@wsl8297: Found a '100% local' open-source alternative to Manus AI on GitHub: AgenticSeek. It bundles a local inference model with an intelligent agent system—capable of browsing the web, searching information, filling forms, writing code, making plans, and supporting voice conversations. The overall experience is very close to the movie-like Jarvis assistant that 'understands and takes action.'

English | 中文 | 繁體中文 | Français | 日本語 | Português (Brasil) | Español | Türkçe A 100% local alternative to Manus AI, this voice-enabled AI assistant autonomously browses the web, writes code, and plans tasks while keeping all data on your device. Tailored for local reasoning models, it runs entirely on your hardware, ensuring complete privacy and zero cloud dependency. Visit AgenticSeek (https://fosowl.github.io/agenticSeek.html) License Discord (https://discord.gg/8hGDaME3TC) Twitter (https://x.com/Martin993886460) GitHub stars (https://github.com/Fosowl/agenticSeek/stargazers) ### Why AgenticSeek ? * 🔒 Fully Local & Private - Everything runs on your machine — no cloud, no data sharing. Your files, conversations, and searches stay private. * 🌐 Smart Web Browsing - AgenticSeek can browse the internet by itself — search, read, extract info, fill web form — all hands-free. * 💻 Autonomous Coding Assistant - Need code? It can write, debug, and run programs in Python, C, Go, Java, and more — all without supervision. * 🧠 Smart Agent Selection - You ask, it figures out the best agent for the job automatically. Like having a team of experts ready to help. * 📋 Plans & Executes Complex Tasks - From trip planning to complex projects — it can split big tasks into steps and get things done using multiple AI agents. * 🎙️ Voice-Enabled - Clean, fast, futuristic voice and speech to text allowing you to talk to it like it’s your personal AI from a sci-fi movie. (In progress) ### Demo > Can you search for the agenticSeek project, learn what skills are required, then open the CV_candidates.zip and then tell me which match best the project https://github.com/user-attachments/assets/b8ca60e9-7b3b-4533-840e-08f9ac426316 Disclaimer: This demo, including all the files that appear (e.g: CV_candidates.zip), are entirely fictional. We are not a corporation, we seek open-source contributors not candidates. > 🛠⚠️️ Active Work in Progress > 🙏 This project started as a side-project and has zero roadmap and zero funding. It’s grown way beyond what I expected by ending in GitHub Trending. Contributions, feedback, and patience are deeply appreciated. ## Prerequisites Before you begin, ensure you have the following software installed: * Git: For cloning the repository. Download Git (https://git-scm.com/downloads) * Python 3.10.x: We strongly recommend using Python version 3.10.x. Using other versions might lead to dependency errors. Download Python 3.10 (https://www.python.org/downloads/release/python-3100/) (pick a 3.10.x version). * Docker Engine & Docker Compose: For running bundled services like SearxNG. * Install Docker Desktop (which includes Docker Compose V2): Windows (https://docs.docker.com/desktop/install/windows-install/) | Mac (https://docs.docker.com/desktop/install/mac-install/) | Linux (https://docs.docker.com/desktop/install/linux-install/) * Alternatively, install Docker Engine and Docker Compose separately on Linux: Docker Engine (https://docs.docker.com/engine/install/) | Docker Compose (https://docs.docker.com/compose/install/) (ensure you install Compose V2, e.g., sudo apt-get install docker-compose-plugin). ### 1. Clone the repository and setup sh git clone https://github.com/Fosowl/agenticSeek.git cd agenticSeek mv .env.example .env ### 2. Change the .env file content sh SEARXNG_BASE_URL="http://searxng:8080" # http://127.0.0.1:8080 if running on host REDIS_BASE_URL="redis://redis:6379/0" WORK_DIR="/Users/mlg/Documents/workspace_for_ai" OLLAMA_PORT="11434" LM_STUDIO_PORT="1234" CUSTOM_ADDITIONAL_LLM_PORT="11435" OPENAI_API_KEY='optional' DEEPSEEK_API_KEY='optional' OPENROUTER_API_KEY='optional' TOGETHER_API_KEY='optional' GOOGLE_API_KEY='optional' ANTHROPIC_API_KEY='optional' Update the .env file with your own values as needed: - SEARXNG_BASE_URL: Leave unchanged unless running on host with CLI mode. - REDIS_BASE_URL: Leave unchanged - WORK_DIR: Path to your working directory on your local machine. AgenticSeek will be able to read and interact with these files. - OLLAMA_PORT: Port number for the Ollama service. - LM_STUDIO_PORT: Port number for the LM Studio service. - CUSTOM_ADDITIONAL_LLM_PORT: Port for any additional custom LLM service. API Key are totally optional for user who choose to run LLM locally. Which is the primary purpose of this project. Leave empty if you have sufficient hardware ### 3. Start Docker Make sure Docker is installed and running on your system. You can start Docker using the following commands: - On Linux/macOS: Open a terminal and run: sh sudo systemctl start docker Or launch Docker Desktop from your applications menu if installed. - On Windows: Start Docker Desktop from the Start menu. You can verify Docker is running by executing: sh docker info If you see information about your Docker installation, it is running correctly. See the table of Local Providers below for a summary. Next step: Run AgenticSeek locally See the Troubleshooting section if you are having issues. If your hardware can’t run LLMs locally, see Setup to run with an API. For detailed config.ini explanations, see Config Section. — ## Setup for running LLM locally on your machine Hardware Requirements: To run LLMs locally, you’ll need sufficient hardware. At a minimum, a GPU capable of running Magistral, Qwen or Deepseek 14B is required. See the FAQ for detailed model/performance recommendations. Setup your local provider Start your local provider (for example with ollama): Unless you wish to to run AgenticSeek on host (CLI mode), export or set the provider listen address: sh export OLLAMA_HOST=0.0.0.0:11434 Then, start you provider: sh ollama serve See below for a list of local supported provider. Update the config.ini Change the config.ini file to set the provider_name to a supported provider and provider_model to a LLM supported by your provider. We recommend reasoning model such as Magistral or Deepseek. See the FAQ at the end of the README for required hardware. sh [MAIN] is_local = True # Whenever you are running locally or with remote provider. provider_name = ollama # or lm-studio, openai, etc.. provider_model = deepseek-r1:14b # choose a model that fit your hardware provider_server_address = 127.0.0.1:11434 agent_name = Jarvis # name of your AI recover_last_session = True # whenever to recover the previous session save_session = True # whenever to remember the current session speak = False # text to speech listen = False # Speech to text, only for CLI, experimental jarvis_personality = False # Whenever to use a more "Jarvis" like personality (experimental) languages = en zh # The list of languages, Text to speech will default to the first language on the list [BROWSER] headless_browser = True # leave unchanged unless using CLI on host. stealth_mode = True # Use undetected selenium to reduce browser detection Warning: - The config.ini file format does not support comments. Do not copy and paste the example configuration directly, as comments will cause errors. Instead, manually modify the config.ini file with your desired settings, excluding any comments. - Do NOT set provider_name to openai if using LM-studio for running LLMs. Set it to lm-studio. - Some provider (eg: lm-studio) require you to have http:// in front of the IP. For example http://127.0.0.1:1234 List of local providers | Provider | Local? | Description | |———–|––––|———————————————————–| | ollama | Yes | Run LLMs locally with ease using ollama as a LLM provider | | lm-studio | Yes | Run LLM locally with LM studio (set provider_name to lm-studio)| | openai | Yes | Use openai compatible API (eg: llama.cpp server) | Next step: Start services and run AgenticSeek See the Troubleshooting section if you are having issues. If your hardware can’t run LLMs locally, see Setup to run with an API. For detailed config.ini explanations, see Config Section. ## Setup to run with an API This setup uses external, cloud-based LLM providers. You’ll need an API key from your chosen service. 1. Choose an API Provider and Get an API Key: Refer to the List of API Providers below. Visit their websites to sign up and obtain an API key. 2. Set Your API Key as an Environment Variable: * Linux/macOS: Open your terminal and use the export command. It’s best to add this to your shell’s profile file (e.g., ~/.bashrc, ~/.zshrc) for persistence. sh export PROVIDER_API_KEY="your_api_key_here" # Replace PROVIDER_API_KEY with the specific variable name, e.g., OPENAI_API_KEY, GOOGLE_API_KEY Example for TogetherAI: sh export TOGETHER_API_KEY="xxxxxxxxxxxxxxxxxxxxxx" * Windows: * Command Prompt (Temporary for current session): cmd set PROVIDER_API_KEY=your_api_key_here * PowerShell (Temporary for current session): powershell $env:PROVIDER_API_KEY="your_api_key_here" * Permanently: Search for “environment variables” in the Windows search bar, click “Edit the system environment variables,” then click the “Environment Variables…” button. Add a new User variable with the appropriate name (e.g., OPENAI_API_KEY) and your key as the value. (See FAQ: How do I set API keys? for more details). 3. Update config.ini: ini [MAIN] is_local = False provider_name = openai # Or google, deepseek, togetherAI, huggingface provider_model = gpt-3.5-turbo # Or gemini-1.5-flash, deepseek-chat, mistralai/Mixtral-8x7B-Instruct-v0.1 etc. provider_server_address = # Typically ignored or can be left blank when is_local = False for most APIs # ... other settings ... Warning: Make sure there are no trailing spaces in the config.ini values. List of API Providers | Provider | provider_name | Local? | Description | API Key Link (Examples) | |–––––––|—————–|––––|—————————————————|———————————————| | OpenAI | openai | No | Use ChatGPT models via OpenAI’s API. | platform.openai.com/signup (https://platform.openai.com/signup) | | Google Gemini| google | No | Use Google Gemini models via Google AI Studio. | aistudio.google.com/keys (https://aistudio.google.com/keys) | | Deepseek | deepseek | No | Use Deepseek models via their API. | platform.deepseek.com (https://platform.deepseek.com) | | Hugging Face | huggingface | No | Use models from Hugging Face Inference API. | huggingface.co/settings/tokens (https://huggingface.co/settings/tokens) | | TogetherAI | togetherAI | No | Use various open-source models via TogetherAI API.| api.together.ai/settings/api-keys (https://api.together.ai/settings/api-keys) | | OpenRouter | openrouter | No | Use OpenRouter Models| https://openrouter.ai/ | | MiniMax | minimax | No | Use MiniMax models (e.g., MiniMax-M2.7, MiniMax-M2.5).| platform.minimax.io (https://platform.minimax.io/user-center/basic-information) | Note: * We advise against using gpt-4o or other OpenAI models for complex web browsing and task planning as current prompt optimizations are geared towards models like Deepseek. * Coding/bash tasks might encounter issues with Gemini, as it may not strictly follow formatting prompts optimized for Deepseek. * The provider_server_address in config.ini is generally not used when is_local = False as the API endpoint is usually hardcoded in the respective provider’s library. Next step: Start services and run AgenticSeek See the Known issues section if you are having issues See the Config section for detailed config file explanation. — ## Start services and Run By default AgenticSeek is run fully in docker. Option 1: Run in Docker, use web interface: Start required services. This will start all services from the docker-compose.yml, including: - searxng - redis (required by searxng) - frontend - backend (if using full when using the web interface) sh ./start_services.sh full # MacOS start start_services.cmd full # Window Warning: This step will download and load all Docker images, which may take up to 30 minutes. After starting the services, please wait until the backend service is fully running (you should see backend: “GET /health HTTP/1.1” 200 OK in the log) before sending any messages. The backend services might take 5 minute to start on first run. Go to http://localhost:3000/ and you should see the web interface. Troubleshooting service start: If these scripts fail, ensure Docker Engine is running and Docker Compose (V2, docker compose) is correctly installed. Check the output in the terminal for error messages. See FAQ: Help! I get an error when running AgenticSeek or its scripts. Option 2: CLI mode: To run with CLI interface you would have to install package on host: sh ./install.sh ./install.bat # windows Then you must change the SEARXNG_BASE_URL in config.ini to: sh SEARXNG_BASE_URL="http://localhost:8080" Start required services. This will start some services from the docker-compose.yml, including: - searxng - redis (required by searxng) - frontend sh ./start_services.sh # MacOS start start_services.cmd # Window Run: uv run: uv run python -m ensurepip to ensure uv has pip enabled. Use the CLI: uv run cli.py — ## Usage Make sure the services are up and running with ./start_services.sh full and go to localhost:3000 for web interface. You can also use speech to text by setting listen = True in the config. Only for CLI mode. To exit, simply say/type goodbye. Here are some example usage: > Make a snake game in python! > Search the web for top cafes in Rennes, France, and save a list of three with their addresses in rennes_cafes.txt. > Write a Go program to calculate the factorial of a number, save it as factorial.go in your workspace > Search my summer_pictures folder for all JPG files, rename them with today’s date, and save a list of renamed files in photos_list.txt > Search online for popular sci-fi movies from 2024 and pick three to watch tonight. Save the list in movie_night.txt. > Search the web for the latest AI news articles from 2025, select three, and write a Python script to scrape their titles and summaries. Save the script as news_scraper.py and the summaries in ai_news.txt in /home/projects > Friday, search the web for a free stock price API, register with [email protected] then write a Python script to fetch using the API daily prices for Tesla, and save the results in stock_prices.csv Note that form filling capabilities are still experimental and might fail. After you type your query, AgenticSeek will allocate the best agent for the task. Because this is an early prototype, the agent routing system might not always allocate the right agent based on your query. Therefore, you should be very explicit in what you want and how the AI might proceed for example if you want it to conduct a web search, do not say: Do you know some good countries for solo-travel? Instead, ask: Do a web search and find out which are the best country for solo-travel — ## Setup to run the LLM on your own server If you have a powerful computer or a server that you can use, but you want to use it from your laptop you have the options to run the LLM on a remote server using our custom llm server. On your “server” that will run the AI model, get the ip address sh ip a | grep "inet " | grep -v 127.0.0.1 | awk '{print $2}' | cut -d/ -f1 # local ip