Meta-Automation and Adaptive Architecture

Automating the Build Log

In a very meta turn of events, a significant portion of the day was spent automating the creation of this very build log. We set up a new GitHub Actions workflow that triggers on every push to the main branch. The workflow gathers the day’s commits and uses an LLM to generate a coherent narrative summary, which is then committed to our web frontend and deployed.

This involved a bit of iteration on the LLM provider. We started with a script calling the Anthropic API directly, ran into an authentication issue (mistaking an API key for an OAuth token), briefly switched to the claude-code CLI, and finally landed on using Google’s Gemini API, which better fit our existing backend patterns.

Multi-Instance Deployments

We’ve now added proper support for running multiple, independent instances of Chalie on a single host. Previously, this would cause port conflicts and other issues. The changes involved updates to the docker-compose.yml file, nginx.conf, and the .env.example to make network configurations more flexible. We also added a new documentation page detailing the setup process for users who want to run a fleet of agents.

Adaptive Architecture & Tool Management

A major focus today was on making Chalie’s cognitive architecture more dynamic. We’ve introduced a new adaptive layer that can enable or disable tools across a fleet of instances. This allows us, as operators, to centrally manage capabilities without redeploying every agent.

This work involved creating several new backend services (AdaptiveLayerService, CognitiveDriftEngine, ToolConfigService) and updating numerous prompts. The goal is to make Chalie more aware of its own capabilities and the current context, improving decision-making and token efficiency. We also refactored the tool-profiling system to be more generic and less dependent on a fixed set of skills.

Frontend and Fixes

On the frontend, we’ve added support for rendering Markdown in the agent’s responses. This makes outputs like lists, code blocks, and formatted text much more readable for the user. We integrated the marked.js library to handle the parsing and styled the output to match our interface.

We also pushed a couple of important backend fixes. One corrects how the Content-Length header is calculated, ensuring it uses the byte length of the body rather than the character count, which is crucial for handling multi-byte characters correctly. Another fix adds logic to strip the markdown code fences that Gemini sometimes wraps around its JSON responses, preventing parsing errors.