# Open-Source Alternatives to Cluely

> AI-ranked search results for `open source alternatives to cluely` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 117 total matches; showing the top 12.

Explore on the web: https://awesome-repositories.com/q/open-source-alternatives-to-cluely

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/open-source-alternatives-to-cluely).**

## Results

- [sevask/ecoute](https://awesome-repositories.com/repository/sevask-ecoute.md) (6,036 ⭐) — Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and the user's speakers output (Speaker) in a textbox.
- [zackriya-solutions/meeting-minutes](https://awesome-repositories.com/repository/zackriya-solutions-meeting-minutes.md) (12,757 ⭐) — This project is a self-hosted meeting transcription and summarization tool that converts audio recordings into text transcripts and structured notes using large language models. It functions as an enterprise meeting documentation manager, allowing for the organization and editing of timestamped records.

The system prioritizes data privacy through local-first processing and the ability to deploy on private infrastructure. It supports a provider-agnostic architecture, enabling users to connect to local AI engines, self-hosted servers, or cloud-based API endpoints for both transcription and summ
- [quentinfuxa/whisperlivekit](https://awesome-repositories.com/repository/quentinfuxa-whisperlivekit.md) (10,475 ⭐) — WhisperLiveKit is a real-time speech-to-text server that transcribes streaming audio into text with ultra-low latency using Whisper models. It serves transcription capabilities through REST endpoints and WebSocket connections, enabling external applications to send audio and receive transcriptions as words are spoken, making it suitable for live captioning or voice interfaces.

The project distinguishes itself by combining real-time transcription with speaker diarization, assigning transcribed words to individual speakers during live audio streams for meeting or interview transcripts. It also
- [thewh1teagle/vibe](https://awesome-repositories.com/repository/thewh1teagle-vibe.md) (5,298 ⭐) — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models directly on your device, with no cloud dependency. It can transcribe audio from files, microphones, system output, and network streams, and supports both batch processing of multiple files and real-time captioning from continuous input.

Beyond basic transcription, Vibe identifies and labels different speakers through speaker diarization, and offers a choice of Command-Line Interface or HTTP API for automated and remote workflows. It also includes plugins to export transcripts to c
- [sohzm/cheating-daddy](https://awesome-repositories.com/repository/sohzm-cheating-daddy.md) (5,387 ⭐) — This project is an AI meeting assistant and interview copilot that monitors system audio and screen content to generate real-time responses during video calls. It functions as a system audio transcription tool and a context-aware prompt manager, injecting user documents and behavioral profiles into large language model prompts to tailor AI outputs.

The system features a stealth screen overlay, utilizing a transparent window that displays information on top of other applications while remaining invisible to screen-sharing software and proctoring tools. It employs a process-hiding mechanism to
- [basedhardware/omi](https://awesome-repositories.com/repository/basedhardware-omi.md) (12,869 ⭐) — Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions.

The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio
- [chenyme/chenyme-aavt](https://awesome-repositories.com/repository/chenyme-chenyme-aavt.md) (2,928 ⭐) — Chenyme-AAVT is an AI-powered video transcription tool and translation platform. It converts speech from media files into editable text transcripts using speech recognition models and voice activity detection to ensure accurate phrasing and timing.

The system functions as a content generator that transforms video transcripts into structured blog posts and marketing graphics using large language models. It also includes a subtitle formatting editor that allows for the modification of subtitle styles with a real-time video preview.

The platform provides multilingual translation capabilities th
- [collabora/whisperlive](https://awesome-repositories.com/repository/collabora-whisperlive.md) (3,819 ⭐) — WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It functions as a backend service that receives microphone input via WebSockets and provides incremental transcriptions with word-level timestamps.

The system utilizes a GPU-accelerated inference engine and a keyword-boosted transcription API to improve the recognition accuracy of domain-specific jargon, acronyms, and product names. It also includes a speaker diarization tool that clusters audio embeddings to identify and label different participants within a recording.

Additiona
- [moonshine-ai/moonshine](https://awesome-repositories.com/repository/moonshine-ai-moonshine.md) (8,527 ⭐) — Moonshine is a complete on-device voice interface toolkit that provides speech recognition, text-to-speech synthesis, phonetic processing, speaker diarization, and intent recognition, all running locally on edge hardware without any cloud dependency. It executes quantized neural networks for speech and language tasks directly on the device, enabling fully offline conversational AI capabilities.

The toolkit distinguishes itself by orchestrating multi-turn spoken exchanges through a conversational flow manager that maintains context across interactions and manages branching dialog flows. It inc
- [leetcode-mafia/cheetah](https://awesome-repositories.com/repository/leetcode-mafia-cheetah.md) (4,262 ⭐) — Cheetah is an LLM technical interview assistant composed of a native macOS application and a browser extension. It provides real-time coding and answering suggestions during technical interviews by combining live audio transcription with web-based context extraction.

The system functions as a real-time interview coach that converts spoken questions into text using on-device speech-to-text processing. It uses a browser-integrated DOM scraper to extract live code and console logs, allowing the AI to analyze the current coding state and generate technical solutions based on the specific environm
- [ten-framework/ten-framework](https://awesome-repositories.com/repository/ten-framework-ten-framework.md) (10,701 ⭐) — Ten Framework is a multimodal large language model agent framework designed for building low-latency conversational agents. It integrates voice, text, and visual inputs in real time to facilitate human interaction.

The project includes a real-time speech processing pipeline for streaming transcription, voice activity detection, and speaker diarization. It also features an avatar synchronization engine that coordinates character lip animations and visual outputs with synthesized speech.

The framework covers edge AI deployment through containerized packaging and direct integration with embedde
- [ggerganov/whisper.cpp](https://awesome-repositories.com/repository/ggerganov-whisper-cpp.md) (50,791 ⭐) — whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning inference engine and quantized runtime. It provides high-performance automatic speech recognition and real-time audio transcription without requiring a Python environment.

The project utilizes model quantization to reduce memory usage and increase inference speed on local hardware. It incorporates hardware acceleration to optimize processing speed across different processors.

The system covers audio processing capabilities including voice activity detection, speaker diarization,