Back

The Assistant                               PR#169

This started with the struggle of choosing what feature to add.
Between the time I wrote my proposal and had to actually implement it, so many things had been added in the codebase, that my initial "plan" did not make much sense anymore.
Luckily, my mentors were ultra flexible on what I could build. An assistant that remembers the conversation and lets you save/restore sessions felt like a missing feature, and a great way to stress-test the agent.

This task consisted of different parts: making the conversational loop truly interactive, giving it a friendly console, and making sessions reliable so you can leave and come back later without losing anything.

Building the Interactive Loop

The loop came first. Instead of just calling runStep() manually and inspecting outputs, each user message is added to the conversation state, the agent runs through its reasoning and tool calls, returns a reply, and the session stays open for the next turn. It's the same pattern as before (messages + tools + reflection), just packaged into something you can keep running.

Session Management

Sessions came next. The idea was to manage them properly and be able to restore them: saving conversations under custom names, listing past sessions, and loading them back whenever needed. Each one is stored as JSON for exact state and Markdown for readability, with safeguards against filename clashes so you never overwrite old work.

User Experience

Finally, I spent some time on the user experience. A CLI is just text, but it can still be friendly: colorized prompts, a welcome banner, recent sessions shown when you start. Little details, but they help make it feel like more than just a debugging tool.

One of the learnings from this task was switching over to the new error handling system that's gradually being introduced in the project. Instead of ad-hoc exceptions, the assistant now uses proper ADTs (AssistantError, LLMError) with structured context. This makes errors type-safe, easier to format, and much cleaner to recover from inside the loop.

File Breakdown

Core Components

1. AssistantAgent.scala

  • Purpose: Orchestrates the chat loop; routes commands vs. queries; runs the agent to completion
  • Impact: Provides the main conversational interface that keeps the session interactive and responsive

2. ConsoleInterface.scala

  • Purpose: Minimal terminal UI with fansi styling, prompts, help, and friendly messages
  • Impact: Creates a welcoming CLI experience that feels polished and user-friendly

3. SessionManager.scala

  • Purpose: Reads/writes session JSON + Markdown, handles collisions, lists recent sessions
  • Impact: Enables reliable persistence so conversations can be saved and restored seamlessly

State and Error Management

4. SessionState.scala

  • Purpose: Immutable session model with typed wrappers, plus helpers to spin up a fresh session
  • Impact: Provides type-safe session management with clean state transitions

5. error/AssistantError.scala

  • Purpose: Domain errors with context; bridges nicely with core LLMError
  • Impact: Delivers structured error handling that's both developer-friendly and user-facing

Demonstration

6. samples/AssistantAgentExample.scala

  • Purpose: Runnable demo wiring local tools + MCP (Playwright, Email) and launching the assistant
  • Impact: Shows real-world integration patterns and provides a working reference implementation

Reflections

Building this helped me look at the project from both sides. MCP showed me how the agent discovers and calls tools; the assistant made me care about how humans discover and call the agent.

In the end, the assistant agent turned into a kind of showcase for LLM4S: it uses the core agent loop, exercises tool integration, persists state, and gives you a fun tool to use in your terminal.

Demo


An instant later:

Email received notification