Show HN: AgenticSeek – Self-hosted Manus alternative
github.comI’ve spent the last two months building AgenticSeek, a privacy-focused alternative to cloud-based AI tools like ManusAI. It runs entirely on your machine—no API calls, no data leaks.
Why AgenticSeek?
Optimized for local LLMs (developed mostly on an RTX 3060 running deepseek r1 14b).
Truly private: All components (TTS, STT, planner) run locally.
More responsive than alternatives (we respond fast to issues + active Discord).
Designed to be fun—think JARVIS-like voice control, multi-agent workflows, and a slick web UI.
Current Features:
Web browsing (research + form filling), code write/fix, file management/search. Planning capabilites to use multiple agents for complex task.
Is it stable? Prototype-stage—great for tinkerers.
Hoping to get feedback!
How is this secure? Can the agent run `rm -rf /` and destroy my machine by chance?
No it can't because we check the bash the AI try to execute against a list of pattern for dangerous command. Also all commands are executed within a folder specified in the configuration file, so that you can choose which files it has access to. However, we currently have no containerization meaning that code execution unlike bash could be harmful. I do think about improving the safety by running all code/commands within a docker and then having some kind of file transfer upon user validation once a task is done.
I have not used this one yet but as a rule of thumb I always test this type of software in a VM, for example I have an Ubuntu Linux desktop running in Virtualbox on my mac to install and test stuff like this, which set up to be isolated and much less likely to have access to my primary Mac OS environment.
How does AgenticSeek handle agent communication and memory management?"
AgenticSeek only have agent communication for planning task but it isn't really communication. Planning task are simply a json list of task and how they related with each other. During execution output from each agents will be saved and "added" to the prompt of the following agents that depends on the information from the previous agent. Really dead simple but work quite well. For memory management, we store message as an array of {"role": "user", "content":...}. Where we take a more innovative approach if how we process message before adding to the history: We remove the <think>..reasoning..</think> pattern to keep only useful informations in the context (we don't really need the reasoning that was done on previous task for the current task). We also have memory summarization that is used when loading a previous session.