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Architecture Overview

Drevon follows a simple but powerful pattern: single source of truth → compile → native agent configs.

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The Compilation Pipeline

When you run drevon sync, the following happens:
1

Load Config

Reads drevon.config.json and validates it against the Zod schema. If the config is invalid, you get clear error messages.
2

Resolve Skills

Reads skills-lock.json to find all installed skills and their descriptions. Skills are injected as a formatted table into each agent’s instructions.
3

Iterate Enabled Agents

For each agent with enabled: true, the compiler instantiates the appropriate adapter.
4

Generate Content

Each adapter builds the agent-specific config by combining:
  • Identity — role, description, posture
  • Memory protocol — instructions for reading/writing memory files
  • Custom instructions — your project-specific rules
  • Skills — installed skill descriptions and usage instructions
  • Workspace rules — workspace organization (hub mode)
  • Prompts — available workflow templates
5

Diff & Write

The compiler compares generated content against existing files. Only changed files are written, avoiding unnecessary Git noise.

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The Adapter Pattern

Each supported agent has a dedicated adapter that knows its native config format: All adapters extend a shared BaseAdapter class that provides common content generation methods. This means every agent receives the same instructions, just formatted differently.
Cursor’s adapter is the most complex — it generates multiple .mdc files with YAML frontmatter, supporting glob-targeted conditional rules that only activate for specific file types.

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Write-Only-If-Changed

The compiler intelligently diffs each generated file against the existing one on disk. This means:
  • Running drevon sync repeatedly is safe — it won’t create noisy Git diffs
  • The output tells you exactly what was created, updated, or unchanged
  • You can commit agent config files to Git without churn

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The Self-Evolution Loop

Drevon doesn’t just generate static config files — it creates a feedback loop where agents improve over time:
  1. Session start — agents read only INDEX.md to restore context, then load individual topic files on demand
  2. During work — agents make decisions informed by past context
  3. Session end — agents write back what they learned (decisions, patterns, discoveries)
  4. Over time — repeated workflows get extracted into reusable prompts
This creates compounding returns: the more you use your workspace, the more useful it becomes.