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

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)

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"])

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

Store episode_id: Keep the episode ID for logging subsequent steps

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