Long-horizon agents drown in their own history: every observation gets appended, every call gets slower and costlier, and stale context corrupts decisions. Lethe fixes this with three rules. (1) Explicit mutable state: a one-page Mission Ledger the agent rewrites every cycle instead of a transcript, with a hard 6k-token cap per LLM call. (2) The agent edits its own context through tools: update_plan, record_decision, add_lesson, set_recommendation, mark_stale. (3) A clear split between persist and discard: raw pages die with the worker that read them, extracted facts carry a TTL, facts confirmed by two sources persist, and a sold listing wipes everything about it.
The demo mission prices a dealership's used RVs against the live web. Scouts use Nimble to search and read listings and return a card under 200 tokens. A Liquid LFM2.5 model running locally on a laptop assigns every fact its TTL and confidence. An Auditor checks comparability and, when a strategy fails, writes a lesson and the agent switches strategy on its own. OpenAI's gpt-oss models plan and decide. All state and every event, including token counts per call, live in a RawTree database, so kill -9 followed by restart resumes in under a second with no replay.
Results: max context 2,876 tokens over hours of live runs. The naive baseline (same tools, append-everything) hits the provider's wall by step 6. The dashboard shows both lines, the board, the facts being expired and contradicted, and the self-corrections.
