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.