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Integration of AI agent router, Speech to text, casual agent and basic web search tool.
🚀 agenticSeek: Local AI Assistant Powered by DeepSeek Agents
A fully local AI assistant using Deepseek R1 agents.
🛠️ Work in Progress – Looking for contributors! 🚀
Features:
- Privacy-first: Runs 100% locally – no data leaves your machine
- ️ Voice-enabled: Speak and interact naturally
- Coding abilities: Code in Python, Bash, C, Golang, and soon more
- Self-correcting: Automatically fixes errors by itself
- Agent routing: Select the best agent for the task
- Multi-agent: For complex tasks, divide and conquer with multiple agents
- Web browsing (not implemented yet): Browse the web and search the internet
Installation
1️⃣ Install Dependencies
Make sure you have Ollama installed, then run:
pip3 install -r requirements.txt
2️⃣ Download Models
Download the deepseek-r1:7b
model from DeepSeek
ollama pull deepseek-r1:7b
3️⃣ Run the Assistant (Ollama)
Start the ollama server
ollama serve
Change the config.ini file to set the provider_name to ollama
and provider_model to deepseek-r1:7b
[MAIN]
is_local = True
provider_name = ollama
provider_model = deepseek-r1:7b
Run the assistant:
python3 main.py
4️⃣ Alternative: Run the Assistant (Own Server)
On the other machine that will run the model execute the script in stream_llm.py
python3 stream_llm.py
Get the ip address of the machine that will run the model
ip a | grep "inet " | grep -v 127.0.0.1 | awk '{print $2}' | cut -d/ -f1
Change the config.ini
file to set the provider_name
to server
and provider_model
to deepseek-r1:7b
.
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-r1:14b
provider_server_address = x.x.x.x:5000
Run the assistant:
python3 main.py
Current capabilities
- All running locally
- Reasoning with deepseek R1
- Code execution capabilities (Python, Golang, C)
- Shell control capabilities in bash
- Will try to fix errors by itself
- Routing system, select the best agent for the task
- Fast text-to-speech using kokoro.
- Memory compression (reduce history as interaction progresses using summary model)
- Recovery: recover last session from memory
UNDER DEVELOPMENT
- Web browsing
- Knowledge base RAG
- Graphical interface
- Speech-to-text using distil-whisper/distil-medium.en
Languages
Python
86.8%
JavaScript
5.2%
Shell
3.6%
CSS
3%
Batchfile
1.1%
Other
0.2%