agenticSeek Agent工作流

1. **Clone the repository and setup**

git clone https://github.com/Fosowl/agenticSeek.git
cd agenticSeek
mv .env.example .env

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
    

- On Windows: Start Docker Desktop from the Start menu.

You can verify Docker is running by executing:

docker info

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.

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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

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
    

    export TOGETHER_API_KEY="xxxxxxxxxxxxxxxxxxxxxx"
    

        set PROVIDER_API_KEY=your_api_key_here
        

        $env:PROVIDER_API_KEY="your_api_key_here"
        

(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

**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

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.

Clone the repository and enter the server/folder.

git clone --depth 1 https://github.com/Fosowl/agenticSeek.git
cd agenticSeek/llm_server/

Install server specific requirements:

pip3 install -r requirements.txt

Run the server script.

python3 app.py --provider ollama --port 3333

You have the choice between using ollama and llamacpp as a LLM service.

Now on your personal computer:

Change the config.ini file to set the provider_name to server and provider_model to deepseek-r1:xxb. Set the provider_server_address to the ip address of the machine that will run the model.

[MAIN]
is_local = False
provider_name = server
provider_model = deepseek-r1:70b
provider_server_address = http://x.x.x.x:3333

Next step: Start services and run AgenticSeek

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Docker-Specific Notes

⚠️ Important for Docker Users: - The Docker volume mount approach may not work with stealth mode (undetected_chromedriver) - Solution: Place ChromeDriver in the project root directory as ./chromedriver - The application will automatically detect and use this binary - You should see: "Using ChromeDriver from project root: ./chromedriver" in the logs

Ollama connection failed from Docker backend

An error occurred: Provider ollama failed:
Ollama connection failed at http://host.docker.internal:11434. Check if the server is running.

The frontend shows Error: Unable to get a response. and the backend returns HTTP 500 on /query.

• Cause: When AgenticSeek runs in Docker, the backend reaches your host's Ollama via host.docker.internal. Only the port of provider_server_address is used in Docker mode; the host part is replaced by DOCKER_INTERNAL_URL from .env. So the failure is not a config typo — it's that Ollama on the host is only bound to 127.0.0.1:11434 and refuses connections coming in over the Docker bridge.
• Do NOT change provider_server_address = 127.0.0.1:11434 in config.ini — the host portion is ignored inside the container.
• Solution: Bind Ollama to all interfaces by setting OLLAMA_HOST=0.0.0.0:11434 on the host:

Manual ollama serve:

    OLLAMA_HOST=0.0.0.0:11434 ollama serve
    

Definitive solution:

systemd (Linux):

    sudo systemctl edit ollama
    

    [Service]
    Environment="OLLAMA_HOST=0.0.0.0:11434"
    

    sudo systemctl daemon-reload
    sudo systemctl restart ollama
    

macOS (Ollama.app):

    launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
    

* Verify from inside the backend container:

    docker compose exec backend curl -sS http://host.docker.internal:11434/api/tags
    

* Linux-only caveat: host.docker.internal only resolves inside the container if docker-compose maps it. Make sure the backend service in docker-compose.yml includes:

    extra_hosts:
      - "host.docker.internal:host-gateway"
    

* Firewall: if ufw / firewalld is active, allow the Docker bridge to reach 11434:

    sudo ufw allow from 172.16.0.0/12 to any port 11434
    

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 supersuper7434567@gmail.com 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

---

**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.

2. Change the .env file content

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. > Note on SEARXNG_PORT vs SEARXNG_BASE_URL: SEARXNG_PORT only changes the host port that Docker binds to (e.g., 8001). The searxng container always listens on port 8080 internally. Therefore, when running fully in Docker, SEARXNG_BASE_URL should always remain http://searxng:8080 (the internal Docker network address). Only change SEARXNG_BASE_URL if you are running CLI mode on the host. - 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

... 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.

---

Config

Example config:

[MAIN]
is_local = True
provider_name = ollama
provider_model = deepseek-r1:32b
provider_server_address = http://127.0.0.1:11434 # Example for Ollama; use http://127.0.0.1:1234 for LM-Studio
agent_name = Friday
recover_last_session = False
save_session = False
speak = False
listen = False

jarvis_personality = False
languages = en zh # List of languages for TTS and potentially routing.
[BROWSER]
headless_browser = False
stealth_mode = False

Explanation of config.ini Settings:

• [MAIN] Section:
• is_local: True if using a local LLM provider (Ollama, LM-Studio, local OpenAI-compatible server) or the self-hosted server option. False if using a cloud-based API (OpenAI, Google, etc.).
• provider_name: Specifies the LLM provider.
• Local options: ollama, lm-studio, openai (for local OpenAI-compatible servers), server (for the self-hosted server setup).
• API options: openai, google, deepseek, huggingface, togetherAI.
• provider_model: The specific model name or ID for the chosen provider (e.g., deepseekcoder:6.7b for Ollama, gpt-3.5-turbo for OpenAI API, mistralai/Mixtral-8x7B-Instruct-v0.1 for TogetherAI).
• provider_server_address: The address of your LLM provider.
• For local providers: e.g., http://127.0.0.1:11434 for Ollama, http://127.0.0.1:1234 for LM-Studio.
• For the server provider type: The address of your self-hosted LLM server (e.g., http://your_server_ip:3333).
• For cloud APIs (is_local = False): This is often ignored or can be left blank, as the API endpoint is usually handled by the client library.
• agent_name: Name of the AI assistant (e.g., Friday). Used as a trigger word for speech-to-text if enabled.
• recover_last_session: True to attempt to restore the previous session's state, False to start fresh.
• save_session: True to save the current session's state for potential recovery, False otherwise.
• speak: True to enable text-to-speech voice output, False to disable.
• listen: True to enable speech-to-text voice input (CLI mode only), False to disable.
• work_dir: Crucial: The directory where AgenticSeek will read/write files. Ensure this path is valid and accessible on your system.
• jarvis_personality: True to use a more "Jarvis-like" system prompt (experimental), False for the standard prompt.
• languages: A comma-separated list of languages (e.g., en, zh, fr). Used for TTS voice selection (defaults to the first) and can assist the LLM router. Avoid too many or very similar languages for router efficiency.
• [BROWSER] Section:
• headless_browser: True to run the automated browser without a visible window (recommended for web interface or non-interactive use). False to show the browser window (useful for CLI mode or debugging).
• stealth_mode: True to enable measures to make browser automation harder to detect. May require manual installation of browser extensions like anticaptcha.

This section summarizes the supported LLM provider types. Configure them in config.ini.

Local Providers (Run on Your Own Hardware):

API Providers (Cloud-Based):

---

AgenticSeek: Private, Local Manus Alternative.

<p align="center"> <img align="center" src="./media/agentic_seek_logo.png" width="300" height="300" alt="Agentic Seek Logo"> <p>

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.

Troubleshooting

If you encounter issues, this section provides guidance.

Troubleshooting Tips

1. Still getting version mismatch? - Verify the ChromeDriver is executable: ls -la ./chromedriver - Check the ChromeDriver version: ./chromedriver --version - Ensure it matches your Chrome browser version

2. Docker container issues? - Check backend logs: docker logs backend - Look for the message: "Using ChromeDriver from project root" - If not found, verify the file exists and is executable

3. Chrome for Testing versions - Use the exact version match when possible - For version 134.0.6998.88, use ChromeDriver 134.0.6998.165 (closest available) - Major version numbers must match (134 = 134)

FAQ

Q: What hardware do I need?

Q: I get an error what do I do?

Ensure local is running (ollama serve), your config.ini matches your provider, and dependencies are installed. If none work feel free to raise an issue.

Q: Can it really run 100% locally?

Yes with Ollama, lm-studio or server providers, all speech to text, LLM and text to speech model run locally. Non-local options (OpenAI or others API) are optional.

Q: Why should I use AgenticSeek when I have Manus?

Unlike Manus, AgenticSeek prioritizes independence from external systems, giving you more control, privacy and avoid api cost.

Q: Who is behind the project ?

The project was created by me, along with two friends who serve as maintainers and contributors from the open-source community on GitHub. We’re just a group of passionate individuals, not a startup or affiliated with any organization.

Any AgenticSeek account on X other than my personal account (https://x.com/Martin993886460) is an impersonation.