Memory-based long conversation handling (no compaction)

[kovtcharov]

Problem

Long conversations overflow the model's context window. The naive solution (context compaction / summarization) loses critical information — OpenClaw's biggest failure was losing safety instructions during compaction.

Approach

Use the memory system + RAG instead of compaction. Important context is offloaded to persistent storage and retrieved via RAG when needed. The memory system IS the solution to long conversations, not summarization/pruning.

Design:

• As conversation grows, agent proactively saves important facts/decisions to ~/.gaia/memory/
• When context approaches limit, oldest messages are dropped BUT their key content is already in memory
• RAG retrieves relevant memory when the agent needs past context
• No information is permanently lost — it moves from conversation to memory

Dependencies

• MemoryStore: persistent memory data layer (SQLite + FTS5) #542 (MemoryStore)
• MemoryMixin: 5 memory tools + 3 lifecycle hooks for any agent #543 (MemoryMixin)

Acceptance Criteria

• Agent saves important context to memory as conversation grows
• Past context retrievable via RAG after messages are dropped
• No critical information lost during long conversations
• Safety instructions and user preferences always preserved
• Works transparently — user doesn't need to manually save context