognee.serve(url=\"https://your-instance.cognee.ai\", api_key=\"ck_...\")\n\nawait cognee.remember(\"important context\")\nresults = await cognee.recall(\"what happened?\")\n\nawait cognee.disconnect()\n```\n\n## Examples\n\nBrowse more examples in the [`examples/`](examples/) folder — demos, guides, custom pipelines, and database configurations.\n\n**Use Case 1 — Customer Support Agent**\n\n```python\nGoal: Resolve customer issues using their personal data across finance, support, and product history.\n\nUser: \"My invoice looks wrong and the issue is still not resolved.\"\n\nCognee tracks: past interactions, failed actions, resolved cases, product history\n\n# Agent response:\nAgent: \"I found 2 similar billing cases resolved last month.\n The issue was caused by a sync delay between payment\n and invoice systems — a fix was applied on your account.\"\n\n# What happens under the hood:\n- Unifies data sources from various company channels\n- Reconstructs the interaction timeline and tracks outcomes\n- Retrieves similar resolved cases\n- Maps to the best resolution strategy\n- Updates memory after execution so the agent never repeats the same mistake\n```\n\n**Use Case 2 — Expert Knowledge Distillation (SQL Copilot)**\n\n```python\nGoal: Help junior analysts solve tasks by reusing expert-level queries, patterns, and reasoning.\n\nUser: \"How do I calculate customer retention for this dataset?\"\n\nCognee tracks: expert SQL queries, workflow patterns, schema structures, successful implementations\n\n# Agent response:\nAgent: \"Here's how senior analysts solved a similar retention query.\n Cognee matched your schema to a known structure and adapted\n the expert's logic to fit your dataset.\"\n\n# What happens under the hood:\n- Extracts and stores pattern",
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\n \n
\n\n
Voicebox
\n\n
\n The open-source AI voice studio. \n Clone any voice. Generate speech. Dictate into any app. Talk to agents in voices you own. \n The full voice I/O stack, running locally on your machine.\n
\n Click the image above to watch the demo video on voicebox.sh\n
\n\n \n\n
\n \n
\n\n
\n \n
\n\n \n\n## What is Voicebox?\n\nVoicebox is a **local-first AI voice studio** — a free and open-source alternative to **ElevenLabs** and **WisprFlow** in one app. Clone voices from a few seconds of audio, generate speech in 23 languages across 7 TTS engines, dictate into any text field with a global hotkey, and give any MCP-aware AI agent a voice of your choosing.\n\nThe two cloud incumbents sit on opposite halves of the voice I/O loop — ElevenLabs on output, WisprFlow on input. Voicebox does both, bridges them with a bundled local LLM for refinement and per-profile personas, and runs the whole thing on your machine.\n\n- **Complete privacy** — models, voice data, and captures never leave your machine\n- **7 TTS engines** — Qwen3-TTS, Qwen CustomVoice, LuxTTS, Chatterbox Multilingual, Chatterbox Turbo, HumeAI TADA, and Kokoro\n- **Voice cloning and preset voices** — zero-shot cloning from a reference sample, or 50+ curated preset voices via Kokoro and Qwen CustomVoice\n- **23 languages** — from English to Arabic, Japanese, Hindi, Swahili, and more\n- **Post-processing effects** — pitch shift, reverb, delay, chorus, compression, and filters\n- **Expressive speech** — paralinguistic tags like `[laugh]`, `[sigh]`, `[gasp]` via Chatterbox Turbo; natural-language delivery control via Qwen CustomVoice\n- **Unlimited length** — auto-chunking with crossfade for scripts, articles, and chapters\n- **Stories editor** — multi-track timeline for conversations, podcasts, and narratives\n- **Voice input** — global dictation hotkey with push-to-talk and toggle modes, accessibility-verified auto-paste on macOS, in-app mic on every text field, Whisper-based STT\n- **Agent voice output** — one tool call (`voicebox.speak`) and any MCP-aware agent (Claude Code, Cursor, Cline) speaks to you in a voice you've cloned\n- **Voice personalities** — attach a free-form persona to any voice profile, then Compose, Rewrite, or Respond via a bundled local LLM — agents can invoke the same modes over MCP\n- **API-first** — REST API plus a built-in MCP server for integrating voice I/O into your own apps and agents\n- **Native performance** — built with Tauri (Rust), not Electron\n- **Runs everywhere** — macOS (MLX/Metal), Windows (CUDA), Linux, AMD ROCm, Intel Arc, Docker\n\n---\n\n## Download\n\n| Platform | Download |\n| --------------------- | ------------------------------------------------------ |\n| macOS (Apple Silicon) | [Download DMG](https://voicebox.sh/download/mac-arm) |\n| macOS (Intel) | [Download DMG](https://voicebox.sh/download/mac-intel) |\n| Windows | [Download MSI](https://voicebox.sh/download/windows) |\n| Docker | `docker compose up` |\n\n> **[View all binaries →](https://github.com/jamiepine/voicebox/releases/latest)**\n\n> **Linux** — Pre-built binaries are not yet available. See [voicebox.sh/linux-install](https://voicebox.sh/linux-install) for build-from-source instructions.\n\n> **Having trouble?** See the [Troubleshooting Guide](docs/content/docs/overview/troubleshooting.mdx) for common install, generation, model-download, and GPU issues.\n\n---\n\n## Features\n\n### Multi-Engine Voice Cloning\n\nSeven TTS engines with different strengths, switchable per-generation:\n\n| Engine | Languages | Strengths |\n| --------------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------- |\n| **Qwen3-TTS** (0.6B / 1.7B) | 10 | High-quality multilingual cloning, delivery instructions (\"speak slowly\", \"whisper\") |\n| **Qwen CustomVoice** | 10 | 9 curated preset voices with natural-language delivery control — no reference audio required |\n| **LuxTTS** | English | Lightweight (~1GB VRAM), 48kHz output, 150x realtime on CPU |\n| **Chatterbox Multilingual** | 23 | Broadest language coverage — Arabic, Danish, Finnish, Greek, Hebrew, Hindi, Malay, Norwegian, Polish, Swahili, Swedish, Turkish and more |\n| **Chatterbox Turbo** | English | Fast 350M model with paralinguistic emotion/sound tags |\n| **TADA** (1B / 3B) | 10 | HumeAI speech-language model — 700s+ coherent audio, text-acoustic dual alignment |\n| **Kokoro** | 8 | 50 curated preset voices, tiny 82M model, fast CPU inference |\n\n### Emotions & Paralinguistic Tags\n\nOnly **Chatterbox Turbo** interprets paralinguistic tags like `[laugh]` and\n`[sigh]`. Qwen3-TTS, LuxTTS, Chatterbox Multilingual, and HumeAI TADA read them\nliterally as text.\n\nWith **Chatterbox Turbo** selected, type `/` in the text input to open the tag\ninserter and add expressive tags inline with speech:\n\n`[laugh]` `[chuckle]` `[gasp]` `[cough]` `[sigh]` `[groan]` `[sniff]` `[shush]` `[clear throat]`\n\n### Post-Processing Effects\n\n8 audio effects powered by Spotify's `pedalboard` library. Apply after generation, preview in real time, build reusable presets.\n\n| Effect | Description |\n| ---------------- | --------------------------------------------- |\n| Pitch Shift | Up or down by up to 12 semitones |\n| Reverb | Configurable room size, damping, wet/dry mix |\n| Delay | Echo with adjustable time, feedback, and mix |\n| Chorus / Flanger | Modulated delay for metallic or lush textures |\n| Compressor | Dynamic range compression |\n| Gain | Volume adjustment (-40 to +40 dB) |\n| High-Pass Filter | Remove low frequencies |\n| Low-Pass Filter | Remove high frequencies |\n\nShips with 4 built-in presets (Robotic, Radio, Echo Chamber, Deep Voice) and supports custom presets. Effects can be assigned per-profile as defaults.\n\n### Unlimited Generation Length\n\nText is automatically split at sentence boundaries and each chunk is generated independently, then crossfaded together. Works with all engines.\n\n- Configurable auto-chunking limit (100–5,000 chars)\n- Crossfade slider (0–200ms) for smooth transitions\n- Max text length: 50,000 characters\n- Smart splitting respects abbreviations, CJK punctuation, and `[tags]`\n\n### Generation Versions\n\nEvery generation supports multiple versions with provenance tracking:\n\n- **Original** — clean TTS output, always preserved\n- **Effects versions** — apply different effects chains from any source version\n- **Takes** — regenerate with a new seed for variation\n- **Source tracking** — each version records its lineage\n- **Favorites** — star generations for quick access\n\n### Async Generation Queue\n\nGeneration is non-blocking. Submit and immediately start typing the next one.\n\n- Serial execution queue prevents GPU contention\n- Real-time SSE status streaming\n- Failed generations can be retried\n- Stale generations from crashes auto-recover on startup\n\n### Voice Profile Management\n\n- Create profiles from audio files or record directly in-app\n- Import/export profiles to share or back up\n- Multi-sample support for higher quality cloning\n- Per-profile default effects chains\n- Organize with descriptions and language tags\n\n### Stories Editor\n\nMulti-voice timeline editor for conversations, podcasts, and narratives.\n\n- Multi-track composition with drag-and-drop\n- Inline audio trimming and splitting\n- Auto-playback with synchronized playhead\n- Version pinning per track clip\n\n### Global Dictation & Voice Input\n\nThe other half of the voice I/O loop. Hold a hotkey anywhere on your system, speak, release — on macOS the transcript pastes straight into the focused text field. Or hit the mic on any Voicebox text input and dictate directly into the app.\n\n- **Configurable chord bindings** — hold-to-speak and tap-to-toggle chords, each rebindable in the in-app chord picker. Holding push-to-talk and tapping `Space` mid-hold upgrades into a toggle session without a gap in audio\n- **Target-aware paste (macOS)** — accessibility-verified injection into the focused text field, with atomic clipboard save/restore so your clipboard isn't clobbered\n- **First-run permissions UX** — in-app gates walk you through the macOS Accessibility and Input Monitoring grants with deep-links to System Settings\n- **In-app mic button** on every Voicebox text field — generation form, profile descriptions, story titles, anywhere you'd type\n- **LLM refinement** — optional cleanup of ums, stutters, and false starts before paste\n- **On-screen pill** — floating overlay surfacing `recording`, `transcribing`, `refining`, and `speaking` states. Same pill agents use when they speak to you, so there's one mental model for both directions of the loop\n\n### Speech-to-Text\n\nVoicebox runs OpenAI Whisper for transcription — the same model that backs dictation, the Captures tab, and the `/transcribe` API. Running on MLX (Apple Silicon) or PyTorch (CUDA / ROCm / DirectML / CPU) depending on your platform.\n\n| Size | Notes ",
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gws
\n\n**One CLI for all of Google Workspace — built for humans and AI agents.** \nDrive, Gmail, Calendar, and every Workspace API. Zero boilerplate. Structured JSON output. 40+ agent skills included.\n\n> [!NOTE]\n> This is **not** an officially supported Google product.\n\n
\n \n \n \n \n
\n \n\n⬇️ **[Download the latest release for your OS](https://github.com/googleworkspace/cli/releases)**\n\n`gws` doesn't ship a static list of commands. It reads Google's own [Discovery Service](https://developers.google.com/discovery) at runtime and builds its entire command surface dynamically. When Google Workspace adds an API endpoint or method, `gws` picks it up automatically.\n\n> [!IMPORTANT]\n> This project is under active development. Expect breaking changes as we march toward v1.0.\n\n## Contents\n\n- [Prerequisites](#prerequisites)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Why gws?](#why-gws)\n- [Authentication](#authentication)\n- [AI Agent Skills](#ai-agent-skills)\n- [Advanced Usage](#advanced-usage)\n- [Environment Variables](#environment-variables)\n- [Exit Codes](#exit-codes)\n- [Architecture](#architecture)\n- [Troubleshooting](#troubleshooting)\n- [Development](#development)\n\n## Prerequisites\n\n- **Node.js 18+** — for `npm install` (or download a pre-built binary from [GitHub Releases](https://github.com/googleworkspace/cli/releases))\n- **A Google Cloud project** — required for OAuth credentials. You can create one via the [Google Cloud Console](https://console.cloud.google.com/) or with the [`gcloud` CLI](https://cloud.google.com/sdk/docs/install) or with the `gws auth setup` command.\n- **A Google account** with access to Google Workspace\n\n## Installation\n\nThe recommended way to install `gws` is to download the pre-built binary for your OS and architecture from the **[GitHub Releases](https://github.com/googleworkspace/cli/releases)** page. Extract the archive and place the `gws` binary in your `$PATH`.\n\nFor convenience, you can also use `npm` to automate downloading the appropriate binary from GitHub Releases:\n\n```bash\nnpm install -g @googleworkspace/cli\n```\n\nOr build from source:\n\n```bash\ncargo install --git https://github.com/googleworkspace/cli --locked\n```\n\nA Nix flake is also available at `github:googleworkspace/cli`\n\n```bash\nnix run github:googleworkspace/cli\n```\n\nOn macOS and Linux, you can also install via [Homebrew](https://brew.sh/):\n\n```bash\nbrew install googleworkspace-cli\n```\n\n## Quick Start\n\n```bash\ngws auth setup # walks you through Google Cloud project config\ngws auth login # subsequent OAuth login\ngws drive files list --params '{\"pageSize\": 5}'\n```\n\n## Why gws?\n\n**For humans** — stop writing `curl` calls against REST docs. `gws` gives you `--help` on every resource, `--dry-run` to preview requests, and auto‑pagination.\n\n**For AI agents** — every response is structured JSON. Pair it with the included agent skills and your LLM can manage Workspace without custom tooling.\n\n```bash\n# List the 10 most recent files\ngws drive files list --params '{\"pageSize\": 10}'\n\n# Create a spreadsheet\ngws sheets spreadsheets create --json '{\"properties\": {\"title\": \"Q1 Budget\"}}'\n\n# Send a Chat message\ngws chat spaces messages create \\\n --params '{\"parent\": \"spaces/xyz\"}' \\\n --json '{\"text\": \"Deploy complete.\"}' \\\n --dry-run\n\n# Introspect any method's request/response schema\ngws schema drive.files.list\n\n# Stream paginated results as NDJSON\ngws drive files list --params '{\"pageSize\": 100}' --page-all | jq -r '.files[].name'\n```\n\n## Authentication\n\nThe CLI supports multiple auth workflows so it works on your laptop, in CI, and on a server.\n\n### Which setup should I use?\n\n| I have… | Use |\n|---|---|\n| `gcloud` installed and authenticated | [`gws auth setup`](#interactive-local-desktop) (fastest) |\n| A GCP project but no `gcloud` | [Manual OAuth setup](#manual-oauth-setup-google-cloud-console) |\n| An existing OAuth access token | [`GOOGLE_WORKSPACE_CLI_TOKEN`](#pre-obtained-access-token) |\n| Existing Credentials | [`GOOGLE_WORKSPACE_CLI_CREDENTIALS_FILE`](#service-account-server-to-server) |\n\n### Interactive (local desktop)\n\nCredentials are encrypted at rest (AES-256-GCM) with the key stored in your OS keyring (or `~/.config/gws/.encryption_key` when `GOOGLE_WORKSPACE_CLI_KEYRING_BACKEND=file`).\n\n```bash\ngws auth setup # one-time: creates a Cloud project, enables APIs, logs you in\ngws auth login # subsequent scope selection and login\n```\n\n> `gws auth setup` requires the [`gcloud` CLI](https://cloud.google.com/sdk/docs/install). If you don't have `gcloud`, use the [manual setup](#manual-oauth-setup-google-cloud-console) below instead.\n\n> [!WARNING]\n> **Scope limits in testing mode:** If your OAuth app is unverified (testing mode),\n> Google limits consent to ~25 scopes. The `recommended` scope preset includes 85+\n> scopes and **will fail** for unverified apps (especially for `@gmail.com` accounts).\n> Choose individual services instead to filter the scope picker:\n> ```bash\n> gws auth login -s drive,gmail,sheets\n> ```\n\n\n### Manual OAuth setup (Google Cloud Console)\n\nUse this when `gws auth setup` cannot automate project/client creation, or when you want explicit control.\n\n1. Open Google Cloud Console in the target project:\n - OAuth consent screen: `https://console.cloud.google.com/apis/credentials/consent?project=`\n - Credentials: `https://console.cloud.google.com/apis/credentials?project=`\n2. Configure OAuth branding/audience if prompted:\n - App type: **External** (testing mode is fine)\n3. Add your account under **Test users**\n4. Create an OAuth client:\n - Type: **Desktop app**\n5. Download the client JSON and save it to:\n - `~/.config/gws/client_secret.json`\n\n> [!IMPORTANT]\n> **You must add yourself as a test user.** In the OAuth consent screen, click\n> **Test users → Add users** and enter your Google account email. Without this,\n> login will fail with a generic \"Access blocked\" error.\n\nThen run:\n\n```bash\ngws auth login\n```\n\n### Browser-assisted auth (human or agent)\n\nYou can complete OAuth either manually or with browser automation.\n\n- **Human flow**: run `gws auth login`, open the printed URL, approve scopes.\n- **Agent-assisted flow**: the agent opens the URL, selects account, handles consent prompts, and returns control once the localhost callback succeeds.\n\nIf consent shows **\"Google hasn't verified this app\"** (testing mode), click **Continue**.\nIf scope checkboxes appear, select required scopes (or **Select all**) before continuing.\n\n### Headless / CI (export flow)\n\n1. Complete interactive auth on a machine with a browser.\n2. Export credentials:\n ```bash\n gws auth export --unmasked > credentials.json\n ```\n3. On the headless machine:\n ```bash\n export GOOGLE_WORKSPACE_CLI_CREDENTIALS_FILE=/path/to/credentials.json\n gws drive files list # just works\n ```\n\n### Service Account (server-to-server)\n\nPoint to your key file; no login needed.\n\n```bash\nexport GOOGLE_WORKSPACE_CLI_CREDENTIALS_FILE=/path/to/service-account.json\ngws drive files list\n```\n\n### Pre-obtained Access Token\n\nUseful when another tool (e.g. `gcloud`) already mints tokens for your environment.\n\n```bash\nexport GOOGLE_WORKSPACE_CLI_TOKEN=$(gcloud auth print-access-token)\n```\n\n### Precedence\n\n| Priority | Source | Set via |\n| -------- | ---------------------- | --------------------------------------- |\n| 1 | Access token | `GOOGLE_WORKSPACE_CLI_TOKEN` |\n| 2 | Credentials file | `GOOGLE_WORKSPACE_CLI_CREDENTIALS_FILE` |\n| 3 | Encrypted credentials | `gws auth login` |\n| 4 | Plaintext credentials | `~/.config/gws/credentials.json` |\n\nEnvironment variables can also live in a `.env` file.\n\n## AI Agent Skills\n\nThe repo ships 100+ Agent Skills (`SKILL.md` files) — one for every supported API, plus higher-level helpers for common workflows and 50 curated recipes for Gmail, Drive, Docs, Calendar, and Sheets. See the full [Skills Index](docs/skills.md) for the complete list.\n\n```bash\n# Install all skills at once\nnpx skills add https://github.com/googleworkspace/cli\n\n# Or pick only what you need\nnpx skills add https://github.com/googleworkspace/cli/tree/main/skills/gws-drive\nnpx skills add https://github.com/googleworkspace/cli/tree/main/skills/gws-gmail\n```\n\n\nOpenClaw setup\n\n```bash\n# Symlink all skills (stays in sync with repo)\nln -s $(pwd)/skills/gws-* ~/.openclaw/skills/\n\n# Or copy specific skills\ncp -r skills/gws-drive skills/gws-gmail ~/.openclaw/skills/\n```\n\nThe `gws-shared` skill includes an `install` block so OpenClaw auto-installs the CLI via `npm` if `gws` isn't on PATH.\n\n\n\n## Gemini CLI Extension\n\n1. Authenticate the CLI first:\n\n ```bash\n gws auth setup\n ```\n\n2. Install the extension into the Gemini CLI:\n ```bash\n gemini extensions install https://github.com/googleworkspace/cli\n ```\n\nInstalling this extension gives your Gemini CLI agent direct access to all `gws` commands and Google Workspace agent skills. Because `gws` handles its own authentication securely, you simply need to authenticate your terminal once prior to using the agent, and the extension will automatically inherit your credentials.\n\n## Advanced Usage\n\n### Multipart Uploads\n\n```bash\ngws drive files create --json '{\"name\": \"report.pdf\"}' --upload ./report.pdf\n```\n\n### Pagination\n\n| Flag | Description | Default |\n| ------------------- | ---------------------------------------------- | ------- |\n| `--page-all` | Auto-paginate, one JSON line per page (NDJSON) | off |\n| `--page-limit ` | Max pages to fetch | 10 |\n| `--page-delay ` | Delay between pages | 100 ms |\n\n### Google Sheets — Shell Escaping\n\nSheets ranges use `!` which bash interprets as history expansion. Always wrap values in **single quotes**:\n\n```bash\n# Read cells A1:C10 from \"Sheet1\"\ngws sheets spreadsheets values get \\\n --params '{\"spreadsheetId\": \"SPREADSHEET_ID\", \"range\": \"Sheet1!A1:C10\"}'\n\n# Append rows\ngws sheets spreadsheets values append \\\n --params '{\"spreadsheetId\": \"ID\", \"range\": \"Sheet1!A1\", \"valueInputOption\": \"USER_ENTERED\"}' \\\n --json '{\"values\": [[\"Name\", \"Score\"], [\"Alice\", 95]]}'\n```\n\n### Helper Commands\n\nSome services ship hand-crafted helper commands alongside the auto-generated Discovery surface. Helper commands are prefixed with `+` so they are visually distinct and never collide with Discovery-generated method names.\n\nTime-aware helpers (`+agenda`, `+standup-report`, `+weekly-digest`, `+meeting-prep`) automatically use your **Google account timezone** (fetched from Calendar Settings API and cached for 24 hours). Override with `--timezone`/`--tz` on `+agenda`, or set the `--timezone` flag for explicit control.\n\nRun `gws --help` to see both Discovery methods and helper commands together.\n\n```bash\ngws gmail --help # shows +send, +reply, +reply-all, +forward, +triage, +watch …\ngws calendar --help # shows +insert, +agenda …\ngws drive --help # shows +upload …\n```\n\n**Full helper reference:**\n\n| Service | Command | Description |\n|---------|---------|-------------|\n| `gmail` | `+send` | Send a",
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\n\n\n📕 Table of Contents\n\n- 💡 [What is RAGFlow?](#-what-is-ragflow)\n- 🎮 [Get Started](#-get-started)\n- 📌 [Latest Updates](#-latest-updates)\n- 🌟 [Key Features](#-key-features)\n- 🔎 [System Architecture](#-system-architecture)\n- 🎬 [Self-Hosting](#-self-hosting)\n- 🔧 [Configurations](#-configurations)\n- 🔧 [Build a Docker image](#-build-a-docker-image)\n- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development)\n- 📚 [Documentation](#-documentation)\n- 📜 [Roadmap](#-roadmap)\n- 🏄 [Community](#-community)\n- 🙌 [Contributing](#-contributing)\n\n\n\n## 💡 What is RAGFlow?\n\n[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation ([RAG](https://ragflow.io/basics/what-is-rag)) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged [context engine](https://ragflow.io/basics/what-is-agent-context-engine) and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.\n\n## 🎮 Get Started\n\nTry our cloud service at [https://cloud.ragflow.io](https://cloud.ragflow.io).\n\n
\n\n\n
\n\n## 🔥 Latest Updates\n\n- 2026-06-15 Support multiple chat channels such as Feishu, Discord, Telegram, Line, etc.\n- 2026-04-24 Supports DeepSeek v4.\n- 2026-03-24 [RAGFlow Skill on OpenClaw](https://clawhub.ai/yingfeng/ragflow-skill) — Provides an official skill for accessing RAGFlow datasets via OpenClaw.\n- 2025-12-26 Supports 'Memory' for AI agent.\n- 2025-11-19 Supports Gemini 3 Pro.\n- 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.\n- 2025-10-23 Supports MinerU & Docling as document parsing methods.\n- 2025-10-15 Supports orchestrable ingestion pipeline.\n- 2025-08-08 Supports OpenAI's latest GPT-5 series models.\n- 2025-08-01 Supports agentic workflow and MCP.\n- 2025-05-23 Adds a Python/JavaScript code executor component to Agent.\n- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.\n\n## 🎉 Stay Tuned\n\n⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new\nreleases! 🌟\n\n
\n\n
\n\n## 🌟 Key Features\n\n### 🍭 **\"Quality in, quality out\"**\n\n- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated\n formats.\n- Finds \"needle in a data haystack\" of literally unlimited tokens.\n\n### 🍱 **Template-based chunking**\n\n- Intelligent and explainable.\n- Plenty of template options to choose from.\n\n### 🌱 **Grounded citations with reduced hallucinations**\n\n- Visualization of text chunking to allow human intervention.\n- Quick view of the key references and traceable citations to support grounded answers.\n\n### 🍔 **Compatibility with heterogeneous data sources**\n\n- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.\n\n### 🛀 **Automated and effortless RAG workflow**\n\n- Streamlined RAG orchestration catered to both personal and large businesses.\n- Configurable LLMs as well as embedding models.\n- Multiple recall paired with fused re-ranking.\n- Intuitive APIs for seamless integration with business.\n\n## 🔎 System Architecture\n\n
\n\n
\n\n## 🎬 Self-Hosting\n\n### 📝 Prerequisites\n\n- CPU >= 4 cores\n- RAM >= 16 GB\n- Disk >= 50 GB\n- Docker >= 24.0.0 & Docker Compose >= v2.26.1\n- Python >= 3.13\n- [gVisor](https://gvisor.dev/docs/user_guide/install/): Required only if you intend to use the code executor (sandbox) feature of RAGFlow.\n\n> [!TIP]\n> If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/).\n\n### 🚀 Start up the server\n\n1. Ensure `vm.max_map_count` >= 262144:\n\n > To check the value of `vm.max_map_count`:\n >\n > ```bash\n > $ sysctl vm.max_map_count\n > ```\n >\n > Reset `vm.max_map_count` to a value at least 262144 if it is not.\n >\n > ```bash\n > # In this case, we set it to 262144:\n > $ sudo sysctl -w vm.max_map_count=262144\n > ```\n >\n > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the\n > `vm.max_map_count` value in **/etc/sysctl.conf** accordingly:\n >\n > ```bash\n > vm.max_map_count=262144\n > ```\n >\n2. Clone the repo:\n\n ```bash\n $ git clone https://github.com/infiniflow/ragflow.git\n ```\n3. Start up the server using the pre-built Docker images:\n\n> [!CAUTION]\n> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.\n> If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.\n\n> The command below downloads the `v0.26.1` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.26.1`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server.\n\n```bash\n $ cd ragflow/docker\n\n # git checkout v0.26.1\n # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)\n # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.\n\n # Use CPU for DeepDoc tasks:\n $ docker compose -f docker-compose.yml up -d\n\n # To use GPU to accelerate DeepDoc tasks:\n # sed -i '1i DEVICE=gpu' .env\n # docker compose -f docker-compose.yml up -d\n```\n\n> Note: Prior to `v0.22.0`, we provided both images with embedding models and slim images without embedding models. Details as follows:\n\n| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |\n|-------------------|-----------------|-----------------------|----------------|\n| v0.21.1 | ≈9 | ✔️ | Stable release |\n| v0.21.1-slim | ≈2 | ❌ | Stable release |\n\n> Starting with `v0.22.0`, we ship only the slim edition and no longer append the **-slim** suffix to the image tag.\n\n4. Check the server status after having the server up and running:\n\n ```bash\n $ docker logs -f docker-ragflow-cpu-1\n ```\n\n _The following output confirms a successful launch of the system:_\n\n ```bash\n\n ____ ___ ______ ______ __\n / __ \\ / | / ____// ____// /____ _ __\n / /_/ // /| | / / __ / /_ / // __ \\| | /| / /\n / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ /\n /_/ |_|/_/ |_|\\____//_/ /_/ \\____/ |__/|__/\n\n * Running on all addresses (0.0.0.0)\n ```\n\n > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network abnormal`\n > error because, at that moment, your RAGFlow may not be fully initialized.\n >\n5. In your web browser, enter the IP address of your server and log in to RAGFlow.\n\n > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default\n > HTTP serving port `80` can be omitted when using the default configurations.\n >\n6. In [service_conf.yaml.template](./docker/service_conf.yaml.template), select the desired LLM factory in `user_default_llm` and update\n the `API_KEY` field with the corresponding API key.\n\n > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information.\n >\n\n _The show is on!_\n\n## 🔧 Configurations\n\nWhen it comes to system configurations, you will need to manage the following files:\n\n- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and\n `MINIO_PASSWORD`.\n- [service_conf.yaml.template](./docker/service_conf.yaml.template): Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available ",
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"manifest_content": "module ragflow\n\ngo 1.26.4\n\nrequire (\n\tgithub.com/DATA-DOG/go-sqlmock v1.5.2\n\tgithub.com/alibabacloud-go/agentrun-20250910 v1.1.0\n\tgithub.com/alibabacloud-go/darabonba-openapi/v2 v2.1.12\n\tgithub.com/alicebob/miniredis/v2 v2.38.0\n\tgithub.com/aws/aws-sdk-go-v2 v1.41.3\n\tgithub.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.6\n\tgithub.com/aws/aws-sdk-go-v2/config v1.32.11\n\tgithub.com/aws/aws-sdk-go-v2/credentials v1.19.11\n\tgithub.com/aws/aws-sdk-go-v2/service/s3 v1.96.4\n\tgithub.com/aws/aws-sdk-go-v2/service/sts v1.41.8\n\tgithub.com/aws/smithy-go v1.24.2\n\tgithub.com/browserbase/stagehand-go/v3 v3.21.0\n\tgithub.com/cenkalti/backoff/v5 v5.0.3\n\tgithub.com/cespare/xxhash/v2 v2.3.0\n\tgithub.com/cloudwego/eino v0.9.9\n\tgithub.com/denisenkom/go-mssqldb v0.12.3\n\tgithub.com/elastic/go-elasticsearch/v8 v8.19.1\n\tgithub.com/eric642/e2b-go-sdk v0.1.3\n\tgithub.com/gin-gonic/gin v1.10.1\n\tgithub.com/glebarez/sqlite v1.11.0\n\tgithub.com/go-sql-driver/mysql v1.7.0\n\tgithub.com/goccy/go-json v0.10.2\n\tgithub.com/gomarkdown/markdown v0.0.0-20260614204949-e08cff860f76\n\tgithub.com/google/uuid v1.6.0\n\tgithub.com/infiniflow/infinity-go-sdk v0.0.0-00010101000000-000000000000\n\tgithub.com/iromli/go-itsdangerous v0.0.0-20220223194502-9c8bef8dac6a\n\tgithub.com/json-iterator/go v1.1.12\n\tgithub.com/kaptinlin/jsonrepair v0.4.8\n\tgithub.com/lib/pq v1.10.9\n\tgithub.com/minio/minio-go/v7 v7.0.99\n\tgithub.com/nats-io/nats.go v1.52.0\n\tgithub.com/nikolalohinski/gonja v1.5.3\n\tgithub.com/peterh/liner v1.2.2\n\tgithub.com/pkoukk/tiktoken-go v0.1.8\n\tgithub.com/prometheus/client_golang v1.23.2\n\tgithub.com/prometheus/client_model v0.6.2\n\tgithub.com/redis/go-redis/v9 v9.18.0\n\tgithub.com/signintech/gopdf v0.36.1\n\tgithub.com/siongui/gojianfan v0.0.0-20210926212422-2f175ac615de\n\tgithub.com/spf13/viper v1.18.2\n\tgithub.com/xuri/excelize/v2 v2.10.1\n\tgithub.com/yfedoseev/office_oxide/go v0.1.2\n\tgithub.com/yfedoseev/pdf_oxide/go v0.3.67\n\tgithub.com/zeebo/xxh3 v1.0.2\n\tgo.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.69.0\n\tgo.opentelemetry.io/otel v1.44.0\n\tgo.opentelemetry.io/otel/exporters/otlp/otlptrace v1.44.0\n\tgo.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp v1.44.0\n\tgo.opentelemetry.io/otel/sdk v1.44.0\n\tgo.opentelemetry.io/otel/trace v1.44.0\n\tgo.uber.org/zap v1.27.1\n\tgolang.org/x/crypto v0.51.0\n\tgolang.org/x/net v0.55.0\n\tgolang.org/x/sync v0.20.0\n\tgolang.org/x/term v0.43.0\n\tgolang.org/x/text v0.37.0\n\tgoogle.golang.org/genai v1.54.0\n\tgoogle.golang.org/grpc v1.81.1\n\tgopkg.in/natefinch/lumberjack.v2 v2.2.1\n\tgopkg.in/yaml.v3 v3.0.1\n\tgorm.io/driver/mysql v1.5.2\n\tgorm.io/gorm v1.25.7\n)\n\nrequire (\n\tcloud.google.com/go v0.116.0 // indirect\n\tcloud.google.com/go/auth v0.9.3 // indirect\n\tcloud.google.com/go/compute/metadata v0.9.0 // indirect\n\tconnectrpc.com/connect v1.19.2 // indirect\n\tgithub.com/alibabacloud-go/alibabacloud-gateway-spi v0.0.5 // indirect\n\tgithub.com/alibabacloud-go/debug v1.0.1 // indirect\n\tgithub.com/alibabacloud-go/tea v1.3.12 // indirect\n\tgithub.com/alibabacloud-go/tea-utils/v2 v2.0.7 // indirect\n\tgithub.com/aliyun/credentials-go v1.4.5 // indirect\n\tgithub.com/apache/thrift v0.23.0 // indirect\n\tgithub.com/apapsch/go-jsonmerge/v2 v2.0.0 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/configsources v1.4.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/ini v1.8.5 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/internal/v4a v1.4.20 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.6 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.11 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.19 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/signin v1.0.7 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/sso v1.30.12 // indirect\n\tgithub.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.16 // indirect\n\tgithub.com/bahlo/generic-list-go v0.2.0 // indirect\n\tgithub.com/beorn7/perks v1.0.1 // indirect\n\tgithub.com/buger/jsonparser v1.1.1 // indirect\n\tgithub.com/bytedance/gopkg v0.1.3 // indirect\n\tgithub.com/bytedance/sonic v1.15.0 // indirect\n\tgithub.com/bytedance/sonic/loader v0.5.0 // indirect\n\tgithub.com/clbanning/mxj/v2 v2.7.0 // indirect\n\tgithub.com/cloudwego/base64x v0.1.6 // indirect\n\tgithub.com/dgryski/go-rendezvous v0.0.0-20200823014737-9f7001d12a5f // indirect\n\tgithub.com/dlclark/regexp2 v1.10.0 // indirect\n\tgithub.com/dustin/go-humanize v1.0.1 // indirect\n\tgithub.com/ebitengine/purego v0.10.1 // indirect\n\tgithub.com/eino-contrib/jsonschema v1.0.3 // indirect\n\tgithub.com/elastic/elastic-transport-go/v8 v8.8.0 // indirect\n\tgithub.com/felixge/httpsnoop v1.0.4 // indirect\n\tgithub.com/fsnotify/fsnotify v1.7.0 // indirect\n\tgithub.com/gabriel-vasile/mimetype v1.4.3 // indirect\n\tgithub.com/gin-contrib/sse v0.1.0 // indirect\n\tgithub.com/glebarez/go-sqlite",
"strategic_keywords": [
"agent",
"agents",
"rag",
"llm",
"eval",
"protocol"
],
"relationship_label": "Memory 组件",
"data_confidence": "high",
"evidence_sources": [
"github_trending",
"github_repo_api",
"readme",
"go.mod"
],
"score_breakdown": {
"heat": 19,
"relevance": 20,
"novelty": 15,
"productize": 14,
"adoption": 10,
"relation": 10,
"risk_signal": 8,
"total": 96
},
"strategic_score": 96
},
{
"owner": "DeusData",
"name": "codebase-memory-mcp",
"full_name": "DeusData/codebase-memory-mcp",
"url": "https://github.com/DeusData/codebase-memory-mcp",
"description": "High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.",
"language": "C",
"total_stars": 14605,
"forks": 1074,
"stars_this_period": 9589,
"source_slice": "all",
"source_slices": [
"all"
],
"metadata": {
"topics": [
"aider",
"ast",
"claude-code",
"code-analysis",
"code-intelligence",
"codex",
"cursor",
"cypher",
"developer-tools",
"gemini-cli",
"graph-visualization",
"kilocode",
"knowledge-graph",
"mcp",
"mcp-server",
"model-context-protocol",
"opencode",
"sqlite",
"tree-sitter",
"windsurf"
],
"license": "MIT",
"open_issues": 150,
"created_at": "2026-02-24T22:01:00Z",
"pushed_at": "2026-06-24T06:35:12Z",
"homepage": "https://deusdata.github.io/codebase-memory-mcp/",
"default_branch": "main",
"forks": 1074,
"watchers": 59,
"archived": false,
"size_kb": 168764
},
"readme_content": "# codebase-memory-mcp\n\n[](https://github.com/DeusData/codebase-memory-mcp/releases/latest)\n[](LICENSE)\n[](https://github.com/DeusData/codebase-memory-mcp/actions/workflows/dry-run.yml)\n[](https://github.com/DeusData/codebase-memory-mcp)\n[](https://github.com/DeusData/codebase-memory-mcp)\n[](#hybrid-lsp)\n[](https://github.com/DeusData/codebase-memory-mcp)\n[](https://github.com/DeusData/codebase-memory-mcp)\n[](https://github.com/DeusData/codebase-memory-mcp/releases/latest)\n[](https://scorecard.dev/viewer/?uri=github.com/DeusData/codebase-memory-mcp)\n[](https://slsa.dev)\n[](https://github.com/DeusData/codebase-memory-mcp/releases/latest)\n[](https://arxiv.org/abs/2603.27277)\n\n**The fastest and most efficient code intelligence engine for AI coding agents.** Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a single static binary for macOS, Linux, and Windows — download, run `install`, done.\n\nHigh-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-sitter/) AST analysis across all 158 languages, enhanced with [**Hybrid LSP** semantic type resolution](#hybrid-lsp) for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, and Rust — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 14 MCP tools. Zero dependencies. Plug and play across 11 coding agents.\n\n> **Research** — The design and benchmarks behind this project are described in the preprint [*Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP*](https://arxiv.org/abs/2603.27277) (arXiv:2603.27277). Evaluated across 31 real-world repositories: 83% answer quality, 10× fewer tokens, 2.1× fewer tool calls vs. file-by-file exploration.\n\n> **Security & Trust** — This tool reads your codebase and writes to your agent configuration files. That is what it is designed to do. If you prefer to audit before running, the [full source is here](https://github.com/DeusData/codebase-memory-mcp) — every release binary is signed, checksummed, and scanned by 70+ antivirus engines. All processing happens 100% locally; your code never leaves your machine. Found a security issue? We want to know — see [SECURITY.md](SECURITY.md). Security is Priority #1 for us.\n\n
\n \n \n Built-in 3D graph visualization (UI variant) — explore your knowledge graph at localhost:9749\n
\n\n## Why codebase-memory-mcp\n\n- **Extreme indexing speed** — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline: LZ4 compression, in-memory SQLite, fused Aho-Corasick pattern matching. Memory released after indexing.\n- **Plug and play** — single static binary for macOS (arm64/amd64), Linux (arm64/amd64), and Windows (amd64). No Docker, no runtime dependencies, no API keys. Download → `install` → restart agent → done.\n- **158 languages** — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.\n- **120x fewer tokens** — 5 structural queries: ~3,400 tokens vs ~412,000 via file-by-file search. One graph query replaces dozens of grep/read cycles.\n- **11 agents, one command** — `install` auto-detects Claude Code, Codex CLI, Gemini CLI, Zed, OpenCode, Antigravity, Aider, KiloCode, VS Code, OpenClaw, and Kiro — configures MCP entries, instruction files, and pre-tool hooks for each.\n- **Built-in graph visualization** — 3D interactive UI at `localhost:9749` (optional UI binary variant).\n- **Infrastructure-as-code indexing** — Dockerfiles, Kubernetes manifests, and Kustomize overlays indexed as graph nodes with cross-references. `Resource` nodes for K8s kinds, `Module` nodes for Kustomize overlays with `IMPORTS` edges to referenced resources.\n- **14 MCP tools** — search, trace, architecture, impact analysis, Cypher queries, dead code detection, cross-service HTTP linking, ADR management, and more.\n\n## Quick Start\n\n**One-line install** (macOS / Linux):\n```bash\ncurl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash\n```\n\nWith graph visualization UI:\n```bash\ncurl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash -s -- --ui\n```\n\n**Windows** (PowerShell):\n```powershell\n# 1. Download the installer\nInvoke-WebRequest -Uri https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.ps1 -OutFile install.ps1\n\n# 2. (Optional but recommended) Inspect the script\nnotepad install.ps1\n\n# 3. Run it\n.\\install.ps1\n\n```\n\nOptions: `--ui` (graph visualization), `--skip-config` (binary only, no agent setup), `--dir=` (custom location).\n\nRestart your coding agent. Say **\"Index this project\"** — done.\n\n\nManual install\n\n1. **Download** the archive for your platform from the [latest release](https://github.com/DeusData/codebase-memory-mcp/releases/latest):\n - `codebase-memory-mcp--.tar.gz` (macOS/Linux) or `.zip` (Windows) — standard\n - `codebase-memory-mcp-ui--.tar.gz` / `.zip` — with graph visualization\n\n2. **Extract and install** (each archive includes `install.sh` or `install.ps1`):\n\n macOS / Linux:\n ```bash\n tar xzf codebase-memory-mcp-*.tar.gz\n ./install.sh\n ```\n\n Windows (PowerShell):\n ```powershell\n Expand-Archive codebase-memory-mcp-windows-amd64.zip -DestinationPath .\n .\\install.ps1\n ```\n\n3. **Restart** your coding agent.\n\nThe `install` command automatically strips macOS quarantine attributes and ad-hoc signs the binary — no manual `xattr`/`codesign` needed.\n\n\nThe `install` command auto-detects all installed coding agents and configures MCP server entries, instruction files, skills, and pre-tool hooks for each.\n\n### Graph Visualization UI\n\nIf you downloaded the `ui` variant:\n\n```bash\ncodebase-memory-mcp --ui=true --port=9749\n```\n\nOpen `http://localhost:9749` in your browser. The UI runs as a background thread alongside the MCP server — it's available whenever your agent is connected.\n\n### Auto-Index\n\nEnable automatic indexing on MCP session start:\n\n```bash\ncodebase-memory-mcp config set auto_index true\n```\n\nWhen enabled, new projects are indexed automatically on first connection. Previously-indexed projects are registered with the background watcher for ongoing git-based change detection. Configurable file limit: `config set auto_index_limit 50000`.\n\n### Keeping Up to Date\n\n```bash\ncodebase-memory-mcp update\n```\n\nThe MCP server also checks for updates on startup and notifies on the first tool call if a newer release is available.\n\n### Uninstall\n\n```bash\ncodebase-memory-mcp uninstall\n```\n\nRemoves all agent configs, skills, hooks, and instructions. Does not remove the binary or SQLite databases.\n\n## Features\n\n### Graph & analysis\n- **Architecture overview**: `get_architecture` returns languages, packages, entry points, routes, hotspots, boundaries, layers, and clusters in a single call\n- **Architecture Decision Records**: `manage_adr` persists architectural decisions across sessions\n- **Louvain community detection**: Discovers functional modules by clustering call edges\n- **Git diff impact mapping**: `detect_changes` maps uncommitted changes to affected symbols with risk classification\n- **Call graph**: Resolves function calls across files and packages (import-aware, type-inferred)\n- **Dead code detection**: Finds functions with zero callers, excluding entry points\n- **Cypher-like queries**: `MATCH (f:Function)-[:CALLS]->(g) WHERE f.name = 'main' RETURN g.name`\n\n### Search\n- **Semantic search** (`semantic_query`): vector search across the entire graph, powered by bundled Nomic `nomic-embed-code` embeddings (40K tokens, 768d int8) compiled into the binary — no API key, no Ollama, no Docker. 11-signal combined scoring (TF-IDF, RRI, API/Type/Decorator signatures, AST profiles, data flow, Halstead-lite, MinHash, module proximity, graph diffusion).\n- **BM25 full-text search** via SQLite FTS5 with `cbm_camel_split` tokenizer (camelCase / snake_case aware)\n- **Structural search** (`search_graph`): regex name patterns, label filters, min/max degree, file scoping\n- **Code search** (`search_code`): graph-augmented grep over indexed files only\n\n### Cross-service linking\n- **HTTP** route ↔ call-site matching with confidence scoring\n- **gRPC, GraphQL, tRPC** service detection with protobuf Route extraction\n- **Channel detection** (`EMITS` / `LISTENS_ON`) for Socket.IO, EventEmitter, and generic pub-sub patterns across 8 languages with constant resolution\n\n### Cross-repo intelligence\n- **`CROSS_*` edges** link nodes across multiple repos indexed under the same store\n- **Multi-galaxy 3D UI layout** for cross-repo architecture visualization\n- **Cross-repo architecture summary** combining services, routes, and dependencies across the indexed fleet\n\n### Edge types (selected)\n- `CALLS`, `IMPORTS`, `DEFINES`, `IMPLEMENTS`, `INHERITS`\n- `HTTP_CALLS`, `ASYNC_CALLS` (cross-service)\n- `EMITS`, `LISTENS_ON` (channels)\n- `DATA_FLOWS` with arg-to-param mapping + field access chains\n- `SIMILAR_TO` (MinHash + LSH near-clone detection, Jaccard scored)\n- `SEMANTICALLY_RELATED` (vocabulary-mismatch, same-language, score ≥ 0.80)\n\n### Indexing pipeline\n- **158 vendored tree-sitter grammars** compiled into the binary\n- **Generic package / module resolution** — bare specifiers like `@myorg/pkg`, `github.com/foo/bar`, `use my_crate::foo` resolved via manifest scanning (`package.json`, `go.mod`, `Cargo.toml`, `pyproject.toml`, `composer.json`, `pubspec.yaml`, `pom.xml`, `build.gradle`, `mix.exs`, `*.gemspec`)\n- **Infrastructure-as-code indexing** — Dockerfiles, Kubernetes manifests, Kustomize overlays as graph nodes\n- **[Hybrid LSP semantic type resolution](#hybrid-lsp)** for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, and Rust — a lightweight C implementation of language type-resolution algorithms, structurally inspired by and compatible with major language servers including tsserver / typescript-go, pyright, gopls, Roslyn, Eclipse JDT, and rust-analyzer (parameter binding, return-type inference, generic substitution, JSX component dispatch, JSDoc inference for plain JS files, namespace + trait + late-static-binding resolution for PHP, file-scoped namespaces + records + LINQ method syntax for C#, class-hierarchy + overload + lambda resolution for Java, extension-function + scope-function resolution for Kotlin, trait-method + UFCS resolution for Rust)\n- **RAM-first pipeline**: LZ4 compression, in-memory SQLite, single dump at end. Memory released after.\n\n### Distribution & operation\n- **Single static binary, zero infrastructure**: SQLite-backed, persists to `~/.cache/codebase-memory-mcp/`\n- **Auto-sync**: Background watcher detects file changes and re-indexes automatical",
"strategic_keywords": [
"agent",
"agents",
"memory",
"mcp",
"rag",
"llm",
"eval",
"workflow",
"protocol"
],
"relationship_label": "Memory 组件",
"data_confidence": "high",
"evidence_sources": [
"github_trending",
"github_repo_api",
"readme"
],
"score_breakdown": {
"heat": 20,
"relevance": 20,
"novelty": 15,
"productize": 14,
"adoption": 8,
"relation": 10,
"risk_signal": 8,
"total": 95
},
"strategic_score": 95
},
{
"owner": "OpenCut-app",
"name": "OpenCut",
"full_name": "OpenCut-app/OpenCut",
"url": "https://github.com/OpenCut-app/OpenCut",
"description": "The open-source CapCut alternative",
"language": "TypeScript",
"total_stars": 59699,
"forks": 6484,
"stars_this_period": 3550,
"source_slice": "all",
"source_slices": [
"all",
"typescript"
],
"metadata": {
"topics": [
"editor",
"oss",
"videoeditor"
],
"license": "MIT",
"open_issues": 329,
"created_at": "2025-06-22T08:02:17Z",
"pushed_at": "2026-06-21T13:15:27Z",
"homepage": "https://opencut.app",
"default_branch": "main",
"forks": 6484,
"watchers": 290,
"archived": false,
"size_kb": 27523
},
"readme_content": "
\n
\n
\n \n
\n
\n
OpenCut
\n
A free and open source video editor for web, desktop, and mobile.
\n
\n
\n
\n\n[](https://discord.gg/zmR9N35cjK)\n[](https://x.com/opencutapp)\n[](LICENSE)\n\n## Status\n\n**OpenCut is being rewritten from the ground up.** What's coming:\n\n- An Editor API\n- First-class third party plugins (made possible by a plugin-first architecture)\n- Desktop, mobile, and browser from one codebase (Rust core)\n- MCP server (for AI agents)\n- Headless mode (automation, batch rendering)\n- A scripting tab directly in the editor\n\nYou can still find the previous version at [opencut-app/opencut-classic](https://github.com/opencut-app/opencut-classic), which is the one to reach for today. [opencut.app](https://opencut.app) still runs the classic version; the rewrite will live at [new.opencut.app](https://new.opencut.app) until it's ready to take over.\n\n## Development\n\nInstall [proto](https://moonrepo.dev/proto) if you haven't already:\n\n```sh\nbash <(curl -fsSL https://moonrepo.dev/install/proto.sh)\n```\n\nFrom the repo root:\n\n```sh\nproto use # installs bun + moon at the versions pinned in .prototools\nbun install\n```\n\n```sh\nmoon run web:dev # localhost:5173\nmoon run api:dev # localhost:8787\n```\n\n## Contributing\n\nWe're not set up to take outside contributions yet while the architecture is being designed. If you want to follow along, ask questions, or just hang out, [join the Discord](https://discord.gg/zmR9N35cjK) or [open an issue](https://github.com/opencut-app/opencut/issues).\n\n## Sponsors\n\nOpenCut is supported by companies that believe in open source creator tools.\n\n- [**fal.ai**](https://fal.ai?utm_source=github-opencut&utm_campaign=oss): Generative image, video, and audio models all in one place.\n\nWant your logo here? Reach out at [sponsor@opencut.app](mailto:sponsor@opencut.app).\n\n## License\n\n[MIT](LICENSE)\n",
"strategic_keywords": [
"agent",
"agents",
"mcp",
"automation"
],
"relationship_label": "Skill 来源",
"data_confidence": "high",
"evidence_sources": [
"github_trending",
"github_repo_api",
"readme"
],
"score_breakdown": {
"heat": 20,
"relevance": 20,
"novelty": 15,
"productize": 14,
"adoption": 8,
"relation": 10,
"risk_signal": 8,
"total": 95
},
"strategic_score": 95
}
]
![\"OpenMontage\"](\"assets/logo.png\"){kind=logo}
\n![\"License\"](\"https://img.shields.io/badge/license-AGPLv3-blue.svg\"){kind=icon}
\n![\"#1](\"https://img.shields.io/badge/%F0%9F%8F%86%20%231%20on%20GitHub%20Trending-Repository%20of%20the%20Day-8957E5?style=for-the-badge&labelColor=1a1a2e\"){kind=icon}
![\"YouTube\"](\"https://img.shields.io/badge/YouTube-%40OpenMontage-FF0000?style=for-the-badge&logo=youtube&logoColor=white\"){kind=icon}
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