engram-memory/openclaw.plugin.json at main · EngramMemory/engram-memory · GitHub

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{
  "name": "engram",
  "version": "1.2.0",
  "description": "Engram - Persistent semantic memory and context management",
  "type": "python",
  "main": "plugin.py",
  "tools": [
    {
      "name": "memory_store",
      "description": "Store text in long-term memory with semantic embedding",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "text": {
            "type": "string",
            "description": "Text content to store"
          },
          "category": {
            "type": "string",
            "enum": ["preference", "fact", "decision", "entity", "other"],
            "default": "other",
            "description": "Memory category"
          },
          "importance": {
            "type": "number",
            "default": 0.5,
            "minimum": 0.0,
            "maximum": 1.0,
            "description": "Importance score (0-1)"
          }
        },
        "required": ["text"]
      },
      "command": {
        "script": "scripts/memory_store_wrapper.py",
        "args": ["--text", "{text}", "--category", "{category}", "--importance", "{importance}"]
      }
    },
    {
      "name": "memory_search",
      "description": "Search stored memories using semantic similarity",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "Natural language search query"
          },
          "limit": {
            "type": "integer",
            "default": 10,
            "description": "Maximum number of results"
          },
          "min_score": {
            "type": "number",
            "default": 0.0,
            "description": "Minimum similarity score (0-1)"
          },
          "category": {
            "type": "string",
            "enum": ["preference", "fact", "decision", "entity", "other"],
            "description": "Filter by category"
          }
        },
        "required": ["query"]
      },
      "command": {
        "script": "scripts/memory_search_wrapper.py",
        "args": ["--query", "{query}", "--limit", "{limit}", "--min-score", "{min_score}"]
      }
    },
    {
      "name": "memory_forget",
      "description": "Delete a memory by ID or by searching for the closest match",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "memory_id": {
            "type": "string",
            "description": "UUID of memory to delete"
          },
          "query": {
            "type": "string",
            "description": "Search query — deletes the best match"
          }
        }
      },
      "command": {
        "script": "plugin.py",
        "args": ["memory_forget", "{\"query\": \"{query}\", \"memory_id\": \"{memory_id}\"}"]
      }
    },
    {
      "name": "memory_ingest",
      "description": "Ingest a file (PDF, DOCX, Markdown, or plain text) into memory. Splits the file into chunks and stores each as a searchable memory with the source filename tracked.",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "path": {
            "type": "string",
            "description": "Absolute or relative path to the file to ingest"
          },
          "category": {
            "type": "string",
            "enum": ["preference", "fact", "decision", "entity", "goal", "plan", "error", "insight", "skill", "event", "question", "relationship", "other"],
            "default": "fact",
            "description": "Memory category for all chunks from this file"
          }
        },
        "required": ["path"]
      },
      "command": {
        "script": "plugin.py",
        "args": ["memory_ingest", "{\"path\": \"{path}\", \"category\": \"{category}\"}"]
      }
    },
    {
      "name": "context_search",
      "description": "Search the current project's .context/ files for information about architecture, patterns, APIs, and workflows",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "What to search for in context files"
          },
          "limit": {
            "type": "integer",
            "default": 5,
            "description": "Maximum results"
          }
        },
        "required": ["query"]
      },
      "command": {
        "script": "plugin.py",
        "args": ["context_search", "{\"query\": \"{query}\", \"limit\": \"{limit}\"}"]
      }
    },
    {
      "name": "context_ask",
      "description": "Ask a natural language question about the current project's codebase and get an answer from context files",
      "implementation": "python_exec",
      "parameters": {
        "type": "object",
        "properties": {
          "question": {
            "type": "string",
            "description": "Natural language question about the codebase"
          }
        },
        "required": ["question"]
      },
      "command": {
        "script": "plugin.py",
        "args": ["context_ask", "{\"question\": \"{question}\"}"]
      }
    }
  ]
}