openclaw - 💡(How to fix) Fix Feature: faster local docs/workspace retrieval before browser/web fallback [1 comments, 1 participants]

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openclaw/openclaw#63291Fetched 2026-04-09 07:55:42
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Root Cause

A lot of agent work is blocked less by reasoning and more by finding the right local context quickly. Better local retrieval reduces cost, latency, and unnecessary browser/web traffic.

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Pattern to steal

Inspired by fast local docs / notes search tools (for example, qmd-style local retrieval).

Problem

OpenClaw is strong once the right data is already in context, but there is still too much friction between:

  • local docs / workspace knowledge
  • memory retrieval
  • browser / web fallback

We need a better local-first retrieval layer for workspace docs, notes, manuals, and project knowledge before escalating to web/browser flows.

Proposed feature

Improve local retrieval with:

  • fast docs / notes / markdown search over configured roots
  • strong ranking for “search before fetch” flows
  • lightweight indexing where helpful
  • clean integration with existing memory/workspace search paths
  • better developer ergonomics for local manuals, READMEs, and project docs

Why this matters

A lot of agent work is blocked less by reasoning and more by finding the right local context quickly. Better local retrieval reduces cost, latency, and unnecessary browser/web traffic.

Acceptance ideas

  • define the scope of local retrieval vs memory_search
  • define indexing/search behavior and roots
  • define UI/tool ergonomics for “retrieve local docs first” workflows
  • produce an initial implementation slice with measurable latency/quality improvements

extent analysis

TL;DR

Implement a local-first retrieval layer with fast search, ranking, and lightweight indexing to reduce friction between local docs and memory retrieval.

Guidance

  • Define the scope of local retrieval versus memory search to determine the boundaries of the new feature.
  • Determine the indexing and search behavior, including configured roots, to ensure efficient and relevant results.
  • Design clean integration with existing memory and workspace search paths to provide a seamless user experience.
  • Develop an initial implementation slice with measurable latency and quality improvements to test and refine the feature.

Example

No specific code example is provided as the issue focuses on high-level feature design and implementation.

Notes

The success of this feature relies on careful consideration of the trade-offs between local retrieval, memory search, and web/browser fallbacks. The implementation should balance latency, quality, and developer ergonomics.

Recommendation

Apply a workaround by implementing a minimal local retrieval layer with fast search and ranking, and then iterate on the feature based on user feedback and performance metrics. This approach allows for rapid testing and refinement of the feature without requiring a complete overhaul of the existing system.

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