Research · analysis · added 9 Mar 2026
Trajectory-Informed Memory Generation for Self-Improving Agent Systems — Technical Analysis
A research note, not a framework evaluation. It carries no architecture score and is not part of the directory or the comparison table.
LLM agents are amnesiac: they repeat the same failures, miss reusable successful strategies, and cannot automatically apply lessons from past executions. Existing approaches are inadequate:
Noted
- Rule-based systems: brittle, manually maintained, can't adapt
- Prompt engineering: generic guidance, no automatic improvement
- Generic memory systems (Mem0, Letta/MemGPT): store conversational facts, not execution patterns; no causal attribution; no tip categories; no provenance
- RL approaches: expensive, black-box, don't distinguish tip categories