The choice is made by a separate router: you state the task, it decides who gets it. The feature list calls this smart agent selection - like having a team of experts ready to help.

Code interpreters are separate per language: Bash, C, Go, Java and Python. Plus tools for finding files, searching the web, finding MCP servers, and a dedicated safety module.

What Has to Be Installed

There are more requirements here than for any other agent in this topic, and that is the price of running locally.

You need Git, Python at exactly version 3.10 - the authors warn that other versions produce dependency errors - and Docker with Docker Compose. The containers bring up the SearXNG search engine, Redis for it, the web interface and, optionally, the application server itself.

Then you pick one of two ways to run it. Everything in Docker with a web interface at localhost:3000:

Or the command line, where the server itself runs on the machine and only the supporting services stay in Docker:

The README's warning deserves to be taken seriously: downloading and loading the images may take up to thirty minutes, and the first server start up to five.

The Search Address Trap

The difference between the two launch modes produces the first mistake almost everyone makes, and the README devotes a table to it.

The search service address is set by the SEARXNG_BASE_URL variable in the environment file - not in the main configuration. And its value differs:

The reason is that the name searxng exists only inside Docker's network. Outside it there is no such host, and the request simply will not resolve.

It is also stated that the environment file is read only at startup, so after editing it the server has to be restarted.

The Configuration Does Not Tolerate Comments

The second common mistake, and the README covers it with a warning.

The config.ini format does not support comments. And the example configuration in the documentation is given with comments - for explanation. Copying it wholesale means an error.

The configuration itself is small:

Two more caveats from the same place. If you use LM Studio, the provider name must be lm-studio, not openai - despite the compatible interface. And some providers need the address with an explicit http:// in front.

How Much Hardware It Takes

The FAQ gives a table the project deserves credit for: it is honest.

Note the bottom row: getting the promised quality needs a graphics card most people do not have. The saving on a subscription runs into the cost of hardware, and the project does not hide it.

Local providers supported are Ollama, LM Studio, and any server with a compatible interface such as llama.cpp. The cloud keys in the environment file are marked optional - it can work without a single one.

Browser Stealth Mode

One setting deserves a separate mention, because it touches not technique but boundaries.

The stealth_mode parameter turns on measures that make browser automation harder to detect, and the description states outright that it may require manually installing extensions such as a captcha bypass. The source tree contains a dedicated spoofing script for it.

A tool is a tool, but applying it to someone else's site is worth doing with the understanding that you are getting around a defence its owner put there deliberately.

What to Know in Advance

The project has no releases. Not a single tag. It installs by cloning the main branch, which puts build reproducibility on you.

There are few open issues - 36. Against the thousands at its neighbours that looks good, though the project also has far fewer stars than Cline has issues.

The last commit was 11 August 2026. Two weeks before this was written. Not abandoned, but not the pace of Crush with a release every few days either.

Voice input is marked unfinished. The feature list appends "in progress" to it, and in the configuration speech recognition is called experimental and works only in the command line.

There are eighteen test files. For a project that runs generated code in five languages that is thin, but they exist - including tests for safe mode and the safety module.

The working directory is set explicitly. The WORK_DIR variable says where the agent may read and change files. That is the only boundary, and it is worth setting deliberately before the first run.

First-Encounter Mistakes

Installing a Python other than 3.10. The authors warn about dependency errors right in the requirements.

Copying the example configuration with its comments. The format does not support them; it will error.

Leaving the Docker search address when running from the command line. The name searxng does not resolve outside Docker.

Setting openai as the provider for LM Studio. It has to be lm-studio, despite the compatible interface.

Expecting results from a 7B model. The FAQ table says outright that the planner will likely fail.

Forgetting to restart the server after editing the environment. The file is read only at startup.

Not setting the working directory. That is the only boundary on where the agent can reach.

Embedding the code in a product you distribute without reading the licence. GPL-3.0 will require opening the derivative work's sources.

To put it in one sentence: AgenticSeek honestly does what it promises - it moves the whole agent onto your machine; the only question is whether you own a graphics card on which the promised quality is reachable.

Sources

This article was written from the Fosowl/agenticSeek repository (branch main) on 25 August 2026: the README, config.ini, .env.example, the sources/ and tests/ directory trees, and the repository metadata. There was no prepared dossier for this project; the facts were gathered from the sources. Stars, the open-issue count and the last commit date were taken the same day through the GitHub API. The project has no releases, so its state is described from the main branch on that date.

• GitHub: Fosowl/agenticSeek
• The project site
• The contributing guide
• The agent sources
• The tool sources
• Ollama
• LM Studio
• SearXNG