Context Memory

Zene employs a sophisticated tiered memory system to handle large codebases and long-running sessions without overwhelming the LLM's context window.

Tiered Architecture

Context is loaded dynamically based on task depth to save tokens and improve accuracy.

Tier 1: Project Structure (L1) / Hot Context

• Tool: ignore (File walking).
• Content: Directory tree, filenames, and immediate conversation history.
• Purpose: Macro understanding and immediate dialogue.
• Trigger: Session initialization.

Tier 2: Syntax Structure (L2) - Core

• Tool: tree-sitter (Parsing).
• Content: Function signatures, struct/enum definitions, and cross-file references.
• Purpose: Interface contracts without reading full source code.
• Trigger: Dependency analysis and definition lookups.

Tier 3: Semantic Vector Memory (RAG)

• What: Semantic search over the entire project codebase.
• Status: Opt-in (Disabled by default to save ~200MB RAM).
• Mechanism:
  • Uses fastembed-rs to generate embeddings for code chunks.
  • Uses usearch for high-performance local vector searching.
  • Enables the agent to find relevant code snippets based on natural language queries.
• Tools: memory_search and context_search.

Tier 4: Session Compaction

• What: Summarization of older conversation history.
• Mechanism:
  • When history exceeds a threshold, the SessionCompactor summarizes the middle portion, preserving system prompt and recent context.

Performance & Opt-in

By default, Zene aims for a minimalist footprint. Semantic memory requires loading transformer models which consume significant memory.

IMPORTANT

Tier 3 Semantic Memory is a compile-time feature. You must enable it during cargo build to include the necessary dependencies:

cargo build --release --features knowledge

Once compiled with the feature, you can activate it at runtime:

export ZENE_USE_SEMANTIC_MEMORY=true

Dynamic Management

• Smart Pruning: Automatically removes AST nodes unrelated to the current task.
• Token Monitoring: Real-time counting and summarization.
• Relevance Ranking: Retrieved snippets are sorted by importance.