{"id":"912fe310-49e1-48bc-bca4-0e2f56463dbb","entityType":"agent","slug":"memorylake-ai-memorylake-skills","name":"memorylake","canonicalUrl":"https://www.xpersona.co/agent/memorylake-ai-memorylake-skills","canonicalPath":"/agent/memorylake-ai-memorylake-skills","generatedAt":"2026-10-09T13:41:06.187Z","source":"GITHUB_OPENCLEW","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-15T05:21:22.124Z","emptyReason":null},"description":"Search, retrieve, and analyze data from a MemoryLake Streamable HTTP MCP Server — the memory layer for AI Agents that provides intelligent unstructured file content retrieval and data analysis. Access the server directly via HTTP/curl (not via pre-configured MCP tools). 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Trigger phrases include: \"search my files\",\n  \"find in memorylake\", \"analyze my data\", \"what files do I have\", \"look up\", \"summarize my documents\",\n  \"compare data across files\", \"run analysis on my data\".\n---\n\n# MemoryLake Skill\n\nMemoryLake is the memory layer for AI Agents. It ingests unstructured files (Excel, PDF, text, etc.),\nchunks and indexes them, and exposes them through a Streamable HTTP MCP Server for intelligent\nretrieval and analysis.\n\nThis repo also includes an up-to-date OpenAPI spec for MemoryLake's Project/Drive APIs (see\n`references/memorylake-openapi.json`).\n\n## Prerequisites\n\n### 1) Get a MemoryLake API key\n\n1. Go to https://app.memorylake.ai/ and apply for a MemoryLake API key.\n2. Use the REST API base URL:\n\n```\nhttps://app.memorylake.ai/openapi/memorylake\n```\n\n3. Authenticate requests with:\n\n- `Authorization: Bearer <your API key>`\n- `X-User-ID: <your user id>` (required for most endpoints)\n\n### 2) (Later) Get a Streamable HTTP MCP secret\n\nAfter you create a project, you can create a project API key that becomes a Streamable HTTP MCP secret:\n\n```\nhttps://ai.data.cloud/memorylake/mcp/v1?apikey=<secret>\n```\n\n## Client Scripts\n\n### REST API client (projects, uploads, documents)\n\nUse `scripts/memorylake_rest_client.sh` to:\n- Create/list projects\n- Create a project API key (MCP secret)\n- Upload documents (multipart)\n- Quick-add documents to a project\n- Poll project documents until `status=okay`\n\nIt expects env vars:\n\n```bash\nexport MEMORYLAKE_BASE_URL=\"https://app.memorylake.ai/openapi/memorylake\"\nexport MEMORYLAKE_API_KEY=\"<your api key>\"\nexport MEMORYLAKE_USER_ID=\"<your user id>\"\n```\n\n### MCP client (search + fetch + code runner)\n\nUse `scripts/memorylake_client.sh` for Streamable HTTP MCP interactions. It handles MCP session\ninitialization, JSON-RPC protocol, and SSE response parsing.\n\n```bash\n# Initialize a session (required before any tool calls)\nSESSION=$(./scripts/memorylake_client.sh \"$MCP_URL\" init)\n\n# Call any tool\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" <tool_name> ['<json_arguments>']\n```\n\n**Session management:** Sessions can expire if idle. If a call returns empty or an error,\nre-initialize with `init` before retrying. Minimize delay between init and the first tool call.\n\n## Available Tools\n\n| Tool | Arguments | Purpose |\n|------|-----------|---------|\n| `get_memorylake_metadata` | *(none)* | Explore memorylake: file counts by type, sample memories |\n| `search_memory` | `{\"parsed_query\":{...}}` | Semantic + keyword search across all files |\n| `fetch_memory` | `{\"memory_ids\":[\"id1\",...]}` | Detailed metadata for specific memories |\n| `create_memory_code_runner` | *(none)* | Create a Python executor, returns `executor_id` |\n| `run_memory_code` | `{\"executor_id\":\"...\",\"code\":\"...\"}` | Execute Python code against data |\n\nSee:\n- [references/mcp-tools.md](references/mcp-tools.md) for detailed MCP tool parameters and response formats.\n- `references/memorylake-openapi.json` for the REST API surface (Projects/Drives/Connectors/etc.).\n\n**Note:** The REST API requires `X-User-ID` on most endpoints (per OpenAPI spec).\n\n## Typical End-to-End Workflow (REST → MCP)\n\nFollow this flow to create a project, ingest documents, then query/analyze them via MCP.\n\n### 1) Create a project (REST)\n\n```bash\n./scripts/memorylake_rest_client.sh projects:create '{\n  \"name\": \"My Research Project\",\n  \"description\": \"Optional description\"\n}'\n```\n\n### 2) List projects (REST)\n\n```bash\n./scripts/memorylake_rest_client.sh projects:list\n```\n\n### 3) Create a project API key (this becomes the MCP secret) (REST)\n\n```bash\n./scripts/memorylake_rest_client.sh projects:create-apikey <project_id> '{\"description\":\"mcp\"}'\n```\n\nSave the returned `secret` locally. That secret is used like:\n\n```\nhttps://ai.data.cloud/memorylake/mcp/v1?apikey=<secret>\n```\n\n### 4) Upload a document (multipart) (REST)\n\n```bash\n# 1) Ask server for presigned part upload URLs (file_size in bytes)\n./scripts/memorylake_rest_client.sh upload:create-multipart '{\"file_size\": 123456}' > upload.json\n\n# 2) Upload parts to presigned URLs, then complete multipart\n./scripts/memorylake_rest_client.sh upload:complete-multipart upload.json /path/to/file.pdf\n```\n\nYou will end up with an `object_key` (from create-multipart), which is the server-side key for the uploaded file.\n\n### 5) Add the uploaded document into the project (quick-add) (REST)\n\n```bash\n./scripts/memorylake_rest_client.sh projects:quick-add <project_id> '{\n  \"object_key\": \"<object_key>\",\n  \"file_name\": \"file.pdf\"\n}'\n```\n\nIf you have multiple documents, upload + quick-add **one by one**.\n\n### 6) Poll project documents until processed (REST)\n\nCheck:\n\n```bash\n./scripts/memorylake_rest_client.sh projects:list-documents <project_id>\n```\n\nDocument `status` values: `error`, `okay`, `running`, `pending`.\n\nRecommended polling interval: **5s** until all documents are `okay`.\n\n### 7) Use Streamable HTTP MCP to search/retrieve/analyze\n\n```bash\nMCP_URL=\"https://ai.data.cloud/memorylake/mcp/v1?apikey=<secret>\"\nSESSION=$(./scripts/memorylake_client.sh \"$MCP_URL\" init)\n\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" get_memorylake_metadata\n```\n\nThen do search/fetch/code-runner as usual.\n\n---\n\n## MCP Workflow (inside the MCP phase)\n\n### 1. Initialize session and orient\n\n```bash\nMCP_URL=\"<user-provided-url>\"\nSESSION=$(./scripts/memorylake_client.sh \"$MCP_URL\" init)\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" get_memorylake_metadata\n```\n\n### 2. Search for relevant content\n\nBuild a structured query with both BM25 keywords and a semantic dense query:\n\n```bash\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" search_memory '{\n  \"parsed_query\": {\n    \"bm25_cleaned_query\": \"recruitment positions master degree\",\n    \"named_entities\": [],\n    \"bm25_keywords\": [\"recruitment\", \"positions\", \"master\", \"degree\"],\n    \"bm25_boost_keywords\": [\"master\", \"recruitment\"],\n    \"rewritten_query_for_dense_model\": \"Job positions requiring a master degree or higher\"\n  }\n}'\n```\n\n**Query construction tips:**\n- Extract all named entities into `named_entities` and `bm25_keywords`\n- Clean BM25 query: remove stop words, punctuation, normalize spaces\n- Dense query: rewrite to capture intent, expand with synonyms\n- Boost keywords: 3-5 most distinctive terms\n\n### 3. Fetch memory details\n\n```bash\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" fetch_memory '{\"memory_ids\":[\"ds-abc123\"]}'\n```\n\n### 4. Analyze with code execution\n\n```bash\n# Create executor (once per session)\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" create_memory_code_runner\n\n# Run code (use executor_id from above)\n./scripts/memorylake_client.sh \"$MCP_URL\" \"$SESSION\" run_memory_code '{\n  \"executor_id\": \"executor-...\",\n  \"code\": \"import pandas as pd\\npath = get_memory_path(\\\"ds-abc\\\", \\\"file.xlsx\\\")\\ndf = pd.read_excel(path)\\nprint(df.describe())\"\n}'\n```\n\n**Available packages:** pandas, numpy, openpyxl, xlrd, scipy, scikit-learn, xgboost.\nAlways `print()` results — not an interactive environment. `matplotlib` is NOT available.\n\n## Parsing Responses\n\nThe script outputs JSON-RPC result lines. 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