dsh-chapters

Long memory for local AI: a DeepSeek Harness plugin that archives agent conversations as verbatim chapters, continues sessions from a Table of Contents at zero inference-token cost, and turns that archive into a shared, searchable knowledge pool. Published on npm as @treeseed/dsh-chapters; source at treeseed-ai/dsh-chapters.

All documentation lives on the documentation site — this README is a doorway, not a duplicate.

📖 https://treeseed-ai.github.io/dsh-chapters/

Install (the whole setup)

dsh plugin --profile web add @treeseed/dsh-chapters      # install into your harness profile
dsh plugin --profile web update @treeseed/dsh-chapters   # keep current — restart `dsh web` after

--profile <name> is required on every dsh plugin command (web = the browser harness profile). After restart you have the fork button, six agent tools, four /chapters-* commands — and the optional chapters preset that routes /compact through the zero-token engine.

Find the right page for your question

You want to…	Go to
understand what this is / why (the ten benefits)	Home → “What it gives you”
install, upgrade, uninstall, verify provenance	§23 Install, upgrade, provenance
run your first compaction/continuation	§1 Quickstart → §2 Your first continuation
link a shared knowledge pool (git or TreeDX)	§3 Link a knowledge pool · §10 The pool
run any command — exact syntax, scope, outputs	§15 Commands
know what the agent-facing tools do	§16 Agent tools
tune a threshold, budget, or trigger	§17 Configuration
understand why not summaries / the economics	§5 Why not summaries?
read the design rules the system won't break	§6 The four invariants
the theory behind retrieval-over-compression (RLM)	§8 Recursive context theory
propose/approve rules for agents	§13 Rules & governance
debug something (“seems broken, is it?”)	§4 FAQ & troubleshooting
prompt-cache / llama.cpp behavior	§21 Cache behavior on llama.cpp
see measured results (production run + benchmarks)	§19 Benchmarks · §20 Field study
compare with other memory approaches	§22 The alternatives, compared
check security/privacy boundaries	§27 Security & privacy
release history & known-fixed incidents	§26 Changelog
develop the plugin itself	§24 Developing → the engineering specs below

Developing / contributing

The site's Part VI covers the daily loop; the repository itself holds the engineering truth for agents:

• AGENTS.md — working spec for AI agents developing this repo (invariants, budget, hard rules)
• docs/ — deep specs: contract.md (host API facts), architecture.md (mechanics), development.md (build/test loop + tape system), verify.md (every claim's procedure), knowledge-repo.md (design record), host-compaction-seam.md, provider.md
• benchmarks: npm run bench — three arms, local model, never CI (protocol)

Feature requests and bugs: open an issue — measured reports get measured answers.

License

Apache-2.0 — see LICENSE. Your conversations, chapters, and knowledge pools stay yours.