Your Forest Is a Vault
The series closer: a self-hosted forest, scoped keys, one row per person, and a locked gate. Twenty days of measured claims and published failures, recapped in three sentences.
3 min read
Blog
Notes from the forest
Engineering notes on navigation, ingestion, agent memory and honest benchmarking, written by the people who ran the experiments.
Make the Forest Your Agent's Memory
Ten typed tools over MCP, a skills file that teaches the agent the terrain, and one scoped key per agent: how a self-hosted forest becomes memory your agents can search, walk and grow.
4 min read
Ten Typed Tools over MCP
Why agents get ten small, typed, token-budgeted tools over MCP instead of one giant search box, and what that buys in tokens, latency, and legible failures.
4 min read
Agent Amnesia and the Second Bill
Every agent session starts by rebuilding context the last one already paid for. What a persistent, growable forest memory changes about agent workflows, and the skills file that makes agents actually use it.
4 min read
A Three-Hop Question, Worked Example
One question, three documents, traced twice: watch top-k retrieval dead-end on a three-hop question, then watch the same corpus answer it when an agent walks it node by node.
4 min read
Navigate, Don't Retrieve: What a Hunt Actually Looks Like
Three moves, one worked hunt through a small company's corpus, and why the same 12B model goes from 0/11 to 11/11 when it walks a forest instead of reading a top-k paste.
4 min read
Your agentic RAG loop answers slower and spends more per correct answer. We measured it.
Iterative RAG loops hide their cost in the wrong denominator. Measured per correct answer: 0.58x the tokens and 8.4 s p95 vs 17.5 s, same 12B model.
3 min read
Want the long version?
The paper carries the full architecture, the benchmark tables and the findings that failed their criteria.