RobotMem
Enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. It supports hybrid vector search with structured filtering and spatial sorting to help robotic agents learn from past s
What is RobotMem?
RobotMem is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. it supports hybrid vector search with structured filtering and spatia...
Enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. It supports hybrid vector search with structured filtering and spatial sorting to help robotic agents learn from past s
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Enables robots to store, retrieve, and consolidate episodic
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx robotmemConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use RobotMem
RobotMem is a specialized MCP server that gives robotic AI agents persistent episodic memory backed by a local SQLite database with vector search capabilities. It allows robots and autonomous agents to store physical experiences (parameters, trajectories, outcomes), retrieve relevant past experiences using hybrid BM25 plus vector search with spatial sorting, and learn from repeated tasks over time. RobotMem is particularly useful for manipulation tasks, locomotion experiments, and any scenario where an agent should improve from its history of successes and failures.
Prerequisites
- Python 3.9+ installed
- pip for installing the robotmem package
- An MCP-compatible client such as Claude Desktop or Claude Code
- Sufficient disk space in ~/.robotmem/ for the SQLite memory database (no GPU required)
Install RobotMem
Install the robotmem Python package from PyPI. This installs the server and CLI tools.
pip install robotmemVerify installation and launch the web UI
Confirm the install succeeded and optionally open the web UI to browse stored memories.
robotmem --version
robotmem webAdd to Claude Desktop config
Configure robotmem as an MCP server in Claude Desktop. The database is stored at ~/.robotmem/memory.db by default.
{
"mcpServers": {
"robotmem": {
"command": "python",
"args": ["-m", "robotmem"],
"env": {}
}
}
}Start a memory session
Begin an episode by calling start_session with context metadata about the robot and current task.
start_session(context='{"robot_id": "arm-01", "task": "pick-and-place"}')Store and retrieve experiences
Use learn() to record insights and save_perception() to store sensor data. Use recall() with hybrid BM25+vector search to retrieve relevant past experiences.
learn(insight="grip_force=12.5N yields highest grasp success rate",
context='{"params": {"grip_force": {"value": 12.5, "unit": "N"}}, "task": {"success": true}}')
memories = recall(query="grip force parameters",
context_filter='{"task.success": true}',
spatial_sort='{"field": "spatial.position", "target": [1.3, 0.7, 0.42]}')End the session
Call end_session to close the episode, which automatically triggers memory consolidation and proactive recall of relevant past experiences.
end_session(session_id=session["session_id"])RobotMem Examples
Client configuration
Claude Desktop configuration for the RobotMem MCP server using the installed Python module.
{
"mcpServers": {
"robotmem": {
"command": "python",
"args": ["-m", "robotmem"],
"env": {}
}
}
}Prompts to try
Example prompts for robotic memory workflows using RobotMem.
- "Store what we learned from today's grasping experiments: the optimal grip force was 12.5N for cylindrical objects"
- "Recall all past experiences where the robot arm succeeded at pick-and-place near coordinates [1.3, 0.7, 0.4]"
- "What grip strategies have worked best for delicate objects based on past sessions?"
- "Forget the incorrect memory about using 20N force for foam objects"
- "Start a new session for the locomotion experiment on terrain type: gravel"Troubleshooting RobotMem
Vector search returns no results even with relevant stored memories
Vector embeddings are generated using FastEmbed ONNX models on first run, which downloads model files. Ensure internet access is available on first use and allow time for the model download to complete.
Database file grows very large over many sessions
Use the forget() tool to remove incorrect or outdated memories. You can also use context_filter queries to scope recall operations and use the robotmem web UI to inspect and prune the memory database at ~/.robotmem/memory.db.
CJK characters in memory queries return no results
RobotMem includes jieba tokenization for CJK full-text search. Ensure you are on version 0.1.0+ where CJK support is included. If issues persist, also provide an English translation in your recall query.
Frequently Asked Questions about RobotMem
What is RobotMem?
RobotMem is a Model Context Protocol (MCP) server that enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. it supports hybrid vector search with structured filtering and spatial sorting to help robotic agents learn from past s It connects AI assistants to external tools and data sources through a standardized interface.
How do I install RobotMem?
Follow the installation instructions on the RobotMem GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with RobotMem?
RobotMem works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is RobotMem free to use?
Yes, RobotMem is open source and available under the Apache 2.0 license. You can use it freely in both personal and commercial projects.
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Set Up RobotMem in Your Editor
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Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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