dsh-continual-evolve
中文 | English
Continual self-evolution for DeepSeek Harness: a versioned, auditable, rollback-safe harness state layer — prompt notes, memories, skills, subagent specs — refined from session trajectories.
The model proposes, the code guarantees. Every mechanical safety property — schema validation, atomic writes, snapshots, versioning, audit trail, acceptance decisions — is enforced in code, never by prompt discipline.
Why
Agents accumulate reusable experience (repeated failures, durable facts, reusable procedures) and forget it next session. This plugin turns that experience into first-class state:
- Local scope per session; global scope across sessions with merge semantics — plus mechanical promotion guards so only portable, substantial, non-duplicate knowledge reaches global
- Deterministic rollback: inverse edits generated from applied results — no LLM re-guessing
- Benchmark loop: candidate refinements are evaluated against frozen cases by a separate scorer before acceptance (rubric encrypted at rest)
- Store hygiene:
/evolve consolidate turns write-time conflict hints and zero-use staleness into one approved, fully reversible batch of archives — with merge, near-duplicate content folds into the surviving original
How it works
- Sediment — the model creates entries via
evolve_add, or the automatic review gate proposes them from the session trajectory (turn-interval + compaction checkpoints).
- Guard — code-enforced validation: edit schema, blast-radius/scope coherence, and the promotion policy (project-scoped markers, thin content, near-duplicate detection, credential screening keep the global store clean — secrets are rejected at every write sink, including mount materialization). Global creates that near-duplicate an existing entry are rejected at write time (≥0.8 similarity); moderate overlaps carry a
conflictHint for later consolidation.
- Approve — global writes require explicit human approval; local-fate proposals are consulted before they land.
- Apply & inject — atomic apply with snapshot + audit event. Prompt notes and delegation specs inject into the system prompt (capped, relevance-ranked, contradicted entries demoted, zero tokens when empty); memories/skills appear as a capped directory index.
- Validate & roll back — benchmarks score candidates against frozen cases; rejected candidates roll back deterministically and are captured as draft regression cases (
auto_regression benchmark).
Install
# from npm (installs and activates — ships its own bundle patch)
dsh plugin add dsh-continual-evolve
# or from source (first GitHub installs require approving the allowBuilds step)
dsh plugin add ZK-Andy/dsh-continual-evolve
Restart dsh web after installing or updating.
Usage
Commands (in-session):
| Command | Effect |
|---|
/evolve | help + current local store |
/evolve list · history · rollback <id> | inspect and revert (add global for the cross-session store) |
/evolve plan [msg] | run the LLM planner against the store |
/evolve wrapup | assess this session's local entries: promote / archive / keep |
/evolve archive · unarchive · demote <id> | hide from injection (data kept, restorable) — demote targets global noise |
/evolve consolidate [apply] [merge] | report (or apply) one batch archive of conflict-hinted + stale zero-use global entries; merge folds near-duplicate content into the survivors |
/evolve failures | aggregated failure classes (gate + benchmark) |
/evolve log [tail N] [session <id>] | plugin log |
/evolve export · import <path> | backup / restore a store |
/evolve mount · unmount <skillId> | hot-mount an executable skill as a live plugin |
/evolve goal [objective · done · block] | round-driven auto-review goal |
/evolve benchmark … | case lifecycle, runs, acceptance |
Model tools: evolve_list / add / update / delete / rollback.
For third-party consumers: every applied evolution (gate or manual) appends a structured evolve_complete event to reviews.jsonl (src/evolve-event.ts defines the shape) alongside the human-readable audit records.
Injection shape: prompt notes and delegation specs inject with content (≤6/kind × 180 chars, relevance-ranked). Memories and skills appear as a directory index ([kind:id] title, capped at 15 lines with a fold counter) — full text via evolve_list. Empty store = zero injected tokens.
Configuration
| Key | Default | Meaning |
|---|
baseDir | resolved DSH home | root for the evolve/ stores |
autoReview | false | enable the automatic review gate |
reviewIntervalTurns | 6 | gate cadence on the turn-interval path |
maxReviewInputChars | 40000 | trajectory slice handed to the gate |
reviewBudgetTokens | 4096 | output budget for the gate call |
notifyOnAutoReview | true | visible follow-up notice after an applied gate run |
requireGlobalApproval | true | global edits ask for explicit approval |
localFate | true | gate audits local entries and proposes promote/archive (consulted, never silent) |
fateIntervalTurns | follows reviewIntervalTurns | minimum turns between fate assessments |
goalBlockedWrapupTurns | 3 | consecutive blocked-goal gate runs trigger one fate assessment (0 disables) |
promotionBlockPatterns | POSIX paths, session ids, ~/.dsh | content matching these is project-scoped and never promoted to global |
promotionMinChars | 100 | whole promotions below this length stay local |
injectionDirectoryLines | 15 | entry-directory lines per build before folding into a counter |
sectionOrder | 118 | system-prompt section order |
skillsDir | <dshHome>/skills | where skill entries materialize as SKILL.md bundles |
rubricKey | auto-generated key file | AES-256-GCM passphrase for benchmark rubrics ( overrides) |
Example profile patch:
- id: continual-evolve
config:
autoReview: true
reviewIntervalTurns: 6
Development
pnpm install && pnpm build # deps + tsc -> lib/
pnpm test # vitest (573 tests)
pnpm test:coverage # v8 coverage, thresholds enforced in CI
pnpm lint # oxlint src test
Project layout:
├── src/ # engine, tools, commands, gate, fate, benchmark, usage…
├── test/ # vitest suites (36 files)
├── lib/ # build output (tsc)
├── docs/
│ ├── design.md # full design doc (hardening matrix)
│ ├── FAQ.md # real failure/fix records
│ ├── gap-analysis.md # vs prime-agent /refine + penguin-harness
│ ├── research/pi-dsh-competitor-gap-analysis.md # pi/dsh ecosystem competitors
│ ├── experiment-bootstrap.md
│ ├── archive/ # closed point-in-time reports
│ └── research/ # penguin report + prime-agent annotated source
├── examples/README.md # seed benchmark cases
└── .agents/ # AI collaboration layer (AGENTS.md, skills, ADR notes)
Docs & provenance
License
MIT
