Agenticx Memory Architect
DemonDamon/AgenticX
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents.
Searches Mem0 memories saved in earlier Kimi sessions of the same repository, with options for scope, category, result count and a specific session, to avoid repeating work.
Install Mem0 Memory Search
$ npx skills add mem0ai/mem0 --skill search -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mem0ai/mem0 search --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .claude/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .claude/skills/search && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .claude/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/searchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mem0ai/mem0 --skill search -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mem0ai/mem0 search --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .agents/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .agents/skills/search && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .agents/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mem0ai/mem0 --skill search -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mem0ai/mem0 search --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .cursor/skills/search && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .cursor/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mem0ai/mem0.git --path integrations/kimi-plugin/skills/search--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mem0ai/mem0 --skill search -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mem0ai/mem0 search --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .gemini/skills/search && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .gemini/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mem0ai/mem0 searchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mem0ai/mem0 --skill search -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .github/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .github/skills/search && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .github/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mem0ai/mem0 --skill search -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mem0ai/mem0 search --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mem0ai/mem0.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/integrations/kimi-plugin/skills/search .opencode/skills/search && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "search" agent skill from https://github.com/mem0ai/mem0/tree/main/integrations/kimi-plugin/skills/search into .opencode/skills/search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
search- GitHub stars
- 67k
- Token cost
- ~337 tokens
- SKILL.md length
- 142 words
- Files
- 1
- Skills in repo
- 26
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Searches Mem0 memories saved in earlier Kimi sessions of the same repository, with options for scope, category, result count and a specific session, to avoid repeating work.
- Checking whether an earlier session already explains a piece of code or an error
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Recalling a past decision or command before repeating file reads or experiments
- Searching only the package you are working in within a monorepo
What it does
The skill calls the search_memories tool with the user's question and returns the tool's result directly. The options top-k, category, scope and run-id are passed as tool arguments rather than written into the query; leaving top_k out uses Mem0's configured default, and leaving category out searches every category.
Scope defaults to the repository's shared memory, which everyone working in it contributes to, plus your own preferences. Setting scope to dir narrows the shared memory to the current directory, such as a package in a monorepo, and mine searches only your own preferences. Passing run_id with any scope retrieves memories saved in a specific coding-agent session; leave it out to search across sessions, and use only a known session ID, never an invented one.
When your agent uses it
- Checking whether an earlier session already explains a piece of code or an error
- Recalling a past decision or command before repeating file reads or experiments
- Searching only the package you are working in within a monorepo
- Looking up your own saved preferences
Example prompts
- “Search memory for why we switched the queue worker to retry with backoff.”
- “Did an earlier session already work out this migration error? Check Mem0 before reading files again.”
- “Look up my own saved preferences about commit message style.”
- “Search the memories saved for this package only, for notes on the auth middleware.”
Requirements
- Mem0 with the search_memories tool available through the Mem0 Kimi plugin
What it can do on your machine
Read from SKILL.md and the folder at commit b7ad69a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mem0 Memory Search loads about 337 tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 142 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from mem0ai/mem0 at commit b7ad69a, republished under its Apache-2.0 licence (© mem0ai). 142 words, ~337 tokens.
Download SKILL.mdSave it as
.claude/skills/search/SKILL.md (or your agent's skills folder).- name
- search
- description
- Search memories from earlier Kimi sessions in this repository. Use it when earlier work may already explain the code, error, decision, or command you need, so you can avoid repeating file reads, searches, or experiments.
- argument-hint
- [question] [--top-k number] [--category category-name] [--scope repo|dir|mine] [--run-id session-id]
- disable-model-invocation
- true
Search memories
Call search_memories with the user's question. Treat --top-k, --category,
--scope, and --run-id as tool arguments instead of including them in the
query.
Omit top_k to use Mem0's configured default. Omit category to search every
category. Omit scope to use the configured default, normally repo: this
repository's shared memory, which everyone who works in it contributes to,
plus your own preferences.
Pass scope when the question needs something else: dir to narrow the
shared memory to the directory you are working in (a package inside a
monorepo), mine for your own preferences alone.
Pass run_id with any scope to retrieve memories saved in a specific coding-agent
session. Omit run_id to search across sessions. It filters the memories returned;
it does not identify the session making the search request. Use a known session ID,
never invent one. Return the tool's result directly.
© mem0ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in integrations/kimi-plugin/skills/search of mem0ai/mem0.
Open the folder on GitHubat commit b7ad69a
Compare with similar skills
Mem0 Memory Search next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mem0 Memory Search this skillmem0ai/mem0 | 67k | — | ~337 | Automated safety check: Pass | Apache-2.0 | |
| Agenticx Memory ArchitectDemonDamon/AgenticX | 294 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Hyperspacedb MemoryYARlabs/hyperspace-db | 161 | — | ~880 | Automated safety check: Pass | MIT | |
| Self Improving Systemsooiyeefei/ccc | 494 | — | ~5.2k | Automated safety check: Pass | MIT | |
| Mem0Anil-matcha/awesome-muse-connectors | 1.3k | — | ~867 | Automated safety check: Pass | MIT | |
| Mem0majiayu000/claude-skill-registry | 666 | 1 repos | ~1.8k | Automated safety check: Pass | Apache-2.0 |
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YARlabs/hyperspace-db
Zero-overhead cognitive long-term and episodic memory for AI agents (Mem0 drop-in and mcp-hyperspace-memory).
Self Improving Systems
ooiyeefei/ccc
Decide whether your agent actually needs persistent memory, feedback loops, or closed-loop learning, then design the smallest thing that pays for itself.
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Anil-matcha/awesome-muse-connectors
Mem0 memory CLI: add memories from messages, semantic search, read or delete memories, poll async events.
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More from mem0ai/mem0
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Adds persistent memory to AI apps with the Mem0 Python and TypeScript SDKs: store, search, update and delete user memories, with framework integrations.
Mem0 CLI Memory Commands
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Adds, searches, lists, updates and deletes memories on the Mem0 platform from the terminal with the mem0 command, including a JSON mode built for agents.
Mem0 Provider for Vercel AI SDK
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Adds persistent memory to Vercel AI SDK apps with the Mem0 provider, using a wrapped model or standalone retrieve and store utilities.
Mem0 Memory Deletion
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Finds and deletes specific mem0 memories by search query or ID, always asking for confirmation first, and can undo the most recent memories added this session.
Mem0 Remember Command
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Saves a fact, decision or preference the user states into mem0 as written, labeled with a memory type such as decision, convention or user_preference.
Mem0 Memory Scope
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Shows or changes the default Mem0 memory scope, project, session or global, which decides where memories are saved and searched.
Related
Categories
Questions about Mem0 Memory Search
What does Mem0 Memory Search do?
Searches Mem0 memories saved in earlier Kimi sessions of the same repository, with options for scope, category, result count and a specific session, to avoid repeating work. The skill calls the search_memories tool with the user's question and returns the tool's result directly. The options top-k, category, scope and run-id are passed as tool arguments rather than written into the query; leaving top_k out uses Mem0's configured default, and leaving category out searches every category.
When should I use Mem0 Memory Search?
Mem0 Memory Search fits situations like: checking whether an earlier session already explains a piece of code or an error; recalling a past decision or command before repeating file reads or experiments; searching only the package you are working in within a monorepo; looking up your own saved preferences.
How do I install Mem0 Memory Search in Claude Code?
Run `npx skills add mem0ai/mem0 --skill search -a claude-code`. Or copy the skill folder (integrations/kimi-plugin/skills/search in mem0ai/mem0) into .claude/skills/search in your project. Claude Code loads it when a task matches its description.
How do I install Mem0 Memory Search in Codex?
Run `npx skills add mem0ai/mem0 --skill search -a codex`. Or copy the skill folder (integrations/kimi-plugin/skills/search in mem0ai/mem0) into .agents/skills/search in your project. Codex loads it when a task matches its description.
Can I use Mem0 Memory Search in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mem0ai/mem0 --skill search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/search, .gemini/skills/search, .github/skills/search and .opencode/skills/search in your project.
What does Mem0 Memory Search need to run?
SKILL.md names no scripts, command-line tools or credentials: Mem0 Memory Search is instructions for the agent only. Our summary lists: Mem0 with the search_memories tool available through the Mem0 Kimi plugin.
Does Mem0 Memory Search access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Mem0 Memory Search safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Mem0 Memory Search use?
Mem0 Memory Search is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mem0 Memory Search use?
About 337 tokens (SKILL.md is roughly 1.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Mem0 Memory Search?
Skills that share tags, products or a category with Mem0 Memory Search: Agenticx Memory Architect (DemonDamon/AgenticX, 294 stars), Hyperspacedb Memory (YARlabs/hyperspace-db, 161 stars), Self Improving Systems (ooiyeefei/ccc, 494 stars) and Mem0 (Anil-matcha/awesome-muse-connectors, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mem0 Memory Search?
mem0ai (a GitHub organization) maintains it in mem0ai/mem0, which has 66,788 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 7, 2026.
Source: mem0ai/mem0 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.