Cognithor v0.26.6 — mein lokaler KI-Assistent versteht jetzt, wenn ich genervt bin

Hey everyone,

I’ve been working on Cognithor, a self hosted AI assistant, for quite a while now and wanted to share the latest update. With v0.26.6 a lot has changed, both technically and in terms of overall experience.

For those who do not know the project yet: Cognithor is a locally executable AI agent written in Python that can be reached through 17 different channels, including Telegram, Discord, WhatsApp, Signal, Matrix, CLI, Web UI and Voice. As an LLM backend you can run it fully local with Ollama, or connect to providers such as OpenAI, Anthropic, Gemini, Groq or DeepSeek.

Architecturally it is built around a Planner Gatekeeper Executor structure. Every tool execution is risk assessed before it actually runs. In total there are 51 tools available, ranging from filesystem access and shell execution to web search, browser automation, encrypted vault storage and long term memory.

What changed most noticeably in this version is the overall feel of the system.

Previously the flow was simple: send a message, wait, receive an answer. Sometimes you would wait for up to two minutes with no intermediate feedback. If something failed, you would get a generic error message with no context. The tone was identical whether you asked a casual question or were clearly frustrated.

This has been fundamentally reworked.

There are now status callbacks across all channels. When Cognithor performs a web search, you see “Searching the web…”. If a tool call is retried, you get “Attempt 2 of 3…”. Discord shows the typing indicator, the CLI displays a spinner, and the Web UI pushes live status updates via WebSocket. Technically these are small changes, but from a user perspective they make a substantial difference.

In addition, I implemented a lightweight keyword based sentiment detection system. It does not rely on any additional models or dependencies. Phrases like “this still does not work” trigger a calmer and more explanatory tone. Urgent wording leads to more concise responses. Signs of confusion result in step by step explanations. The impact on perceived quality has been significant.

On top of that, there is now a configurable personality engine. It manages greetings, optional humor, contextual success confirmations and similar elements. Cognithor also adapts to individual users over time. If someone consistently writes short messages, responses become more compact. If a user prefers detailed input, answers become correspondingly more extensive. This adjustment happens automatically.

Error handling has been refined as well. Timeouts, connection issues and Gatekeeper blocks are now clearly differentiated. When an action is blocked, the system explains why and suggests alternatives instead of just stating that it was denied.

The PWA interface has been fully overhauled. Previous issues such as incorrect imports, empty canvas components and unconnected dialogs have been fixed. The UI now runs on a structured CSS design system with dark mode, service worker support and a proper PWA manifest for mobile installation.

The Control Center includes a global search accessible via Ctrl K, a theme toggle, styled confirmation dialogs, auto save for unsaved changes and improved accessibility with ARIA labels and full keyboard navigation.

In parallel, a codebase audit resulted in 23 fixes across security, gateway, channel handling and the A2A protocol. This includes file size limits, token encryption, TLS support and several race condition fixes.

The test suite currently runs 8470 tests with zero failures, which provides a reasonable level of confidence for a release of this scope.

If you want to explore the project, you can find the full repository here:
https://github.com/Alex8791-cyber/cognithor

I would especially appreciate feedback on the “human feel” approach. Does this kind of behavioral refinement meaningfully improve daily usage, or do you see it more as a cosmetic layer.