ZENITH: I Was Too Lazy to Manage My AI Agents, So I Built an AI to Manage Them

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

I have a terrible habit of starting interesting projects. I also have a less interesting habit of coming back to them and asking myself the same three questions:

What was I doing? Why did we make that decision? And when did "all tests pass" stop being true?

The answers were scattered across Git repositories, notes, task lists, and AI conversations. Every time I returned, I became the person responsible for reminding my tools what my other tools had done.

Which seemed like an unreasonable amount of work for someone trying to be lazy. 🦥

So I built Zenith, an open-source, local-first personal intelligence and agent-orchestration platform. And I built Misa, the assistant that sits on top of it.

Zenith is not another chatbot with a bigger prompt. Its memory belongs to the system, not the language model. It tracks projects, decisions, unfinished tasks, evidence, source freshness, and execution history. It also provides a bounded workflow for planning, implementing, and independently checking engineering work.

Two ideas hold the whole thing together:

For natural-language reasoning, Misa can use Gemma 3 4B locally through Ollama, on my laptop's GPU. No hosted AI service is needed for that mode. When the model is unavailable or its answer fails validation, Misa can still respond from its records.

The goal isn't to work faster every waking minute. It's to be able to walk away from work—and come back without reconstructing my entire life first.

The demo uses fictional projects, Harbor and Lighthouse, in a disposable workspace. No personal repositories, private notes, or credentials are included.

Scene 1: Misa remembers what I no longer remember.

Misa finds an optimistic old note, a passing test from an earlier revision, uncommitted changes, two open tasks, and a source directory it can no longer inspect.

It doesn't merge these into one confident "everything is fine." It tells me what I reported, what was actually verified, and what is now uncertain.

Then I ask show sources, and it shows the records behind the answer.

Scene 2: A small local model reasons over the evidence.

With Ollama running on 127.0.0.1:11434, I ask:

Me: Why did we stay on SQLite in Harbor?

In the recorded local Gemma run, Misa connects the question to the stored decision: SQLite was chosen for single-node personal use and simpler migration auditing.

It also mentions that the notes source is unavailable, that documentation remains unfinished, and that an earlier "done" claim is not verified.

The answer took 7.73 seconds with 3,928 input tokens and 316 output tokens in that recorded run.

This isn't a polished answer secretly written by a larger hosted model. It's a real 4B model, with all its limitations.

In another recorded reply, Gemma produced a wildly incorrect day count and stray JSON-like text. Zenith's current lexical validator catches nonexistent citations and certain unsupported specifics, and can fall back to a deterministic answer.

It doesn't reliably detect incorrect arithmetic or every wrong attribution.

I kept those failures in the raw transcript instead of editing them out.

When I ask about a project named Orion, which isn't registered, Zenith refuses to invent its history—and doesn't even need to call the model.

Scene 3: An AI agent says the tests pass. Zenith disagrees.

Zenith includes an engineering workflow:

In one, the verification passes and the run completes.

In another, the Implementer claims it fixed the issue—but the verifier finds a failing test and Zenith blocks the run.

Important: This orchestration demo uses scripted fake Director and Implementer adapters.

The state machine, approval handling, worktree isolation, and sandboxed verification are real code paths; the coding agents in this demonstration are simulated.

I have not demonstrated the complete loop using live Claude Code and Codex agents.

To reproduce the story on Linux after installing and starting Ollama:

git clone https://github.com/miclaldogan/zenith-v1 cd zenith-v1 scripts/install.sh ollama pull gemma3:4b zenith demo story --md transcript.md Enter fullscreen mode Exit fullscreen mode The complete demo transcript includes the model's raw outputs, validation results, and measured timings.

The synthetic demo doesn't require access to anyone's real files.

I was too lazy to manage my AI agents, so I built an AI to manage them.

Zenith is an open-source, local-first personal intelligence and agent-orchestration platform. It remembers your projects with evidence and provenance, tells you where you left off, coordinates coding agents inside hard boundaries, verifies their claims independently, and never acts without your approval. Misa is the assistant you talk to on top of it, in Turkish or English, offline by default, with an optional local open-weight model (Google Gemma via Ollama) for natural reasoning and your own Claude Code login for engineering work.

Everything is a single Python package with zero dependencies (SQLite, stdlib only), tested by 243 unit and integration tests, running on Linux.