fix(openclaw-plugin): enforce token budget and reduce context bloat (#730)#796
Conversation
…get behavior (volcengine#730) Extract resolveMemoryContent() helper to eliminate duplicate content-resolution logic between buildMemoryLines and buildMemoryLinesWithBudget. Add JSDoc and inline comment documenting intentional first-line budget overshoot (spec §6.2). Tighten test assertion from <=120 to <=106 tokens.
Resolve conflict in index.ts: keep buildMemoryLinesWithBudget approach inside main's timeout wrapper (AUTO_RECALL_TIMEOUT_MS).
…olcengine#730) Change nullish coalescing (??) to truthy fallback (||) in resolveMemoryContent() so empty-string abstracts fall back to item.uri instead of producing empty content lines.
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Hey @chethanuk, thanks for this excellent contribution! The 5-slice decomposition is really well thought out — each slice being independently revertable is a great design choice, and the before/after pipeline diagrams make the problem and solution crystal clear. The test coverage is thorough too. One request: could you retarget this PR from You can do this via: gh pr edit 796 --base refactor/openclaw-memoryOr just click Edit next to the base branch on the PR page. Thanks again for the detailed analysis and clean implementation! |
@qin-ctx Do you still want me to do this? |
Hi,@chethanuk,thank you for your pull request. We are improving the memory functionality in the context engine and will continue to evolve based on your modifications. We will be working directly on the openclaw-memory branch and expect to submit a version to main next Tuesday. We hope you can participate in the evolution of the solution. |
Sure, let me know. thank you :) |
…budget and reduce context bloat (#891) * fix(openclaw-plugin): enforce token budget and reduce context bloat (#730) (#796) * test(openclaw-plugin): add vitest test infrastructure for #730 * fix(openclaw-plugin): raise recallScoreThreshold default from 0.01 to 0.15 (#730) * fix(openclaw-plugin): narrow isLeafLikeMemory boost to level-2 only (#730) * fix(openclaw-plugin): prefer abstract over full content fetch in memory injection (#730) * feat(openclaw-plugin): add recallMaxContentChars and recallPreferAbstract config (#730) * feat(openclaw-plugin): enforce tokenBudget in injection with decrement loop (#730) * fix(openclaw-plugin): update recallScoreThreshold placeholder to match new default (#730) * fix(openclaw-plugin): deduplicate content resolution and document budget behavior (#730) Extract resolveMemoryContent() helper to eliminate duplicate content-resolution logic between buildMemoryLines and buildMemoryLinesWithBudget. Add JSDoc and inline comment documenting intentional first-line budget overshoot (spec §6.2). Tighten test assertion from <=120 to <=106 tokens. * fix(openclaw-plugin): use truthy fallback for empty abstract strings (#730) Change nullish coalescing (??) to truthy fallback (||) in resolveMemoryContent() so empty-string abstracts fall back to item.uri instead of producing empty content lines. * docs: add openclaw context engine refactor design Co-authored-by: Mijamind <mijamind@163.com> Co-authored-by: GPT-5.4 <noreply@openai.com> * add afterTurn refactor in design_doc * add afterTurn compact in design_doc --------- Co-authored-by: chethanuk <chethanuk@outlook.com> Co-authored-by: GPT-5.4 <noreply@openai.com> Co-authored-by: wlff123 <wulf234@163.com> Co-authored-by: xuwengui <huangxun375@gmail.com>
Problem
The OpenClaw plugin's memory injection pipeline has 5 compounding issues that inject 16K+ tokens of memory context per LLM call with no budget enforcement — inflating cost and degrading response quality.
Before: Unbounded Injection Pipeline
flowchart TD subgraph "❌ Current Pipeline — No Guards" A["🔍 client.find()\nReturns all matching memories"] --> B B{"⚠️ recallScoreThreshold = 0.01\n<i>Effectively no filter</i>"} B -->|"~70% irrelevant memories\npass through"| C C{"⚠️ isLeafLikeMemory()\nBoosts .md URIs + level-2"} C -->|"False positives\nranked artificially high"| D D{"⚠️ client.read(uri)\nAlways fetches full content"} D -->|"2K+ chars per memory\nfull .md files loaded"| E E{"⚠️ No truncation\nper memory item"} E -->|"Unbounded content\npassed through"| F F{"⚠️ No token budget\nin injection loop"} F -->|"All memories\nconcatenated"| G G["💥 16,384+ tokens\ninjected per LLM call"] end style A fill:#4a90d9,color:#fff,stroke:#2c5f8a style B fill:#e74c3c,color:#fff,stroke:#c0392b style C fill:#e74c3c,color:#fff,stroke:#c0392b style D fill:#e74c3c,color:#fff,stroke:#c0392b style E fill:#e74c3c,color:#fff,stroke:#c0392b style F fill:#e74c3c,color:#fff,stroke:#c0392b style G fill:#8b0000,color:#fff,stroke:#5c0000After: Budget-Enforced Pipeline
flowchart TD subgraph "✅ Fixed Pipeline — 5 Defense Layers" A["🔍 client.find()\nReturns matching memories"] --> B B{"✅ Slice A\nrecallScoreThreshold ≥ 0.15"} B -->|"~70% irrelevant\nfiltered out"| C C{"✅ Slice C\nisLeafLikeMemory: level-2 only"} C -->|"No false .md\nURI boosting"| D D{"✅ Slice B\nPrefer item.abstract"} D -->|"100-300 chars\nvs full file fetch"| E E{"✅ Slice D\nrecallMaxContentChars ≤ 500"} E -->|"Per-memory\ntruncation"| F F{"✅ Slice E\nrecallTokenBudget ≤ 2000"} F -->|"Decrement loop\nstops at limit"| G G["✨ < 2,000 tokens\nbudget-enforced injection"] end style A fill:#4a90d9,color:#fff,stroke:#2c5f8a style B fill:#27ae60,color:#fff,stroke:#1e8449 style C fill:#27ae60,color:#fff,stroke:#1e8449 style D fill:#27ae60,color:#fff,stroke:#1e8449 style E fill:#27ae60,color:#fff,stroke:#1e8449 style F fill:#27ae60,color:#fff,stroke:#1e8449 style G fill:#1a5e2f,color:#fff,stroke:#0d3b1aFix — 5 Independent Slices
Each slice is an atomic commit that can be reverted independently without affecting the others.
raise recallScoreThreshold 0.01→0.15narrow isLeafLikeMemory to level-2.mdURI extension check from boost logicprefer abstract over full contentitem.abstractwhen available, skipclient.read()add recallMaxContentCharsenforce tokenBudget with decrement loopAdditional Improvements (Review Feedback)
resolveMemoryContent()helper eliminates duplicate content-resolution logic betweenbuildMemoryLinesandbuildMemoryLinesWithBudget??to `recallMaxContentChars)Testing
10 regression tests (vitest) — one or more per slice, all passing:
flowchart LR subgraph "Test Coverage Map" direction TB subgraph SliceA["Slice A — Score Filter"] T1["① Default threshold\nfilters scores < 0.15"] T2["② Backward compat\nexplicit 0.01 preserved"] end subgraph SliceC["Slice C — Ranking"] T3["③ Level-2 only boost\nno .md URI false positives"] end subgraph SliceB["Slice B — Abstract-First"] T4["④ client.read() skipped\nwhen abstract available"] end subgraph SliceD["Slice D — Truncation"] T5["⑤ Content truncated\nat recallMaxContentChars"] T6["⑥ Config defaults\nrecallMaxContentChars=500\nrecallPreferAbstract=true"] end subgraph SliceE["Slice E — Budget"] T7["⑦ Budget enforcement\ndecrement loop stops"] T8["⑧ First-line overshoot\n≤2 lines, ≤106 tokens"] T9["⑨ estimateTokenCount\nceil(chars/4) heuristic"] T10["⑩ Config default\nrecallTokenBudget=2000"] end end style SliceA fill:#e8f5e9,stroke:#27ae60 style SliceC fill:#e8f5e9,stroke:#27ae60 style SliceB fill:#e8f5e9,stroke:#27ae60 style SliceD fill:#e8f5e9,stroke:#27ae60 style SliceE fill:#e8f5e9,stroke:#27ae60recallScoreThreshold: 0.01config is preserved and respectedisLeafLikeMemoryranking.mdURI withlevel≠2does NOTclient.read()not called whenitem.abstractis populated"..."(503 total)recallMaxContentChars=500,recallPreferAbstract=trueestimateTokenCountaccuracy""→0,"abcd"→1,"abcde"→2,"A"×100→25recallTokenBudget=2000Impact
graph LR subgraph "Before" B1["16K+ tokens/call"] B2["~$13.50/day"] B3["No budget control"] end subgraph "After" A1["< 2K tokens/call"] A2["< $1.50/day"] A3["3 config knobs"] end B1 -.->|"87% reduction"| A1 B2 -.->|"89% savings"| A2 B3 -.->|"user-tunable"| A3 style B1 fill:#e74c3c,color:#fff style B2 fill:#e74c3c,color:#fff style B3 fill:#e74c3c,color:#fff style A1 fill:#27ae60,color:#fff style A2 fill:#27ae60,color:#fff style A3 fill:#27ae60,color:#fffinjecting N memoriesinjecting N memories (~T tokens, budget=B)New Configuration Options
All options have backward-compatible defaults — zero config changes required for existing users.
recallScoreThresholdnumber0.15(was 0.01)recallPreferAbstractbooleantrueitem.abstractinstead of fetching full content viaclient.read()recallMaxContentCharsnumber500...)recallTokenBudgetnumber2000Files Changed
examples/openclaw-plugin/config.tsexamples/openclaw-plugin/index.tsresolveMemoryContent()helper, budget-enforced injection loop, abstract-first resolutionexamples/openclaw-plugin/memory-ranking.tsisLeafLikeMemorynarrowed tolevel === 2onlyexamples/openclaw-plugin/openclaw.plugin.jsonexamples/openclaw-plugin/__tests__/context-bloat-730.test.tsexamples/openclaw-plugin/vitest.config.tsexamples/openclaw-plugin/package.jsonCloses #730