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## Description Please include a summary of the change, the problem it solves, the implementation approach, and relevant context. List any dependencies required for this change. Related Issue (Required): Fixes #issue_number ## Type of change Please delete options that are not relevant. - [ ] Bug fix (non-breaking change which fixes an issue) - [ ] New feature (non-breaking change which adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] Refactor (does not change functionality, e.g. code style improvements, linting) - [ ] Documentation update ## How Has This Been Tested? Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration - [ ] Unit Test - [ ] Test Script Or Test Steps (please provide) - [ ] Pipeline Automated API Test (please provide) ## Checklist - [ ] I have performed a self-review of my own code | 我已自行检查了自己的代码 - [ ] I have commented my code in hard-to-understand areas | 我已在难以理解的地方对代码进行了注释 - [ ] I have added tests that prove my fix is effective or that my feature works | 我已添加测试以证明我的修复有效或功能正常 - [ ] I have created related documentation issue/PR in [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) (if applicable) | 我已在 [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) 中创建了相关的文档 issue/PR（如果适用） - [ ] I have linked the issue to this PR (if applicable) | 我已将 issue 链接到此 PR（如果适用） - [ ] I have mentioned the person who will review this PR | 我已提及将审查此 PR 的人 ## Reviewer Checklist - [ ] closes #xxxx (Replace xxxx with the GitHub issue number) - [ ] Made sure Checks passed - [ ] Tests have been provided
…tecture
Complete end-to-end rewrite of the memos-local-plugin package into a
layered, agent-agnostic memory runtime with support for both OpenClaw
and Hermes adapters.
Highlights:
- New `core/` package (agent-agnostic): capture, embedding, feedback,
hub, LLM client, logger, memory (L1/L2/L3), pipeline, reward, recall,
retrieval, skill, session, storage, config modules — each with its
own README + ALGORITHMS notes.
- New `adapters/` layer with `openclaw/` and `hermes/` integrations
isolated from core. Agent-specific concepts (turns, installers,
bridge clients) live only here.
- New `agent-contract/` — single shared contract (dto, errors, events,
jsonrpc, log-record, memory-core) between core and adapters.
- New `bridge/` — JSON-RPC stdio bridge (methods.ts, stdio.ts).
- New `server/` — HTTP/SSE server for the viewer.
- New `site/` — Vite-built public product site + release notes index.
- New `web/` — Vite-built viewer app with memory/skill/timeline/world
model views.
- New `docs/` — ALGORITHM, DATA-MODEL, LOGGING, MANUAL_E2E_TESTING,
Reflect2Skill design core, multi-agent viewer, etc.
- New `tests/` — vitest unit/integration + python bridge tests.
- Tooling: TypeScript multi-project build (tsconfig.{json,site,web}),
Vite + Vitest, cross-platform install.sh / install.ps1, npm release
checker, package-lock committed.
- Removes legacy `src/` and `www/` structure from main branch; the new
layout replaces it entirely.
This change is fully scoped to apps/memos-local-plugin/ and does not
touch any other package.
- Remove unused imports (Iterable, Dict, List) in memos_provider/__init__.py - Move Callable into TYPE_CHECKING block in bridge_client.py - Replace try/except/pass with contextlib.suppress in bridge_client.py - Combine nested if in test_bridge_client.py - Apply ruff format to 4 Python files (hermes adapter + tests) All files now pass `ruff check` and `ruff format --check`.
…tecture (MemTensor#1511) ## Summary Complete end-to-end rewrite of `apps/memos-local-plugin/` into a layered, agent-agnostic memory runtime with adapters for both OpenClaw and Hermes. This replaces the previous `src/` + `www/` layout on `main` entirely. The change is **fully scoped to `apps/memos-local-plugin/`** — no other package is modified. ### What's new - **`core/`** — agent-agnostic runtime: capture, embedding, feedback, hub, LLM client, logger, memory (L1/L2/L3), pipeline, reward, recall, retrieval, session, skill, storage, config. Each module ships with a `README.md` and, where relevant, an `ALGORITHMS.md`. - **`adapters/`** — agent-specific integrations isolated from core: - `openclaw/` — TS bridge + tools + installer - `hermes/` — Python `memos_provider` + bridge client + install script - **`agent-contract/`** — the only shared contract between core and adapters (dto / errors / events / jsonrpc / log-record / memory-core). - **`bridge/`** — JSON-RPC stdio bridge (`methods.ts`, `stdio.ts`). - **`server/`** — HTTP/SSE server backing the viewer. - **`web/`** — Vite viewer app (Overview / Memories / Policies / Skills / Tasks / WorldModels / Settings views). - **`site/`** — Vite-built public product site + per-version release notes index. - **`docs/`** — algorithm alignment, data model, logging, manual E2E scenario, Reflect2Skill design core, multi-agent viewer. - **`tests/`** — vitest unit + integration + Python bridge tests. - **Tooling** — multi-project TS build (`tsconfig.{json,site,web}.json`), Vite + Vitest, cross-platform `install.sh` / `install.ps1`, npm release checker, `package-lock.json` committed. ### Structural changes vs `main` - Removes the legacy `src/` and `www/` layout on `main` — the new module layout supersedes it. - Both `.gitignore` and `.npmignore` updated to cover new build outputs (`site/dist/`, `web/dist/`, `coverage/`, etc.). ### Stats 615 files changed · +94,489 / −36,040. ## Type of change - [x] New feature (non-breaking change which adds functionality) - [x] Refactor (significant restructuring of `apps/memos-local-plugin/`) ## Test plan - [ ] `cd apps/memos-local-plugin && npm install` - [ ] `npm run lint` - [ ] `npm test` (vitest unit + integration) - [ ] Manual E2E per `apps/memos-local-plugin/docs/MANUAL_E2E_TESTING.md` - [ ] OpenClaw adapter install: `./install.sh` on macOS/Linux; `install.ps1` on Windows - [ ] Hermes adapter install: `adapters/hermes/install.hermes.sh` - [ ] Viewer smoke test: `npm run dev:web` → open Overview / Memories / Skills views - [ ] Site build: `npm run build:site`
…rovements Capture path (thinking-between-tool-calls): - Adapter extractTurn now flushes thinking + assistant-text that appears between consecutive tool calls into the next ToolCallDTO's `thinkingBefore`, preserving the model's natural-language bridge (e.g. "nproc failed, let me try sysctl") in the trace. - Adapter flattenMessages: when pi-ai `content[toolCall]` coexists with legacy top-level `tool_calls`, skip the legacy path so each call is emitted once (prior double-push clobbered the first stub's `thinkingBefore` via pendingCalls.set, making the field silently go missing and doubling tool-call rows in the DB). - Orchestrator: tool turns now persist `thinkingBefore` in EpisodeTurn.meta so the capture step-extractor can re-attach it. - Step extractor: only the first tool sub-step carries `userText`; subsequent sub-steps leave it empty so the viewer's flattenChat doesn't render the same user bubble N times. - Step extractor: `toolCallFromTurn` + `coerceToolCall` now read `thinkingBefore` back from meta. - Normalizer: sub-step candidates skip the generic dedup path — their intentionally-identical empty userText/agentText plus 1-tool shape used to collapse two distinct tools into one whenever their input prefixes matched under 200 chars. - Agent contract DTO: `ToolCallDTO.thinkingBefore?: string` added; no schema migration needed (stored inside `tool_calls_json`). - Web flattenChat: renders per-tool `thinkingBefore` bubbles before each tool call for the user↔agent timeline; retains legacy `agentThinking` single-bubble fallback for pure-reply traces. Retrieval path: - LLM-filter: refactored prompt templates and schema shape. - Ranker: reworked scoring with new blend knobs. - Retrieve + pipeline wiring updated to match the new types. - Config defaults + schema expose the new retrieval knobs. - Viewer LogsView surfaces new filter fields; i18n updated. Tests: - New regression tests for extractTurn interleaved thinking, pi-ai + OpenAI-legacy double-push avoidance, and flattenChat sub-step rendering. - Retrieval / llm-filter / ranker tests updated for the new shape.
…oning capture (MemTensor#1515) ## Summary This PR lands the **memos-local-plugin v2** rewrite and iterates on top of it with multi-tool-call reasoning capture + retrieval improvements. ### Scope (only `apps/memos-local-plugin/`) Three commits on top of `main`: 1. `feat(memos-local-plugin): v2.0 full rewrite with Reflect2Evolve architecture` — the base v2 rewrite (layered L1/L2/L3 memory, Reflect2Evolve capture/reward/skill pipeline, tier-1/2/3 retrieval, OpenClaw + Hermes adapters). 2. `style(memos-local-plugin): apply ruff check + format to Python files` — Python adapter pass (Hermes provider). 3. `feat(memos-local-plugin): preserve mid-turn reasoning + retrieval improvements` — the capture+retrieval work described below. ### Capture path — mid-turn reasoning is no longer lost A multi-tool-call turn looks like `user → [thinking?, text?, tool_1] → result_1 → [thinking?, text?, tool_2] → result_2 → [text final]`. The plugin used to collapse every `assistant` text into one `agentText` and drop the bridge narration ("tool_1 failed, let me try tool_2") entirely. This PR fixes that end-to-end: - **Adapter** `extractTurn` flushes `thinking` + inline assistant text into the *next* tool call's new `thinkingBefore` field so the bridge reasoning is preserved per-tool, not smashed into the final reply. - **Adapter** `flattenMessages` no longer double-emits tool calls when pi-ai's `content[toolCall]` and OpenAI-legacy top-level `tool_calls` coexist. Prior double-push clobbered the first stub via `pendingCalls.set`, making `thinkingBefore` silently go missing *and* doubling tool-call rows in the DB. - **Orchestrator** persists `thinkingBefore` in the tool `EpisodeTurn.meta` so capture can read it back. - **Step extractor** (per-decision-point sub-step capture): - Only the first tool sub-step carries `userText`; subsequent sub-steps leave it empty so `flattenChat` doesn't render the same user bubble N times. - `toolCallFromTurn` + `coerceToolCall` now hydrate `thinkingBefore` from meta. - **Normalizer** skips the generic dedup path for sub-steps — their intentionally-identical empty userText/agentText + 1-tool shape was incorrectly collapsing two distinct tools whenever their input prefixes matched under 200 chars. - **Agent contract** `ToolCallDTO.thinkingBefore?: string` added; no SQL migration (stored inside `tool_calls_json`). - **Web** `flattenChat` renders per-tool `thinkingBefore` bubbles before each tool call; retains `agentThinking` single-bubble fallback for pure-reply traces. ### Retrieval path - Refactored LLM-filter prompt + schema, reworked ranker scoring with new blend knobs, updated retrieve + pipeline wiring. - Config defaults / schema expose the new retrieval knobs; viewer LogsView surfaces the new fields; i18n updated. ### Tests - New regression tests for: - `extractTurn` interleaved thinking per tool call - pi-ai + OpenAI-legacy double-push avoidance in `flattenMessages` - `flattenChat` sub-step rendering (per-tool thinking bubble, no duplicate user) - Existing retrieval / llm-filter / ranker unit tests updated to the new shape. ## Test plan - [x] `apps/memos-local-plugin/` unit tests: `flattenChat` suite passes (12/12). - [x] Live OpenClaw integration: clean DB, issue a multi-tool-call query (e.g. "list git status + recent commits + current branch"), verify in the Tasks drawer: - Single `user` bubble (no N-fold repetition) - `thinking` bubble before each tool call **when the model produced one** (models may also emit tool calls without any mid-step text — that's model-level behavior and cannot be back-filled) - Tool call bubbles in chronological order with correct input/output - Final `assistant` reply at the end - [x] Confirmed no duplicated tool-call rows in `tool_calls_json` after the double-push fix. ## Non-scope - No changes outside `apps/memos-local-plugin/`. - No `pnpm-lock.yaml` / `uv.lock` bumps.
…ledge + L2/L3 boundary prompts UI: one user turn = one memory card - New `traces.turn_id INTEGER` column (migration 013) stamped by `step-extractor` with the user turn's ts; every sub-step of the same user message shares the same turnId. - `MemoryGroup` aggregation in `web/src/views/MemoriesView.tsx` collapses rows by (episodeId, turnId): one card per turn, role pill chosen by group-level rule (any tool → "tool"), aggregate V/α displayed as the member-row mean. - Drawer rewritten as `<StepList>`: every member step renders as a collapsible <details> block with its own ts / V / α / agentThinking / toolCalls / reflection. First step expanded, rest collapsed so a 10-tool turn doesn't drown the user. - Bulk actions (select / delete / share / export) operate on whole cards: card checkbox toggles the full set of member ids; delete / share / export bulk over `g.ids` so a card never half-disappears. - Algorithm layer untouched — every L1 trace stays step-level so V/α reflection-weighted backprop, L2 incremental association, Tier-2 error-signature retrieval, and Decision Repair keep their per-step granularity (V7 §0.1). Per-tool reasoning capture (carryover, see PR MemTensor#1515) - ToolCallDTO carries `value` / `reflection` / `thinkingBefore` so the drawer's per-step section can show the per-tool intermediate thinking and any LLM-assigned per-tool score without a schema change. - StepCandidate.meta.turnId / subStep / subStepIdx / subStepTotal threaded through capture.ts → traces.turn_id; `pickTurnId` falls back to the trace's own ts so old fixtures still produce singleton groups instead of crashing. Knowledge generation in user's language - `core/llm/prompts/index.ts` adds `detectDominantLanguage(samples, {minSignal})` — counts CJK ideographs + ASCII letters and returns "zh" / "en" / "auto" (allocation-free, runs on every gen call). - All five knowledge-generation sites now emit a `languageSteeringLine` system message keyed off their evidence: * core/capture/alpha-scorer.ts ← reflection-quality reason * core/capture/batch-scorer.ts ← per-step batch reflections * core/memory/l2/induce.ts ← L2 policy fields * core/memory/l3/abstract.ts ← L3 (ℰ, ℐ, C) bullets * core/skill/crystallize.ts ← skill body + scope - Effect: a Chinese-speaking user no longer gets a half-English skill card. An English user no longer gets a 中文-mixed reflection. L2 / L3 prompts: hard boundary against drift - `L2_INDUCTION_PROMPT` v1 → v2: explicit "what NOT to write" guard rejects environment topology, declarative behavioural rules, and generic taboos. New same-fact-two-framings example shows how to re-fold an env fact into a state-level trigger or step-level caveat. - `L3_ABSTRACTION_PROMPT` v1 → v2: bans imperative verbs (do/should/use/ install/run) under any of ℰ/ℐ/C; reworked all three example sets to pure declarative ("loading a glibc-linked binary wheel inside Alpine raises a dynamic-link error" instead of "if pip fails, install dev libs and retry"). Same-fact contrast example included. - Test mock keys updated v1 → v2 in induce.test.ts / l2.integration.test.ts / openclaw-full-chain.test.ts / v7-full-chain.e2e.test.ts. Historical `inducedBy` audit strings intentionally left at v1 — they're metadata recording the prompt version a row was generated under, not call-time keys. Retrieval injector: heading hierarchy - `# User's conversation history (from memory system)` is now H1, with `## Memories` / `## Skills` / `## Environment Knowledge` as H2 so the injected block has a clean outline in the LLM's context (previously the inner sections used H1 too, breaking the visual hierarchy). Migration runner: SQLite defensive mode - better-sqlite3 ≥ v11 enables `SQLITE_DBCONFIG_DEFENSIVE` which blocks writes to `sqlite_master` even with `PRAGMA writable_schema=ON`. Migration 012 (status unification) needs that pragma to swap CHECK constraints in-place. `runMigrations` now flips `db.raw.unsafeMode` on at the outer boundary if any pending migration uses `writable_schema`, then off again in `finally`. Migrations are shipped with the plugin (never user input) so this is safe. - Migration 012 SQL itself rewritten to use single-quote string literals with doubled inner quotes (instead of double quotes that better-sqlite3 strict mode treats as identifiers). Documentation - New `docs/GRANULARITY-AND-MEMORY-LAYERS.md` — mental-model alignment doc explaining: 小步/轮/任务 三个粒度的关系、打分粒度（每步 α/V， 每任务 R_human，"轮"无独立分）、检索粒度（技能/单步/子任务序列/ 环境认知，没有"按轮"召回）、生成链路（小步→经验→环境认知→技能）、 以及 §6 "经验 vs 环境认知 边界裁剪" 章节回答"该不该合并"问题：7 条 反对合并的理由 + 三种折中方案对比 + 同事实多框架对照判别表。 - `docs/Reflect2Skill_算法设计核心.md` 头部加阅读顺序提示，引导新人 先看上面那篇粒度对齐文档。 - `docs/README.md` 索引同步更新，标粗 GRANULARITY-AND-MEMORY-LAYERS。 Tests - `tests/unit/capture/step-extractor.test.ts`: turnId stability assertions across sub-steps; multi-tool turn shares one turnId. - All other test fixtures' LLM mock keys synchronized with new prompt versions; non-mock `inducedBy` audit fields kept at v1 by design.
…ledge + L2/L3 boundary prompts (MemTensor#1516) ## Summary This PR continues the v2 Reflect2Evolve plugin work merged in MemTensor#1515 with three orthogonal improvements that landed together because they share the same trace fixtures and tests: 1. **UI: one user turn = one memory card** — frontend collapses sub-step rows by `(episodeId, turnId)`. Algorithm layer (V/α backprop, L2 induction, Tier-2 retrieval, Decision Repair) keeps step-level granularity per V7 §0.1. 2. **Knowledge generation in user's language** — every L1/L2/L3/Skill/reflection generation site now detects the dominant language of its evidence and emits a `languageSteeringLine` so a Chinese user no longer gets half-English memos. 3. **L2 / L3 prompts: hard boundary against drift** — `L2_INDUCTION_PROMPT` and `L3_ABSTRACTION_PROMPT` bumped v1 → v2 with explicit "what NOT to write" guards plus same-fact-two-framings examples to keep procedural ↔ declarative knowledge cleanly separated. Plus two infrastructure fixes the v2 plugin needed to actually run on better-sqlite3 ≥ v11 (defensive-mode block on `sqlite_master`) and a documentation alignment doc explaining the 小步/轮/任务 + 经验/环境认知/技能 mental model so future contributors stop conflating UI, storage, and algorithm granularities. ## What changed ### `traces.turn_id` + per-turn UI grouping - New migration `013-trace-turn-id.sql`: adds `turn_id INTEGER` + `idx_traces_episode_turn` index. - `step-extractor.ts` stamps every sub-step from the same user message with the user turn's `ts` as `meta.turnId`; `capture.ts::pickTurnId` threads it into `traces.turn_id`. - `MemoriesView.tsx` introduces `MemoryGroup` aggregation + `<StepList>` drawer so a 5-tool turn renders as one card with five collapsible step blocks (each carrying its own V / α / reflection / toolCalls), instead of five sibling cards. Bulk select / delete / share / export operate at card level. - DB rows from before this migration get NULL `turn_id` and fall back to per-row rendering. ### Language-aware knowledge generation - `core/llm/prompts/index.ts`: new `detectDominantLanguage(samples, {minSignal})` — counts CJK ideographs vs ASCII letters, returns `"zh" | "en" | "auto"`. Allocation-free, runs on every gen call. - All five gen sites inject `languageSteeringLine`: - `capture/alpha-scorer.ts` — reflection-quality reason - `capture/batch-scorer.ts` — per-step batch reflections - `memory/l2/induce.ts` — L2 policy fields - `memory/l3/abstract.ts` — L3 (ℰ, ℐ, C) bullets - `skill/crystallize.ts` — skill body + scope ### L2 / L3 boundary prompts (v1 → v2) - `L2_INDUCTION_PROMPT`: new "Boundaries — what NOT to write" section explicitly rejects environment topology / declarative behavioural rules / generic taboos. Includes same-fact-two-framings example (procedural vs declarative for the same underlying truth). - `L3_ABSTRACTION_PROMPT`: bans imperative verbs (do / should / use / install / run) under any of ℰ/ℐ/C. All three example sets rewritten as pure declarative ("loading a glibc-linked binary wheel inside Alpine raises a dynamic-link error" instead of "if pip fails, install dev libs and retry"). - Test mock keys updated v1 → v2; historical `inducedBy` audit strings intentionally left at v1 (they record the prompt version a row was generated under, not a call-time match key). ### Retrieval injector heading hierarchy - `# User's conversation history (from memory system)` is H1; `## Memories` / `## Skills` / `## Environment Knowledge` are H2 — restores the visual outline the LLM consumes. ### Migration runner: better-sqlite3 ≥ v11 compatibility - `runMigrations` now flips `db.raw.unsafeMode(true)` at the outer boundary if any pending migration uses `PRAGMA writable_schema` (resets in `finally`). Migration 012 (status unification) needs this to swap CHECK constraints in-place; defensive mode otherwise blocked it at runtime. - Migration 012 SQL uses single-quote literals with doubled inner quotes (was double-quoted, which strict mode treats as identifiers). ### Documentation - New `docs/GRANULARITY-AND-MEMORY-LAYERS.md` (~365 lines, zh-CN) — the foundational mental-model doc that should be read before any other algorithm doc: - 小步 / 轮 / 任务 三个交互粒度 + 与代码层的对应 - 打分粒度（每步 α/V，每任务 R_human，"轮"无独立分） - 检索粒度（技能/单步/子任务序列/环境认知，没有"按轮"召回 + 三层判别） - "结构性不确定" vs "操作性疑问" 判别表 - 经验 / 环境认知 / 技能 五者关系 - §6 "经验 vs 环境认知 边界裁剪"：7 条不该合并的理由 + 三种折中方案对比 + 7 维度判别速查 + 同事实多框架对照表 + 反例 - `docs/Reflect2Skill_算法设计核心.md` 头部加阅读顺序提示。 - `docs/README.md` 索引同步更新。 ## Algorithm alignment Per V7 §0.1, the L1 trace is the minimum learning unit and stays step-level — one tool call → one trace, one final reply → one trace. The "one round = one memory" view is purely a frontend display concern using `turn_id` as a stable group key. Reflection-weighted backprop, cross-task L2 association, error-signature retrieval, and Decision Repair all continue to operate per-step. Documented end-to-end in the new GRANULARITY doc §6. ## Test plan - [x] `npx vitest run tests/unit/capture/step-extractor.test.ts` — turnId stamped on every sub-step, multi-tool turn shares one turnId (11/11 pass) - [x] `npx vitest run tests/unit/memory/l2/ tests/unit/memory/l3/ tests/unit/llm/prompts.test.ts` — prompt v2 mock keys + L2/L3 induction (74/74 pass) - [x] `npx vitest run tests/unit/storage/` — migration 013 applies cleanly (106/106 pass) - [x] `npx vitest run tests/unit/` — full unit sweep: 802/806 pass; 4 failures are pre-existing on `main` (mock LLM behavior in reward integration + an outdated `capture.lite.done` event-list assertion), unchanged by this PR - [x] Local install via `bash install.sh --version ./memtensor-memos-local-plugin-2.0.0-beta.1.tgz`: gateway + viewer come up clean, `traces.turn_id` column present, migration 013 logged as applied - [x] Manual end-to-end: ran a 3-tool query in OpenClaw, verified the memory page shows ONE card with `工具 · 4 步` chip, drawer expands into 4 collapsible step sections with per-step V/α/thinking/tool I-O ## Notes - No backward compat for the schema change is required — fresh installs run all 13 migrations on first open. Existing local DBs auto-pick up 013 the next time the gateway opens them. - Only `apps/memos-local-plugin/` is touched. No changes to other packages.
Bring the plugin runtime, API bootstrap integration, and related tests from aliyun-ee/dev-20260423-v2.0.14.post onto upstream/main so the upstream branch can review the plugin architecture independently of the broader enterprise history. Made-with: Cursor
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Description
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