watchllm-klyd 0.1.1

Architectural memory system for autonomous coding agents.

klyd

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Persistent architectural memory for autonomous coding agents.

Current status

Early design & specification phase. Subsystem boundary defined; codebase scaffolded.

Overview

Large Language Models (LLMs) tend to lose architectural context and make regression decisions over long coding sessions. klyd solves this by acting as a persistent architectural memory layer. It extracts architectural decisions from git history/diffs, stores them locally, and retrieves relevant constraints at write time to keep autonomous agents aligned with project guidelines.

klyd remembers. The kernel enforces.

Core Features

• Architectural Decision Extraction: Auto-extracts design constraints and guidelines from repository changes and commit history.
• Local Memory Store: Uses a lightweight local SQLite database to persist constraints offline.
• Context Retrieval: Queries relevant architectural constraints based on the files currently being edited.
• Agent Prompt Injection: Injects retrieved guidelines into agent execution loops to guide code generation.

Relationship to Kernel

• klyd (Memory): Tracks and provides the historical context and architectural rules.
• Kernel (Enforcement): Deteministically evaluates code changes at the write-path to ensure those rules are not violated.

Non-goals

• Write-path interception: klyd does not block files or handle save hooks; that is the sole responsibility of the Kernel.
• Code Generation: klyd does not generate or rewrite source code.

Installation

pip install -e .

Usage

klyd --help
python -m klyd --help

Links

• WatchLLM Organization
• Central Documentation
• watchllm.dev