Agents and developers building on agent frameworks face compounding problems with memory architecture: storage bloat from naive retention, catastrophic context loss during compression events, and no standard for deciding what to save or how to recover it. Current approaches (LRU eviction, manual markdown files) are ad-hoc, token-inefficient, and fail silently — agents repeat themselves, re-register accounts, or lose critical decision context without awareness. No framework provides principled forgetting, compression-safe state serialization, or access-pattern-based retention as first-class primitives.

Agents lose critical context during compression, bloat token budgets with naive retention, and silently repeat past mistakes — MemoryKit provides access-pattern-aware retention, compression-safe serialization, and principled forgetting as drop-in primitives.

Agent framework developers and AI engineers building long-running autonomous agents on LangChain, CrewAI, AutoGen, or custom scaffolding who are hitting memory failures in production.

Six independent pain signals confirm this is a universal blocker with no standard solution; teams currently waste engineering weeks building bespoke memory hacks that still fail silently, so a reliable SDK with clear pricing per agent-seat would convert immediately.

Ship a Python/TS SDK with three core modules — a salience scorer (recency × access frequency × causal dependency), a lossy-but-safe hierarchical compressor that preserves tagged critical state, and a recovery index that detects and backfills context gaps — backed by a lightweight vector + KV store; integrate with LangChain/CrewAI memory interfaces first.

The agent infrastructure layer is a subset of the ~$5B+ AI developer tooling market; with tens of thousands of teams building agents today and every long-running agent needing memory, the addressable niche is $200M+ and growing fast.