ATXP Memory — Agent Memory Management

Manage your agent's

.md

memory files: back up and restore to/from ATXP cloud servers, and

search your local memories

using zvec vector similarity search.

Capabilities

Capability

Description

Cloud Backup

Push/pull

.md

files to ATXP servers for disaster recovery

Local Search

Index

.md

files into a local zvec vector database, then search by natural language query

Status

View cloud backup info and local index statistics

Security Model

Only

.md

files

are collected and transmitted (push/pull). No credentials, JSON configs, binaries, or other file types are ever sent.

Files are sent to ATXP servers over

HTTPS

, associated with the authenticated agent's identity.

push

replaces

the server snapshot entirely (latest snapshot only, no history).

pull

is

non-destructive

— it writes server files to the local directory but does not delete local files absent from the server.

Local search index

is stored in a

.atxp-memory-index/

subdirectory inside

--path

. It never leaves the local machine.

index

and

search

do not require authentication or network access.

Filesystem access

reads from --path directory (push/index), writes to --path directory (pull) and --path/.atxp-memory-index/ (index). No other directories are touched. No modification of OpenClaw config or auth files. When to Use Situation Command After meaningful changes to SOUL.md, MEMORY.md, or at end of session push Bootstrapping a fresh workspace or recovering from environment loss pull After updating memory files and before starting a task that requires recall index Looking for relevant context in past memories search Verify backup exists before risky operations status Commands Reference Command Description npx atxp@latest memory push --path Recursively collect all *.md files from and upload to server npx atxp@latest memory pull --path Download backup from server and write files to npx atxp@latest memory index --path Chunk .md files by heading and build a local zvec search index npx atxp@latest memory search --path Search indexed memories by similarity npx atxp@latest memory status [--path ] Show cloud backup info and/or local index stats Options Option Required Description --path Yes (push/pull/index/search) Directory to operate on --topk No (search only) Number of results to return (default: 10) How Local Search Works Indexing ( memory index ): Scans all .md files recursively from --path Splits each file into chunks at heading boundaries (h1/h2/h3) Converts each chunk into a 256-dimensional feature vector using locality-sensitive hashing (unigrams + bigrams) Stores vectors and metadata in a local zvec database (HNSW index) at /.atxp-memory-index/ Searching ( memory search ): Converts the query text into the same vector representation Performs approximate nearest neighbor search via zvec's HNSW index Returns the top-k most similar chunks with file paths, headings, line numbers, and similarity scores The search is purely local — no network requests, no API keys, no cost. Re-index after modifying memory files. Path Conventions Typical OpenClaw workspace paths: ~/.openclaw/workspace-/ ~/.openclaw/workspace-/SOUL.md ~/.openclaw/workspace-/MEMORY.md ~/.openclaw/workspace-/memory/ ~/.openclaw/workspace-/AGENTS.md ~/.openclaw/workspace-/USER.md Backward Compatibility The backup command is still accepted as an alias for memory : npx atxp@latest backup push --path < dir

works, same as memory push

npx atxp@latest backup pull --path < dir

works, same as memory pull

npx atxp@latest backup status

works, same as memory status

Limitations .md files only — all other file types are ignored during push/index and not present in pull. Latest snapshot only — each push overwrites the previous backup. There is no version history. Requires ATXP auth for cloud operations — run npx atxp@latest login or npx atxp@latest agent register first. --path is required — there is no auto-detection of workspace location. Local search requires @zvec/zvec — install with npm install @zvec/zvec before using index/search. Feature-hash embeddings — local search uses statistical text hashing, not neural embeddings. It works well for keyword and phrase matching but is not a full semantic search. For best results, use specific terms from your memory files.