Episode Start

Start a new learning episode in the self-learning memory system.

Purpose

Create a new episode record with proper context for the memory backend to learn from execution patterns.

Steps

1. •

   Understand the task: Parse the task description and identify:

   • •Task type (implementation, debugging, refactoring, testing)
   • •Domain (storage, patterns, retrieval, testing, etc.)
   • •Language context (Rust/Tokio/async patterns)
2. •

   Prepare TaskContext: Ensure you have:

   • •language: "rust"
   • •domain: One of [storage, patterns, retrieval, embedding, testing, ci]
   • •tags: Array of relevant tags (e.g., ["turso", "async", "tokio"])
3. •

   Create episode: Call SelfLearningMemory::start_episode(task_description, context)

   • •Task description should be clear and concise (1-2 sentences)
   • •Include relevant context from the user's request
4. •

   Store episode_id: Keep the episode ID for logging subsequent steps

5. •

   Initialize step logging: Prepare to log execution steps with:

   • •Tool used
   • •Action taken
   • •Latency/tokens (if applicable)
   • •Success status
   • •Observations

Storage Requirements

• •Persist to Turso (durable storage)
• •Cache in redb (fast access)
• •Store context as JSON blob

Example

let context = TaskContext {
    language: "rust".to_string(),
    domain: "storage".to_string(),
    tags: vec!["turso".to_string(), "async".to_string()],
};

let episode_id = memory
    .start_episode(
        "Implement async batch pattern updates",
        context
    )
    .await?;

Notes

• •Always validate that both Turso and redb connections are healthy
• •Use anyhow::Result for error handling
• •Log any initialization failures with context