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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
{
  "name": "engram",
  "version": "1.0.0",
  "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": "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}\"}"]
      }
    }
  ]
}