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fix(openclaw): migrate example plugin memory search to the v1 POST + Filters DSL API#245

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fix(openclaw): migrate example plugin memory search to the v1 POST + Filters DSL API#245
Fearvox wants to merge 668 commits into
EverMind-AI:mainfrom
Fearvox:fix/openclaw-plugin-v1-search-migration

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@Fearvox Fearvox commented Jun 3, 2026

What

The OpenClaw example plugin's memory recall never worked against the v1
backend. searchMemories (examples/openclaw-plugin/src/api.js) still spoke the
v0 dialect on both the request and the response side:

Request — it sent GET /api/v1/memories/search with a flat top-level
user_id and a retrieve_method field. Against the v1 API that fails twice:

  • the route is POST-only, so the GET returns 405 Method Not Allowed;
  • even as a POST, SearchMemoriesRequest requires a Filters DSL object
    (filters must carry user_id/group_id at the first level), so a flat
    top-level user_id returns 422 "Field required: filters";
  • the retrieval-method field is method, not retrieve_method.

Response — it read r.result.memories / r.result.pending_messages, but the
v1 response is { data: { episodes, profiles, raw_messages, ... } }. So even a
successful call surfaced zero memories.

This PR migrates searchMemories to the v1 contract end-to-end while preserving
the function's return shape (so parseSearchResponse and the assembler are
untouched):

  • Build the v1 request envelope: POST + filters: { user_id, group_id? } +
    method (mapped from retrieve_method) + query / top_k / memory_types.
  • Map the v1 response: data.episodes → memories (tagging memory_type: "episodic_memory" so the downstream episodic filter matches; carrying
    summary/episode/subject/score/timestamp), and data.raw_messages → pending_messages (flattening content_items to text).

Verification

Live against a running EverCore (:1995):

  • old form GET …/search405; flat top-level user_id (POST) → 422
    "Field required: filters"
    .
  • the fixed body (POST + filters:{user_id} + method) → HTTP 200, with
    the response echoing "filters_applied": {"user_id": "…"}.
  • the real branch searchMemories() driven end-to-end against :1995
    (method=keyword) returns status: "ok" and maps the response with no error.

Note: method=hybrid/vector additionally require a reachable embedding
backend; on a host without one they 500 at the embedding step (an environment
dependency, not this code). keyword (ES-only) needs no embeddings and was
used for the live 200.

Offline regression — adds test/search-memories.test.js (node --test, no
live stack):

  • asserts the request is a POST whose body carries filters: { user_id } (no
    top-level user_id) and method (no retrieve_method), with only
    backend-searchable memory_types;
  • feeds a canned v1 { data: { episodes, raw_messages } } response and asserts
    it maps into { memories, pending_messages } and that the existing
    parseSearchResponse consumes the mapped shape
    into the expected episodic /
    pending text.
cd methods/EverCore/examples/openclaw-plugin
node --test test/search-memories.test.js

Result: 2 passed.

Scope

Surgical: src/api.js (searchMemories only) + its new regression test. The v1
demo migration (#191, PR #196) covered methods/evermemos/demo/* and docs but
not this example plugin, so the plugin's search path was left on v0. This PR
finishes that migration for the plugin. Does not touch saveMemories (shipped
separately as the #237 fix).

Credit

Builds on prior art:

Co-authored-by: CZH-THU CZH-THU@users.noreply.github.com
Co-Authored-By: Claude Opus 4.8 (1M context) noreply@anthropic.com

🤖 Generated with Claude Code

libin.zhang and others added 30 commits December 31, 2025 18:23
Use self-deployed embedding and rerank APIs by default

See merge request npc-work/aic/ai/evermemos-opensource!64
vLLM Rerank API adopts an instruction-tuned approach

See merge request npc-work/aic/ai/evermemos-opensource!65
feat: metrics client and rerank/vectorize/retrieve metrics

See merge request npc-work/aic/ai/evermemos-opensource!66
fix:update episode prompt

See merge request npc-work/aic/ai/evermemos-opensource!68
feat: add rerank metrics

See merge request npc-work/aic/ai/evermemos-opensource!69
cyfyifanchen and others added 25 commits April 27, 2026 03:42
* chore: rename project from evermemos to EverCore

This commit renames the project directory and updates all internal references from "evermemos" to "EverCore". The changes include:
- Renaming the main directory from `methods/evermemos` to `methods/EverCore`
- Updating all import paths and module references
- Maintaining the same code structure and functionality
- Adding new configuration files (.vscode/settings.json, .pylintrc, pyrightconfig.json)
- Updating Dockerfile and project metadata

* docs: update references from evermemos to EverCore

Update documentation files to reflect the renaming of the 'evermemos' directory to 'EverCore'. This includes fixing clone commands, directory paths, and documentation links across multiple files to ensure consistency and correct navigation for users.

* chore: rename EverMemOS to EverCore across codebase

This is a project-wide rebranding from EverMemOS to EverCore. The changes include:
- Update project name in source files, documentation, and configuration
- Rename API references, environment variables, and service names
- Modify demo descriptions and benchmark configurations
- Update URLs and citations to reflect new project identity

All functionality remains identical; only naming has changed to align with the new project branding.

* docs: update README with EverCore focus and restructured TOC

- Add line break before Table of Contents for better visual separation
- Rewrite project description to highlight EverCore as the central component
- Reorder directory tree to prioritize benchmarks and methods over use-cases
- Update use-cases list with more examples and clarify they are templates
- Improve flow from Quick Start to use-cases to benchmarks

* docs: update README with clearer methods description and benchmarks

Add benchmark numbers directly in the method summaries for better visibility.
Clarify introductory text to emphasize choice and composition of methods.

* docs: fix markdown formatting in README table of contents

Adjust whitespace and line breaks to ensure proper rendering of the collapsible table of contents section.
…d-AI#204)

- Replace specific EverMemBench-Dynamic badge with general EverMind-AI HuggingFace badge
- Remove redundant License badge
- Change "Methods" section heading to "Architecture Methods"
- Update sub-section headings from h4 (####) to h3 (###) for better hierarchy
…rMind-AI#208)

* docs: restructure README and add AGENTS.md for better navigation

- Reorder sections to emphasize architecture methods and use cases
- Move use cases section before quick start for better flow
- Rename "Methods" to "Architecture Methods" for clarity
- Add AGENTS.md with quick commands and key entry points
- Update section headers to improve document hierarchy
- Maintain all existing content while improving organization

* docs: add community and contribution files

* docs: reorder README directory tree for logical grouping

* docs: move community files to .github/ and update references

* ci: change deploy workflow trigger from feature branch to main
* docs: restructure README and add subdirectory guides

Move the directory tree from the main README to new dedicated README files for each top-level folder (use-cases, methods, benchmarks). Add detailed introductions and tables to guide users to the appropriate subprojects. This improves navigation and provides clear entry points for different use cases.

* docs: expand showcase section with new projects and links

Add six new project entries to the README showcase, each with a banner image, description, and code/plugin link. Also update an existing benchmark entry to include a dataset link. This enhances the repository's demonstration of real-world applications and available resources.
* docs(readme): update project links and formatting

* docs(use-cases): enhance README with visual catalogue of demos

Expand the use cases section from a simple table to a detailed visual catalogue with project banners, descriptions, and links. This improves user engagement and provides a better showcase of community integrations and demos.

* docs: update READMEs and add validation for use-case links
* docs: update plugin repository link in README

* docs(readme): update banner gif link
)

* docs(readme): update code example link to pinned commit

pin the reference to the voice assistant example code to a specific commit hash and fix folder name capitalization

* docs: update voice assistant demo link in README
* docs(readme): add four new use case entries

* docs(readme): update outdated banner links to correct github repos
…e-demo-content-payload

Fix EverCore demo memory payload
…actions-hygiene

Harden GitHub Actions workflows
…adme-quickstart

docs: verify EverCore quickstart path
…I#236)

Delete deprecated EvoAgentBench, EverMemBench benchmark suites and
HyperMem memory system implementation, including all associated
configurations, scripts, and supporting assets.
…Filters DSL API

The example plugin's memory recall never worked against the v1 backend.
searchMemories spoke the v0 dialect on both sides:

- Request: GET /api/v1/memories/search with a flat top-level user_id and a
  retrieve_method field. The v1 route is POST-only (GET -> 405); even as POST,
  SearchMemoriesRequest requires a Filters DSL object (user_id/group_id must sit
  inside `filters`), so a flat top-level user_id -> 422 "Field required: filters";
  and the method field is `method`, not `retrieve_method`.
- Response: it read r.result.memories / r.result.pending_messages, but the v1
  response is { data: { episodes, profiles, raw_messages, ... } }, so even a
  successful call surfaced zero memories.

Migrate searchMemories to the v1 contract end-to-end while preserving its return
shape (parseSearchResponse and the assembler are untouched): build the v1 POST
envelope with filters:{user_id,group_id?} + method, and map data.episodes ->
memories (tagging memory_type so the episodic filter matches) and
data.raw_messages -> pending_messages (flattening content_items to text).

Verified live against EverCore on :1995: old GET -> 405; flat top-level user_id
(POST) -> 422 "Field required: filters"; fixed body (POST + filters + method) ->
200 with "filters_applied":{"user_id":...}; the real searchMemories() driven
end-to-end (method=keyword) returns status:"ok" and maps the response with no
error. Adds offline test/search-memories.test.js (node --test) asserting the v1
request envelope and that the v1 response maps into the caller contract and is
consumable by the existing parseSearchResponse (2 passed).

The v1 demo migration (EverMind-AI#191, PR EverMind-AI#196) covered methods/evermemos/demo/* and docs
but not this example plugin; this finishes that migration for the plugin's search
path. Scope: src/api.js (searchMemories only) + its regression test. saveMemories
is migrated separately as the EverMind-AI#237 fix.

Co-authored-by: CZH-THU <CZH-THU@users.noreply.github.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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