Open Source C Artificial Intelligence Software
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C Artificial Intelligence Software
View 17128 business solutionsBrowse free open source C Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source C Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Ollama
Run models like Kimi-K2.5, GLM-5, DeepSeek, gpt-oss, Gemma, Qwen etc.
Ollama is an open-source platform that enables developers to run large language models locally on their own machines. It simplifies working with modern AI models by providing a unified interface to download, manage, and interact with them. Users can run models like Llama, Gemma, Qwen, and others directly from the command line or through APIs. Ollama also integrates with popular developer tools and AI agents, allowing seamless workflows across coding environments and applications. It supports REST APIs, Python, and JavaScript SDKs, making it easy to build AI-powered features into software projects. Overall, Ollama focuses on privacy, local-first AI execution, and developer-friendly tooling for building with open models. -
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. -
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CLIPS Rule Based Programming Language
Expert System Tool
CLIPS is a forward-chaining rule-based programming language written in C that also provides procedural and object-oriented programming facilities. -
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WanGP
AI video generator optimized for low VRAM and older GPUs use
Wan2GP is an open source AI video generation toolkit designed to make modern generative models accessible on consumer-grade hardware with limited GPU memory. It acts as a unified interface for running multiple video, image, and audio generation models, including Wan-based models as well as other systems like Hunyuan Video, Flux, and Qwen. A key focus of the project is reducing VRAM requirements, enabling some workflows to run on as little as 6 GB while still supporting older Nvidia and certain AMD GPUs. Wan2GP provides a full web-based interface that simplifies interaction with complex generative pipelines, making it easier to configure prompts, models, and rendering settings. It also integrates a wide range of utilities such as prompt enhancement, mask editing, motion design, and extraction tools for pose, depth, and flow data to support advanced video workflows. -
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llama.cpp
Port of Facebook's LLaMA model in C/C++
The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments. -
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PNotes is light-weight, flexible, skinnable manager of virtual notes on your desktop. It supports multiple languages, individual note's settings, transparency and scheduling. Absolutely portable as well - no traces in registry. PNotes.NET edition requires .NET framework 4 Client Profile
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CLISP is a portable ANSI Common Lisp implementation and development environment by Bruno Haible. Interpreter, compiler, debugger, CLOS, MOP, FFI, Unicode, sockets, CLX. UI in English, German, French, Spanish, Dutch, Russian, and Danish.
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Colibrì
Run frontier MoE models on hardware you already own
Colibrì is an open-source inference engine designed to run frontier-scale mixture-of-experts (MoE) models on consumer and heterogeneous hardware. It treats VRAM, system RAM, and NVMe storage as a unified memory hierarchy, dynamically placing and streaming model weights where they can be accessed most efficiently. This architecture enables users to run models ranging from 7 billion to 2.8 trillion parameters without requiring the entire model to fit in expensive GPU memory. Written primarily in pure C, Colibrì has no core engine dependencies and can operate without a GPU, although CUDA, Metal, Vulkan, and other acceleration options can improve performance. A consistent command-line interface, OpenAI-compatible API, web dashboard, and desktop application support multiple model families, including GLM, DeepSeek, Kimi, Qwen, Inkling, and OLMoE. Colibrì also serves as an open research platform for testing model placement, caching, compression, speculative decoding, storage I/O, & more. -
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eGuideDog project develops free software for the blind. Currently, we focus on WebSpeech, Ekho TTS and WebAnywhere.
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Open JTalk is a Japanese text-to-speech synthesis system. This software is released under the Modified BSD license.
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pgvector
Open-source vector similarity search for Postgres
pgvector is an open-source PostgreSQL extension that equips PostgreSQL databases with vector data storage, indexing, and similarity search capabilities—ideal for embeddings-based applications like semantic search and recommendations. You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index. An HNSW index creates a multilayer graph. It has better query performance than IVFFlat (in terms of speed-recall tradeoff), but has slower build times and uses more memory. Also, an index can be created without any data in the table since there isn’t a training step like IVFFlat. -
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Darknet YOLO
Real-Time Object Detection for Windows and Linux
This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project. -
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IIDC Camera Control Library
Capture and control API for IIDC compliant cameras
libdc1394 is a library that provides a high level programming interface for application developers who wish to control and capture streams from IEEE 1394 based cameras that conform to the 1394-based Digital Camera Specifications (also known as the IIDC or DCAM Specifications). libdc1394 also supports some USB cameras that are IIDC compliant. Besides capture and control, libdc1394 provides a full set of colour space conversion functions (including RAW decoding), vendor specific functions and direct camera register access. Keywords: ieee1394, IIDC, DCAM, firewire, USB, machine vision, computer vision, video capture, library -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint. -
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codebase-memory-mcp
High-performance code intelligence MCP server
codebase-memory-mcp is a high-performance code intelligence MCP server built for AI coding agents. It indexes codebases into a persistent knowledge graph so agents can understand architecture, symbols, call chains, routes, and cross-service relationships without reading every file repeatedly. The project uses tree-sitter AST analysis across 158 languages and adds Hybrid LSP type resolution for major languages like Python, TypeScript, JavaScript, Go, C#, Java, Rust, C, and C++. It is built for speed, with average repositories indexed in milliseconds and structural queries answered in under one millisecond. It ships as a single static binary for macOS, Linux, and Windows with no shared library dependencies. It also includes semantic search, BM25 search, graph visualization, auto-indexing, and cross-repository intelligence. -
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Kimi K3 in C
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU
Kimi K3 in C is a portable C99 inference engine built to run the 2.78-trillion-parameter Kimi K3 model on CPUs without BLAS, machine-learning frameworks, or GPUs. It demonstrates inference from a roughly 1.56 TB checkpoint with measured memory use as low as 8.24 GB. The runtime streams model trunk layers and routed experts from disk instead of keeping all weights resident in memory. Memory presets balance pinned layers and an expert LRU cache for laptops, desktops, workstations, and servers. Incremental generation preserves KV cache and recurrent state between tokens. The repository also includes tokenization, safetensors loading, diagnostics, benchmarks, trace replay, and comparisons against a PyTorch reference. -
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FLUX.2-klein-4B
Flux 2 image generation model pure C inference
FLUX.2-klein-4B is a compact, high-performance C library implementation of the Flux optimization algorithm — an iterative approach for solving large-scale optimization problems common in scientific computing, machine learning, and numerical simulation. Written with a strong emphasis on simplicity, correctness, and performance, it abstracts the core logic of flux-based optimization into a minimal C API that can be embedded in broader applications without pulling in heavy dependencies. Because the implementation is in plain C and focuses on data locality and vectorized operations, flux2.c can be integrated into performance-critical code paths where control over memory layout and execution behavior matters, such as GPU kernels, embedded systems, or custom ML runtime engines. -
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reacTIVision is a computer vision framework for the fast and robust tracking of markers attached on physical objects, and the creation of multi-touch surfaces. It was designed for the rapid development of table-based tangible user interfaces.
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Presage
the intelligent predictive text entry platform
Presage (formerly Soothsayer) is an intelligent predictive text entry system. Presage generates predictions by modelling natural language as a combination of redundant information sources. Presage computes probabilities for words which are most likely to be entered next by merging predictions generated by the different predictive algorithms. Presage's modular and extensible architecture allows its language model to be extended and customized to utilize statistical, syntactic, and semantic predictive algorithms. Presage's predictive capabilities are implemented by predictive plugins. Predictive plugins use services provided by the platform to implement multiple prediction techniques. -
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Mozilla JPEG Encoder Project
Improved JPEG encoder
MozJPEG improves JPEG compression efficiency achieving higher visual quality and smaller file sizes at the same time. It is compatible with the JPEG standard, and the vast majority of the world's deployed JPEG decoders. MozJPEG is compatible with the libjpeg API and ABI. It is intended to be a drop-in replacement for libjpeg. MozJPEG is a strict superset of libjpeg-turbo's functionality. All MozJPEG's improvements can be disabled at run time, and in that case it behaves exactly like libjpeg-turbo. MozJPEG is meant to be used as a library in graphics programs and image processing tools. We include a demo cjpeg command-line tool, but it's not intended for serious use. We encourage authors of graphics programs to use libjpeg's C API and link with MozJPEG library instead. Progressive encoding with "jpegrescan" optimization. It can be applied to any JPEG file (with jpegtran) to losslessly reduce file size. -
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GNU Prolog
The GNU Prolog native compiler
GNU Prolog is a free implementation (under GPL) of the logic programming language PROLOG. It can compile to native machine code which is extremely fast in execution. Another feature is the included constraint solver. -
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Kaldi
kaldi-asr/kaldi is the official location of the Kaldi project
Kaldi is an open source toolkit for speech recognition research. It provides a powerful framework for building state-of-the-art automatic speech recognition (ASR) systems, with support for deep neural networks, Gaussian mixture models, hidden Markov models, and other advanced techniques. The toolkit is widely used in both academia and industry due to its flexibility, extensibility, and strong community support. Kaldi is designed for researchers who need a highly customizable environment to experiment with new algorithms, as well as for practitioners who want robust, production-ready ASR pipelines. It includes extensive tools for data preparation, feature extraction, acoustic and language modeling, decoding, and evaluation. With its modular design, Kaldi allows users to adapt the system to a wide range of languages and domains. As one of the most influential projects in speech recognition, it has become a foundation for much of the modern work in ASR. -
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Flora-2 is a powerful knowledge representation and reasoning system designed for building knowledge-intensive applications. It is based on F-logic, HiLog, Transaction Logic, and also supports defeasible reasoning. Applications include intelligent agents, Semantic Web, ontology management, and more. If you use Flora-2 and like it, please acknowledge it in your project!
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llama2.c
Inference Llama 2 in one file of pure C
llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). While it can technically load Meta’s official Llama 2 models, current support is limited to fp32 precision, meaning practical use is capped at models up to around 7B parameters. The goal of llama2.c is to demonstrate how a compact and transparent implementation can perform meaningful inference even with small models, emphasizing simplicity, clarity, and accessibility. The project builds upon lessons from nanoGPT and takes inspiration from llama.cpp, focusing instead on minimalism and educational value over large-scale performance. -
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shadowhook
Android inline hook library which supports thumb, arm32 and arm64
shadowhook is an open-source native code hooking library for Android — designed to let developers intercept and override native (C/C++) functions inside Android apps at runtime. It supports both ARM32 and ARM64 architectures (and the respective “thumb” instruction set) and works across a wide range of Android OS versions. The library allows you to specify hook targets either by function address or by library name + function name, and it automatically handles newly loaded shared libraries (ELFs), ensuring hooks remain effective even when code is dynamically loaded at runtime. ShadowHook strives to be stable and safe for production use: it automatically avoids circular-call recursion between original and proxy functions, and supports unwind/unwind-safe proxy functions (CFI/FP unwind) so that backtraces, debugging, and stack unwinding remain correct. It also offers optional intercept and logging capabilities, letting developers record hooked calls.
