GitHub Trending Today for python - Python Daily

openai/skills

Mon, 09 Feb 2026 00:07:58 GMT

openai/skills

Skills Catalog for Codex

Language: Python

Stars: 6,950

Forks: 388

Stars today: 1,425 stars today

README

# Agent Skills

Agent Skills are folders of instructions, scripts, and resources that AI agents can discover and use to perform at specific tasks. Write once, use everywhere.

Codex uses skills to help package capabilities that teams and individuals can use to complete specific tasks in a repeatable way. This repository catalogs skills for use and distribution with Codex.

Learn more:
- [Using skills in Codex](https://developers.openai.com/codex/skills)
- [Create custom skills in Codex](https://developers.openai.com/codex/skills/create-skill)
- [Agent Skills open standard](https://agentskills.io)

## Installing a skill

Skills in [`.system`](skills/.system/) are automatically installed in the latest version of Codex.

To install [curated](skills/.curated/) or [experimental](skills/.experimental/) skills, you can use the `$skill-installer` inside Codex.

Curated skills can be installed by name (defaults to `skills/.curated`):

```
$skill-installer gh-address-comments
```

For experimental skills, specify the skill folder. For example:

```
$skill-installer install the create-plan skill from the .experimental folder
```

Or provide the GitHub directory URL:

```
$skill-installer install https://github.com/openai/skills/tree/main/skills/.experimental/create-plan
```

After installing a skill, restart Codex to pick up new skills.

## License

The license of an individual skill can be found directly inside the skill's directory inside the `LICENSE.txt` file.

google/langextract

Mon, 09 Feb 2026 00:07:57 GMT

google/langextract

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

Language: Python

Stars: 24,740

Forks: 1,711

Stars today: 438 stars today

README

# LangExtract [![PyPI version](https://img.shields.io/pypi/v/langextract.svg)](https://pypi.org/project/langextract/) [![GitHub stars](https://img.shields.io/github/stars/google/langextract.svg?style=social&label=Star)](https://github.com/google/langextract) ![Tests](https://github.com/google/langextract/actions/workflows/ci.yaml/badge.svg) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17015089.svg)](https://doi.org/10.5281/zenodo.17015089) ## Table of Contents - [Introduction](#introduction) - [Why LangExtract?](#why-langextract) - [Quick Start](#quick-start) - [Installation](#installation) - [API Key Setup for Cloud Models](#api-key-setup-for-cloud-models) - [Adding Custom Model Providers](#adding-custom-model-providers) - [Using OpenAI Models](#using-openai-models) - [Using Local LLMs with Ollama](#using-local-llms-with-ollama) - [More Examples](#more-examples) - [*Romeo and Juliet* Full Text Extraction](#romeo-and-juliet-full-text-extraction) - [Medication Extraction](#medication-extraction) - [Radiology Report Structuring: RadExtract](#radiology-report-structuring-radextract) - [Community Providers](#community-providers) - [Contributing](#contributing) - [Testing](#testing) - [Disclaimer](#disclaimer) ## Introduction LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text. ## Why LangExtract? 1. **Precise Source Grounding:** Maps every extraction to its exact location in the source text, enabling visual highlighting for easy traceability and verification. 2. **Reliable Structured Outputs:** Enforces a consistent output schema based on your few-shot examples, leveraging controlled generation in supported models like Gemini to guarantee robust, structured results. 3. **Optimized for Long Documents:** Overcomes the "needle-in-a-haystack" challenge of large document extraction by using an optimized strategy of text chunking, parallel processing, and multiple passes for higher recall. 4. **Interactive Visualization:** Instantly generates a self-contained, interactive HTML file to visualize and review thousands of extracted entities in their original context. 5. **Flexible LLM Support:** Supports your preferred models, from cloud-based LLMs like the Google Gemini family to local open-source models via the built-in Ollama interface. 6. **Adaptable to Any Domain:** Define extraction tasks for any domain using just a few examples. LangExtract adapts to your needs without requiring any model fine-tuning. 7. **Leverages LLM World Knowledge:** Utilize precise prompt wording and few-shot examples to influence how the extraction task may utilize LLM knowledge. The accuracy of any inferred information and its adherence to the task specification are contingent upon the selected LLM, the complexity of the task, the clarity of the prompt instructions, and the nature of the prompt examples. ## Quick Start > **Note:** Using cloud-hosted models like Gemini requires an API key. See the [API Key Setup](#api-key-setup-for-cloud-models) section for instructions on how to get and configure your key. Extract structured information with just a few lines of code. ### 1. Define Your Extraction Task First, create a prompt that clearly describes what you want to extract. Then, provide a high-quality example to guide the model. ```python import langextract as lx import textwrap # 1. Define the prompt and extraction rules prompt = textwrap.dedent("""\ Extract characters, emotions, and relationships in order of appearance. Use exact text for extractions. Do not paraphrase or overlap entities. Provide meaningful attributes for each entity to add context.""") # 2. Provide a high-quality example to guide the model examples = [ lx.data.ExampleData( text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.", extractions=[ lx.data.Extraction( extraction_class="character", extraction_text="ROMEO", attributes={"emotional_state": "wonder"} ), lx.data.Extraction( extraction_class="emotion", extraction_text="But soft!", attributes={"feeling": "gentle awe"} ), lx.data.Extraction( extraction_class="relationship", extraction_text="Juliet is the sun", attributes={"type": "metaphor"} ), ] ) ] ``` > **Note:** Examples drive model behavior. Each `extraction_text` should ideally be verbatim from the example's `text` (no paraphrasing), listed in order of appearance. LangExtract raises `Prompt alignment` warnings by default if examples don't follow this pattern—resolve these for best results. ### 2. Run the Extraction Provide your input text and the prompt materials to the `lx.extract` function. ```python # The input text to be processed input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo" # Run the extraction result = lx.extract( text_or_documents=input_text, prompt_description=prompt, examples=examples, model_id="gemini-2.5-flash", ) ``` > **Model Selection**: `gemini-2.5-flash` is the recommended default, offering an excellent balance of speed, cost, and quality. For highly complex tasks requiring deeper reasoning, `gemini-2.5-pro` may provide superior results. For large-scale or production use, a Tier 2 Gemini quota is suggested to increase throughput and avoid rate limits. See the [rate-limit documentation](https://ai.google.dev/gemini-api/docs/rate-limits#tier-2) for details. > > **Model Lifecycle**: Note that Gemini models have a lifecycle with defined retirement dates. Users should consult the [official model version documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions) to stay informed about the latest stable and legacy versions. ### 3. Visualize the Results The extractions can be saved to a `.jsonl` file, a popular format for working with language model data. LangExtract can then generate an interactive HTML visualization from this file to review the entities in context. ```python # Save the results to a JSONL file lx.io.save_annotated_documents([result], output_name="extraction_results.jsonl", output_dir=".") # Generate the visualization from the file html_content = lx.visualize("extraction_results.jsonl") with open("visualization.html", "w") as f: if hasattr(html_content, 'data'): f.write(html_content.data) # For Jupyter/Colab else: f.write(html_content) ``` This creates an animated and interactive HTML file: ![Romeo and Juliet Basic Visualization ](https://raw.githubusercontent.com/google/langextract/main/docs/_static/romeo_juliet_basic.gif) > **Note on LLM Knowledge Utilization:** This example demonstrates extractions that stay close to the text evidence - extracting "longing" for Lady Juliet's emotional state and identifying "yearning" from "gazed longingly at the stars." The task could be modified to generate attributes that draw more heavily from the LLM's world knowledge (e.g., adding `"identity": "Capulet family daughter"` or `"literary_context": "tragic heroine"`). The balance between text-evidence and knowledge-inference is controlled by your prompt instructions and example attributes. ### Scaling to Longer Documents For larger texts, you can process entire documents directly from URLs with parallel processing and enhanced sensitivity: ```python # Process Romeo & Juliet directly from Project Gutenberg result = lx.extract( text_or_documents="https://www.gutenberg.org/files/1513/1513-0.txt", prompt_description=prompt, examples=examples, model_id="gemini-2.5-flash", extraction_passes=3, # Improves recall through multiple passes max_workers=20, # Parallel processing for speed max_char_buffer=1000 # Smaller contexts for better accuracy ) ``` This approach can extract hundreds of entities from full novels while maintaining high accuracy. The interactive visualization seamlessly handles large result sets, making it easy to explore hundreds of entities from the output JSONL file. **[See the full *Romeo and Juliet* extraction example →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)** for detailed results and performance insights. ### Vertex AI Batch Processing Save costs on large-scale tasks by enabling Vertex AI Batch API: `language_model_params={"vertexai": True, "batch": {"enabled": True}}`. See an example of the Vertex AI Batch API usage in [this example](docs/examples/batch_api_example.md). ## Installation ### From PyPI ```bash pip install langextract ``` *Recommended for most users. For isolated environments, consider using a virtual environment:* ```bash python -m venv langextract_env source langextract_env/bin/activate # On Windows: langextract_env\Scripts\activate pip install langextract ``` ### From Source LangExtract uses modern Python packaging with `pyproject.toml` for dependency management: *Installing with `-e` puts the package in development mode, allowing you to modify the code without reinstalling.* ```bash git clone https://github.com/google/langextract.git cd langextract # For basic installation: pip install -e . # For development (includes linting tools): pip install -e ".[dev]" # For testing (includes pytest): pip install -e ".[test]" ``` ### Docker ```bash docker build -t langextract . docker run --rm -e LANGEXTRACT_API_KEY="your-api-key" langextract python your_script.py ``` ## API Key Setup for Cloud Models When using LangExtract with cloud-hosted models (like Gemini or OpenAI), you'll need to set up an API key. On-device models don't require an API key. For developers using local LLMs, LangExtract offers built-in support for Ollama and can be extended to other third-party APIs by updating the inference endpoints. ### API Key Sources Get API keys from: * [AI Studio](https://aistudio.google.com/app/apikey) for Gemini models * [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview) for enterprise use * [OpenAI Platform](https://platform.openai.com/api-keys) for OpenAI models ### Setting up API key in your environment **Option 1: Environment Variable** ```bash export LANGEXTRACT_API_KEY="your-api-key-here" ``` **Option 2: .env File (Recommended)** Add your API key to a `.env` file: ```bash # Add API key to .env file cat >> .env << 'EOF' LANGEXTRACT_API_KEY=your-api-key-here EOF # Keep your API key secure echo '.env' >> .gitignore ``` In your Python code: ```python import langextract as lx result = lx.extract( text_or_documents=input_text, prompt_description="Extract information...", examples=[...], model_id="gemini-2.5-flash" ) ``` **Option 3: Direct API Key (Not Recommended for Production)** You can also provide the API key directly in your code, though this is not recommended for production use: ```python result = lx.extract( text_or_documents=input_text, prompt_description="Extract information...", examples=[...], model_id="gemini-2.5-flash", api_key="your-api-key-here" # Only use this for testing/development ) ``` **Option 4: Vertex AI (Service Accounts)** Use [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) for authentication with service accounts: ```python result = lx.extract( text_or_documents=input_text, prompt_description="Extract information...", examples=[...], model_id="gemini-2.5-flash", language_model_params={ "vertexai": True, "project": "your-project-id", "location": "global" # or regional endpoint } ) ``` ## Adding Custom Model Providers LangExtract supports custom LLM providers via a lightweight plugin system. You can add support for new models without changing core code. - Add new model support independently of the core library - Distribute your provider as a separate Python package - Keep custom dependencies isolated - Override or extend built-in providers via priority-based resolution See the detailed guide in [Provider System Documentation](langextract/providers/README.md) to learn how to: - Register a provider with `@registry.register(...)` - Publish an entry point for discovery - Optionally provide a schema with `get_schema_class()` for structured output - Integrate with the factory via `create_model(...)` ## Using OpenAI Models LangExtract supports OpenAI models (requires optional dependency: `pip install langextract[openai]`): ```python import langextract as lx result = lx.extract( text_or_documents=input_text, prompt_description=prompt, examples=examples, model_id="gpt-4o", # Automatically selects OpenAI provider api_key=os.environ.get('OPENAI_API_KEY'), fence_output=True, use_schema_constraints=False ) ``` Note: OpenAI models require `fence_output=True` and `use_schema_constraints=False` because LangExtract doesn't implement schema constraints for OpenAI yet. ## Using Local LLMs with Ollama LangExtract supports local inference using Ollama, allowing you to run models without API keys: ```python import langextract as lx result = lx.extract( text_or_documents=input_text, prompt_description=prompt, examples=examples, model_id="gemma2:2b", # Automatically selects Ollama provider model_url="http://localhost:11434", fence_output=False, use_schema_constraints=False ) ``` **Quick setup:** Install Ollama from [ollama.com](https://ollama.com/), run `ollama pull gemma2:2b`, then `ollama serve`. For detailed installation, Docker setup, and examples, see [`examples/ollama/`](examples/ollama/). ## More Examples Additional examples of LangExtract in action: ### *Romeo and Juliet* Full Text Extraction LangExtract can process complete documents directly from URLs. This example demonstrates extraction from the full text of *Romeo and Juliet* from Project Gutenberg (147,843 characters), showing parallel processing, sequential extraction passes, and performance optimization for long document processing. **[View *Romeo and Juliet* Full Text Example →](https://github.com/google/langextract/blob/main/docs/examples/longer_text_example.md)** ### Medication Extraction > **Disclaimer:** This demonstration is for illustrative purposes of LangExtract's baseline capability only. It does not represent a finished or approved product, is not intended to diagnose or suggest treatment of any disease or condition, and should not be used for medical advice. LangExtract excels at extracting structured medical information from clinical text. These examples demonstrate both basic entity recognition (medication names, dosages, routes) and relationship extraction (connecting medications to their attributes), showing LangExtract's effectiveness for healthcare applications. **[View Medication Examples →](https://github.com/google/langextract/blob/main/docs/examples/medication_examples.md)** ### Radiology Report Structuring: RadExtract Explore RadExtract, a live interactive demo on HuggingFace Spaces that shows how LangExtract can automatically structure radiology reports. Try it directly in your browser with no setup required. **[View RadExtract Demo →](https://huggingface.co/spaces/google/radextract)** ## Community Providers Extend LangExtract with custom model providers! Check out our [Community Provider Plugins](COMMUNITY_PROVIDERS.md) registry to discover providers created by the community or add your own. For detailed instructions on creating a provider plugin, see the [Custom Provider Plugin Example](examples/custom_provider_plugin/). ## Contributing Contributions are welcome! See [CONTRIBUTING.md](https://github.com/google/langextract/blob/main/CONTRIBUTING.md) to get started with development, testing, and pull requests. You must sign a [Contributor License Agreement](https://cla.developers.google.com/about) before submitting patches. ## Testing To run tests locally from the source: ```bash # Clone the repository git clone https://github.com/google/langextract.git cd langextract # Install with test dependencies pip install -e ".[test]" # Run all tests pytest tests ``` Or reproduce the full CI matrix locally with tox: ```bash tox # runs pylint + pytest on Python 3.10 and 3.11 ``` ### Ollama Integration Testing If you have Ollama installed locally, you can run integration tests: ```bash # Test Ollama integration (requires Ollama running with gemma2:2b model) tox -e ollama-integration ``` This test will automatically detect if Ollama is available and run real inference tests. ## Development ### Code Formatting This project uses automated formatting tools to maintain consistent code style: ```bash # Auto-format all code ./autoformat.sh # Or run formatters separately isort langextract tests --profile google --line-length 80 pyink langextract tests --config pyproject.toml ``` ### Pre-commit Hooks For automatic formatting checks: ```bash pre-commit install # One-time setup pre-commit run --all-files # Manual run ``` ### Linting Run linting before submitting PRs: ```bash pylint --rcfile=.pylintrc langextract tests ``` See [CONTRIBUTING.md](CONTRIBUTING.md) for full development guidelines. ## Disclaimer This is not an officially supported Google product. If you use LangExtract in production or publications, please cite accordingly and acknowledge usage. Use is subject to the [Apache 2.0 License](https://github.com/google/langextract/blob/main/LICENSE). For health-related applications, use of LangExtract is also subject to the [Health AI Developer Foundations Terms of Use](https://developers.google.com/health-ai-developer-foundations/terms). --- **Happy Extracting!**

OpenBMB/MiniCPM-o

Mon, 09 Feb 2026 00:07:56 GMT

OpenBMB/MiniCPM-o

A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone

Language: Python

Stars: 23,386

Forks: 1,787

Stars today: 212 stars today

README



 

**A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone**

  [中文](./README_zh.md) |
  English




  
   WeChat  |

 


   Discord  





   MiniCPM-o 4.5 🤗 📞 🤖 | MiniCPM-V 4.0 🤗  | 🍳 Cookbook




**MiniCPM-o** is the latest series of on-device multimodal LLMs (MLLMs) ungraded from MiniCPM-V. The models can now take image, video, text, and audio as inputs and provide high-quality text and speech outputs in an end-to-end fashion. The model series is designed for **strong performance and efficient deployment**. The most notable models in the series currently include:


- **MiniCPM-o 4.5**: 🔥🔥🔥 The latest and most capable model in the series. With a total of 9B parameters, this end-to-end model **approaches Gemini 2.5 Flash in vision, speech, and full-duplex multimodal live streaming**, making it one of the most versatile and performant models in the open-source community. The new full-duplex multimodal live streaming capability means that the output streams (speech and text), and the real-time input streams (video and audio) do not block each other. This **enables MiniCPM-o 4.5 to see, listen, and speak simultaneously** in a real-time omnimodal conversation, and perform **proactive interactions** such as proactive reminding. The improved voice mode supports bilingual real-time speech conversation in a more natural, expressive, and stable way, and also allows for voice cloning. It also advances MiniCPM-V's visual capabilities such as strong OCR capability, trustworthy behavior and multilingual support, etc. We also rollout a **high-performing llama.cpp-omni inference framework together with a WebRTC Demo**, to bring this full-duplex multimodal live streaming experience [available on local devices such as Macs](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/WebRTC_Demo/README.md).

- **MiniCPM-V 4.0**: ⭐️⭐️⭐️ An efficient model in the MiniCPM-V series. With a total of 4B parameters, the model surpasses GPT-4.1-mini-20250414 in image understanding on the OpenCompass evaluation. With its small parameter-size and efficient architecure, MiniCPM-V 4.0 is an ideal choice for on-device deployment on the phone.




## News 

#### 📌 Pinned

> [!NOTE]
> [2026.02.06] 🥳 🥳 🥳 MiniCPM-o 4.5 Local & Ready-to-Run! Experience **low-latency full-duplex communication** directly **on your own Mac** using our new official Docker image. [Try it now](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/WebRTC_Demo/README.md)!


* [2026.02.05] 📢📢📢 We note the web demo may experience latency issues due to network conditions. We are working actively to provide a Docker image for local deployment of the real-time interactive Demo as soon as possible. Please stay tuned!

* [2026.02.03] 🔥🔥🔥 We open-source MiniCPM-o 4.5, which matches Gemini 2.5 Flash on vision and speech, and supports full-duplex multimodal live streaming. Try it now!


* [2025.09.18] 📢📢📢 MiniCPM-V 4.5 technical report is now released! See [here](./docs/MiniCPM_V_4_5_Technical_Report.pdf).

* [2025.08.26] 🔥🔥🔥 We open-source MiniCPM-V 4.5, which outperforms GPT-4o-latest, Gemini-2.0 Pro, and Qwen2.5-VL 72B. It advances popular capabilities of MiniCPM-V, and brings useful new features. Try it now!

* [2025.08.01] ⭐️⭐️⭐️ We open-sourced the [MiniCPM-V & o Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook)! It provides comprehensive guides for diverse user scenarios, paired with our new [Docs Site](https://minicpm-o.readthedocs.io/en/latest/index.html) for smoother onboarding.

* [2025.03.01] 🚀🚀🚀 RLAIF-V, the alignment technique of MiniCPM-o, is accepted by CVPR 2025 Highlights！The [code](https://github.com/RLHF-V/RLAIF-V), [dataset](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset), [paper](https://arxiv.org/abs/2405.17220) are open-sourced!

* [2025.01.24] 📢📢📢 MiniCPM-o 2.6 technical report is released! See [here](https://openbmb.notion.site/MiniCPM-o-2-6-A-GPT-4o-Level-MLLM-for-Vision-Speech-and-Multimodal-Live-Streaming-on-Your-Phone-185ede1b7a558042b5d5e45e6b237da9).

* [2025.01.19] ⭐️⭐️⭐️ MiniCPM-o tops GitHub Trending and reaches top-2 on Hugging Face Trending!


* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!




 
Click to view more news.

* [2025.09.01] ⭐️⭐️⭐️ MiniCPM-V 4.5 has been officially supported by [llama.cpp](https://github.com/ggml-org/llama.cpp/pull/15575), [vLLM](https://github.com/vllm-project/vllm/pull/23586), and [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory/pull/9022). You are welcome to use it directly through these official channels! Support for additional frameworks such as [Ollama](https://github.com/ollama/ollama/pull/12078) and [SGLang](https://github.com/sgl-project/sglang/pull/9610) is actively in progress.
* [2025.08.02] 🚀🚀🚀 We open-source MiniCPM-V 4.0, which outperforms GPT-4.1-mini-20250414 in image understanding. It advances popular features of MiniCPM-V 2.6, and largely improves the efficiency. We also open-source the iOS App on iPhone and iPad. Try it now!
* [2025.06.20] ⭐️⭐️⭐️ Our official [Ollama repository](https://ollama.com/openbmb) is released. Try our latest models with [one click](https://ollama.com/openbmb/minicpm-o2.6)！
* [2025.01.23] 💡💡💡 MiniCPM-o 2.6 is now supported by [Align-Anything](https://github.com/PKU-Alignment/align-anything), a framework by PKU-Alignment Team for aligning any-to-any modality large models with human intentions. It supports DPO and SFT fine-tuning on both vision and audio. Try it now!
* [2025.01.19] 📢 **ATTENTION!** We are currently working on merging MiniCPM-o 2.6 into the official repositories of llama.cpp, Ollama, and vllm. Until the merge is complete, please USE OUR LOCAL FORKS of [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md), [Ollama](https://github.com/OpenBMB/ollama/blob/minicpm-v2.6/examples/minicpm-v2.6/README.md), and [vllm](https://github.com/OpenBMB/MiniCPM-o?tab=readme-ov-file#efficient-inference-with-llamacpp-ollama-vllm). **Using the official repositories before the merge may lead to unexpected issues**.
* [2025.01.17] We have updated the usage of MiniCPM-o 2.6 int4 quantization version and resolved the model initialization error. Click [here](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and try it now!
* [2025.01.13] 🔥🔥🔥 We open-source MiniCPM-o 2.6, which matches GPT-4o-202405 on vision, speech and multimodal live streaming. It advances popular capabilities of MiniCPM-V 2.6, and supports various new fun features. Try it now!
* [2024.08.15] We now also support multi-image SFT. For more details, please refer to the [document](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune).
* [2024.08.14] MiniCPM-V 2.6 now also supports [fine-tuning](https://github.com/modelscope/ms-swift/issues/1613) with the SWIFT framework!
* [2024.08.17] 🚀🚀🚀 MiniCPM-V 2.6 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf).
* [2024.08.10] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 is now fully supported by [official](https://github.com/ggerganov/llama.cpp) llama.cpp! GGUF models of various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf).
* [2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now!
* [2024.08.03] MiniCPM-Llama3-V 2.5 technical report is released! See [here](https://arxiv.org/abs/2408.01800).
* [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See [here](#inference-with-vllm).

* [2024.06.03] Now, you can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs(12 GB or 16 GB) by distributing the model's layers across multiple GPUs. For more details, check this [link](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md).
* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and Ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [Ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). MiniCPM-Llama3-V 2.5 series is **not supported by the official repositories yet**, and we are working hard to merge PRs. Please stay tuned!

* [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics).

* [2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage)!
* [2024.05.24] We release the MiniCPM-Llama3-V 2.5 [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!

* [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmark evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](./docs/compare_with_phi-3_vision.md) to view more details.

* [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](./finetune/readme.md). Try it now!
* [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click [here](#inference-with-vllm) to view more details.
* [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at [here](https://huggingface.co/spaces/openbmb/MiniCPM-V-2)!
* [2024.04.17] MiniCPM-V-2.0 supports deploying [WebUI Demo](#webui-demo) now!
* [2024.04.15] MiniCPM-V-2.0 now also supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md) with the SWIFT framework!
* [2024.04.12] We open-source MiniCPM-V 2.0, which achieves comparable performance with Gemini Pro in understanding scene text and outperforms strong Qwen-VL-Chat 9.6B and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. Click here to view the MiniCPM-V 2.0 technical blog.
* [2024.03.14] MiniCPM-V now supports [fine-tuning](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md) with the SWIFT framework. Thanks to [Jintao](https://github.com/Jintao-Huang) for the contribution！
* [2024.03.01] MiniCPM-V can now be deployed on Mac!
* [2024.02.01] We open-source MiniCPM-V and OmniLMM-12B, which support efficient end-side deployment and powerful multimodal capabilities correspondingly.
 


## Contents 


- [MiniCPM-o 4.5](#minicpm-o-45)
- [MiniCPM-V 4.0](#minicpm-v-40)
- [MiniCPM-V \& o Cookbook](#minicpm-v--o-cookbook)
- [Model Zoo](#model-zoo)
- [Local Interactive Demo](#local-interactive-demo)
- [Inference with Transformers](#inference-with-transformers)
  - [Model Initialization](#model-initialization)
  - [Duplex Omni Mode](#duplex-omni-mode)
  - [Simplex Omni Mode](#simplex-omni-mode)
  - [Simplex Realtime Speech Conversation Mode](#simplex-realtime-speech-conversation-mode)
  - [Visual Understanding](#visual-understanding)
  - [Structured Content Input](#structured-content-input)
- [Supported Frameworks](#supported-frameworks)
  - [FlagOS](#flagos)
  - [vLLM, SGLang, llama.cpp, Ollama](#vllm-sglang-llamacpp-ollama)
  - [LLaMA-Factory, SWIFT](#llama-factory-swift)
- [Awesome work using MiniCPM-V \& MiniCPM-o](#awesome-work-using-minicpm-v--minicpm-o)
- [Limitations](#limitations)
- [Acknowledgements](#acknowledgements)


## MiniCPM-o 4.5

**MiniCPM-o 4.5** is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip2, Whisper-medium, CosyVoice2, and Qwen3-8B with a total of 9B parameters. It exhibits a significant performance improvement, and introduces new features for full-duplex multimodal live streaming. Notable features of MiniCPM-o 4.5 include:

- 🔥 **Leading Visual Capability.**
  MiniCPM-o 4.5 achieves an average score of 77.6 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 9B parameters, it surpasses widely used proprietary models like GPT-4o, Gemini 2.0 Pro, and approaches Gemini 2.5 Flash** for vision-language capabilities. It supports instruct and thinking modes in a single model, better covering efficiency and performance trade-offs in different user scenarios.

- 🎙 **Strong Speech Capability.** 
  MiniCPM-o 4.5 supports **bilingual real-time speech conversation with configurable voices** in English and Chinese. It features **more natural, expressive and stable speech conversation**. The model also allows for fun features such as **voice cloning and role play via a simple reference audio clip**, where the cloning performance surpasses strong TTS tools such as CosyVoice2.

- 🎬 **New Full-Duplex and Proactive Multimodal Live Streaming Capability.** 
  As a new feature, MiniCPM-o 4.5 can process real-time, continuous video and audio input streams simultaneously while generating concurrent text and speech output streams in an end-to-end fashion, without mutual blocking. This **allows MiniCPM-o 4.5 to see, listen, and speak simultaneously**, creating a fluid, real-time omnimodal conversation experience. Beyond reactive responses, the model can also perform **proactive interaction**, such as initiating reminders or comments based on its continuous understanding of the live scene. 

- 💪 **Strong OCR Capability, Efficiency and Others.**
Advancing popular visual capabilities from MiniCPM-V series, MiniCPM-o 4.5 can process **high-resolution images** (up to 1.8 million pixels) and **high-FPS videos** (up to 10fps) in any aspect ratio efficiently. It achieves **state-of-the-art peformance for end-to-end English document parsing** on OmniDocBench, outperforming proprietary models such as Gemini-3 Flash and GPT-5, and specialized tools such as DeepSeek-OCR 2. It also features **trustworthy behaviors**, matching Gemini 2.5 Flash on MMHal-Bench, and supports **multilingual capabilities** on more than 30 languages.

-  💫  **Easy Usage.**
  MiniCPM-o 4.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-o4_5_llamacpp.md) and [Ollama](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-o4_5_ollama.md) support for efficient CPU inference on local devices, (2) [int4](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/awq/minicpm-o4_5_awq_quantize.md) and [GGUF](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-o4_5_gguf_quantize.md) format quantized models in 16 sizes, (3) [vLLM](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-o4_5_vllm.md) and [SGLang](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-o4_5_sglang.md) support for high-throughput and memory-efficient inference, (4) [FlagOS](#flagos) support for the unified multi-chip backend plugin, (5) fine-tuning on new domains and tasks with [LLaMA-Factory](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/llama-factory/finetune_llamafactory.md), and (6) online web demo on [server](https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/gradio/README_o45.md). We also rollout a high-performing [llama.cpp-omni](https://github.com/tc-mb/llama.cpp-omni) inference framework together with a [WebRTC Demo](https://minicpm-omni.openbmb.cn/), which **enables the full-duplex multimodal live streaming experience on local devices** such as [PCs](https://github.com/tc-mb/llama.cpp-omni/blob/master/README.md) (e.g., on a MacBook).

**Model Architecture.**
- **End-to-end Omni-modal Architecture.** The modality encoders/decoders and LLM are densely connected via hidden states in an end-to-end fashion. This enables better information flow and control, and also facilitates full exploitation of rich multimodal knowledge during training.
- **Full-Duplex Omni-modal Live Streaming Mechanism.** (1) We turn the offline modality encoder/decoders into online and full-duplex ones for streaming inputs/outputs. The speech token decoder models text and speech tokens in an interleaved fashion to support full-duplex speech generation (i.e., sync timely with new input). This also facilitates more stable long speech generation (e.g., > 1min).
(2) **We sync all the input and output streams on timeline in milliseconds**, which are jointly modeled by a time-division multiplexing (TDM) mechanism for omni-modality streaming processing in the LLM backbone. It divides parallel omni-modality streams into sequential info groups within small periodic time slices.
- **Proactive Interaction Mechanism.** The LLM continuously monitors the input video and audio streams, and decides at a frequency of 1Hz to speak or not. This high decision-making frequency together with full-duplex nature are curcial to enable the proactive interaction capability.
- **Configurable Speech Modeling Design.** We inherent the multimodal system prompt design of MiniCPM-o 2.6, which includes a traditional text system prompt, and a new audio system prompt to determine the assistant voice. This enables cloning new voices and role play in inference time for speech conversation.




  



### Evaluation  



  




  

Note: Scores marked with ∗ are from our evaluation; others are cited from referenced reports. n/a indicates that the model does not support the corresponding modality. All results are reported in instruct mode/variant.

 




Click to view visual understanding results.

**Image Understanding (Instruct)**
  
  

  Model
  OpenCompass

... [README content truncated due to size. Visit the repository for the complete README] ...
          


p-e-w/heretic
Mon, 09 Feb 2026 00:07:55 GMT

            p-e-w/heretic
            Fully automatic censorship removal for language models
            Language: Python
            Stars: 4,899
            Forks: 473
            Stars today: 308 stars today
            README
# Heretic: Fully automatic censorship removal for language models

[![Discord](https://img.shields.io/discord/1447831134212984903?color=5865F2&label=discord&labelColor=black&logo=discord&logoColor=white&style=for-the-badge)](https://discord.gg/gdXc48gSyT)

Heretic is a tool that removes censorship (aka "safety alignment") from
transformer-based language models without expensive post-training.
It combines an advanced implementation of directional ablation, also known
as "abliteration" ([Arditi et al. 2024](https://arxiv.org/abs/2406.11717)),
with a TPE-based parameter optimizer powered by [Optuna](https://optuna.org/).

This approach enables Heretic to work **completely automatically.** Heretic
finds high-quality abliteration parameters by co-minimizing the number of
refusals and the KL divergence from the original model. This results in a
decensored model that retains as much of the original model's intelligence
as possible. Using Heretic does not require an understanding of transformer
internals. In fact, anyone who knows how to run a command-line program
can use Heretic to decensor language models.



 

Running unsupervised with the default configuration, Heretic can produce
decensored models that rival the quality of abliterations created manually
by human experts:

| Model | Refusals for "harmful" prompts | KL divergence from original model for "harmless" prompts |
| :--- | ---: | ---: |
| [google/gemma-3-12b-it](https://huggingface.co/google/gemma-3-12b-it) (original) | 97/100 | 0 *(by definition)* |
| [mlabonne/gemma-3-12b-it-abliterated-v2](https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated-v2) | 3/100 | 1.04 |
| [huihui-ai/gemma-3-12b-it-abliterated](https://huggingface.co/huihui-ai/gemma-3-12b-it-abliterated) | 3/100 | 0.45 |
| **[p-e-w/gemma-3-12b-it-heretic](https://huggingface.co/p-e-w/gemma-3-12b-it-heretic) (ours)** | **3/100** | **0.16** |

The Heretic version, generated without any human effort, achieves the same
level of refusal suppression as other abliterations, but at a much lower
KL divergence, indicating less damage to the original model's capabilities.
*(You can reproduce those numbers using Heretic's built-in evaluation functionality,
e.g. `heretic --model google/gemma-3-12b-it --evaluate-model p-e-w/gemma-3-12b-it-heretic`.
Note that the exact values might be platform- and hardware-dependent.
The table above was compiled using PyTorch 2.8 on an RTX 5090.)*

Of course, mathematical metrics and automated benchmarks never tell the whole
story, and are no substitute for human evaluation. Models generated with
Heretic have been well-received by users (links and emphasis added):

> "I was skeptical before, but I just downloaded
> [**GPT-OSS 20B Heretic**](https://huggingface.co/p-e-w/gpt-oss-20b-heretic)
> model and holy shit. It gives properly formatted long responses to sensitive topics,
> using the exact uncensored words that you would expect from an uncensored model,
> produces markdown format tables with details and whatnot. Looks like this is
> the best abliterated version of this model so far..."
> [*(Link to comment)*](https://old.reddit.com/r/LocalLLaMA/comments/1oymku1/heretic_fully_automatic_censorship_removal_for/np6tba6/)

> "[**Heretic GPT 20b**](https://huggingface.co/p-e-w/gpt-oss-20b-heretic)
> seems to be the best uncensored model I have tried yet. It doesn't destroy a
> the model's intelligence and it is answering prompts normally would be
> rejected by the base model."
> [*(Link to comment)*](https://old.reddit.com/r/LocalLLaMA/comments/1oymku1/heretic_fully_automatic_censorship_removal_for/npe9jng/)

> "[[**Qwen3-4B-Instruct-2507-heretic**](https://huggingface.co/p-e-w/Qwen3-4B-Instruct-2507-heretic)]
> Has been the best unquantized abliterated model that I have been able to run on 16gb vram."
> [*(Link to comment)*](https://old.reddit.com/r/LocalLLaMA/comments/1phjxca/im_calling_these_people_out_right_now/nt06tji/)

Heretic supports most dense models, including many multimodal models, and
several different MoE architectures. It does not yet support SSMs/hybrid models,
models with inhomogeneous layers, and certain novel attention systems.

You can find a collection of models that have been decensored using Heretic
[on Hugging Face](https://huggingface.co/collections/p-e-w/the-bestiary).


## Usage

Prepare a Python 3.10+ environment with PyTorch 2.2+ installed as appropriate
for your hardware. Then run:

```
pip install -U heretic-llm
heretic Qwen/Qwen3-4B-Instruct-2507
```

Replace `Qwen/Qwen3-4B-Instruct-2507` with whatever model you want to decensor.

The process is fully automatic and does not require configuration; however,
Heretic has a variety of configuration parameters that can be changed for
greater control. Run `heretic --help` to see available command-line options,
or look at [`config.default.toml`](config.default.toml) if you prefer to use
a configuration file.

At the start of a program run, Heretic benchmarks the system to determine
the optimal batch size to make the most of the available hardware.
On an RTX 3090, with the default configuration, decensoring Llama-3.1-8B
takes about 45 minutes.

After Heretic has finished decensoring a model, you are given the option to
save the model, upload it to Hugging Face, chat with it to test how well it works,
or any combination of those actions.


## Research features

In addition to its primary function of removing model censorship, Heretic also
provides features designed to support research into the semantics of model internals
(interpretability). To use those features, you need to install Heretic with the
optional `research` extra:

```
pip install -U heretic-llm[research]
```

This gives you access to the following functionality:

### Generate plots of residual vectors by passing `--plot-residuals`

When run with this flag, Heretic will:

1. Compute residual vectors (hidden states) for the first output token,
   for each transformer layer, for both "harmful" and "harmless" prompts.
2. Perform a [PaCMAP projection](https://github.com/YingfanWang/PaCMAP)
   from residual space to 2D-space.
3. Left-right align the projections of "harmful"/"harmless" residuals
   by their geometric medians to make projections for consecutive layers
   more similar. Additionally, PaCMAP is initialized with the previous
   layer's projections for each new layer, minimizing disruptive transitions.
4. Scatter-plot the projections, generating a PNG image for each layer.
5. Generate an animation showing how residuals transform between layers,
   as an animated GIF.



See [the configuration file](config.default.toml) for options that allow you
to control various aspects of the generated plots.

Note that PaCMAP is an expensive operation that is performed on the CPU.
For larger models, it can take an hour or more to compute projections
for all layers.

### Print details about residual geometry by passing `--print-residual-geometry`

If you are interested in a quantitative analysis of how residual vectors
for "harmful" and "harmless" prompts relate to each other, this flag gives you
the following table, packed with metrics that can facilitate understanding
the same (for [gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it)
in this case):

```
┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃ Layer ┃ S(g,b) ┃ S(g*,b*) ┃  S(g,r) ┃ S(g*,r*) ┃  S(b,r) ┃ S(b*,r*) ┃      |g| ┃     |g*| ┃      |b| ┃     |b*| ┃     |r| ┃    |r*| ┃   Silh ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│     1 │ 1.0000 │   1.0000 │ -0.4311 │  -0.4906 │ -0.4254 │  -0.4847 │   170.29 │   170.49 │   169.78 │   169.85 │    1.19 │    1.31 │ 0.0480 │
│     2 │ 1.0000 │   1.0000 │  0.4297 │   0.4465 │  0.4365 │   0.4524 │   768.55 │   768.77 │   771.32 │   771.36 │    6.39 │    5.76 │ 0.0745 │
│     3 │ 0.9999 │   1.0000 │ -0.5699 │  -0.5577 │ -0.5614 │  -0.5498 │  1020.98 │  1021.13 │  1013.80 │  1014.71 │   12.70 │   11.60 │ 0.0920 │
│     4 │ 0.9999 │   1.0000 │  0.6582 │   0.6553 │  0.6659 │   0.6627 │  1356.39 │  1356.20 │  1368.71 │  1367.95 │   18.62 │   17.84 │ 0.0957 │
│     5 │ 0.9987 │   0.9990 │ -0.6880 │  -0.6761 │ -0.6497 │  -0.6418 │   766.54 │   762.25 │   731.75 │   732.42 │   51.97 │   45.24 │ 0.1018 │
│     6 │ 0.9998 │   0.9998 │ -0.1983 │  -0.2312 │ -0.1811 │  -0.2141 │  2417.35 │  2421.08 │  2409.18 │  2411.40 │   43.06 │   43.47 │ 0.0900 │
│     7 │ 0.9998 │   0.9997 │ -0.5258 │  -0.5746 │ -0.5072 │  -0.5560 │  3444.92 │  3474.99 │  3400.01 │  3421.63 │   86.94 │   94.38 │ 0.0492 │
│     8 │ 0.9990 │   0.9991 │  0.8235 │   0.8312 │  0.8479 │   0.8542 │  4596.54 │  4615.62 │  4918.32 │  4934.20 │  384.87 │  377.87 │ 0.2278 │
│     9 │ 0.9992 │   0.9992 │  0.5335 │   0.5441 │  0.5678 │   0.5780 │  5322.30 │  5316.96 │  5468.65 │  5466.98 │  265.68 │  267.28 │ 0.1318 │
│    10 │ 0.9974 │   0.9973 │  0.8189 │   0.8250 │  0.8579 │   0.8644 │  5328.81 │  5325.63 │  5953.35 │  5985.15 │  743.95 │  779.74 │ 0.2863 │
│    11 │ 0.9977 │   0.9978 │  0.4262 │   0.4045 │  0.4862 │   0.4645 │  9644.02 │  9674.06 │  9983.47 │  9990.28 │  743.28 │  726.99 │ 0.1576 │
│    12 │ 0.9904 │   0.9907 │  0.4384 │   0.4077 │  0.5586 │   0.5283 │ 10257.40 │ 10368.50 │ 11114.51 │ 11151.21 │ 1711.18 │ 1664.69 │ 0.1890 │
│    13 │ 0.9867 │   0.9874 │  0.4007 │   0.3680 │  0.5444 │   0.5103 │ 12305.12 │ 12423.75 │ 13440.31 │ 13432.47 │ 2386.43 │ 2282.47 │ 0.1293 │
│    14 │ 0.9921 │   0.9922 │  0.3198 │   0.2682 │  0.4364 │   0.3859 │ 16929.16 │ 17080.37 │ 17826.97 │ 17836.03 │ 2365.23 │ 2301.87 │ 0.1282 │
│    15 │ 0.9846 │   0.9850 │  0.1198 │   0.0963 │  0.2913 │   0.2663 │ 16858.58 │ 16949.44 │ 17496.00 │ 17502.88 │ 3077.08 │ 3029.60 │ 0.1611 │
│    16 │ 0.9686 │   0.9689 │ -0.0029 │  -0.0254 │  0.2457 │   0.2226 │ 18912.77 │ 19074.86 │ 19510.56 │ 19559.62 │ 4848.35 │ 4839.75 │ 0.1516 │
│    17 │ 0.9782 │   0.9784 │ -0.0174 │  -0.0381 │  0.1908 │   0.1694 │ 27098.09 │ 27273.00 │ 27601.12 │ 27653.12 │ 5738.19 │ 5724.21 │ 0.1641 │
│    18 │ 0.9184 │   0.9196 │  0.1343 │   0.1430 │  0.5155 │   0.5204 │   190.16 │   190.35 │   219.91 │   220.62 │   87.82 │   87.59 │ 0.1855 │
└───────┴────────┴──────────┴─────────┴──────────┴─────────┴──────────┴──────────┴──────────┴──────────┴──────────┴─────────┴─────────┴────────┘
g = mean of residual vectors for good prompts
g* = geometric median of residual vectors for good prompts
b = mean of residual vectors for bad prompts
b* = geometric median of residual vectors for bad prompts
r = refusal direction for means (i.e., b - g)
r* = refusal direction for geometric medians (i.e., b* - g*)
S(x,y) = cosine similarity of x and y
|x| = L2 norm of x
Silh = Mean silhouette coefficient of residuals for good/bad clusters
```


## How Heretic works

Heretic implements a parametrized variant of directional ablation. For each
supported transformer component (currently, attention out-projection and
MLP down-projection), it identifies the associated matrices in each transformer
layer, and orthogonalizes them with respect to the relevant "refusal direction",
inhibiting the expression of that direction in the result of multiplications
with that matrix.

Refusal directions are computed for each layer as a difference-of-means between
the first-token residuals for "harmful" and "harmless" example prompts.

The ablation process is controlled by several optimizable parameters:

* `direction_index`: Either the index of a refusal direction, or the special
  value `per layer`, indicating that each layer should be ablated using the
  refusal direction associated with that layer.
* `max_weight`, `max_weight_position`, `min_weight`, and `min_weight_distance`:
  For each component, these parameters describe the shape and position of the
  ablation weight kernel over the layers. The following diagram illustrates this:



 

Heretic's main innovations over existing abliteration systems are:

* The shape of the ablation weight kernel is highly flexible, which, combined with
  automatic parameter optimization, can improve the compliance/quality tradeoff.
  Non-constant ablation weights were previously explored by Maxime Labonne in
  [gemma-3-12b-it-abliterated-v2](https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated-v2).
* The refusal direction index is a float rather than an integer. For non-integral
  values, the two nearest refusal direction vectors are linearly interpolated.
  This unlocks a vast space of additional directions beyond the ones identified
  by the difference-of-means computation, and often enables the optimization
  process to find a better direction than that belonging to any individual layer.
* Ablation parameters are chosen separately for each component. I have found that
  MLP interventions tend to be more damaging to the model than attention interventions,
  so using different ablation weights can squeeze out some extra performance.


## Prior art

I'm aware of the following publicly available implementations of abliteration
techniques:

* [AutoAbliteration](https://huggingface.co/posts/mlabonne/714992455492422)
* [abliterator.py](https://github.com/FailSpy/abliterator)
* [wassname's Abliterator](https://github.com/wassname/abliterator)
* [ErisForge](https://github.com/Tsadoq/ErisForge)
* [Removing refusals with HF Transformers](https://github.com/Sumandora/remove-refusals-with-transformers)
* [deccp](https://github.com/AUGMXNT/deccp)

Note that Heretic was written from scratch, and does not reuse code from
any of those projects.


## Acknowledgments

The development of Heretic was informed by:

* [The original abliteration paper (Arditi et al. 2024)](https://arxiv.org/abs/2406.11717)
* [Maxime Labonne's article on abliteration](https://huggingface.co/blog/mlabonne/abliteration),
  as well as some details from the model cards of his own abliterated models (see above)
* [Jim Lai's article describing "projected abliteration"](https://huggingface.co/blog/grimjim/projected-abliteration)


## Citation

If you use Heretic for your research, please cite it using the following BibTeX entry:

```bibtex
@misc{heretic,
  author = {Weidmann, Philipp Emanuel},
  title = {Heretic: Fully automatic censorship removal for language models},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/p-e-w/heretic}}
}
```


## License

Copyright © 2025  Philipp Emanuel Weidmann ()

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see .

**By contributing to this project, you agree to release your
contributions under the same license.**

          


karpathy/nanoGPT
Mon, 09 Feb 2026 00:07:54 GMT

            karpathy/nanoGPT
            The simplest, fastest repository for training/finetuning medium-sized GPTs.
            Language: Python
            Stars: 52,751
            Forks: 8,934
            Stars today: 40 stars today
            README
# nanoGPT

![nanoGPT](assets/nanogpt.jpg)


---

**Update Nov 2025** nanoGPT has a new and improved cousin called [nanochat](https://github.com/karpathy/nanochat). It is very likely you meant to use/find nanochat instead. nanoGPT (this repo) is now very old and deprecated but I will leave it up for posterity.

---

The simplest, fastest repository for training/finetuning medium-sized GPTs. It is a rewrite of [minGPT](https://github.com/karpathy/minGPT) that prioritizes teeth over education. Still under active development, but currently the file `train.py` reproduces GPT-2 (124M) on OpenWebText, running on a single 8XA100 40GB node in about 4 days of training. The code itself is plain and readable: `train.py` is a ~300-line boilerplate training loop and `model.py` a ~300-line GPT model definition, which can optionally load the GPT-2 weights from OpenAI. That's it.

![repro124m](assets/gpt2_124M_loss.png)

Because the code is so simple, it is very easy to hack to your needs, train new models from scratch, or finetune pretrained checkpoints (e.g. biggest one currently available as a starting point would be the GPT-2 1.3B model from OpenAI).

## install

```
pip install torch numpy transformers datasets tiktoken wandb tqdm
```

Dependencies:

- [pytorch](https://pytorch.org) <3
- [numpy](https://numpy.org/install/) <3
-  `transformers` for huggingface transformers <3 (to load GPT-2 checkpoints)
-  `datasets` for huggingface datasets <3 (if you want to download + preprocess OpenWebText)
-  `tiktoken` for OpenAI's fast BPE code <3
-  `wandb` for optional logging <3
-  `tqdm` for progress bars <3

## quick start

If you are not a deep learning professional and you just want to feel the magic and get your feet wet, the fastest way to get started is to train a character-level GPT on the works of Shakespeare. First, we download it as a single (1MB) file and turn it from raw text into one large stream of integers:

```sh
python data/shakespeare_char/prepare.py
```

This creates a `train.bin` and `val.bin` in that data directory. Now it is time to train your GPT. The size of it very much depends on the computational resources of your system:

**I have a GPU**. Great, we can quickly train a baby GPT with the settings provided in the [config/train_shakespeare_char.py](config/train_shakespeare_char.py) config file:

```sh
python train.py config/train_shakespeare_char.py
```

If you peek inside it, you'll see that we're training a GPT with a context size of up to 256 characters, 384 feature channels, and it is a 6-layer Transformer with 6 heads in each layer. On one A100 GPU this training run takes about 3 minutes and the best validation loss is 1.4697. Based on the configuration, the model checkpoints are being written into the `--out_dir` directory `out-shakespeare-char`. So once the training finishes we can sample from the best model by pointing the sampling script at this directory:

```sh
python sample.py --out_dir=out-shakespeare-char
```

This generates a few samples, for example:

```
ANGELO:
And cowards it be strawn to my bed,
And thrust the gates of my threats,
Because he that ale away, and hang'd
An one with him.

DUKE VINCENTIO:
I thank your eyes against it.

DUKE VINCENTIO:
Then will answer him to save the malm:
And what have you tyrannous shall do this?

DUKE VINCENTIO:
If you have done evils of all disposition
To end his power, the day of thrust for a common men
That I leave, to fight with over-liking
Hasting in a roseman.
```

lol  `¯\_(ツ)_/¯`. Not bad for a character-level model after 3 minutes of training on a GPU. Better results are quite likely obtainable by instead finetuning a pretrained GPT-2 model on this dataset (see finetuning section later).

**I only have a macbook** (or other cheap computer). No worries, we can still train a GPT but we want to dial things down a notch. I recommend getting the bleeding edge PyTorch nightly ([select it here](https://pytorch.org/get-started/locally/) when installing) as it is currently quite likely to make your code more efficient. But even without it, a simple train run could look as follows:

```sh
python train.py config/train_shakespeare_char.py --device=cpu --compile=False --eval_iters=20 --log_interval=1 --block_size=64 --batch_size=12 --n_layer=4 --n_head=4 --n_embd=128 --max_iters=2000 --lr_decay_iters=2000 --dropout=0.0
```

Here, since we are running on CPU instead of GPU we must set both `--device=cpu` and also turn off PyTorch 2.0 compile with `--compile=False`. Then when we evaluate we get a bit more noisy but faster estimate (`--eval_iters=20`, down from 200), our context size is only 64 characters instead of 256, and the batch size only 12 examples per iteration, not 64. We'll also use a much smaller Transformer (4 layers, 4 heads, 128 embedding size), and decrease the number of iterations to 2000 (and correspondingly usually decay the learning rate to around max_iters with `--lr_decay_iters`). Because our network is so small we also ease down on regularization (`--dropout=0.0`). This still runs in about ~3 minutes, but gets us a loss of only 1.88 and therefore also worse samples, but it's still good fun:

```sh
python sample.py --out_dir=out-shakespeare-char --device=cpu
```
Generates samples like this:

```
GLEORKEN VINGHARD III:
Whell's the couse, the came light gacks,
And the for mought you in Aut fries the not high shee
bot thou the sought bechive in that to doth groan you,
No relving thee post mose the wear
```

Not bad for ~3 minutes on a CPU, for a hint of the right character gestalt. If you're willing to wait longer, feel free to tune the hyperparameters, increase the size of the network, the context length (`--block_size`), the length of training, etc.

Finally, on Apple Silicon Macbooks and with a recent PyTorch version make sure to add `--device=mps` (short for "Metal Performance Shaders"); PyTorch then uses the on-chip GPU that can *significantly* accelerate training (2-3X) and allow you to use larger networks. See [Issue 28](https://github.com/karpathy/nanoGPT/issues/28) for more.

## reproducing GPT-2

A more serious deep learning professional may be more interested in reproducing GPT-2 results. So here we go - we first tokenize the dataset, in this case the [OpenWebText](https://openwebtext2.readthedocs.io/en/latest/), an open reproduction of OpenAI's (private) WebText:

```sh
python data/openwebtext/prepare.py
```

This downloads and tokenizes the [OpenWebText](https://huggingface.co/datasets/openwebtext) dataset. It will create a `train.bin` and `val.bin` which holds the GPT2 BPE token ids in one sequence, stored as raw uint16 bytes. Then we're ready to kick off training. To reproduce GPT-2 (124M) you'll want at least an 8X A100 40GB node and run:

```sh
torchrun --standalone --nproc_per_node=8 train.py config/train_gpt2.py
```

This will run for about 4 days using PyTorch Distributed Data Parallel (DDP) and go down to loss of ~2.85. Now, a GPT-2 model just evaluated on OWT gets a val loss of about 3.11, but if you finetune it it will come down to ~2.85 territory (due to an apparent domain gap), making the two models ~match.

If you're in a cluster environment and you are blessed with multiple GPU nodes you can make GPU go brrrr e.g. across 2 nodes like:

```sh
# Run on the first (master) node with example IP 123.456.123.456:
torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
# Run on the worker node:
torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
```

It is a good idea to benchmark your interconnect (e.g. iperf3). In particular, if you don't have Infiniband then also prepend `NCCL_IB_DISABLE=1` to the above launches. Your multinode training will work, but most likely _crawl_. By default checkpoints are periodically written to the `--out_dir`. We can sample from the model by simply `python sample.py`.

Finally, to train on a single GPU simply run the `python train.py` script. Have a look at all of its args, the script tries to be very readable, hackable and transparent. You'll most likely want to tune a number of those variables depending on your needs.

## baselines

OpenAI GPT-2 checkpoints allow us to get some baselines in place for openwebtext. We can get the numbers as follows:

```sh
$ python train.py config/eval_gpt2.py
$ python train.py config/eval_gpt2_medium.py
$ python train.py config/eval_gpt2_large.py
$ python train.py config/eval_gpt2_xl.py
```

and observe the following losses on train and val:

| model | params | train loss | val loss |
| ------| ------ | ---------- | -------- |
| gpt2 | 124M         | 3.11  | 3.12     |
| gpt2-medium | 350M  | 2.85  | 2.84     |
| gpt2-large | 774M   | 2.66  | 2.67     |
| gpt2-xl | 1558M     | 2.56  | 2.54     |

However, we have to note that GPT-2 was trained on (closed, never released) WebText, while OpenWebText is just a best-effort open reproduction of this dataset. This means there is a dataset domain gap. Indeed, taking the GPT-2 (124M) checkpoint and finetuning on OWT directly for a while reaches loss down to ~2.85. This then becomes the more appropriate baseline w.r.t. reproduction.

## finetuning

Finetuning is no different than training, we just make sure to initialize from a pretrained model and train with a smaller learning rate. For an example of how to finetune a GPT on new text go to `data/shakespeare` and run `prepare.py` to download the tiny shakespeare dataset and render it into a `train.bin` and `val.bin`, using the OpenAI BPE tokenizer from GPT-2. Unlike OpenWebText this will run in seconds. Finetuning can take very little time, e.g. on a single GPU just a few minutes. Run an example finetuning like:

```sh
python train.py config/finetune_shakespeare.py
```

This will load the config parameter overrides in `config/finetune_shakespeare.py` (I didn't tune them much though). Basically, we initialize from a GPT2 checkpoint with `init_from` and train as normal, except shorter and with a small learning rate. If you're running out of memory try decreasing the model size (they are `{'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}`) or possibly decreasing the `block_size` (context length). The best checkpoint (lowest validation loss) will be in the `out_dir` directory, e.g. in `out-shakespeare` by default, per the config file. You can then run the code in `sample.py --out_dir=out-shakespeare`:

```
THEODORE:
Thou shalt sell me to the highest bidder: if I die,
I sell thee to the first; if I go mad,
I sell thee to the second; if I
lie, I sell thee to the third; if I slay,
I sell thee to the fourth: so buy or sell,
I tell thee again, thou shalt not sell my
possession.

JULIET:
And if thou steal, thou shalt not sell thyself.

THEODORE:
I do not steal; I sell the stolen goods.

THEODORE:
Thou know'st not what thou sell'st; thou, a woman,
Thou art ever a victim, a thing of no worth:
Thou hast no right, no right, but to be sold.
```

Whoa there, GPT, entering some dark place over there. I didn't really tune the hyperparameters in the config too much, feel free to try!

## sampling / inference

Use the script `sample.py` to sample either from pre-trained GPT-2 models released by OpenAI, or from a model you trained yourself. For example, here is a way to sample from the largest available `gpt2-xl` model:

```sh
python sample.py \
    --init_from=gpt2-xl \
    --start="What is the answer to life, the universe, and everything?" \
    --num_samples=5 --max_new_tokens=100
```

If you'd like to sample from a model you trained, use the `--out_dir` to point the code appropriately. You can also prompt the model with some text from a file, e.g. ```python sample.py --start=FILE:prompt.txt```.

## efficiency notes

For simple model benchmarking and profiling, `bench.py` might be useful. It's identical to what happens in the meat of the training loop of `train.py`, but omits much of the other complexities.

Note that the code by default uses [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/). At the time of writing (Dec 29, 2022) this makes `torch.compile()` available in the nightly release. The improvement from the one line of code is noticeable, e.g. cutting down iteration time from ~250ms / iter to 135ms / iter. Nice work PyTorch team!

## todos

- Investigate and add FSDP instead of DDP
- Eval zero-shot perplexities on standard evals (e.g. LAMBADA? HELM? etc.)
- Finetune the finetuning script, I think the hyperparams are not great
- Schedule for linear batch size increase during training
- Incorporate other embeddings (rotary, alibi)
- Separate out the optim buffers from model params in checkpoints I think
- Additional logging around network health (e.g. gradient clip events, magnitudes)
- Few more investigations around better init etc.

## troubleshooting

Note that by default this repo uses PyTorch 2.0 (i.e. `torch.compile`). This is fairly new and experimental, and not yet available on all platforms (e.g. Windows). If you're running into related error messages try to disable this by adding `--compile=False` flag. This will slow down the code but at least it will run.

For some context on this repository, GPT, and language modeling it might be helpful to watch my [Zero To Hero series](https://karpathy.ai/zero-to-hero.html). Specifically, the [GPT video](https://www.youtube.com/watch?v=kCc8FmEb1nY) is popular if you have some prior language modeling context.

For more questions/discussions feel free to stop by **#nanoGPT** on Discord:

[![](https://dcbadge.vercel.app/api/server/3zy8kqD9Cp?compact=true&style=flat)](https://discord.gg/3zy8kqD9Cp)

## acknowledgements

All nanoGPT experiments are powered by GPUs on [Lambda labs](https://lambdalabs.com), my favorite Cloud GPU provider. Thank you Lambda labs for sponsoring nanoGPT!

          


public-apis/public-apis
Mon, 09 Feb 2026 00:07:53 GMT

            public-apis/public-apis
            A collective list of free APIs
            Language: Python
            Stars: 396,574
            Forks: 42,446
            Stars today: 663 stars today
            README
# Try Public APIs for free
The Public APIs repository is manually curated by community members like you and folks working at [APILayer](https://apilayer.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo). It includes an extensive list of public APIs from many domains that you can use for your own products. Consider it a treasure trove of APIs well-managed by the community over the years.





    
        

            
        

    
  

APILayer is the fastest way to integrate APIs into any product. Explore [APILayer APIs](https://apilayer.com/products/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo) here for your next project.

Join our [Discord server](https://discord.com/invite/hgjA78638n/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo) to get updates, ask questions, get answers, random community calls, and more.




## APILayer APIs
| API | Description | Call this API |
|:---|:---|:---|
| [IPstack](https://ipstack.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers) | Locate and Identify Website Visitors by IP Address | [](https://god.gw.postman.com/run-collection/10131015-55145132-244c-448c-8e6f-8780866e4862?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D10131015-55145132-244c-448c-8e6f-8780866e4862%26entityType%3Dcollection%26workspaceId%3D2b7498b6-6d91-4fa8-817f-608441fe42a8)|
| [Marketstack](https://marketstack.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers) | Free, easy-to-use REST API interface delivering worldwide stock market data in JSON format | [](https://god.gw.postman.com/run-collection/10131015-9cbac391-3611-4f50-9bfd-d24ae41c97c1?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D10131015-9cbac391-3611-4f50-9bfd-d24ae41c97c1%26entityType%3Dcollection%26workspaceId%3D2b7498b6-6d91-4fa8-817f-608441fe42a8)|
| [Weatherstack](https://weatherstack.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers) | Retrieve instant, accurate weather information for any location in the world in lightweight JSON format | [](https://god.gw.postman.com/run-collection/10131015-276c4312-f682-425d-b6b1-0f82c0a7f2b3?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D10131015-276c4312-f682-425d-b6b1-0f82c0a7f2b3%26entityType%3Dcollection%26workspaceId%3D2b7498b6-6d91-4fa8-817f-608441fe42a8)|
| [Numverify](https://numverify.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers ) | Global Phone Number Validation & Lookup JSON API |[](https://god.gw.postman.com/run-collection/10131015-0760d25e-b802-412e-b0e4-26e5ca3b9ffa?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D10131015-0760d25e-b802-412e-b0e4-26e5ca3b9ffa%26entityType%3Dcollection%26workspaceId%3D2b7498b6-6d91-4fa8-817f-608441fe42a8)|
| [Fixer](https://fixer.io/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers) | Fixer is a simple and lightweight API for current and historical foreign exchange (forex) rates. |[](https://god.gw.postman.com/run-collection/10131015-0d9c66b3-5f1a-42ed-a5ca-379217bd629d?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D10131015-0d9c66b3-5f1a-42ed-a5ca-379217bd629d%26entityType%3Dcollection%26workspaceId%3D2b7498b6-6d91-4fa8-817f-608441fe42a8)|
| [Aviationstack](https://aviationstack.com/?utm_source=Github&utm_medium=Referral&utm_campaign=Public-apis-repo-Best-sellers) | Free, real-time flight status and global Aviation data API |[](https://god.gw.postman.com/run-collection/10131015-72ee0d35-018e-4370-a2b6-a66d3ebd5b5a?action=collection/fork)|




## Learn more about Public APIs

Get Involved

* [Contributing Guide](CONTRIBUTING.md)
* [API for this project](https://github.com/davemachado/public-api)
* [Issues](https://github.com/public-apis/public-apis/issues)
* [Pull Requests](https://github.com/public-apis/public-apis/pulls)
* [LICENSE](LICENSE) 




## Index

* [Animals](#animals)
* [Anime](#anime)
* [Anti-Malware](#anti-malware)
* [Art & Design](#art--design)
* [Authentication & Authorization](#authentication--authorization)
* [Blockchain](#blockchain)
* [Books](#books)
* [Business](#business)
* [Calendar](#calendar)
* [Cloud Storage & File Sharing](#cloud-storage--file-sharing)
* [Continuous Integration](#continuous-integration)
* [Cryptocurrency](#cryptocurrency)
* [Currency Exchange](#currency-exchange)
* [Data Validation](#data-validation)
* [Development](#development)
* [Dictionaries](#dictionaries)
* [Documents & Productivity](#documents--productivity)
* [Email](#email)
* [Entertainment](#entertainment)
* [Environment](#environment)
* [Events](#events)
* [Finance](#finance)
* [Food & Drink](#food--drink)
* [Games & Comics](#games--comics)
* [Geocoding](#geocoding)
* [Government](#government)
* [Health](#health)
* [Jobs](#jobs)
* [Machine Learning](#machine-learning)
* [Music](#music)
* [News](#news)
* [Open Data](#open-data)
* [Open Source Projects](#open-source-projects)
* [Patent](#patent)
* [Personality](#personality)
* [Phone](#phone)
* [Photography](#photography)
* [Programming](#programming)
* [Science & Math](#science--math)
* [Security](#security)
* [Shopping](#shopping)
* [Social](#social)
* [Sports & Fitness](#sports--fitness)
* [Test Data](#test-data)
* [Text Analysis](#text-analysis)
* [Tracking](#tracking)
* [Transportation](#transportation)
* [URL Shorteners](#url-shorteners)
* [Vehicle](#vehicle)
* [Video](#video)
* [Weather](#weather)



### Animals
API | Description | Auth | HTTPS | CORS 
|:---|:---|:---|:---|:---|
| [AdoptAPet](https://www.adoptapet.com/public/apis/pet_list.html) | Resource to help get pets adopted | `apiKey` | Yes | Yes |
| [Axolotl](https://theaxolotlapi.netlify.app/) | Collection of axolotl pictures and facts | No | Yes | No |
| [Cat Facts](https://alexwohlbruck.github.io/cat-facts/) | Daily cat facts | No | Yes | No | |
| [Cataas](https://cataas.com/) | Cat as a service (cats pictures and gifs) | No | Yes | No |
| [Cats](https://docs.thecatapi.com/) | Pictures of cats from Tumblr | `apiKey` | Yes | No |
| [Dog Facts](https://dukengn.github.io/Dog-facts-API/) | Random dog facts | No | Yes | Yes |
| [Dog Facts](https://kinduff.github.io/dog-api/) | Random facts of Dogs | No | Yes | Yes |
| [Dogs](https://dog.ceo/dog-api/) | Based on the Stanford Dogs Dataset | No | Yes | Yes |
| [eBird](https://documenter.getpostman.com/view/664302/S1ENwy59) | Retrieve recent or notable birding observations within a region | `apiKey` | Yes | No |
| [FishWatch](https://www.fishwatch.gov/developers) | Information and pictures about individual fish species | No | Yes | Yes |
| [HTTP Cat](https://http.cat/) | Cat for every HTTP Status | No | Yes | Yes |
| [HTTP Dog](https://http.dog/) | Dogs for every HTTP response status code | No | Yes | Yes |
| [IUCN](http://apiv3.iucnredlist.org/api/v3/docs) | IUCN Red List of Threatened Species | `apiKey` | No | No |
| [MeowFacts](https://github.com/wh-iterabb-it/meowfacts) | Get random cat facts | No | Yes | No |
| [Movebank](https://github.com/movebank/movebank-api-doc) | Movement and Migration data of animals | No | Yes | Yes |
| [Petfinder](https://www.petfinder.com/developers/) | Petfinder is dedicated to helping pets find homes, another resource to get pets adopted | `apiKey` | Yes | Yes |
| [PlaceBear](https://placebear.com/) | Placeholder bear pictures | No | Yes | Yes |
| [PlaceDog](https://place.dog) | Placeholder Dog pictures | No | Yes | Yes |
| [PlaceKitten](https://placekitten.com/) | Placeholder Kitten pictures | No | Yes | Yes |
| [RandomDog](https://random.dog/woof.json) | Random pictures of dogs | No | Yes | Yes |
| [RandomDuck](https://random-d.uk/api) | Random pictures of ducks | No | Yes | No |
| [RandomFox](https://randomfox.ca/floof/) | Random pictures of foxes | No | Yes | No |
| [RescueGroups](https://userguide.rescuegroups.org/display/APIDG/API+Developers+Guide+Home) | Adoption | No | Yes | Unknown |
| [Shibe.Online](http://shibe.online/) | Random pictures of Shiba Inu, cats or birds | No | Yes | Yes |
| [The Dog](https://thedogapi.com/) | A public service all about Dogs, free to use when making your fancy new App, Website or Service | `apiKey` | Yes | No |
| [xeno-canto](https://xeno-canto.org/explore/api) | Bird recordings | No | Yes | Unknown |
| [Zoo Animals](https://zoo-animal-api.herokuapp.com/) | Facts and pictures of zoo animals | No | Yes | Yes |

**[⬆ Back to Index](#index)**





### Anime
API | Description | Auth | HTTPS | CORS |
|:---|:---|:---|:---|:---|
| [AniAPI](https://aniapi.com/docs/) | Anime discovery, streaming & syncing with trackers | `OAuth` | Yes | Yes |
| [AniDB](https://wiki.anidb.net/HTTP_API_Definition) | Anime Database | `apiKey` | No | Unknown |
| [AniList](https://github.com/AniList/ApiV2-GraphQL-Docs) | Anime discovery & tracking | `OAuth` | Yes | Unknown |
| [AnimeChan](https://github.com/RocktimSaikia/anime-chan) | Anime quotes (over 10k+) | No | Yes | No |
| [AnimeFacts](https://chandan-02.github.io/anime-facts-rest-api/) | Anime Facts (over 100+) | No | Yes | Yes |
| [AnimeNewsNetwork](https://www.animenewsnetwork.com/encyclopedia/api.php) | Anime industry news | No | Yes | Yes |
| [Catboy](https://catboys.com/api) | Neko images, funny GIFs & more | No | Yes | Yes |
| [Danbooru Anime](https://danbooru.donmai.us/wiki_pages/help:api) | Thousands of anime artist database to find good anime art | `apiKey` | Yes | Yes |
| [Jikan](https://jikan.moe) | Unofficial MyAnimeList API | No | Yes | Yes |
| [Kitsu](https://kitsu.docs.apiary.io/) | Anime discovery platform | `OAuth` | Yes | Yes |
| [MangaDex](https://api.mangadex.org/docs.html) | Manga Database and Community | `apiKey` | Yes | Unknown |
| [Mangapi](https://rapidapi.com/pierre.carcellermeunier/api/mangapi3/) | Translate manga pages from one language to another | `apiKey` | Yes | Unknown |
| [MyAnimeList](https://myanimelist.net/clubs.php?cid=13727) | Anime and Manga Database and Community | `OAuth` | Yes | Unknown |
| [NekosBest](https://docs.nekos.best) | Neko Images & Anime roleplaying GIFs | No | Yes | Yes |
| [Shikimori](https://shikimori.one/api/doc) | Anime discovery, tracking, forum, rates | `OAuth` | Yes | Unknown |
| [Studio Ghibli](https://ghibliapi.herokuapp.com) | Resources from Studio Ghibli films | No | Yes | Yes |
| [Trace Moe](https://soruly.github.io/trace.moe-api/#/) | A useful tool to get the exact scene of an anime from a screenshot | No | Yes | No |
| [Waifu.im](https://waifu.im/docs) | Get waifu pictures from an archive of over 4000 images and multiple tags | No | Yes | Yes |
| [Waifu.pics](https://waifu.pics/docs) | Image sharing platform for anime images | No | Yes | No |

**[⬆ Back to Index](#index)**





### Anti-Malware
API | Description | Auth | HTTPS | CORS |
|:---|:---|:---|:---|:---|
| [AbuseIPDB](https://docs.abuseipdb.com/) | IP/domain/URL reputation | `apiKey` | Yes | Unknown |
| [AlienVault Open Threat Exchange (OTX)](https://otx.alienvault.com/api) | IP/domain/URL reputation | `apiKey` | Yes | Unknown |
| [CAPEsandbox](https://capev2.readthedocs.io/en/latest/usage/api.html) | Malware execution and analysis | `apiKey` | Yes | Unknown |
| [Google Safe Browsing](https://developers.google.com/safe-browsing/) | Google Link/Domain Flagging | `apiKey` | Yes | Unknown |
| [MalDatabase](https://maldatabase.com/api-doc.html) | Provide malware datasets and threat intelligence feeds | `apiKey` | Yes | Unknown |
| [MalShare](https://malshare.com/doc.php) | Malware Archive / file sourcing | `apiKey` | Yes | No |
| [MalwareBazaar](https://bazaar.abuse.ch/api/) | Collect and share malware samples | `apiKey` | Yes | Unknown |
| [Metacert](https://metacert.com/) | Metacert Link Flagging | `apiKey` | Yes | Unknown |
| [NoPhishy](https://rapidapi.com/Amiichu/api/exerra-phishing-check/) | Check links to see if they're known phishing attempts | `apiKey` | Yes | Yes |
| [Phisherman](https://phisherman.gg/) | IP/domain/URL reputation | `apiKey` | Yes | Unknown |
| [Scanii](https://docs.scanii.com/) | Simple REST API that can scan submitted documents/files for the presence of threats | `apiKey` | Yes | Yes |
| [URLhaus](https://urlhaus-api.abuse.ch/) | Bulk queries and Download Malware Samples | No | Yes | Yes |
| [URLScan.io](https://urlscan.io/about-api/) | Scan and Analyse URLs | `apiKey` | Yes | Unknown |
| [VirusTotal](https://www.virustotal.com/en/documentation/public-api/) | VirusTotal File/URL Analysis | `apiKey` | Yes | Unknown |
| [Web of Trust](https://support.mywot.com/hc/en-us/sections/360004477734-API-) | IP/domain/URL reputation | `apiKey` | Yes | Unknown | 

**[⬆ Back to Index](#index)**




### Art & Design
API | Description | Auth | HTTPS | CORS |
|:---|:---|:---|:---|:---|
| [Améthyste](https://api.amethyste.moe/) | Generate images for Discord users | `apiKey` | Yes | Unknown |
| [Art Institute of Chicago](https://api.artic.edu/docs/) | Art | No | Yes | Yes |
| [Colormind](http://colormind.io/api-access/) | Color scheme generator | No | No | Unknown |
| [ColourLovers](http://www.colourlovers.com/api) | Get various patterns, palettes and images | No | No | Unknown |
| [Cooper Hewitt](https://collection.cooperhewitt.org/api) | Smithsonian Design Museum | `apiKey` | Yes | Unknown |
| [Dribbble](https://developer.dribbble.com) | Discover the world’s top designers & creatives | `OAuth` | Yes | Unknown |
| [EmojiHub](https://github.com/cheatsnake/emojihub) | Get emojis by categories and groups | No | Yes | Yes |
| [Europeana](https://pro.europeana.eu/resources/apis/search) | European Museum and Galleries content | `apiKey` | Yes | Unknown |
| [Harvard Art Museums](https://github.com/harvardartmuseums/api-docs) | Art | `apiKey` | No | Unknown |
| [Icon Horse](https://icon.horse) | Favicons for any website, with fallbacks | No | Yes | Yes |
| [Iconfinder](https://developer.iconfinder.com) | Icons | `apiKey` | Yes | Unknown |
| [Icons8](https://img.icons8.com/) | Icons (find "search icon" hyperlink in page) | No | Yes | Unknown |
| [Lordicon](https://lordicon.com/) | Icons with predone Animations | No | Yes | Yes |
| [Metropolitan Museum of Art](https://metmuseum.github.io/) | Met Museum of Art | No | Yes | No |
| [Noun Project](http://api.thenounproject.com/index.html) | Icons | `OAuth` | No | Unknown |
| [PHP-Noise](https://php-noise.com/) | Noise Background Image Generator | No | Yes | Yes |
| [Pixel Encounter](https://pixelencounter.com/api) | SVG Icon Generator | No | Yes | No |
| [Rijksmuseum](https://data.rijksmuseum.nl/object-metadata/api/) | RijksMuseum Data | `apiKey` | Yes | Unknown |
| [Word Cloud](https://wordcloudapi.com/) | Easily create word clouds | `apiKey` | Yes | Unknown |
| [xColors](https://x-colors.herokuapp.com/) | Generate & convert colors | No | Yes | Yes |

**[⬆ Back to Index](#index)**




### Authentication & Authorization
API | Description | Auth | HTTPS | CORS |
|:---|:---|:---|:---|:---|
| [Auth0](https://auth0.com) | Easy to implement, adaptable authentication and authorization platform | `apiKey` | Yes | Yes |
| [GetOTP](https://otp.dev/en/docs/) | Implement OTP flow quickly | `apiKey` | Yes | No |
| [Micro User Service](https://m3o.com/user) | User management and authentication | `apiKey` | Yes | No |
| [MojoAuth](https://mojoauth.com) | Secure and modern passwordless authentication platform | `apiKey` | Yes | Yes |
| [SAWO Labs](https://sawolabs.com) | Simplify login and improve user experience by integrating passwordless authentication in your app | `apiKey` | Yes | Yes |
| [Stytch](https://stytch.com/) | User infrastructure for modern applications | `apiKey` | Yes | No |
| [Warrant](https://warrant.dev/) | APIs for authorization and access control | `apiKey` | Yes | Yes |

**[⬆ Back to Index](#index)**




### Blockchain
| API | Description | Auth | HTTPS | CORS |
|---|:---|:---|:---|:---|
| [Bitquery](https://graphql.bitquery.io/ide) | Onchain GraphQL APIs & DEX APIs | `apiKey` | Yes | Yes |
| [Chainlink](https://chain.link/developer-resources) | Build hybrid smart contracts with Chainlink | No | Yes | Unknown |
| [Chainpoint](https://tierion.com/chainpoint/) | Chainpoint is a global network for anchoring data to the Bitcoin blockchain | No | Yes | Unknown |
| [Covalent](https://www.covalenthq.com/docs/api/) | Multi-blockchain data aggregator platform | `apiKey` | Yes | Unknown |
| [Etherscan](https://etherscan.io/apis) | Ethereum explorer API | `apiKey` | Yes | Yes |
| [Helium](https://docs.helium.com/api/blockchain/introduction/) | Helium is a global, distributed network of Hotspots that create public, long-range wireless coverage | No | Yes | Unknown |
| [Nownodes](https://nownodes.io/) | Blockchain-as-a-service solution that provides high-quality connection via API | `apiKey` | Yes | Unknown |
| [Steem](https://developers.steem.io/) | Blockchain-based blogging and social media website | No | No | No |
| [The Graph](https://thegraph.com) | Indexing protocol for querying networks like Ethereum with GraphQL | `apiKey` | Yes | Unknown |
| [Walltime](https://walltime.info/api.html) | To retrieve Walltime's market info | No | Yes | Unknown |
| [Watchdata](https://docs.watchdata.io) | Provide simple and reliable API access to Ethereum blockchain | `apiKey` | Yes | Unknown |

**[⬆ Back to Index](#index)**




### Books
API | Description | Auth | HTTPS | CORS |
|:---|:---|:---|:---|:---|
| [A Bíblia Digital](https://www.abibliadigital.com.br/en) | Do not worry about managing the multiple versions of the Bible | `apiKey` | Yes | No |
| [Bhagavad Gita](https://docs.bhagavadgitaapi.in) | Open Source Shrimad Bhagavad Gita API including 21+ authors translation in Sanskrit/English/Hindi | `apiKey` | Yes | Yes |
| [Bhagavad Gita](https://bhagavadgita.io/api) | Bhagavad Gita text | `OAuth` | Yes | Yes |
| [Bhagavad Gita telugu](https://gita-api.vercel.app) | Bhagavad Gita API in telugu and odia languages | No | Yes | Yes |
| [Bible-api](https://bible-api.com/) | Free Bible API with multiple languages | No | Yes | Yes |
| [British National Bibliography](http://bnb.data.bl.uk/) | Books | No | No | Unknown |
| [Crossref Metadata Search](https://github.com/CrossRef/rest-api-doc) | Books & Articles Metadata | No | Yes | Unknown |
| [Ganjoor](https://api.ganjoor.net) | Classic Persian poetry works including access to related manuscripts, recitations and music tracks | `OAuth` | Yes | Yes |
| [Google Books](https://developers.google.com/books/) | Books | `OAuth` | Yes | Unknown |
| [GurbaniNow](https://github.com/GurbaniNow/api) | Fast and Accurate Gurbani RESTful API | No | Yes | Unknown |
| [Gutendex](https://gutendex.com/) | Web-API for fetching data from Project Gutenberg Books Library | No | Yes | Unknown |
| [Open Library](https://openlibrary.org/developers/api) | Books, book covers and related data | No | Yes | No |
| [Penguin Publishing](http://www.penguinrandomhouse.biz/webservices/rest/) | Books, book covers and related data | No | Yes | Yes |
| [PoetryDB](https://github.com/thundercomb/poetrydb#readme) | Enables you to get instant data from our vast p

... [README content truncated due to size. Visit the repository for the complete README] ...
          


hsliuping/TradingAgents-CN
Mon, 09 Feb 2026 00:07:52 GMT

            hsliuping/TradingAgents-CN
            基于多智能体LLM的中文金融交易框架 - TradingAgents中文增强版
            Language: Python
            Stars: 15,979
            Forks: 3,539
            Stars today: 278 stars today
            README
# TradingAgents 中文增强版

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/)
[![Version](https://img.shields.io/badge/Version-cn--0.1.15-green.svg)](./VERSION)
[![Documentation](https://img.shields.io/badge/docs-中文文档-green.svg)](./docs/)
[![Original](https://img.shields.io/badge/基于-TauricResearch/TradingAgents-orange.svg)](https://github.com/TauricResearch/TradingAgents)

---

## ⚠️ 重要版权声明与授权说明

### 🚨 版权侵权警告

**我们注意到 `tradingagents-ai.com` 网站未经授权使用了我们的专有代码，并声称是他们公司的产品。**

**⚠️ 重要提醒**：
- ❌ **我们项目组目前没有给任何组织或个人进行过商业授权**
- ❌ **该网站未经授权使用我们的代码，属于侵权行为**
- ⚠️ **请大家注意识别，避免上当受骗**

**✅ 官方唯一渠道**：
- 📦 GitHub 仓库：https://github.com/hsliuping/TradingAgents-CN
- 📧 官方邮箱：hsliup@163.com
- 📱 微信公众号：TradingAgents-CN

如发现任何未经授权的商业使用，请通过上述渠道联系我们。

### 📋 版本授权说明

#### v1.0.0-preview（当前版本）
- ✅ **个人使用**：完全开源，可自由使用
- ❌ **商业使用**：**必须获得商业授权**，未经授权禁止商业使用
- 📧 **授权联系**：[hsliup@163.com](mailto:hsliup@163.com)

#### v2.0.0（开发中）
- 🔄 **开发状态**：已完成两轮内测，接近完工上线阶段
- ⚠️ **开源计划**：**因存在盗版问题，v2.0 版本暂时不进行开源**
- 📢 **发布方式**：将通过官方渠道发布，敬请关注

### 📄 许可证详情

本项目采用**混合许可证**模式：
- 🔓 **开源部分**（Apache 2.0）：除 `app/` 和 `frontend/` 外的所有文件
- 🔒 **专有部分**（需商业授权）：`app/`（FastAPI后端）和 `frontend/`（Vue前端）目录

详细说明请查看：[版权声明](./COPYRIGHT.md) | [许可证文件](./LICENSE)

---

>
> 🎓 **学习中心**: AI基础 | 提示词工程 | 模型选择 | 多智能体分析原理 | 风险与局限 | 源项目与论文 | 实战教程（部分为外链） | 常见问题
> 🎯 **核心功能**: 原生OpenAI支持 | Google AI全面集成 | 自定义端点配置 | 智能模型选择 | 多LLM提供商支持 | 模型选择持久化 | Docker容器化部署 | 专业报告导出 | 完整A股支持 | 中文本地化

面向中文用户的**多智能体与大模型股票分析学习平台**。帮助你系统化学习如何使用多智能体交易框架与 AI 大模型进行合规的股票研究与策略实验，不提供实盘交易指令，平台定位为学习与研究用途。

## 🙏 致敬源项目

感谢 [Tauric Research](https://github.com/TauricResearch) 团队创造的革命性多智能体交易框架 [TradingAgents](https://github.com/TauricResearch/TradingAgents)！

**🎯 我们的定位与使命**: 专注学习与研究，提供中文化学习中心与工具，合规友好，支持 A股/港股/美股 的分析与教学，推动 AI 金融技术在中文社区的普及与正确使用。

## 🎉 v1.0.0-preview 版本上线 - 全新架构升级

> 🚀 **重磅发布**: v1.0.0-preview 版本现已正式！全新的 FastAPI + Vue 3 架构，带来企业级的性能和体验！

### ✨ 核心特性

#### 🏗️ **全新技术架构**
- **后端升级**: 从 Streamlit 迁移到 FastAPI，提供更强大的 RESTful API
- **前端重构**: 采用 Vue 3 + Element Plus，打造现代化的单页应用
- **数据库优化**: MongoDB + Redis 双数据库架构，性能提升 10 倍
- **容器化部署**: 完整的 Docker 多架构支持（amd64 + arm64）

#### 🎯 **企业级功能**
- **用户权限管理**: 完整的用户认证、角色管理、操作日志系统
- **配置管理中心**: 可视化的大模型配置、数据源管理、系统设置
- **缓存管理系统**: 智能缓存策略，支持 MongoDB/Redis/文件多级缓存
- **实时通知系统**: SSE+WebSocket 双通道推送，实时跟踪分析进度和系统状态
- **批量分析功能**: 支持多只股票同时分析，提升工作效率
- **智能股票筛选**: 基于多维度指标的股票筛选和排序系统
- **自选股管理**: 个人自选股收藏、分组管理和跟踪功能
- **个股详情页**: 完整的个股信息展示和历史分析记录
- **模拟交易系统**: 虚拟交易环境，验证投资策略效果

#### 🤖 **智能分析增强**
- **动态供应商管理**: 支持动态添加和配置 LLM 供应商
- **模型能力管理**: 智能模型选择，根据任务自动匹配最佳模型
- **多数据源同步**: 统一的数据源管理，支持 Tushare、AkShare、BaoStock
- **报告导出功能**: 支持 Markdown/Word/PDF 多格式专业报告导出

#### � **重大Bug修复**
- **技术指标计算修复**: 彻底解决市场分析师技术指标计算不准确问题
- **基本面数据修复**: 修复基本面分析师PE、PB等关键财务数据计算错误
- **死循环问题修复**: 解决部分用户在分析过程中触发的无限循环问题
- **数据一致性优化**: 确保所有分析师使用统一、准确的数据源

#### �🐳 **Docker 多架构支持**
- **跨平台部署**: 支持 x86_64 和 ARM64 架构（Apple Silicon、树莓派、AWS Graviton）
- **GitHub Actions**: 自动化构建和发布 Docker 镜像
- **一键部署**: 完整的 Docker Compose 配置，5 分钟快速启动

### 📊 技术栈升级

| 组件 | v0.1.x | v1.0.0-preview |
|------|--------|----------------|
| **后端框架** | Streamlit | FastAPI + Uvicorn |
| **前端框架** | Streamlit | Vue 3 + Vite + Element Plus |
| **数据库** | 可选 MongoDB | MongoDB + Redis |
| **API 架构** | 单体应用 | RESTful API + WebSocket |
| **部署方式** | 本地/Docker | Docker 多架构 + GitHub Actions |



#### 📥 安装部署

**三种部署方式，任选其一**：

| 部署方式 | 适用场景 | 难度 | 文档链接 |
|---------|---------|------|---------|
| 🟢 **绿色版** | Windows 用户、快速体验 | ⭐ 简单 | [绿色版安装指南](https://mp.weixin.qq.com/s/eoo_HeIGxaQZVT76LBbRJQ) |
| 🐳 **Docker版** | 生产环境、跨平台 | ⭐⭐ 中等 | [Docker 部署指南](https://mp.weixin.qq.com/s/JkA0cOu8xJnoY_3LC5oXNw) |
| 💻 **本地代码版** | 开发者、定制需求 | ⭐⭐⭐ 较难 | [本地安装指南](https://mp.weixin.qq.com/s/cqUGf-sAzcBV19gdI4sYfA) |

⚠️ **重要提醒**：在分析股票之前，请按相关文档要求，将股票数据同步完成，否则分析结果将会出现数据错误。



#### 📚 使用指南

在使用前，建议先阅读详细的使用指南：
- **[0、📘 TradingAgents-CN v1.0.0-preview 快速入门视频](https://www.bilibili.com/video/BV1i2CeBwEP7/?vd_source=5d790a5b8d2f46d2c10fd4e770be1594)**

- **[1、📘 TradingAgents-CN v1.0.0-preview 使用指南](https://mp.weixin.qq.com/s/ppsYiBncynxlsfKFG8uEbw)**
- **[2、📘 使用 Docker Compose 部署TradingAgents-CN v1.0.0-preview（完全版）](https://mp.weixin.qq.com/s/JkA0cOu8xJnoY_3LC5oXNw)**
- **[3、📘 从 Docker Hub 更新 TradingAgents‑CN 镜像](https://mp.weixin.qq.com/s/WKYhW8J80Watpg8K6E_dSQ)**
- **[4、📘 TradingAgents-CN v1.0.0-preview绿色版安装和升级指南](https://mp.weixin.qq.com/s/eoo_HeIGxaQZVT76LBbRJQ)**
- **[5、📘 TradingAgents-CN v1.0.0-preview绿色版端口配置说明](https://mp.weixin.qq.com/s/o5QdNuh2-iKkIHzJXCj7vQ)**
- **[6、📘 TradingAgents v1.0.0-preview 源码版安装手册（修订版）](https://mp.weixin.qq.com/s/cqUGf-sAzcBV19gdI4sYfA)**
- **[7、📘 TradingAgents v1.0.0-preview 源码安装视频教程](https://www.bilibili.com/video/BV1FxCtBHEte/?vd_source=5d790a5b8d2f46d2c10fd4e770be1594)**


使用指南包含：
- ✅ 完整的功能介绍和操作演示
- ✅ 详细的配置说明和最佳实践
- ✅ 常见问题解答和故障排除
- ✅ 实际使用案例和效果展示

#### 关注公众号

1. **关注公众号**: 微信搜索 **"TradingAgents-CN"** 并关注
2. 公众号每天推送项目最新进展和使用教程


- **微信公众号**: TradingAgents-CN（推荐）

  


## 🆚 中文增强特色

**相比原版新增**: 智能新闻分析 | 多层次新闻过滤 | 新闻质量评估 | 统一新闻工具 | 多LLM提供商集成 | 模型选择持久化 | 快速切换按钮 | | 实时进度显示 | 智能会话管理 | 中文界面 | A股数据 | 国产LLM | Docker部署 | 专业报告导出 | 统一日志管理 | Web配置界面 | 成本优化

## 📢 招募测试志愿者

### 🎯 我们需要你的帮助！

TradingAgentsCN 已经获得 **13,000+ stars**，但一直由我一个人开发维护。每次发布新版本时，尽管我会尽力测试，但仍然会有一些隐藏的 bug 没有被发现。

**我需要你的帮助来让这个项目变得更好！**

### 🙋 我们需要什么样的志愿者？

- ✅ 对股票分析或 AI 应用感兴趣
- ✅ 愿意在新版本发布前进行测试
- ✅ 能够清晰描述遇到的问题
- ✅ 每周可以投入 2-4 小时（弹性时间）

**不需要编程经验！** 功能测试、文档测试、用户体验测试都非常有价值。

### 🎁 你将获得什么？

1. **优先体验权** - 提前体验新功能和新版本
2. **技术成长** - 深入了解多智能体系统和 LLM 应用开发
3. **社区认可** - 在 README 和发布说明中致谢，获得 "Core Tester" 标签
4. **开源贡献** - 为 13,000+ stars 的项目做出实质性贡献
5. **未来机会** - 如果项目商业化，可能会有相应的报酬

### 🚀 如何加入？

**方式一：微信公众号申请（推荐）**
1. 关注微信公众号：**TradingAgentsCN**
2. 在公众号菜单选择"测试申请"菜单
3. 填写申请信息

**方式二：邮件申请**
- 发送邮件到：hsliup@163.com
- 主题：测试志愿者申请

### 📋 测试内容示例

- **日常测试**（每周 2-4 小时）：测试新功能和 bug 修复，在不同环境下验证功能
- **版本发布前测试**（每月 1-2 次）：完整的功能回归测试、安装和部署流程测试

### 🌟 特别需要的测试方向

- 🪟 **Windows 用户** - 测试 Windows 安装程序和绿色版
- 🍎 **macOS 用户** - 测试 macOS 兼容性
- 🐧 **Linux 用户** - 测试 Linux 兼容性
- 🐳 **Docker 用户** - 测试 Docker 部署
- 📊 **多市场用户** - 测试 A 股、港股、美股数据源
- 🤖 **多 LLM 用户** - 测试不同 LLM 提供商（OpenAI/Gemini/DeepSeek/通义千问等）

**详细信息**: 查看完整招募公告 → [📢 测试志愿者招募](docs/community/CALL_FOR_TESTERS.md)

## 🤝 贡献指南

我们欢迎各种形式的贡献：

### 贡献类型

- 🐛 **Bug修复** - 发现并修复问题
- ✨ **新功能** - 添加新的功能特性
- 📚 **文档改进** - 完善文档和教程
- 🌐 **本地化** - 翻译和本地化工作
- 🎨 **代码优化** - 性能优化和代码重构

### 贡献流程

1. Fork 本仓库
2. 创建特性分支 (`git checkout -b feature/AmazingFeature`)
3. 提交更改 (`git commit -m 'Add some AmazingFeature'`)
4. 推送到分支 (`git push origin feature/AmazingFeature`)
5. 创建 Pull Request

### 📋 查看贡献者

查看所有贡献者和详细贡献内容：**[🤝 贡献者名单](CONTRIBUTORS.md)**

## 📄 许可证详情

本项目采用**混合许可证**模式，详见 [LICENSE](LICENSE) 文件：

### 🔓 开源部分（Apache 2.0）
- **适用范围**：除 `app/` 和 `frontend/` 外的所有文件
- **权限**：商业使用 ✅ | 修改分发 ✅ | 私人使用 ✅ | 专利使用 ✅
- **条件**：保留版权声明 ❗ | 包含许可证副本 ❗

### 🔒 专有部分（需商业授权）
- **适用范围**：`app/`（FastAPI后端）和 `frontend/`（Vue前端）目录
- **商业使用**：需要单独许可协议
- **联系授权**：[hsliup@163.com](mailto:hsliup@163.com)

### 📋 许可证选择建议
- **个人学习/研究**：可自由使用全部功能
- **商业应用**：请联系获取专有组件授权
- **定制开发**：欢迎咨询商业合作方案

### 📚 相关文档

- [版权声明](./COPYRIGHT.md) - 详细的版权信息和使用条款
- [主许可证](./LICENSE) - Apache 2.0 许可证
- [后端专有许可证](./app/LICENSE) - 后端专有组件许可证
- [前端专有许可证](./frontend/LICENSE) - 前端专有组件许可证

## 🙏 致谢与感恩

### 🌟 向源项目开发者致敬

我们向 [Tauric Research](https://github.com/TauricResearch) 团队表达最深的敬意和感谢：

- **🎯 愿景领导者**: 感谢您们在AI金融领域的前瞻性思考和创新实践
- **💎 珍贵源码**: 感谢您们开源的每一行代码，它们凝聚着无数的智慧和心血
- **🏗️ 架构大师**: 感谢您们设计了如此优雅、可扩展的多智能体框架
- **💡 技术先驱**: 感谢您们将前沿AI技术与金融实务完美结合
- **🔄 持续贡献**: 感谢您们持续的维护、更新和改进工作

### 🤝 社区贡献者致谢

感谢所有为TradingAgents-CN项目做出贡献的开发者和用户！

详细的贡献者名单和贡献内容请查看：**[📋 贡献者名单](CONTRIBUTORS.md)**

包括但不限于：

- 🐳 **Docker容器化** - 部署方案优化
- 📄 **报告导出功能** - 多格式输出支持
- 🐛 **Bug修复** - 系统稳定性提升
- 🔧 **代码优化** - 用户体验改进
- 📝 **文档完善** - 使用指南和教程
- 🌍 **社区建设** - 问题反馈和推广
- **🌍 开源贡献**: 感谢您们选择Apache 2.0协议，给予开发者最大的自由
- **📚 知识分享**: 感谢您们提供的详细文档和最佳实践指导

**特别感谢**：[TradingAgents](https://github.com/TauricResearch/TradingAgents) 项目为我们提供了坚实的技术基础。虽然Apache 2.0协议赋予了我们使用源码的权利，但我们深知每一行代码的珍贵价值，将永远铭记并感谢您们的无私贡献。

### 🇨🇳 推广使命的初心

创建这个中文增强版本，我们怀着以下初心：

- **🌉 技术传播**: 让优秀的TradingAgents技术在中国得到更广泛的应用
- **🎓 教育普及**: 为中国的AI金融教育提供更好的工具和资源
- **🤝 文化桥梁**: 在中西方技术社区之间搭建交流合作的桥梁
- **🚀 创新推动**: 推动中国金融科技领域的AI技术创新和应用

### 🌍 开源社区

感谢所有为本项目贡献代码、文档、建议和反馈的开发者和用户。正是因为有了大家的支持，我们才能更好地服务中文用户社区。

### 🤝 合作共赢

我们承诺：

- **尊重原创**: 始终尊重源项目的知识产权和开源协议
- **反馈贡献**: 将有价值的改进和创新反馈给源项目和开源社区
- **持续改进**: 不断完善中文增强版本，提供更好的用户体验
- **开放合作**: 欢迎与源项目团队和全球开发者进行技术交流与合作

## 📈 版本历史

- **v0.1.13** (2025-08-02): 🤖 原生OpenAI支持与Google AI生态系统全面集成 ✨ **最新版本**
- **v0.1.12** (2025-07-29): 🧠 智能新闻分析模块与项目结构优化
- **v0.1.11** (2025-07-27): 🤖 多LLM提供商集成与模型选择持久化
- **v0.1.10** (2025-07-18): 🚀 Web界面实时进度显示与智能会话管理
- **v0.1.9** (2025-07-16): 🎯 CLI用户体验重大优化与统一日志管理
- **v0.1.8** (2025-07-15): 🎨 Web界面全面优化与用户体验提升
- **v0.1.7** (2025-07-13): 🐳 容器化部署与专业报告导出
- **v0.1.6** (2025-07-11): 🔧 阿里百炼修复与数据源升级
- **v0.1.5** (2025-07-08): 📊 添加Deepseek模型支持
- **v0.1.4** (2025-07-05): 🏗️ 架构优化与配置管理重构
- **v0.1.3** (2025-06-28): 🇨🇳 A股市场完整支持
- **v0.1.2** (2025-06-15): 🌐 Web界面和配置管理
- **v0.1.1** (2025-06-01): 🧠 国产LLM集成

📋 **详细更新日志**: [CHANGELOG.md](./docs/releases/CHANGELOG.md)

## 📞 联系方式

- **GitHub Issues**: [提交问题和建议](https://github.com/hsliuping/TradingAgents-CN/issues)
- **邮箱**: hsliup@163.com
- 项目ＱＱ群：1009816091
- 项目微信公众号：TradingAgents-CN

  

- **原项目**: [TauricResearch/TradingAgents](https://github.com/TauricResearch/TradingAgents)
- **文档**: [完整文档目录](docs/)

## ⚠️ 风险提示

**重要声明**: 本框架仅用于研究和教育目的，不构成投资建议。

- 📊 交易表现可能因多种因素而异
- 🤖 AI模型的预测存在不确定性
- 💰 投资有风险，决策需谨慎
- 👨‍💼 建议咨询专业财务顾问

---



**🌟 如果这个项目对您有帮助，请给我们一个 Star！**

[⭐ Star this repo](https://github.com/hsliuping/TradingAgents-CN) | [🍴 Fork this repo](https://github.com/hsliuping/TradingAgents-CN/fork) | [📖 Read the docs](./docs/)



          


resemble-ai/chatterbox
Mon, 09 Feb 2026 00:07:51 GMT

            resemble-ai/chatterbox
            SoTA open-source TTS
            Language: Python
            Stars: 22,483
            Forks: 2,944
            Stars today: 76 stars today
            README
![Chatterbox Turbo Image](./Chatterbox-Turbo.jpg)


# Chatterbox TTS

[![Alt Text](https://img.shields.io/badge/listen-demo_samples-blue)](https://resemble-ai.github.io/chatterbox_turbo_demopage/)
[![Alt Text](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/ResembleAI/chatterbox-turbo-demo)
[![Alt Text](https://static-public.podonos.com/badges/insight-on-pdns-sm-dark.svg)](https://podonos.com/resembleai/chatterbox)
[![Discord](https://img.shields.io/discord/1377773249798344776?label=join%20discord&logo=discord&style=flat)](https://discord.gg/rJq9cRJBJ6)

_Made with ♥️ by 

**Chatterbox** is a family of three state-of-the-art, open-source text-to-speech models by Resemble AI.

We are excited to introduce **Chatterbox-Turbo**, our most efficient model yet. Built on a streamlined 350M parameter architecture, **Turbo** delivers high-quality speech with less compute and VRAM than our previous models. We have also distilled the speech-token-to-mel decoder, previously a bottleneck, reducing generation from 10 steps to just **one**, while retaining high-fidelity audio output.

**Paralinguistic tags** are now native to the Turbo model, allowing you to use `[cough]`, `[laugh]`, `[chuckle]`, and more to add distinct realism. While Turbo was built primarily for low-latency voice agents, it excels at narration and creative workflows.

If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (link). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media.



### ⚡ Model Zoo

Choose the right model for your application.

| Model                                                                                                           | Size | Languages | Key Features                                            | Best For                                     | 🤗                                                                  | Examples |
|:----------------------------------------------------------------------------------------------------------------| :--- | :--- |:--------------------------------------------------------|:---------------------------------------------|:--------------------------------------------------------------------------| :--- |
| **Chatterbox-Turbo**                                                                                            | **350M** | **English** | Paralinguistic Tags (`[laugh]`), Lower Compute and VRAM | Zero-shot voice agents,  Production          | [Demo](https://huggingface.co/spaces/ResembleAI/chatterbox-turbo-demo)        | [Listen](https://resemble-ai.github.io/chatterbox_turbo_demopage/) |
| Chatterbox-Multilingual [(Language list)](#supported-languages)                                                 | 500M | 23+ | Zero-shot cloning, Multiple Languages                   | Global applications, Localization            | [Demo](https://huggingface.co/spaces/ResembleAI/Chatterbox-Multilingual-TTS) | [Listen](https://resemble-ai.github.io/chatterbox_demopage/) |
| Chatterbox [(Tips and Tricks)](#original-chatterbox-tips)                                                       | 500M | English | CFG & Exaggeration tuning                               | General zero-shot TTS with creative controls | [Demo](https://huggingface.co/spaces/ResembleAI/Chatterbox)              | [Listen](https://resemble-ai.github.io/chatterbox_demopage/) |

## Installation
```shell
pip install chatterbox-tts
```

Alternatively, you can install from source:
```shell
# conda create -yn chatterbox python=3.11
# conda activate chatterbox

git clone https://github.com/resemble-ai/chatterbox.git
cd chatterbox
pip install -e .
```
We developed and tested Chatterbox on Python 3.11 on Debian 11 OS; the versions of the dependencies are pinned in `pyproject.toml` to ensure consistency. You can modify the code or dependencies in this installation mode.

## Usage

##### Chatterbox-Turbo

```python
import torchaudio as ta
import torch
from chatterbox.tts_turbo import ChatterboxTurboTTS

# Load the Turbo model
model = ChatterboxTurboTTS.from_pretrained(device="cuda")

# Generate with Paralinguistic Tags
text = "Hi there, Sarah here from MochaFone calling you back [chuckle], have you got one minute to chat about the billing issue?"

# Generate audio (requires a reference clip for voice cloning)
wav = model.generate(text, audio_prompt_path="your_10s_ref_clip.wav")

ta.save("test-turbo.wav", wav, model.sr)
```

##### Chatterbox and Chatterbox-Multilingual

```python

import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
from chatterbox.mtl_tts import ChatterboxMultilingualTTS

# English example
model = ChatterboxTTS.from_pretrained(device="cuda")

text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill."
wav = model.generate(text)
ta.save("test-english.wav", wav, model.sr)

# Multilingual examples
multilingual_model = ChatterboxMultilingualTTS.from_pretrained(device=device)

french_text = "Bonjour, comment ça va? Ceci est le modèle de synthèse vocale multilingue Chatterbox, il prend en charge 23 langues."
wav_french = multilingual_model.generate(spanish_text, language_id="fr")
ta.save("test-french.wav", wav_french, model.sr)

chinese_text = "你好，今天天气真不错，希望你有一个愉快的周末。"
wav_chinese = multilingual_model.generate(chinese_text, language_id="zh")
ta.save("test-chinese.wav", wav_chinese, model.sr)

# If you want to synthesize with a different voice, specify the audio prompt
AUDIO_PROMPT_PATH = "YOUR_FILE.wav"
wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH)
ta.save("test-2.wav", wav, model.sr)
```
See `example_tts.py` and `example_vc.py` for more examples.

## Supported Languages
Arabic (ar) • Danish (da) • German (de) • Greek (el) • English (en) • Spanish (es) • Finnish (fi) • French (fr) • Hebrew (he) • Hindi (hi) • Italian (it) • Japanese (ja) • Korean (ko) • Malay (ms) • Dutch (nl) • Norwegian (no) • Polish (pl) • Portuguese (pt) • Russian (ru) • Swedish (sv) • Swahili (sw) • Turkish (tr) • Chinese (zh)

## Original Chatterbox Tips
- **General Use (TTS and Voice Agents):**
  - Ensure that the reference clip matches the specified language tag. Otherwise, language transfer outputs may inherit the accent of the reference clip’s language. To mitigate this, set `cfg_weight` to `0`.
  - The default settings (`exaggeration=0.5`, `cfg_weight=0.5`) work well for most prompts across all languages.
  - If the reference speaker has a fast speaking style, lowering `cfg_weight` to around `0.3` can improve pacing.

- **Expressive or Dramatic Speech:**
  - Try lower `cfg_weight` values (e.g. `~0.3`) and increase `exaggeration` to around `0.7` or higher.
  - Higher `exaggeration` tends to speed up speech; reducing `cfg_weight` helps compensate with slower, more deliberate pacing.


## Built-in PerTh Watermarking for Responsible AI

Every audio file generated by Chatterbox includes [Resemble AI's Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth) - imperceptible neural watermarks that survive MP3 compression, audio editing, and common manipulations while maintaining nearly 100% detection accuracy.


## Watermark extraction

You can look for the watermark using the following script.

```python
import perth
import librosa

AUDIO_PATH = "YOUR_FILE.wav"

# Load the watermarked audio
watermarked_audio, sr = librosa.load(AUDIO_PATH, sr=None)

# Initialize watermarker (same as used for embedding)
watermarker = perth.PerthImplicitWatermarker()

# Extract watermark
watermark = watermarker.get_watermark(watermarked_audio, sample_rate=sr)
print(f"Extracted watermark: {watermark}")
# Output: 0.0 (no watermark) or 1.0 (watermarked)
```


## Official Discord

👋 Join us on [Discord](https://discord.gg/rJq9cRJBJ6) and let's build something awesome together!

## Evaluation
Chatterbox Turbo was evaluated using Podonos, a platform for reproducible subjective speech evaluation.

We compared Chatterbox Turbo to competitive TTS systems using Podonos' standardized evaluation suite, focusing on overall preference, naturalness, and expressiveness.

Evaluation reports:
- [Chatterbox Turbo vs ElevenLabs Turbo v2.5](https://podonos.com/resembleai/chatterbox-turbo-vs-elevenlabs-turbo)
- [Chatterbox Turbo vs Cartesia Sonic 3](https://podonos.com/resembleai/chatterbox-turbo-vs-cartesia-sonic3)
- [Chatterbox Turbo vs VibeVoice 7B](https://podonos.com/resembleai/chatterbox-turbo-vs-vibevoice7b)

These evaluations were conducted under identical conditions and are publicly accessible via Podonos.

## Acknowledgements
- [Podonos](https://podonos.com) — for supporting reproducible subjective speech evaluation
- [Cosyvoice](https://github.com/FunAudioLLM/CosyVoice)
- [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning)
- [HiFT-GAN](https://github.com/yl4579/HiFTNet)
- [Llama 3](https://github.com/meta-llama/llama3)
- [S3Tokenizer](https://github.com/xingchensong/S3Tokenizer)

## Citation
If you find this model useful, please consider citing.
```
@misc{chatterboxtts2025,
  author       = {{Resemble AI}},
  title        = {{Chatterbox-TTS}},
  year         = {2025},
  howpublished = {\url{https://github.com/resemble-ai/chatterbox}},
  note         = {GitHub repository}
}
```
## Disclaimer
Don't use this model to do bad things. Prompts are sourced from freely available data on the internet.

          


ComposioHQ/awesome-claude-skills
Mon, 09 Feb 2026 00:07:50 GMT

            ComposioHQ/awesome-claude-skills
            A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows
            Language: Python
            Stars: 32,668
            Forks: 3,131
            Stars today: 849 stars today
            README
Awesome Claude Skills



  






  
    
  
  
    
  
  
    
  



  
    
  
  
    
  
  
    
  
  


A curated list of practical Claude Skills for enhancing productivity across Claude.ai, Claude Code, and the Claude API.


> **Want skills that do more than generate text?** Claude can send emails, create issues, post to Slack, and take actions across 1000+ apps. [See how →](./connect/)

---

## Quickstart: Connect Claude to 500+ Apps

The **connect-apps** plugin lets Claude perform real actions - send emails, create issues, post to Slack. It handles auth and connects to 500+ apps using Composio under the hood.

### 1. Install the Plugin

```bash
claude --plugin-dir ./connect-apps-plugin
```

### 2. Run Setup

```
/connect-apps:setup
```

Paste your API key when asked. (Get a free key at [platform.composio.dev](https://platform.composio.dev/?utm_source=Github&utm_content=AwesomeSkills))

### 3. Restart & Try It

```bash
exit
claude
```

> **Want skills that do more than generate text?** Claude can send emails, create issues, post to Slack, and take actions across 1000+ apps. [See how →](./connect/)

If you receive the email, Claude is now connected to 500+ apps.

**[See all supported apps →](https://composio.dev/toolkits)**

---

## Contents

- [What Are Claude Skills?](#what-are-claude-skills)
- [Skills](#skills)
  - [Document Processing](#document-processing)
  - [Development & Code Tools](#development--code-tools)
  - [Data & Analysis](#data--analysis)
  - [Business & Marketing](#business--marketing)
  - [Communication & Writing](#communication--writing)
  - [Creative & Media](#creative--media)
  - [Productivity & Organization](#productivity--organization)
  - [Collaboration & Project Management](#collaboration--project-management)
  - [Security & Systems](#security--systems)
  - [App Automation via Composio](#app-automation-via-composio)
- [Getting Started](#getting-started)
- [Creating Skills](#creating-skills)
- [Contributing](#contributing)
- [Resources](#resources)
- [License](#license)

## What Are Claude Skills?

Claude Skills are customizable workflows that teach Claude how to perform specific tasks according to your unique requirements. Skills enable Claude to execute tasks in a repeatable, standardized manner across all Claude platforms.

## Skills

### Document Processing

- [docx](https://github.com/anthropics/skills/tree/main/skills/docx) - Create, edit, analyze Word docs with tracked changes, comments, formatting.
- [pdf](https://github.com/anthropics/skills/tree/main/skills/pdf) - Extract text, tables, metadata, merge & annotate PDFs.
- [pptx](https://github.com/anthropics/skills/tree/main/skills/pptx) - Read, generate, and adjust slides, layouts, templates.
- [xlsx](https://github.com/anthropics/skills/tree/main/skills/xlsx) - Spreadsheet manipulation: formulas, charts, data transformations.
- [Markdown to EPUB Converter](https://github.com/smerchek/claude-epub-skill) - Converts markdown documents and chat summaries into professional EPUB ebook files. *By [@smerchek](https://github.com/smerchek)*

### Development & Code Tools

- [artifacts-builder](https://github.com/anthropics/skills/tree/main/skills/web-artifacts-builder) - Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui).
- [aws-skills](https://github.com/zxkane/aws-skills) - AWS development with CDK best practices, cost optimization MCP servers, and serverless/event-driven architecture patterns.
- [Changelog Generator](./changelog-generator/) - Automatically creates user-facing changelogs from git commits by analyzing history and transforming technical commits into customer-friendly release notes.
- [Claude Code Terminal Title](https://github.com/bluzername/claude-code-terminal-title) - Gives each Claud-Code terminal window a dynamic title that describes the work being done so you don't lose track of what window is doing what.
- [D3.js Visualization](https://github.com/chrisvoncsefalvay/claude-d3js-skill) - Teaches Claude to produce D3 charts and interactive data visualizations. *By [@chrisvoncsefalvay](https://github.com/chrisvoncsefalvay)*
- [FFUF Web Fuzzing](https://github.com/jthack/ffuf_claude_skill) - Integrates the ffuf web fuzzer so Claude can run fuzzing tasks and analyze results for vulnerabilities. *By [@jthack](https://github.com/jthack)*
- [finishing-a-development-branch](https://github.com/obra/superpowers/tree/main/skills/finishing-a-development-branch) - Guides completion of development work by presenting clear options and handling chosen workflow.
- [iOS Simulator](https://github.com/conorluddy/ios-simulator-skill) - Enables Claude to interact with iOS Simulator for testing and debugging iOS applications. *By [@conorluddy](https://github.com/conorluddy)*
- [jules](https://github.com/sanjay3290/ai-skills/tree/main/skills/jules) - Delegate coding tasks to Google Jules AI agent for async bug fixes, documentation, tests, and feature implementation on GitHub repos. *By [@sanjay3290](https://github.com/sanjay3290)*
- [LangSmith Fetch](./langsmith-fetch/) - Debug LangChain and LangGraph agents by automatically fetching and analyzing execution traces from LangSmith Studio. First AI observability skill for Claude Code. *By [@OthmanAdi](https://github.com/OthmanAdi)*
- [MCP Builder](./mcp-builder/) - Guides creation of high-quality MCP (Model Context Protocol) servers for integrating external APIs and services with LLMs using Python or TypeScript.
- [move-code-quality-skill](https://github.com/1NickPappas/move-code-quality-skill) - Analyzes Move language packages against the official Move Book Code Quality Checklist for Move 2024 Edition compliance and best practices.
- [Playwright Browser Automation](https://github.com/lackeyjb/playwright-skill) - Model-invoked Playwright automation for testing and validating web applications. *By [@lackeyjb](https://github.com/lackeyjb)*
- [prompt-engineering](https://github.com/NeoLabHQ/context-engineering-kit/tree/master/plugins/customaize-agent/skills/prompt-engineering) - Teaches well-known prompt engineering techniques and patterns, including Anthropic best practices and agent persuasion principles.
- [pypict-claude-skill](https://github.com/omkamal/pypict-claude-skill) - Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for requirements or code, generating optimized test suites with pairwise coverage.
- [reddit-fetch](https://github.com/ykdojo/claude-code-tips/tree/main/skills/reddit-fetch) - Fetches Reddit content via Gemini CLI when WebFetch is blocked or returns 403 errors.
- [Skill Creator](./skill-creator/) - Provides guidance for creating effective Claude Skills that extend capabilities with specialized knowledge, workflows, and tool integrations.
- [Skill Seekers](https://github.com/yusufkaraaslan/Skill_Seekers) - Automatically converts any documentation website into a Claude AI skill in minutes. *By [@yusufkaraaslan](https://github.com/yusufkaraaslan)*
- [software-architecture](https://github.com/NeoLabHQ/context-engineering-kit/tree/master/plugins/ddd/skills/software-architecture) - Implements design patterns including Clean Architecture, SOLID principles, and comprehensive software design best practices.
- [subagent-driven-development](https://github.com/NeoLabHQ/context-engineering-kit/tree/master/plugins/sadd/skills/subagent-driven-development) - Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.
- [test-driven-development](https://github.com/obra/superpowers/tree/main/skills/test-driven-development) - Use when implementing any feature or bugfix, before writing implementation code.
- [using-git-worktrees](https://github.com/obra/superpowers/blob/main/skills/using-git-worktrees/) - Creates isolated git worktrees with smart directory selection and safety verification.
- [Connect](./connect/) - Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services.
- [Webapp Testing](./webapp-testing/) - Tests local web applications using Playwright for verifying frontend functionality, debugging UI behavior, and capturing screenshots.

### Data & Analysis

- [CSV Data Summarizer](https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill) - Automatically analyzes CSV files and generates comprehensive insights with visualizations without requiring user prompts. *By [@coffeefuelbump](https://github.com/coffeefuelbump)*
- [deep-research](https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research) - Execute autonomous multi-step research using Gemini Deep Research Agent for market analysis, competitive landscaping, and literature reviews. *By [@sanjay3290](https://github.com/sanjay3290)*
- [postgres](https://github.com/sanjay3290/ai-skills/tree/main/skills/postgres) - Execute safe read-only SQL queries against PostgreSQL databases with multi-connection support and defense-in-depth security. *By [@sanjay3290](https://github.com/sanjay3290)*
- [root-cause-tracing](https://github.com/obra/superpowers/tree/main/skills/root-cause-tracing) - Use when errors occur deep in execution and you need to trace back to find the original trigger.

### Business & Marketing

- [Brand Guidelines](./brand-guidelines/) - Applies Anthropic's official brand colors and typography to artifacts for consistent visual identity and professional design standards.
- [Competitive Ads Extractor](./competitive-ads-extractor/) - Extracts and analyzes competitors' ads from ad libraries to understand messaging and creative approaches that resonate.
- [Domain Name Brainstormer](./domain-name-brainstormer/) - Generates creative domain name ideas and checks availability across multiple TLDs including .com, .io, .dev, and .ai extensions.
- [Internal Comms](./internal-comms/) - Helps write internal communications including 3P updates, company newsletters, FAQs, status reports, and project updates using company-specific formats.
- [Lead Research Assistant](./lead-research-assistant/) - Identifies and qualifies high-quality leads by analyzing your product, searching for target companies, and providing actionable outreach strategies.

### Communication & Writing

- [article-extractor](https://github.com/michalparkola/tapestry-skills-for-claude-code/tree/main/article-extractor) - Extract full article text and metadata from web pages.
- [brainstorming](https://github.com/obra/superpowers/tree/main/skills/brainstorming) - Transform rough ideas into fully-formed designs through structured questioning and alternative exploration.
- [Content Research Writer](./content-research-writer/) - Assists in writing high-quality content by conducting research, adding citations, improving hooks, and providing section-by-section feedback.
- [family-history-research](https://github.com/emaynard/claude-family-history-research-skill) - Provides assistance with planning family history and genealogy research projects.
- [Meeting Insights Analyzer](./meeting-insights-analyzer/) - Analyzes meeting transcripts to uncover behavioral patterns including conflict avoidance, speaking ratios, filler words, and leadership style.
- [NotebookLM Integration](https://github.com/PleasePrompto/notebooklm-skill) - Lets Claude Code chat directly with NotebookLM for source-grounded answers based exclusively on uploaded documents. *By [@PleasePrompto](https://github.com/PleasePrompto)*
- [Twitter Algorithm Optimizer](./twitter-algorithm-optimizer/) - Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit tweets to improve engagement and visibility.

### Creative & Media

- [Canvas Design](./canvas-design/) - Creates beautiful visual art in PNG and PDF documents using design philosophy and aesthetic principles for posters, designs, and static pieces.
- [imagen](https://github.com/sanjay3290/ai-skills/tree/main/skills/imagen) - Generate images using Google Gemini's image generation API for UI mockups, icons, illustrations, and visual assets. *By [@sanjay3290](https://github.com/sanjay3290)*
- [Image Enhancer](./image-enhancer/) - Improves image and screenshot quality by enhancing resolution, sharpness, and clarity for professional presentations and documentation.
- [Slack GIF Creator](./slack-gif-creator/) - Creates animated GIFs optimized for Slack with validators for size constraints and composable animation primitives.
- [Theme Factory](./theme-factory/) - Applies professional font and color themes to artifacts including slides, docs, reports, and HTML landing pages with 10 pre-set themes.
- [Video Downloader](./video-downloader/) - Downloads videos from YouTube and other platforms for offline viewing, editing, or archival with support for various formats and quality options.
- [youtube-transcript](https://github.com/michalparkola/tapestry-skills-for-claude-code/tree/main/youtube-transcript) - Fetch transcripts from YouTube videos and prepare summaries.

### Productivity & Organization

- [File Organizer](./file-organizer/) - Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures.
- [Invoice Organizer](./invoice-organizer/) - Automatically organizes invoices and receipts for tax preparation by reading files, extracting information, and renaming consistently.
- [kaizen](https://github.com/NeoLabHQ/context-engineering-kit/tree/master/plugins/kaizen/skills/kaizen) - Applies continuous improvement methodology with multiple analytical approaches, based on Japanese Kaizen philosophy and Lean methodology.
- [n8n-skills](https://github.com/haunchen/n8n-skills) - Enables AI assistants to directly understand and operate n8n workflows.
- [Raffle Winner Picker](./raffle-winner-picker/) - Randomly selects winners from lists, spreadsheets, or Google Sheets for giveaways and contests with cryptographically secure randomness.
- [Tailored Resume Generator](./tailored-resume-generator/) - Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances.
- [ship-learn-next](https://github.com/michalparkola/tapestry-skills-for-claude-code/tree/main/ship-learn-next) - Skill to help iterate on what to build or learn next, based on feedback loops.
- [tapestry](https://github.com/michalparkola/tapestry-skills-for-claude-code/tree/main/tapestry) - Interlink and summarize related documents into knowledge networks.

### Collaboration & Project Management

- [git-pushing](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/git-pushing) - Automate git operations and repository interactions.
- [google-workspace-skills](https://github.com/sanjay3290/ai-skills/tree/main/skills) - Suite of Google Workspace integrations: Gmail, Calendar, Chat, Docs, Sheets, Slides, and Drive with cross-platform OAuth. *By [@sanjay3290](https://github.com/sanjay3290)*
- [outline](https://github.com/sanjay3290/ai-skills/tree/main/skills/outline) - Search, read, create, and manage documents in Outline wiki instances (cloud or self-hosted). *By [@sanjay3290](https://github.com/sanjay3290)*
- [review-implementing](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/review-implementing) - Evaluate code implementation plans and align with specs.
- [test-fixing](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/test-fixing) - Detect failing tests and propose patches or fixes.

### Security & Systems

- [computer-forensics](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/computer-forensics-skills/skills/computer-forensics) - Digital forensics analysis and investigation techniques.
- [file-deletion](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/computer-forensics-skills/skills/file-deletion) - Secure file deletion and data sanitization methods.
- [metadata-extraction](https://github.com/mhattingpete/claude-skills-marketplace/tree/main/computer-forensics-skills/skills/metadata-extraction) - Extract and analyze file metadata for forensic purposes.
- [threat-hunting-with-sigma-rules](https://github.com/jthack/threat-hunting-with-sigma-rules-skill) - Use Sigma detection rules to hunt for threats and analyze security events.

### App Automation via Composio

Pre-built workflow skills for 78 SaaS apps via [Rube MCP (Composio)](https://composio.dev). Each skill includes tool sequences, parameter guidance, known pitfalls, and quick reference tables — all using real tool slugs discovered from Composio's API.

**CRM & Sales**
- [Close Automation](./close-automation/) - Automate Close CRM: leads, contacts, opportunities, activities, and pipelines.
- [HubSpot Automation](./hubspot-automation/) - Automate HubSpot CRM: contacts, deals, companies, tickets, and email engagement.
- [Pipedrive Automation](./pipedrive-automation/) - Automate Pipedrive: deals, contacts, organizations, activities, and pipelines.
- [Salesforce Automation](./salesforce-automation/) - Automate Salesforce: objects, records, SOQL queries, and bulk operations.
- [Zoho CRM Automation](./zoho-crm-automation/) - Automate Zoho CRM: leads, contacts, deals, accounts, and modules.

**Project Management**
- [Asana Automation](./asana-automation/) - Automate Asana: tasks, projects, sections, assignments, and workspaces.
- [Basecamp Automation](./basecamp-automation/) - Automate Basecamp: to-do lists, messages, people, groups, and projects.
- [ClickUp Automation](./clickup-automation/) - Automate ClickUp: tasks, lists, spaces, goals, and time tracking.
- [Jira Automation](./jira-automation/) - Automate Jira: issues, projects, boards, sprints, and JQL queries.
- [Linear Automation](./linear-automation/) - Automate Linear: issues, projects, cycles, teams, and workflows.
- [Monday Automation](./monday-automation/) - Automate Monday.com: boards, items, columns, groups, and workspaces.
- [Notion Automation](./notion-automation/) - Automate Notion: pages, databases, blocks, comments, and search.
- [Todoist Automation](./todoist-automation/) - Automate Todoist: tasks, projects, sections, labels, and filters.
- [Trello Automation](./trello-automation/) - Automate Trello: boards, cards, lists, members, and checklists.
- [Wrike Automation](./wrike-automation/) - Automate Wrike: tasks, folders, projects, comments, and workflows.

**Communic

... [README content truncated due to size. Visit the repository for the complete README] ...
          


chenyme/grok2api
Mon, 09 Feb 2026 00:07:49 GMT

            chenyme/grok2api
            基于 FastAPI 重构的 Grok2API，全面适配最新 Web 调用格式，支持流/非流式对话、图像生成/编辑、深度思考，号池并发与自动负载均衡一体化。
            Language: Python
            Stars: 1,223
            Forks: 367
            Stars today: 65 stars today
            README
README not available. Either the repository does not have a README or it could not be accessed.
          


suitenumerique/docs
Mon, 09 Feb 2026 00:07:48 GMT

            suitenumerique/docs
            A collaborative note taking, wiki and documentation platform that scales. Built with Django and React.
            Language: Python
            Stars: 15,964
            Forks: 525
            Stars today: 61 stars today
            README

  
    
  


  
    
  
  
  
  
  
    
      


  
    Chat on Matrix
   - 
    Documentation
   - 
    Getting started
   - 
    Reach out
  


# La Suite Docs : Collaborative Text Editing
Docs, where your notes can become knowledge through live collaboration.



## Why use Docs ❓
Docs is a collaborative text editor designed to address common challenges in knowledge building and sharing.

### Write
* 😌 Get simple, accessible online editing for your team.
* 💅 Create clean documents with beautiful formatting options.
* 🖌️ Focus on your content using either the in-line editor, or [the Markdown syntax](https://www.markdownguide.org/basic-syntax/).
* 🧱 Quickly design your page thanks to the many block types, accessible from the `/` slash commands, as well as keyboard shortcuts.
* 🔌 Write offline! Your edits will be synced once you're back online.
* ✨ Save time thanks to our AI actions, such as rephrasing, summarizing, fixing typos, translating, etc. You can even turn your selected text into a prompt!

### Work together
* 🤝 Enjoy live editing! See your team collaborate in real time.
* 🔒 Keep your information secure thanks to granular access control. Only share with the right people.
* 📑 Export your content in multiple formats (`.odt`, `.docx`, `.pdf`) with customizable templates.
* 📚 Turn your team's collaborative work into organized knowledge with Subpages.

### Self-host

#### 🚀 Docs is easy to install on your own servers
We use Kubernetes for our [production instance](https://docs.numerique.gouv.fr/) but also support Docker Compose. The community contributed a couple other methods (Nix, YunoHost etc.) check out the [docs](/docs/installation/README.md) to get detailed instructions and examples.

#### 🌍 Known instances
We hope to see many more, here is an incomplete list of public Docs instances. Feel free to make a PR to add ones that are not listed below🙏

| Url | Org | Public |
| --- | --- | ------- |
| [docs.numerique.gouv.fr](https://docs.numerique.gouv.fr/)    | DINUM    | French public agents working for the central administration and the extended public sphere. ProConnect is required to login in or sign up|
| [docs.suite.anct.gouv.fr](https://docs.suite.anct.gouv.fr/)    | ANCT    | French public agents working for the territorial administration and the extended public sphere. ProConnect is required to login in or sign up|
| [notes.demo.opendesk.eu](https://notes.demo.opendesk.eu)    | ZenDiS    | Demo instance of OpenDesk. Request access to get credentials |
| [notes.liiib.re](https://notes.liiib.re/)    | lasuite.coop    | Free and open demo to all. Content and accounts are reset after one month |
| [docs.federated.nexus](https://docs.federated.nexus/)    | federated.nexus    | Public instance, but you have to [sign up for a Federated Nexus account](https://federated.nexus/register/). |
| [docs.demo.mosacloud.eu](https://docs.demo.mosacloud.eu/)    | mosa.cloud    | Demo instance of mosa.cloud, a dutch company providing services around La Suite apps. |

#### ⚠️ Advanced features
For some advanced features (ex: Export as PDF) Docs relies on XL packages from BlockNote. These are licenced under GPL and are not MIT compatible. You can perfectly use Docs without these packages by setting the environment variable `PUBLISH_AS_MIT` to true. That way you'll build an image of the application without the features that are not MIT compatible. Read the [environment variables documentation](/docs/env.md) for more information.

## Getting started 🔧

### Test it

You can test Docs on your browser by visiting this [demo document](https://impress-preprod.beta.numerique.gouv.fr/docs/6ee5aac4-4fb9-457d-95bf-bb56c2467713/)

### Run Docs locally

> ⚠️ The methods described below for running Docs locally is **for testing purposes only**. It is based on building Docs using [Minio](https://min.io/) as an S3-compatible storage solution. Of course you can choose any S3-compatible storage solution.

**Prerequisite**

Make sure you have a recent version of Docker and [Docker Compose](https://docs.docker.com/compose/install) installed on your laptop, then type:

```shellscript
$ docker -v

Docker version 20.10.2, build 2291f61

$ docker compose version

Docker Compose version v2.32.4
```

> ⚠️ You may need to run the following commands with `sudo`, but this can be avoided by adding your user to the local `docker` group.

**Project bootstrap**

The easiest way to start working on the project is to use [GNU Make](https://www.gnu.org/software/make/):

```shellscript
$ make bootstrap FLUSH_ARGS='--no-input'
```

This command builds the `app-dev` and `frontend-dev` containers, installs dependencies, performs database migrations and compiles translations. It's a good idea to use this command each time you are pulling code from the project repository to avoid dependency-related or migration-related issues.

Your Docker services should now be up and running 🎉

You can access the project by going to .

You will be prompted to log in. The default credentials are:

```
username: impress
password: impress
```

📝 Note that if you need to run them afterwards, you can use the eponymous Make rule:

```shellscript
$ make run
```

⚠️ For the frontend developer, it is often better to run the frontend in development mode locally.

To do so, install the frontend dependencies with the following command:

```shellscript
$ make frontend-development-install
```

And run the frontend locally in development mode with the following command:

```shellscript
$ make run-frontend-development
```

To start all the services, except the frontend container, you can use the following command:

```shellscript
$ make run-backend
```

To execute frontend tests & linting only
```shellscript
$ make frontend-test
$ make frontend-lint
```

**Adding content**

You can create a basic demo site by running this command:

```shellscript
$ make demo
```

Finally, you can check all available Make rules using this command:

```shellscript
$ make help
```

**Django admin**

You can access the Django admin site at:

.

You first need to create a superuser account:

```shellscript
$ make superuser
```

## Feedback 🙋‍♂️🙋‍♀️

We'd love to hear your thoughts, and hear about your experiments, so come and say hi on [Matrix](https://matrix.to/#/#docs-official:matrix.org).

## Roadmap 💡

Want to know where the project is headed? [🗺️ Checkout our roadmap](https://github.com/orgs/numerique-gouv/projects/13/views/11)

## License 📝

This work is released under the MIT License (see [LICENSE](https://github.com/suitenumerique/docs/blob/main/LICENSE)).

While Docs is a public-driven initiative, our license choice is an invitation for private sector actors to use, sell and contribute to the project. 

## Contributing 🙌

This project is intended to be community-driven, so please, do not hesitate to [get in touch](https://matrix.to/#/#docs-official:matrix.org) if you have any question related to our implementation or design decisions.

You can help us with translations on [Crowdin](https://crowdin.com/project/lasuite-docs).

If you intend to make pull requests, see [CONTRIBUTING](https://github.com/suitenumerique/docs/blob/main/CONTRIBUTING.md) for guidelines.

## Directory structure:

```markdown
docs
├── bin - executable scripts or binaries that are used for various tasks, such as setup scripts, utility scripts, or custom commands.
├── crowdin - for crowdin translations, a tool or service that helps manage translations for the project.
├── docker - Dockerfiles and related configuration files used to build Docker images for the project. These images can be used for development, testing, or production environments.
├── docs - documentation for the project, including user guides, API documentation, and other helpful resources.
├── env.d/development - environment-specific configuration files for the development environment. These files might include environment variables, configuration settings, or other setup files needed for development.
├── gitlint - configuration files for `gitlint`, a tool that enforces commit message guidelines to ensure consistency and quality in commit messages.
├── playground - experimental or temporary code, where developers can test new features or ideas without affecting the main codebase.
└── src - main source code directory, containing the core application code, libraries, and modules of the project.
```

## Credits ❤️

### Stack

Docs is built on top of [Django Rest Framework](https://www.django-rest-framework.org/), [Next.js](https://nextjs.org/), [BlockNote.js](https://www.blocknotejs.org/), [HocusPocus](https://tiptap.dev/docs/hocuspocus/introduction) and [Yjs](https://yjs.dev/). We thank the contributors of all these projects for their awesome work!

We are proud sponsors of [BlockNotejs](https://www.blocknotejs.org/) and [Yjs](https://yjs.dev/). 


### Gov ❤️ open source
Docs is the result of a joint effort led by the French 🇫🇷🥖 ([DINUM](https://www.numerique.gouv.fr/dinum/)) and German 🇩🇪🥨 governments ([ZenDiS](https://zendis.de/)). 

We are always looking for new public partners (we are currently onboarding the Netherlands 🇳🇱🧀), feel free to [reach out](mailto:docs@numerique.gouv.fr) if you are interested in using or contributing to Docs.


  


          


airweave-ai/airweave
Mon, 09 Feb 2026 00:07:47 GMT

            airweave-ai/airweave
            Open-source context retrieval layer for AI agents
            Language: Python
            Stars: 5,684
            Forks: 692
            Stars today: 63 stars today
            README

  
    
      
      
      
    
  


Open-source context retrieval layer for AI agents and RAG systems.


  
  
  
  



  
  
  
  
  



  


### What is Airweave?
Airweave connects to your apps, tools, and databases, continuously syncs their data, and exposes it through a unified, LLM-friendly search interface. AI agents query Airweave to retrieve relevant, grounded, up-to-date context from multiple sources in a single request.

### Where it fits
Airweave sits between your data sources and AI systems as shared retrieval infrastructure. It handles authentication, ingestion, syncing, indexing, and retrieval so you don't have to rebuild fragile pipelines for every agent or integration.

### How it works
1. **Connect** your apps, databases, and documents (50+ integrations)
2. **Airweave** syncs, indexes, and exposes your data through a unified retrieval layer
3. **Agents query** Airweave via our SDKs, REST API, MCP, or native integrations with popular agent frameworks
4. **Agents retrieve** relevant, grounded context on demand

## Quickstart

### Cloud-hosted: [app.airweave.ai](https://app.airweave.ai)



### Self-hosted

```bash
git clone https://github.com/airweave-ai/airweave.git
cd airweave
./start.sh
```

→ http://localhost:8080

> Requires Docker and docker-compose

## Supported Integrations



































## SDKs

```bash
pip install airweave-sdk        # Python
npm install @airweave/sdk       # TypeScript
```

```python
from airweave import AirweaveSDK

client = AirweaveSDK(api_key="YOUR_API_KEY")
results = client.collections.search(
    readable_id="my-collection",
    query="Find recent failed payments"
)
```




## Tech Stack

- **Frontend**: [React/TypeScript](https://react.dev/) with [ShadCN](https://ui.shadcn.com/)
- **Backend**: [FastAPI](https://fastapi.tiangolo.com/) (Python)
- **Databases**: [PostgreSQL](https://www.postgresql.org/) (metadata), [Qdrant](https://qdrant.tech/) (vectors)
- **Workers**: [Temporal](https://temporal.io/) (orchestration), [Redis](https://redis.io/) (pub/sub)
- **Deployment**: [Docker Compose](https://docs.docker.com/compose/) (dev), [Kubernetes](https://kubernetes.io/) (prod)

## Contributing

We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md).

## License

[MIT License](LICENSE)


  Discord ·
  Issues ·
  Twitter


          


Lightricks/LTX-Video
Mon, 09 Feb 2026 00:07:46 GMT

            Lightricks/LTX-Video
            Official repository for LTX-Video
            Language: Python
            Stars: 9,268
            Forks: 876
            Stars today: 27 stars today
            README


# LTX-Video

[![Website](https://img.shields.io/badge/Website-LTXV-181717?logo=google-chrome)](https://ltx.video)
[![Model](https://img.shields.io/badge/HuggingFace-Model-orange?logo=huggingface)](https://huggingface.co/Lightricks/LTX-Video)
[![Demo](https://img.shields.io/badge/Demo-Try%20Now-brightgreen?logo=vercel)](https://app.ltx.studio/ltx-2-playground/t2v)
[![Paper](https://img.shields.io/badge/Paper-arXiv-B31B1B?logo=arxiv)](https://arxiv.org/abs/2501.00103)
[![Trainer](https://img.shields.io/badge/LTXV-Trainer-9146FF?logo=github)](https://github.com/Lightricks/LTX-Video-Trainer)
[![Discord](https://img.shields.io/badge/Join-Discord-5865F2?logo=discord)](https://discord.gg/ltxplatform)

This is the official repository for LTX-Video.



---

## 🚀 **New: LTX-2 is Now Available!**

**We're excited to announce [LTX-2](https://github.com/Lightricks/LTX-2) - the next generation of LTX with synchronized audio+video generation!**

LTX-2 is the first DiT-based audio-video foundation model that contains all core capabilities of modern video generation in one model. **LTX-2 is now the primary home for LTX development** and includes significant improvements:

- 🎵 **Synchronized Audio+Video Generation** - Generate videos with perfectly synchronized audio
- 🎬 **Latest Model** - LTX-2 with improved quality and capabilities
- 🔌 **ComfyUI Integration** - Built into ComfyUI core for seamless workflows
- 🎯 **Advanced Features:**
  - Multiple keyframe support
  - IC-LoRA control models for precise generation
  - Standard LoRA support for style customization
  - Latent upsampler for multiscale pipelines
- 🛠️ **Training Tools** - LoRA training capabilities
- 📚 **Comprehensive Documentation** - Full documentation at [https://docs.ltx.video](https://docs.ltx.video)
- 🔄 **Active Development** - Ongoing improvements and community support

**[👉 Check out LTX-2 here](https://github.com/Lightricks/LTX-2)**

**[📖 View Documentation](https://docs.ltx.video)**

---

## Table of Contents

- [Introduction](#introduction)
- [What's New](#news)
- [Models](#models)
- [Quick Start Guide](#quick-start-guide)
  - [Online demo](#online-inference)
  - [Run locally](#run-locally)
    - [Installation](#installation)
    - [Inference](#inference)
  - [ComfyUI Integration](#comfyui-integration)
  - [Diffusers Integration](#diffusers-integration)
- [Model User Guide](#model-user-guide)
- [Community Contribution](#community-contribution)
- [Training](#training)
- [Control Models](#control-models)
- [Join Us!](#join-us)
- [Acknowledgement](#acknowledgement)

# Introduction

LTX-Video is the first DiT-based video generation model that contains all core capabilities of modern video generation in one model: synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access. It can generate up to 50 FPS videos at native 4K resolution with synchronized audio in one pass.
The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content.

The model supports image-to-video, multi-keyframe conditioning, keyframe-based animation, video extension (both forward and backward), video-to-video transformations, and any combination of these features.

### Image-to-video examples
| | | |
|:---:|:---:|:---:|
| ![example1](./docs/_static/ltx-video_i2v_example_00001.gif) | ![example2](./docs/_static/ltx-video_i2v_example_00002.gif) | ![example3](./docs/_static/ltx-video_i2v_example_00003.gif) |
| ![example4](./docs/_static/ltx-video_i2v_example_00004.gif) | ![example5](./docs/_static/ltx-video_i2v_example_00005.gif) |  ![example6](./docs/_static/ltx-video_i2v_example_00006.gif) |
| ![example7](./docs/_static/ltx-video_i2v_example_00007.gif) |  ![example8](./docs/_static/ltx-video_i2v_example_00008.gif) | ![example9](./docs/_static/ltx-video_i2v_example_00009.gif) |

### Controlled video examples
| | | |
|:---:|:---:|:---:|
| ![control0](./docs/_static/ltx-video_ic_2v_example_00000.gif) | ![control1](./docs/_static/ltx-video_ic_2v_example_00001.gif) | ![control2](./docs/_static/ltx-video_ic_2v_example_00002.gif) |

| | |
|:---:|:---:|
| ![control3](./docs/_static/ltx-video_ic_2v_example_00003.gif) | ![control4](./docs/_static/ltx-video_ic_2v_example_00004.gif) |

# News

## October 23, 2025: LTX-2 Announced

Today we announced our newest foundation model, LTX-2. LTX-2 represents a major leap forward from our previous model, LTXV 0.9.8. Here’s what’s new:
* **Audio + Video, Together**: Visuals and sound are generated in one coherent process, with motion, dialogue, ambience, and music flowing simultaneously.
* **4K Fidelity**: Professional-grade precision with native 4K and up to 50 fps, sharp textures, clean motion, and synchronized audio.
* **Longer Generations**: LTX-2 supports longer, continuous clips with synchronized audio up to 10 seconds.
* **Low Cost & Efficiency**: Up to 50% lower compute cost than competing models, powered by a multi-GPU inference stack.
* **Creative Control**: Multi-keyframe conditioning, 3D camera logic, and LoRA fine-tuning deliver frame-level precision and style consistency.

For more details, please see our [blog post](https://website.ltx.video/blog/introducing-ltx-2). LTX-2 model weights, code, and benchmarks will be released to the community later in 2025. 


## July, 16th, 2025: New Distilled models v0.9.8 with up to 60 seconds of video:
- Long shot generation in LTXV-13B!
  * LTX-Video now supports up to 60 seconds of video.
  * Compatible also with the official IC-LoRAs.
  * Try now in [ComfyUI](https://github.com/Lightricks/ComfyUI-LTXVideo/tree/master/example_workflows/ltxv-13b-i2v-long-multi-prompt.json).
- Release a new distilled models:
  * 13B distilled model [ltxv-13b-0.9.8-distilled](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-distilled.yaml)
  * 2B distilled model [ltxv-2b-0.9.8-distilled](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.8-distilled.yaml)
  * Both models are distilled from the same base model [ltxv-13b-0.9.8-dev](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-dev.yaml) and are compatible for use together in the same multiscale pipeline.
  * Improved prompt understanding and detail generation
  * Includes corresponding FP8 weights and workflows.
- Release a new detailer model [LTX-Video-ICLoRA-detailer-13B-0.9.8](https://huggingface.co/Lightricks/LTX-Video-ICLoRA-detailer-13b-0.9.8)
  * Available in [ComfyUI](https://github.com/Lightricks/ComfyUI-LTXVideo/tree/master/example_workflows/ltxv-13b-upscale.json).

## July, 8th, 2025: New Control Models Released!
- Released three new control models for LTX-Video on HuggingFace:
    * **Depth Control**: [LTX-Video-ICLoRA-depth-13b-0.9.7](https://huggingface.co/Lightricks/LTX-Video-ICLoRA-depth-13b-0.9.7)
    * **Pose Control**: [LTX-Video-ICLoRA-pose-13b-0.9.7](https://huggingface.co/Lightricks/LTX-Video-ICLoRA-pose-13b-0.9.7)
    * **Canny Control**: [LTX-Video-ICLoRA-canny-13b-0.9.7](https://huggingface.co/Lightricks/LTX-Video-ICLoRA-canny-13b-0.9.7)


## May, 14th, 2025: New distilled model 13B v0.9.7:
- Release a new 13B distilled model [ltxv-13b-0.9.7-distilled](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-distilled.safetensors)
    * Amazing for iterative work - generates HD videos in 10 seconds, with low-res preview after just 3 seconds (on H100)!
    * Does not require classifier-free guidance and spatio-temporal guidance.
    * Supports sampling with 8 (recommended), or less diffusion steps.
    * Also released a LoRA version of the distilled model, [ltxv-13b-0.9.7-distilled-lora128](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-distilled-lora128.safetensors)
        * Requires only 1GB of VRAM
        * Can be used with the full 13B model for fast inference
- Release a new quantized distilled model [ltxv-13b-0.9.7-distilled-fp8](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-distilled-fp8.safetensors) for *real-time* generation (on H100) with even less VRAM

## May, 5th, 2025: New model 13B v0.9.7:
- Release a new 13B model [ltxv-13b-0.9.7-dev](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors)
- Release a new quantized model [ltxv-13b-0.9.7-dev-fp8](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev-fp8.safetensors) for faster inference with less VRam
- Release a new upscalers
  * [ltxv-temporal-upscaler-0.9.7](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-temporal-upscaler-0.9.7.safetensors)
  * [ltxv-spatial-upscaler-0.9.7](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-spatial-upscaler-0.9.7.safetensors)
- Breakthrough prompt adherence and physical understanding.
- New Pipeline for multi-scale video rendering for fast and high quality results


## April, 15th, 2025: New checkpoints v0.9.6:
- Release a new checkpoint [ltxv-2b-0.9.6-dev-04-25](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-2b-0.9.6-dev-04-25.safetensors) with improved quality
- Release a new distilled model [ltxv-2b-0.9.6-distilled-04-25](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-2b-0.9.6-distilled-04-25.safetensors)
    * 15x faster inference than non-distilled model.
    * Does not require classifier-free guidance and spatio-temporal guidance.
    * Supports sampling with 8 (recommended), or less diffusion steps.
- Improved prompt adherence, motion quality and fine details.
- New default resolution and FPS: 1216 × 704 pixels at 30 FPS
    * Still real time on H100 with the distilled model.
    * Other resolutions and FPS are still supported.
- Support stochastic inference (can improve visual quality when using the distilled model)

## March, 5th, 2025: New checkpoint v0.9.5
- New license for commercial use ([OpenRail-M](https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.5.license.txt))
- Release a new checkpoint v0.9.5 with improved quality
- Support keyframes and video extension
- Support higher resolutions
- Improved prompt understanding
- Improved VAE
- New online web app in [LTX-Studio](https://app.ltx.studio/ltx-video)
- Automatic prompt enhancement

## February, 20th, 2025: More inference options
- Improve STG (Spatiotemporal Guidance) for LTX-Video
- Support MPS on macOS with PyTorch 2.3.0
- Add support for 8-bit model, LTX-VideoQ8
- Add TeaCache for LTX-Video
- Add [ComfyUI-LTXTricks](#comfyui-integration)
- Add Diffusion-Pipe

## December 31st, 2024: Research paper
- Release the [research paper](https://arxiv.org/abs/2501.00103)

## December 20th, 2024: New checkpoint v0.9.1
- Release a new checkpoint v0.9.1 with improved quality
- Support for STG / PAG
- Support loading checkpoints of LTX-Video in Diffusers format (conversion is done on-the-fly)
- Support offloading unused parts to CPU
- Support the new timestep-conditioned VAE decoder
- Reference contributions from the community in the readme file
- Relax transformers dependency

## November 21th, 2024: Initial release v0.9.0
- Initial release of LTX-Video
- Support text-to-video and image-to-video generation


# Models

| Name                    | Notes                                                                                      | inference.py config                                                                                                                                      | ComfyUI workflow (Recommended) |
|-------------------------|--------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------|
| ltxv-13b-0.9.8-dev                   | Highest quality, requires more VRAM                                                        | [ltxv-13b-0.9.8-dev.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-dev.yaml)                                             | [ltxv-13b-i2v-base.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/ltxv-13b-i2v-base.json)             |
| [ltxv-13b-0.9.8-mix](https://app.ltx.studio/motion-workspace?videoModel=ltxv-13b)            | Mix ltxv-13b-dev and ltxv-13b-distilled in the same multi-scale rendering workflow for balanced speed-quality | N/A                                             | [ltxv-13b-i2v-mixed-multiscale.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/ltxv-13b-i2v-mixed-multiscale.json)             |
 [ltxv-13b-0.9.8-distilled](https://app.ltx.studio/motion-workspace?videoModel=ltxv)        | Faster, less VRAM usage, slight quality reduction compared to 13b. Ideal for rapid iterations | [ltxv-13b-0.9.8-distilled.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-distilled.yaml)                                    | [ltxv-13b-dist-i2v-base.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/13b-distilled/ltxv-13b-dist-i2v-base.json) |
ltxv-2b-0.9.8-distilled        | Smaller model, slight quality reduction compared to 13b distilled. Ideal for fast generation with light VRAM usage | [ltxv-2b-0.9.8-distilled.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.8-distilled.yaml)                                    | N/A |
| ltxv-13b-0.9.8-dev-fp8               | Quantized version of ltxv-13b | [ltxv-13b-0.9.8-dev-fp8.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-dev-fp8.yaml) | [ltxv-13b-i2v-base-fp8.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/ltxv-13b-i2v-base-fp8.json) |
| ltxv-13b-0.9.8-distilled-fp8     | Quantized version of ltxv-13b-distilled | [ltxv-13b-0.9.8-distilled-fp8.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-13b-0.9.8-distilled-fp8.yaml) | [ltxv-13b-dist-i2v-base-fp8.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/13b-distilled/ltxv-13b-dist-i2v-base-fp8.json) |
| ltxv-2b-0.9.8-distilled-fp8     | Quantized version of ltxv-2b-distilled | [ltxv-2b-0.9.8-distilled-fp8.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.8-distilled-fp8.yaml) | N/A |
| ltxv-2b-0.9.6                     | Good quality, lower VRAM requirement than ltxv-13b                                         | [ltxv-2b-0.9.6-dev.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.6-dev.yaml)                                                 | [ltxvideo-i2v.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/low_level/ltxvideo-i2v.json)             |
| ltxv-2b-0.9.6-distilled         | 15× faster, real-time capable, fewer steps needed, no STG/CFG required                     | [ltxv-2b-0.9.6-distilled.yaml](https://github.com/Lightricks/LTX-Video/blob/main/configs/ltxv-2b-0.9.6-distilled.yaml)                                     | [ltxvideo-i2v-distilled.json](https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/low_level/ltxvideo-i2v-distilled.json)             |


# Quick Start Guide

## Online inference
The model is accessible right away via the following links:
- [LTX-Studio image-to-video (13B-mix)](https://app.ltx.studio/motion-workspace?videoModel=ltxv-13b)
- [LTX-Studio image-to-video (13B distilled)](https://app.ltx.studio/motion-workspace?videoModel=ltxv)
- [Fal.ai image-to-video (13B full)](https://fal.ai/models/fal-ai/ltx-video-13b-dev/image-to-video)
- [Fal.ai image-to-video (13B distilled)](https://fal.ai/models/fal-ai/ltx-video-13b-distilled/image-to-video)
- [Replicate image-to-video](https://replicate.com/lightricks/ltx-video)

## Run locally

### Installation
The codebase was tested with Python 3.10.5, CUDA version 12.2, and supports PyTorch >= 2.1.2.
On macOS, MPS was tested with PyTorch 2.3.0, and should support PyTorch == 2.3 or >= 2.6.

```bash
git clone https://github.com/Lightricks/LTX-Video.git
cd LTX-Video

# create env
python -m venv env
source env/bin/activate
python -m pip install -e .\[inference\]
```

#### FP8 Kernels (optional)

[FP8 kernels](https://github.com/Lightricks/LTXVideo-Q8-Kernels) developed for LTX-Video provide performance boost on supported graphics cards (Ada architecture and later). To install FP8 kernels, follow the instructions in that repository.

### Inference

📝 **Note:** For best results, we recommend using our [ComfyUI](#comfyui-integration) workflow. We're working on updating the inference.py script to match the high quality and output fidelity of ComfyUI.

To use our model, please follow the inference code in [inference.py](./inference.py):

#### For image-to-video generation:

```bash
python inference.py --prompt "PROMPT" --conditioning_media_paths IMAGE_PATH --conditioning_start_frames 0 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml
```

#### Extending a video:

📝 **Note:** Input video segments must contain a multiple of 8 frames plus 1 (e.g., 9, 17, 25, etc.), and the target frame number should be a multiple of 8.


```bash
python inference.py --prompt "PROMPT" --conditioning_media_paths VIDEO_PATH --conditioning_start_frames START_FRAME --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml
```

#### For video generation with multiple conditions:

You can now generate a video conditioned on a set of images and/or short video segments.
Simply provide a list of paths to the images or video segments you want to condition on, along with their target frame numbers in the generated video. You can also specify the conditioning strength for each item (default: 1.0).

```bash
python inference.py --prompt "PROMPT" --conditioning_media_paths IMAGE_OR_VIDEO_PATH_1 IMAGE_OR_VIDEO_PATH_2 --conditioning_start_frames TARGET_FRAME_1 TARGET_FRAME_2 --height HEIGHT --width WIDTH --num_frames NUM_FRAMES --seed SEED --pipeline_config configs/ltxv-13b-0.9.8-distilled.yaml
```

### Using as a library

```python
from ltx_video.inference import infer, InferenceConfig

infer(
    InferenceConfig(
        pipeline_config="configs/ltxv-13b-0.9.8-distilled.yaml",
        prompt=PROMPT,
        height=HEIGHT,
        width=WIDTH,
        num_frames=NUM_FRAMES,
        output_path="output.mp4",
    )
)
```

## ComfyUI Integration
To use our model with ComfyUI, please follow the instructions at [https://github.com/Lightricks/ComfyUI-LTXVideo/](https://github.com/Lightricks/ComfyUI-LTXVideo/).

## Diffusers Integration
To use our model with the Diffusers Python library, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).

Diffusers also support an 8-bit version of LTX-Video, [see details below](#ltx-videoq8)

# Model User Guide

## 📝 Prompt Engineering

When writing prompts, focus on detailed, chronological descriptions of actions and scenes. Include specific movements, appearances, camera angles, and environmental details - all in a single flowing paragraph. Start directly with the action, and keep descriptions literal and precise. Think like a cinematographer describing a shot list. Keep within 200 words. For best results, build your prompts using this structure:

* Start with main action in a single sentence
* Add specific details about movements and gestures
* Describe character/object appearances precisely
* Include background and environment details
* Specify camera angles and movements
* Describe lighting and colors
* Note any changes or sudden events
* See [examples](#introduction) for more inspiration.

### Automatic Prompt Enhancement

When using `LTXVideoPipeline` directly, you can enable prompt enhancement by setting

... [README content truncated due to size. Visit the repository for the complete README] ...
          


wshobson/agents
Mon, 09 Feb 2026 00:07:45 GMT

            wshobson/agents
            Intelligent automation and multi-agent orchestration for Claude Code
            Language: Python
            Stars: 28,140
            Forks: 3,105
            Stars today: 95 stars today
            README
# Claude Code Plugins: Orchestration and Automation

> **⚡ Updated for Opus 4.5, Sonnet 4.5 & Haiku 4.5** — Three-tier model strategy for optimal performance

[![Run in Smithery](https://smithery.ai/badge/skills/wshobson)](https://smithery.ai/skills?ns=wshobson&utm_source=github&utm_medium=badge)

> **🎯 Agent Skills Enabled** — 146 specialized skills extend Claude's capabilities across plugins with progressive disclosure

A comprehensive production-ready system combining **112 specialized AI agents**, **16 multi-agent workflow orchestrators**, **146 agent skills**, and **79 development tools** organized into **73 focused, single-purpose plugins** for [Claude Code](https://docs.claude.com/en/docs/claude-code/overview).

## Overview

This unified repository provides everything needed for intelligent automation and multi-agent orchestration across modern software development:

- **73 Focused Plugins** - Granular, single-purpose plugins optimized for minimal token usage and composability
- **112 Specialized Agents** - Domain experts with deep knowledge across architecture, languages, infrastructure, quality, data/AI, documentation, business operations, and SEO
- **146 Agent Skills** - Modular knowledge packages with progressive disclosure for specialized expertise
- **16 Workflow Orchestrators** - Multi-agent coordination systems for complex operations like full-stack development, security hardening, ML pipelines, and incident response
- **79 Development Tools** - Optimized utilities including project scaffolding, security scanning, test automation, and infrastructure setup

### Key Features

- **Granular Plugin Architecture**: 73 focused plugins optimized for minimal token usage
- **Comprehensive Tooling**: 79 development tools including test generation, scaffolding, and security scanning
- **100% Agent Coverage**: All plugins include specialized agents
- **Agent Skills**: 146 specialized skills following for progressive disclosure and token efficiency
- **Clear Organization**: 23 categories with 1-6 plugins each for easy discovery
- **Efficient Design**: Average 3.4 components per plugin (follows Anthropic's 2-8 pattern)

### How It Works

Each plugin is completely isolated with its own agents, commands, and skills:

- **Install only what you need** - Each plugin loads only its specific agents, commands, and skills
- **Minimal token usage** - No unnecessary resources loaded into context
- **Mix and match** - Compose multiple plugins for complex workflows
- **Clear boundaries** - Each plugin has a single, focused purpose
- **Progressive disclosure** - Skills load knowledge only when activated

**Example**: Installing `python-development` loads 3 Python agents, 1 scaffolding tool, and makes 16 skills available (~1000 tokens), not the entire marketplace.

## Quick Start

### Step 1: Add the Marketplace

Add this marketplace to Claude Code:

```bash
/plugin marketplace add wshobson/agents
```

This makes all 73 plugins available for installation, but **does not load any agents or tools** into your context.

### Step 2: Install Plugins

Browse available plugins:

```bash
/plugin
```

Install the plugins you need:

```bash
# Essential development plugins
/plugin install python-development          # Python with 16 specialized skills
/plugin install javascript-typescript       # JS/TS with 4 specialized skills
/plugin install backend-development         # Backend APIs with 3 architecture skills

# Infrastructure & operations
/plugin install kubernetes-operations       # K8s with 4 deployment skills
/plugin install cloud-infrastructure        # AWS/Azure/GCP with 4 cloud skills

# Security & quality
/plugin install security-scanning           # SAST with security skill
/plugin install code-review-ai             # AI-powered code review

# Full-stack orchestration
/plugin install full-stack-orchestration   # Multi-agent workflows
```

Each installed plugin loads **only its specific agents, commands, and skills** into Claude's context.

### Plugins vs Agents

You install **plugins**, which bundle agents:

| Plugin                  | Agents                                            |
| ----------------------- | ------------------------------------------------- |
| `comprehensive-review`  | architect-review, code-reviewer, security-auditor |
| `javascript-typescript` | javascript-pro, typescript-pro                    |
| `python-development`    | python-pro, django-pro, fastapi-pro               |
| `blockchain-web3`       | blockchain-developer                              |

```bash
# ❌ Wrong - can't install agents directly
/plugin install typescript-pro

# ✅ Right - install the plugin
/plugin install javascript-typescript@claude-code-workflows
```

### Troubleshooting

**"Plugin not found"** → Use plugin names, not agent names. Add `@claude-code-workflows` suffix.

**Plugins not loading** → Clear cache and reinstall:

```bash
rm -rf ~/.claude/plugins/cache/claude-code-workflows && rm ~/.claude/plugins/installed_plugins.json
```

## Documentation

### Core Guides

- **[Plugin Reference](docs/plugins.md)** - Complete catalog of all 73 plugins
- **[Agent Reference](docs/agents.md)** - All 112 agents organized by category
- **[Agent Skills](docs/agent-skills.md)** - 146 specialized skills with progressive disclosure
- **[Usage Guide](docs/usage.md)** - Commands, workflows, and best practices
- **[Architecture](docs/architecture.md)** - Design principles and patterns

### Quick Links

- [Installation](#quick-start) - Get started in 2 steps
- [Essential Plugins](docs/plugins.md#quick-start---essential-plugins) - Top plugins for immediate productivity
- [Command Reference](docs/usage.md#command-reference-by-category) - All slash commands organized by category
- [Multi-Agent Workflows](docs/usage.md#multi-agent-workflow-examples) - Pre-configured orchestration examples
- [Model Configuration](docs/agents.md#model-configuration) - Haiku/Sonnet hybrid orchestration

## What's New

### Agent Teams Plugin (NEW)

Orchestrate multi-agent teams for parallel workflows using Claude Code's experimental Agent Teams feature:

```bash
/plugin install agent-teams@claude-code-workflows
```

- **7 Team Presets** — `review`, `debug`, `feature`, `fullstack`, `research`, `security`, `migration`
- **Parallel Code Review** — `/team-review src/ --reviewers security,performance,architecture`
- **Hypothesis-Driven Debugging** — `/team-debug "API returns 500" --hypotheses 3`
- **Parallel Feature Development** — `/team-feature "Add OAuth2 auth" --plan-first`
- **Research Teams** — Parallel investigation across codebase and web sources
- **Security Audits** — 4 reviewers covering OWASP, auth, dependencies, and secrets
- **Migration Support** — Coordinated migration with parallel streams and correctness verification

Includes 4 specialized agents, 7 commands, and 6 skills with reference documentation.

[→ View agent-teams documentation](plugins/agent-teams/README.md)

### Conductor Plugin — Context-Driven Development

Transforms Claude Code into a project management tool with a structured **Context → Spec & Plan → Implement** workflow:

```bash
/plugin install conductor@claude-code-workflows
```

- **Interactive Setup** — `/conductor:setup` creates product vision, tech stack, workflow rules, and style guides
- **Track-Based Development** — `/conductor:new-track` generates specifications and phased implementation plans
- **TDD Workflow** — `/conductor:implement` executes tasks with verification checkpoints
- **Semantic Revert** — `/conductor:revert` undoes work by logical unit (track, phase, or task)
- **State Persistence** — Resume setup across sessions with persistent project context
- **3 Skills** — Context-driven development, track management, workflow patterns

[→ View Conductor documentation](plugins/conductor/README.md)

### Agent Skills (146 skills across 21 plugins)

Specialized knowledge packages following Anthropic's progressive disclosure architecture:

**Language Development:**

- **Python** (5 skills): async patterns, testing, packaging, performance, UV package manager
- **JavaScript/TypeScript** (4 skills): advanced types, Node.js patterns, testing, modern ES6+

**Infrastructure & DevOps:**

- **Kubernetes** (4 skills): manifests, Helm charts, GitOps, security policies
- **Cloud Infrastructure** (4 skills): Terraform, multi-cloud, hybrid networking, cost optimization
- **CI/CD** (4 skills): pipeline design, GitHub Actions, GitLab CI, secrets management

**Development & Architecture:**

- **Backend** (3 skills): API design, architecture patterns, microservices
- **LLM Applications** (8 skills): LangGraph, prompt engineering, RAG, evaluation, embeddings, similarity search, vector tuning, hybrid search

**Blockchain & Web3** (4 skills): DeFi protocols, NFT standards, Solidity security, Web3 testing

**Project Management:**

- **Conductor** (3 skills): context-driven development, track management, workflow patterns

**And more:** Framework migration, observability, payment processing, ML operations, security scanning

[→ View complete skills documentation](docs/agent-skills.md)

### Three-Tier Model Strategy

Strategic model assignment for optimal performance and cost:

| Tier       | Model    | Agents | Use Case                                                                                        |
| ---------- | -------- | ------ | ----------------------------------------------------------------------------------------------- |
| **Tier 1** | Opus 4.5 | 42     | Critical architecture, security, ALL code review, production coding (language pros, frameworks) |
| **Tier 2** | Inherit  | 42     | Complex tasks - user chooses model (AI/ML, backend, frontend/mobile, specialized)               |
| **Tier 3** | Sonnet   | 51     | Support with intelligence (docs, testing, debugging, network, API docs, DX, legacy, payments)   |
| **Tier 4** | Haiku    | 18     | Fast operational tasks (SEO, deployment, simple docs, sales, content, search)                   |

**Why Opus 4.5 for Critical Agents?**

- 80.9% on SWE-bench (industry-leading)
- 65% fewer tokens for complex tasks
- Best for architecture decisions and security audits

**Tier 2 Flexibility (`inherit`):**
Agents marked `inherit` use your session's default model, letting you balance cost and capability:

- Set via `claude --model opus` or `claude --model sonnet` when starting a session
- Falls back to Sonnet 4.5 if no default specified
- Perfect for frontend/mobile developers who want cost control
- AI/ML engineers can choose Opus for complex model work

**Cost Considerations:**

- **Opus 4.5**: $5/$25 per million input/output tokens - Premium for critical work
- **Sonnet 4.5**: $3/$15 per million tokens - Balanced performance/cost
- **Haiku 4.5**: $1/$5 per million tokens - Fast, cost-effective operations
- Opus's 65% token reduction on complex tasks often offsets higher rate
- Use `inherit` tier to control costs for high-volume use cases

Orchestration patterns combine models for efficiency:

```
Opus (architecture) → Sonnet (development) → Haiku (deployment)
```

[→ View model configuration details](docs/agents.md#model-configuration)

## Popular Use Cases

### Full-Stack Feature Development

```bash
/full-stack-orchestration:full-stack-feature "user authentication with OAuth2"
```

Coordinates 7+ agents: backend-architect → database-architect → frontend-developer → test-automator → security-auditor → deployment-engineer → observability-engineer

[→ View all workflow examples](docs/usage.md#multi-agent-workflow-examples)

### Security Hardening

```bash
/security-scanning:security-hardening --level comprehensive
```

Multi-agent security assessment with SAST, dependency scanning, and code review.

### Python Development with Modern Tools

```bash
/python-development:python-scaffold fastapi-microservice
```

Creates production-ready FastAPI project with async patterns, activating skills:

- `async-python-patterns` - AsyncIO and concurrency
- `python-testing-patterns` - pytest and fixtures
- `uv-package-manager` - Fast dependency management

### Kubernetes Deployment

```bash
# Activates k8s skills automatically
"Create production Kubernetes deployment with Helm chart and GitOps"
```

Uses kubernetes-architect agent with 4 specialized skills for production-grade configs.

[→ View complete usage guide](docs/usage.md)

## Plugin Categories

**24 categories, 73 plugins:**

- 🎨 **Development** (4) - debugging, backend, frontend, multi-platform
- 📚 **Documentation** (3) - code docs, API specs, diagrams, C4 architecture
- 🔄 **Workflows** (5) - git, full-stack, TDD, **Conductor** (context-driven development), **Agent Teams** (multi-agent orchestration)
- ✅ **Testing** (2) - unit testing, TDD workflows
- 🔍 **Quality** (3) - code review, comprehensive review, performance
- 🤖 **AI & ML** (4) - LLM apps, agent orchestration, context, MLOps
- 📊 **Data** (2) - data engineering, data validation
- 🗄️ **Database** (2) - database design, migrations
- 🚨 **Operations** (4) - incident response, diagnostics, distributed debugging, observability
- ⚡ **Performance** (2) - application performance, database/cloud optimization
- ☁️ **Infrastructure** (5) - deployment, validation, Kubernetes, cloud, CI/CD
- 🔒 **Security** (4) - scanning, compliance, backend/API, frontend/mobile
- 💻 **Languages** (7) - Python, JS/TS, systems, JVM, scripting, functional, embedded
- 🔗 **Blockchain** (1) - smart contracts, DeFi, Web3
- 💰 **Finance** (1) - quantitative trading, risk management
- 💳 **Payments** (1) - Stripe, PayPal, billing
- 🎮 **Gaming** (1) - Unity, Minecraft plugins
- 📢 **Marketing** (4) - SEO content, technical SEO, SEO analysis, content marketing
- 💼 **Business** (3) - analytics, HR/legal, customer/sales
- And more...

[→ View complete plugin catalog](docs/plugins.md)

## Architecture Highlights

### Granular Design

- **Single responsibility** - Each plugin does one thing well
- **Minimal token usage** - Average 3.4 components per plugin
- **Composable** - Mix and match for complex workflows
- **100% coverage** - All 112 agents accessible across plugins

### Progressive Disclosure (Skills)

Three-tier architecture for token efficiency:

1. **Metadata** - Name and activation criteria (always loaded)
2. **Instructions** - Core guidance (loaded when activated)
3. **Resources** - Examples and templates (loaded on demand)

### Repository Structure

```
claude-agents/
├── .claude-plugin/
│   └── marketplace.json          # 73 plugins
├── plugins/
│   ├── python-development/
│   │   ├── agents/               # 3 Python experts
│   │   ├── commands/             # Scaffolding tool
│   │   └── skills/               # 5 specialized skills
│   ├── kubernetes-operations/
│   │   ├── agents/               # K8s architect
│   │   ├── commands/             # Deployment tools
│   │   └── skills/               # 4 K8s skills
│   └── ... (65 more plugins)
├── docs/                          # Comprehensive documentation
└── README.md                      # This file
```

[→ View architecture details](docs/architecture.md)

## Contributing

To add new agents, skills, or commands:

1. Identify or create the appropriate plugin directory in `plugins/`
2. Create `.md` files in the appropriate subdirectory:
   - `agents/` - For specialized agents
   - `commands/` - For tools and workflows
   - `skills/` - For modular knowledge packages
3. Follow naming conventions (lowercase, hyphen-separated)
4. Write clear activation criteria and comprehensive content
5. Update the plugin definition in `.claude-plugin/marketplace.json`

See [Architecture Documentation](docs/architecture.md) for detailed guidelines.

## Resources

### Documentation

- [Claude Code Documentation](https://docs.claude.com/en/docs/claude-code/overview)
- [Plugins Guide](https://docs.claude.com/en/docs/claude-code/plugins)
- [Subagents Guide](https://docs.claude.com/en/docs/claude-code/sub-agents)
- [Agent Skills Guide](https://docs.claude.com/en/docs/agents-and-tools/agent-skills/overview)
- [Slash Commands Reference](https://docs.claude.com/en/docs/claude-code/slash-commands)

### This Repository

- [Plugin Reference](docs/plugins.md)
- [Agent Reference](docs/agents.md)
- [Agent Skills Guide](docs/agent-skills.md)
- [Usage Guide](docs/usage.md)
- [Architecture](docs/architecture.md)

## License

MIT License - see [LICENSE](LICENSE) file for details.

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=wshobson/agents&type=date&legend=top-left)](https://www.star-history.com/#wshobson/agents&type=date&legend=top-left)

          


huggingface/speech-to-speech
Mon, 09 Feb 2026 00:07:44 GMT

            huggingface/speech-to-speech
            Speech To Speech: an effort for an open-sourced and modular GPT4-o
            Language: Python
            Stars: 4,428
            Forks: 500
            Stars today: 35 stars today
            README

   
   


# Speech To Speech: an effort for an open-sourced and modular GPT4-o


## 📖 Quick Index
* [Approach](#approach)
  - [Structure](#structure)
  - [Modularity](#modularity)
* [Setup](#setup)
* [Usage](#usage)
  - [Docker Server approach](#docker-server)
  - [Server/Client approach](#serverclient-approach)
  - [Local approach](#local-approach-running-on-mac)
* [Command-line usage](#command-line-usage)
  - [Model parameters](#model-parameters)
  - [Generation parameters](#generation-parameters)
  - [Notable parameters](#notable-parameters)

## Approach

### Structure
This repository implements a speech-to-speech cascaded pipeline consisting of the following parts:
1. **Voice Activity Detection (VAD)**
2. **Speech to Text (STT)**
3. **Language Model (LM)**
4. **Text to Speech (TTS)**

### Modularity
The pipeline provides a fully open and modular approach, with a focus on leveraging models available through the Transformers library on the Hugging Face hub. The code is designed for easy modification, and we already support device-specific and external library implementations:

**VAD** 
- [Silero VAD v5](https://github.com/snakers4/silero-vad)

**STT**
- Any [Whisper](https://huggingface.co/docs/transformers/en/model_doc/whisper) model checkpoint on the Hugging Face Hub through Transformers 🤗, including [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) and [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
- [Lightning Whisper MLX](https://github.com/mustafaaljadery/lightning-whisper-mlx?tab=readme-ov-file#lightning-whisper-mlx)
- [Paraformer - FunASR](https://github.com/modelscope/FunASR)

**LLM**
- Any instruction-following model on the [Hugging Face Hub](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending) via Transformers 🤗
- [mlx-lm](https://github.com/ml-explore/mlx-examples/blob/main/llms/README.md)
- [OpenAI API](https://platform.openai.com/docs/quickstart)

**TTS**
- [Parler-TTS](https://github.com/huggingface/parler-tts) 🤗
- [MeloTTS](https://github.com/myshell-ai/MeloTTS)
- [ChatTTS](https://github.com/2noise/ChatTTS?tab=readme-ov-file)

## Setup

Clone the repository:
```bash
git clone https://github.com/huggingface/speech-to-speech.git
cd speech-to-speech
```

Install the required dependencies using [uv](https://github.com/astral-sh/uv):
```bash
uv pip install -r requirements.txt
```

For Mac users, use the `requirements_mac.txt` file instead:
```bash
uv pip install -r requirements_mac.txt
```

If you want to use Melo TTS, you also need to run:
```bash
python -m unidic download
```


## Usage

The pipeline can be run in two ways:
- **Server/Client approach**: Models run on a server, and audio input/output are streamed from a client.
- **Local approach**: Runs locally.

### Recommended setup 

### Server/Client Approach

1. Run the pipeline on the server:
   ```bash
   python s2s_pipeline.py --recv_host 0.0.0.0 --send_host 0.0.0.0
   ```

2. Run the client locally to handle microphone input and receive generated audio:
   ```bash
   python listen_and_play.py --host 
   ```

### Local Approach (Mac)

1. For optimal settings on Mac:
   ```bash
   python s2s_pipeline.py --local_mac_optimal_settings
   ```

This setting:
   - Adds `--device mps` to use MPS for all models.
     - Sets LightningWhisperMLX for STT
     - Sets MLX LM for language model
     - Sets MeloTTS for TTS

### Docker Server

#### Install the NVIDIA Container Toolkit

https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

#### Start the docker container
```docker compose up```



### Recommended usage with Cuda

Leverage Torch Compile for Whisper and Parler-TTS. **The usage of Parler-TTS allows for audio output streaming, further reducing the overall latency** 🚀:

```bash
python s2s_pipeline.py \
	--lm_model_name microsoft/Phi-3-mini-4k-instruct \
	--stt_compile_mode reduce-overhead \
	--tts_compile_mode default \
  --recv_host 0.0.0.0 \
	--send_host 0.0.0.0 
```

For the moment, modes capturing CUDA Graphs are not compatible with streaming Parler-TTS (`reduce-overhead`, `max-autotune`).

### Multi-language Support

The pipeline currently supports English, French, Spanish, Chinese, Japanese, and Korean.  
Two use cases are considered:

- **Single-language conversation**: Enforce the language setting using the `--language` flag, specifying the target language code (default is 'en').
- **Language switching**: Set `--language` to 'auto'. In this case, Whisper detects the language for each spoken prompt, and the LLM is prompted with "`Please reply to my message in ...`" to ensure the response is in the detected language.

Please note that you must use STT and LLM checkpoints compatible with the target language(s). For the STT part, Parler-TTS is not yet multilingual (though that feature is coming soon! 🤗). In the meantime, you should use Melo (which supports English, French, Spanish, Chinese, Japanese, and Korean) or Chat-TTS.

#### With the server version:

For automatic language detection:

```bash
python s2s_pipeline.py \
    --stt_model_name large-v3 \
    --language auto \
    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct \
```

Or for one language in particular, chinese in this example

```bash
python s2s_pipeline.py \
    --stt_model_name large-v3 \
    --language zh \
    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct \
```

#### Local Mac Setup

For automatic language detection:

```bash
python s2s_pipeline.py \
    --local_mac_optimal_settings \
    --device mps \
    --stt_model_name large-v3 \
    --language auto \
    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct-4bit \
```

Or for one language in particular, chinese in this example

```bash
python s2s_pipeline.py \
    --local_mac_optimal_settings \
    --device mps \
    --stt_model_name large-v3 \
    --language zh \
    --mlx_lm_model_name mlx-community/Meta-Llama-3.1-8B-Instruct-4bit \
```

## Command-line Usage

> **_NOTE:_** References for all the CLI arguments can be found directly in the [arguments classes](https://github.com/huggingface/speech-to-speech/tree/d5e460721e578fef286c7b64e68ad6a57a25cf1b/arguments_classes) or by running `python s2s_pipeline.py -h`.

### Module level Parameters 
See [ModuleArguments](https://github.com/huggingface/speech-to-speech/blob/d5e460721e578fef286c7b64e68ad6a57a25cf1b/arguments_classes/module_arguments.py) class. Allows to set:
- a common `--device` (if one wants each part to run on the same device)
- `--mode` `local` or `server`
- chosen STT implementation 
- chosen LM implementation
- chose TTS implementation
- logging level

### VAD parameters
See [VADHandlerArguments](https://github.com/huggingface/speech-to-speech/blob/d5e460721e578fef286c7b64e68ad6a57a25cf1b/arguments_classes/vad_arguments.py) class. Notably:
- `--thresh`: Threshold value to trigger voice activity detection.
- `--min_speech_ms`: Minimum duration of detected voice activity to be considered speech.
- `--min_silence_ms`: Minimum length of silence intervals for segmenting speech, balancing sentence cutting and latency reduction.


### STT, LM and TTS parameters

`model_name`, `torch_dtype`, and `device` are exposed for each implementation of the Speech to Text, Language Model, and Text to Speech. Specify the targeted pipeline part with the corresponding prefix (e.g. `stt`, `lm` or `tts`, check the implementations' [arguments classes](https://github.com/huggingface/speech-to-speech/tree/d5e460721e578fef286c7b64e68ad6a57a25cf1b/arguments_classes) for more details).

For example:
```bash
--lm_model_name google/gemma-2b-it
```

### Generation parameters

Other generation parameters of the model's generate method can be set using the part's prefix + `_gen_`, e.g., `--stt_gen_max_new_tokens 128`. These parameters can be added to the pipeline part's arguments class if not already exposed.

## Citations

### Silero VAD
```bibtex
@misc{Silero VAD,
  author = {Silero Team},
  title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/snakers4/silero-vad}},
  commit = {insert_some_commit_here},
  email = {hello@silero.ai}
}
```

### Distil-Whisper
```bibtex
@misc{gandhi2023distilwhisper,
      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling},
      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
      year={2023},
      eprint={2311.00430},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

### Parler-TTS
```bibtex
@misc{lacombe-etal-2024-parler-tts,
  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
  title = {Parler-TTS},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/parler-tts}}
}
```

          


gyoridavid/ai_agents_az
Mon, 09 Feb 2026 00:07:43 GMT

            gyoridavid/ai_agents_az
            
            Language: Python
            Stars: 3,325
            Forks: 818
            Stars today: 49 stars today
            README
# AI Agents A-Z

In this repo, you can find the n8n templates we created for the episodes of [AI Agents A-Z](https://www.youtube.com/channel/UCloXqLhp_KGhHBe1kwaL2Tg).

## Season 1

- [Episode 1: Creating a prescription agent](episode_1)
- [Episode 2: Making a daily digest agent](episode_2)
- [Episode 3: Making LinkedIn posts using Human in the Loop approval process](episode_3)
- [Episode 4: Deep Research Agent using Google](episode_4)
- [Episode 5: Creating a blog writing system using deep research](episode_5)
- [Episode 6: Lead generation with X-Ray search and LinkedIn](episode_6)
- [Episode 7: Creating Youtube short videos using our custom MCP server](episode_7)
- [Episode 8: Creating an AI influencer on Instagram using n8n](episode_8)
- [Episode 9: Create revenge story videos for YouTube](episode_9)
- [Episode 10: n8n best practices](episode_10)
- [Episode 11: Create short (motivational) stories for YouTube and TikTok](episode_11)
- [Episode 12: Scheduling social media posts with Postiz and n8n](episode_12)
- [Episode 13: Create AI videos with MiniMax Hailuo 2 and n8n](episode_13)
- [Episode 14: Create AI videos with Seedance and n8n](episode_14)
- [Episode 15: Generate AI startup ideas from Reddit](episode_15)
- [Episode 16: Create AI poem videos with n8n for TikTok](episode_16)
- [Episode 17: Create Shopify product videos with Seedance, ElevenLabs, Latentsync, Flux Kontext and n8n](episode_17)
- [Episode 18: Scary story TikTok videos workflow](episode_18)
- [Episode 19: Run FLUX.1 Kontext [dev] with modal.com](episode_19)
- [Episode 20: Use Wan 2.2, ComfyUI and n8n to generate videos for free](episode_20)
- [Episode 21: 10 EASY faceless niches that pay well - monetize in a MONTH (2025)](episode_21)
- [Episode 22: Sleep long-form videos with GPT-5, ElevenMusic, Imagen4, Seendance and n8n](episode_22)
- [Episode 23: UGC videos with nanobanana and n8n](episode_23)
- [Episode 24: generate images with Qwen Image, Flux.1 [dev] and Flux.1 Schnell with modal.com and Cloudflare Workers AI](episode_24)
- [Episode 25: Fal.ai n8n subworkflows for Qwen Image Edit Plus and Wan 2.2 animate](episode_25)
- [Episode 31: Veo 3.1 is now in n8n - how to use it for FREE](episode_31)
- [Episode 35: Instagram influencer machine](episode_35)
- [Episode 36: Viral bodycam footage creator with Sora 2](episode_36)
- [Episode 38: Create AI reaction videos with Veo 3.1 and n8n](episode_38)
- [Episode 39: Create infographics with Nano Banana Pro in n8n](episode_39)
- [Episode 40: Flux.2[dev] with n8n](episode_40)
- [Episode 41: FREE z-image-turbo with n8n](episode_41)
- [Episode 42: 100% FREE explainer videos with n8n and Z-Image](episode_42)

## servers

- [AI Agents No-Code Tools](https://hub.docker.com/r/gyoridavid/ai-agents-no-code-tools)
- [Short video maker MCP/REST server](https://github.com/gyoridavid/short-video-maker)
- [Narrated story creator REST/MCP server](https://hub.docker.com/r/gyoridavid/narrated-story-creator)