AgenticSeek: Private, Local Manus Alternative.
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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.
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.
🙏 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
- 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 (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 | Mac | Linux
- Alternatively, install Docker Engine and Docker Compose separately on Linux: Docker Engine | Docker Compose (ensure you install Compose V2, e.g.,
sudo apt-get install docker-compose-plugin).
1. Clone the repository and setup
git clone https://github.com/Fosowl/agenticSeek.git
cd agenticSeek
mv .env.example .env
2. Change the .env file content
SEARXNG_BASE_URL="http://searxng:8080" # value depends on where the backend runs — see the SEARXNG note below
SEARXNG_PORT=8080
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_PORT: The host port Docker publishes SearXNG on. If port
8080 is already taken on your machine, set another one (e.g. 8001). Inside Docker the container always listens on 8080 — this variable never changes that.
- SEARXNG_BASE_URL: The address the backend uses to reach SearXNG. It is set here in
.env (not in config.ini) and depends only on where the backend runs:
| How you run AgenticSeek |
SEARXNG_BASE_URL |
Web interface — backend in Docker (./start_services.sh full) |
http://searxng:8080 — always port 8080, even if you changed SEARXNG_PORT |
CLI mode — backend on host (uv run cli.py) |
http://localhost:8080 — if you changed SEARXNG_PORT, use that port instead (e.g. http://localhost:8001). http://searxng:... does not work here: that hostname only exists inside Docker |
To check SearXNG in a browser, always use the host port: http://localhost:<SEARXNG_PORT>. After changing .env, restart the backend — the file is only read at process start.
- 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:
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:
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:
export OLLAMA_HOST=0.0.0.0:11434
Then, start you provider:
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.
[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.
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:
export TOGETHER_API_KEY="xxxxxxxxxxxxxxxxxxxxxx"
Windows:
(See FAQ: How do I set API keys? for more details).
3. Update config.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
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)
./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:
./install.sh
./install.bat # windows
Then you must change SEARXNG_BASE_URL in your .env file (not config.ini) to the host-mapped address, because in CLI mode the backend runs on your machine, outside Docker:
SEARXNG_BASE_URL="http://localhost:8080"
If you changed SEARXNG_PORT in .env, use that port here instead (e.g. http://localhost:8001). Restart cli.py after editing .env — the value is read at startup.
Start required services. This will start some services from the docker-compose.yml, including:
- searxng
- redis (required by searxng)
- frontend
./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 can run the LLM on the remote machine with a standard LLM server (Ollama, or any OpenAI-compatible server such as llama.cpp's llama-server) and point AgenticSeek at it over the network. No AgenticSeek-specific code needs to run on the server.
On your "server" that will run the AI model, get the ip address
ip a | grep "inet " | grep -v 127.0.0.1 | awk '{print $2}' | cut -d/ -f1 # local ip
curl https://ipinfo.io/ip # public ip
Note: For Windows or macOS, use ipconfig or ifconfig respectively to find the IP address.
Option 1: Ollama (recommended)
On the server, install Ollama, start it so it accepts connections from your network, and pull your model:
OLLAMA_HOST=0.0.0.0 ollama serve
ollama pull deepseek-r1:14b
On your personal computer, change config.ini:
[MAIN]
is_local = False
provider_name = ollama
provider_model = deepseek-r1:14b
provider_server_address = x.x.x.x:11434
is_local must be False here: with True, AgenticSeek looks for Ollama on the machine it runs on and ignores the address.
Option 2: llama.cpp or any other OpenAI-compatible server
On the server, start llama.cpp's llama-server with your GGUF model (LM-Studio, vLLM or any other server exposing an OpenAI-compatible API works the same way):
llama-server -m your-model.gguf --host 0.0.0.0 --port 8080
On your personal computer, use the lm-studio provider — it speaks the plain OpenAI-compatible /v1/chat/completions API without requiring an API key:
[MAIN]
is_local = False
provider_name = lm-studio
provider_model = your-model
provider_server_address = http://x.x.x.x:8080
**Legacy: custom A
AgenticSeek 是一个完全本地运行、支持语音的 Manus AI 替代品:一个自主智能体,可以浏览网页、编写代码和规划任务,同时把所有数据都留在你自己的设备上。