v0.4.0 is live.

This release adds a fully working, embedding-based semantic memory system to the engine.

🧠 What’s new

• Agent-level persistent memory
• Embedding-based storage (local or provider-based)
• Cosine similarity retrieval
• Similarity threshold filtering
• Retention cap per agent
• Token-safe prompt injection
• Ollama fallback for embeddings (no external vector DB)

Memory is opt-in per LLM step:

{
  "type": "llm",
  "prompt": "What is my project name?",
  "useMemory": true,
  "memoryTopK": 5
}

Everything works local-first (MongoDB + Ollama supported).

This lays the foundation for document-based RAG next.

As always, feedback and ideas are welcome.