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mem0

Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK / @mem0/vercel-ai-provider / createMem0 (use mem0-vercel-ai-sdk).

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npx skills add 'https://github.com/mem0ai/mem0/tree/main/skills/mem0'
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main · 0df3e4b ↗Scanned 2026-09-17

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scripts/mem0_doc_search.py

scripts/mem0_doc_search.pyBrowse 14 files
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#!/usr/bin/env python3"""Mem0 Documentation Search Agent (Mintlify-based)On-demand search tool for querying Mem0 documentation without storing content locally. This tool leverages Mintlify's documentation structure to perform just-in-timeretrieval of technical information from docs.mem0.ai. Usage:    python mem0_doc_search.py --query "how to add graph memory"    python mem0_doc_search.py --query "filter syntax for categories"    python mem0_doc_search.py --page "/platform/features/graph-memory"    python mem0_doc_search.py --index    python mem0_doc_search.py --query "webhook events" --section platform Purpose:    - Avoid bloating local context with full documentation    - Enable just-in-time retrieval of technical details    - Query specific documentation pages on demand    - Search across the full Mem0 documentation site""" import argparseimport jsonimport sysimport urllib.errorimport urllib.parseimport urllib.request DOCS_BASE = "https://docs.mem0.ai"SEARCH_ENDPOINT = f"{DOCS_BASE}/api/search"LLMS_INDEX = f"{DOCS_BASE}/llms.txt" # Known documentation sections for targeted retrievalSECTION_MAP = {    "platform": [        "/platform/overview",        "/platform/quickstart",        "/platform/features",        "/platform/features/graph-memory",        "/platform/features/selective-memory",        "/platform/features/custom-categories",        "/platform/features/v2-memory-filters",        "/platform/features/async-client",        "/platform/features/webhooks",        "/platform/features/multimodal-support",    ],    "api": [        "/api-reference/memory/add-memories",        "/api-reference/memory/v2-search-memories",        "/api-reference/memory/v2-get-memories",        "/api-reference/memory/get-memory",        "/api-reference/memory/update-memory",        "/api-reference/memory/delete-memory",    ],    "open-source": [        "/open-source/overview",        "/open-source/python-quickstart",        "/open-source/node-quickstart",        "/open-source/features",        "/open-source/features/graph-memory",        "/open-source/features/rest-api",        "/open-source/configure-components",    ],    "sdks": [        "/sdks/python",        "/sdks/js",    ],    "integrations": [        "/integrations",    ],}  def fetch_url(url: str) -> str:    """Fetch content from a URL."""    req = urllib.request.Request(url, headers={"User-Agent": "Mem0DocSearchAgent/1.0"})    try:        with urllib.request.urlopen(req, timeout=15) as resp:            return resp.read().decode("utf-8")    except urllib.error.HTTPError as e:        return f"HTTP Error {e.code}: {e.reason}"    except urllib.error.URLError as e:        return f"URL Error: {e.reason}"  def search_docs(query: str, section: str | None = None) -> dict:    """    Search Mem0 documentation using Mintlify's search API.    Falls back to the llms.txt index for keyword matching if the API is unavailable.    """    # Try Mintlify search API first    params = urllib.parse.urlencode({"query": query})    search_url = f"{SEARCH_ENDPOINT}?{params}"     try:        result = fetch_url(search_url)        data = json.loads(result)        if isinstance(data, dict) and data.get("results"):            results = data["results"]            if section and section in SECTION_MAP:                section_paths = SECTION_MAP[section]                results = [r for r in results if any(r.get("url", "").startswith(p) for p in section_paths)]            return {"source": "mintlify_search", "results": results}    except (json.JSONDecodeError, Exception):        pass     # Fallback: search llms.txt index for matching URLs    index_content = fetch_url(LLMS_INDEX)    query_lower = query.lower()    matching_urls = []     for line in index_content.splitlines():        line = line.strip()        if not line or line.startswith("#"):            continue        if query_lower in line.lower():            matching_urls.append(line)     if section and section in SECTION_MAP:        section_paths = SECTION_MAP[section]        matching_urls = [u for u in matching_urls if any(p in u for p in section_paths)]     return {        "source": "llms_txt_index",        "query": query,        "matching_urls": matching_urls[:20],        "suggestion": "Fetch specific URLs for detailed content",    }  def fetch_page(page_path: str) -> dict:    """Fetch a specific documentation page."""    url = f"{DOCS_BASE}{page_path}" if page_path.startswith("/") else page_path    content = fetch_url(url)    return {"url": url, "content": content[:10000], "truncated": len(content) > 10000}  def get_index() -> dict:    """Fetch the full documentation index from llms.txt."""    content = fetch_url(LLMS_INDEX)    urls = [line.strip() for line in content.splitlines() if line.strip() and not line.startswith("#")]    return {"total_pages": len(urls), "urls": urls, "sections": list(SECTION_MAP.keys())}  def list_section(section: str) -> dict:    """List all known pages in a documentation section."""    if section not in SECTION_MAP:        return {"error": f"Unknown section: {section}", "available": list(SECTION_MAP.keys())}    return {        "section": section,        "pages": [f"{DOCS_BASE}{p}" for p in SECTION_MAP[section]],    }  def main():    parser = argparse.ArgumentParser(description="Search Mem0 documentation on demand")    parser.add_argument("--query", help="Search query for documentation")    parser.add_argument("--page", help="Fetch a specific page path (e.g., /platform/features/graph-memory)")    parser.add_argument("--index", action="store_true", help="Show full documentation index")    parser.add_argument("--section", help="Filter by section or list section pages")    parser.add_argument("--json", action="store_true", help="Output as JSON")     args = parser.parse_args()     if args.index:        result = get_index()    elif args.section and not args.query:        result = list_section(args.section)    elif args.page:        result = fetch_page(args.page)    elif args.query:        result = search_docs(args.query, section=args.section)    else:        parser.print_help()        sys.exit(1)     if args.json:        print(json.dumps(result, indent=2))    else:        if isinstance(result, dict):            if "results" in result:                print(f"Source: {result.get('source', 'unknown')}")                for r in result["results"]:                    print(f"  - {r.get('title', 'N/A')}: {r.get('url', 'N/A')}")                    if r.get("description"):                        print(f"    {r['description'][:200]}")            elif "matching_urls" in result:                print(f"Source: {result['source']}")                print(f"Query: {result['query']}")                for url in result["matching_urls"]:                    print(f"  - {url}")                if result.get("suggestion"):                    print(f"\n{result['suggestion']}")            elif "urls" in result:                print(f"Total documentation pages: {result['total_pages']}")                print(f"Sections: {', '.join(result['sections'])}")                for url in result["urls"][:30]:                    print(f"  - {url}")                if result["total_pages"] > 30:                    print(f"  ... and {result['total_pages'] - 30} more")            elif "pages" in result:                print(f"Section: {result['section']}")                for page in result["pages"]:                    print(f"  - {page}")            elif "content" in result:                print(f"URL: {result['url']}")                if result.get("truncated"):                    print("[Content truncated to 10000 chars]")                print(result["content"])            elif "error" in result:                print(f"Error: {result['error']}")                if result.get("available"):                    print(f"Available sections: {', '.join(result['available'])}")            else:                print(json.dumps(result, indent=2))  if __name__ == "__main__":    main()
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