Plug-in memory layer that compresses multi-session agent history into token-efficient context prefixes, billed per compression job.

Customer: Solo dev building a coding assistant or research agent with Claude/GPT who ships to 10–200 end users and keeps hitting context limits or paying for repeated re-derivation of prior session state

Problem: Each new agent session re-reads or re-derives what happened before — burning 2k–20k tokens on stale context, causing drift when that context is truncated, and requiring custom memory plumbing the dev doesn’t want to build

Pricing: saas-mrr — $400 MRR in 3 months (8 devs × $50/mo tier, ~500k compressions/mo each)

Why now

Token reduction is the hot axis right now — 91% CLI reduction is a proof point users already cite. sqlite-vec makes local vector search trivially deployable. Haiku pricing makes compression jobs cheap enough to resell at margin. Devs building agents are multiplying faster than memory infrastructure exists for them.

Go-to-market

1. Post a teardown on r/LocalLLaMA and HN Show HN: ‘How I cut session token cost 70% with a background consolidation job’ — include benchmark numbers and link to OSS core
2. Ship open-source CLI wrapper (pip install session-mem) that works standalone; gate multi-user API, dashboard, and hosted storage behind paid tier
3. DM 20 devs who posted about agent memory pain on X/Twitter in last 60 days — offer free beta, ask for async feedback async, convert 2–3 to paying
4. Write one integration guide per week: Claude Code, Cursor custom agent, LangChain — each targets a search term (‘langchain session memory python 2025’) and funnels to hosted tier

Moat (or lack thereof)

No moat. OpenAI, Anthropic, and LangMem ship this eventually. Defensibility is zero at the infrastructure layer. Real advantage is: ship fast, own the early-adopter cohort, and accumulate compression quality data (which prompt templates produce best recall). If sticky workflows form around the schema format, switching cost exists but it’s weak. Build for acquisition or lifestyle — not for Series A.