Persistent memory for AI assistants with semantic search and knowledge graph relationships.
Persistent memory for AI assistants with semantic search and knowledge graph relationships.
AI assistants start every conversation with zero context. You explain your project, your preferences, your decisions — every single time. Octobrain breaks that cycle:
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Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| memorize | Store memories with metadata; optional related_to for inline relationships |
| remember | Semantic search with filters; returns 1-hop graph neighbors |
| forget | Delete memories (requires confirmation) |
| knowledge | Unified tool: search, store, delete, read, match via command field |
{
"mcpServers": {
"octobrain": {
"command": "/path/to/octobrain",
"args": ["mcp"]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| VOYAGE_API_KEY | Credential the server authenticates with. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
| JINA_API_KEY | Credential the server authenticates with. | Yes |
Store entities, relations and observations in a local knowledge graph with the reference Memory MCP server.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Pull Deep Memory from across your Claude Code Sessions — when you need it.
A clean (and possibly naive) implementation of the Model Context Protocol (MCP) for tool integration.
Local-first, persistent memory for AI coding agents — remember, recall, forget, and list, over MCP.
Context-aware MCP prompt compiler with a composable pipeline: clarify_with_user → ground_prompt OR optimize_prompt → critique_prompt, all in one call