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Update README with new AI memory resources #15

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Expand Up @@ -197,6 +197,7 @@ Harness components organized by the problem they solve, not by vendor.

### Memory & State

- [Structured AI coding agents memory](https://github.com/syncable-dev/memtrace-public) - Memtrace is a persistent memory layer for coding agents (not conversation context), built as a bi‑temporal structural knowledge graph over your codebase (AST‑driven symbols and relationships, plus temporal evolution and cross‑service API topology)
- [Building Effective Agents](https://www.anthropic.com/research/building-effective-agents) — Covers in-context, external, and procedural memory patterns as harness-level concerns.
- [Letta (MemGPT)](https://github.com/letta-ai/letta) — The reference architecture for stateful agents: three-tier memory (core / archival / recall) maps directly to harness state management design. Their [agent loop redesign post](https://www.letta.com/blog/letta-v1-agent) is the most thorough public analysis of how memory structure shapes the harness. ![Stars](https://img.shields.io/github/stars/letta-ai/letta?style=flat-square&label=★&color=yellow)
- [mem0](https://github.com/mem0ai/mem0) — Drop-in universal memory layer (YC-backed, AWS Agent SDK's exclusive memory provider) that handles cross-session retention without custom harness-level state management code. Lowest integration cost for production-grade persistent memory. ![Stars](https://img.shields.io/github/stars/mem0ai/mem0?style=flat-square&label=★&color=yellow)
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