@muscular/robotmem

MCP ServerFree

Thin npm wrapper for the robotmem Python CLI and MCP server.

Open Source

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5 capabilities

Best for: python cli wrapping via node.js subprocess bridge, mcp server protocol bridging for robot memory operations, experience replay data persistence and retrieval
Type: MCP Server · Free
Score: 27/100
Best alternative: AWS MCP Servers
Agent-compatible: Yes — MCP protocol

Capabilities5 decomposed

python cli wrapping via node.js subprocess bridge

Medium confidence

Provides a thin npm wrapper that spawns and communicates with the robotmem Python CLI as a child process, enabling Node.js/TypeScript applications to invoke Python-based robot memory functionality without direct Python dependency installation. Uses standard Node.js child_process APIs to marshal arguments, stdin/stdout, and exit codes between JavaScript and Python runtime contexts.

Solves for

I want to use robotmem from a Node.js application without installing Python separatelyI need to call Python CLI tools from TypeScript without managing subprocess complexityI want to integrate robotmem into an existing JavaScript/Node.js project

Best for

Node.js/TypeScript developers building AI agents or robotics systems

Teams with mixed JavaScript and Python codebases needing interop

MCP server implementations requiring Python-based memory backends

Requires

Node.js 14+ (for child_process and Promise support)

Python 3.7+ installed and available in system PATH

robotmem Python package installed (pip install robotmem or equivalent)

Limitations

Subprocess overhead adds latency compared to native Node.js implementation (~50-200ms per invocation)

Requires Python runtime to be installed on the system despite npm packaging

No built-in process pooling or connection reuse — spawns new Python process per call

What makes it unique

Minimal wrapper design that delegates all robotmem logic to Python CLI rather than reimplementing in JavaScript, reducing maintenance burden and ensuring feature parity with Python version

vs alternatives

Simpler than native Node.js ports of robotmem because it reuses existing Python implementation, but introduces subprocess latency vs direct library binding

mcp server protocol bridging for robot memory operations

Medium confidence

Exposes robotmem functionality through the Model Context Protocol (MCP) server interface, allowing Claude and other MCP-compatible AI clients to invoke robot memory operations as tools. Translates MCP tool call schemas into robotmem CLI invocations and marshals results back as MCP responses, enabling AI agents to persistently store and retrieve robot experience data.

Solves for

I want Claude to access and manipulate robot memory as part of an agentic workflowI need to expose robotmem as a tool that AI models can call autonomouslyI want to build an MCP-compatible memory backend for multi-turn robot learning

Best for

AI agent developers building Claude-powered robotics systems

Teams implementing persistent memory layers for embodied AI

MCP ecosystem participants needing memory/experience-replay backends

Requires

Node.js 14+

Python 3.7+ with robotmem installed

MCP client implementation (e.g., Claude desktop app, Cline, or custom MCP client)

Limitations

MCP protocol overhead adds serialization/deserialization latency

Tool schema must be manually defined and kept in sync with robotmem CLI interface

No built-in streaming support for large memory datasets — full responses buffered in memory

What makes it unique

Bridges MCP protocol semantics directly to robotmem CLI without intermediate abstraction layer, preserving robotmem's native command structure while exposing it as AI-callable tools

vs alternatives

More lightweight than building a full REST API wrapper, but less flexible than native MCP implementations because it depends on Python CLI stability and output format

experience replay data persistence and retrieval

Medium confidence

Manages storage and querying of robot experience data (trajectories, state-action pairs, rewards) through robotmem's underlying persistence layer, enabling AI agents to learn from past robot interactions. The wrapper exposes robotmem's experience-replay functionality, which stores structured memory records and supports filtering/retrieval by robot state, action, or temporal windows.

Solves for

I want to record robot trajectories and replay them for learningI need to query past robot experiences by state or action typeI want to build a persistent memory system for multi-episode robot training

Best for

Robotics researchers implementing experience-replay learning algorithms

AI agent developers building long-horizon robot learning systems

Teams needing persistent state management for embodied AI

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Persistent storage backend (file system or database) configured in robotmem Python package

Limitations

No built-in indexing — queries may be slow on large experience datasets (>100k records)

Storage format and schema determined by robotmem Python implementation; no customization via npm wrapper

No transaction support — concurrent writes from multiple processes may corrupt data

What makes it unique

Delegates experience-replay logic entirely to robotmem Python backend, avoiding reimplementation of complex state serialization and query logic in JavaScript

vs alternatives

Simpler integration than building custom experience-replay from scratch, but less performant than native Node.js memory stores because Python CLI overhead applies to every query

robot state serialization and deserialization

Medium confidence

Handles marshaling of robot state objects (sensor readings, joint positions, internal state) between JavaScript and Python representations through robotmem's serialization layer. Converts JavaScript objects to Python-compatible formats (JSON, pickle, or robotmem-native schemas) for storage and retrieves them back as JavaScript objects, enabling seamless state exchange across the language boundary.

Solves for

I want to store robot sensor data and state in a format that Python robotmem can understandI need to convert between JavaScript robot state objects and robotmem's internal representationI want to ensure robot state survives serialization round-trips without data loss

Best for

Node.js robotics frameworks integrating with Python-based memory systems

Teams with heterogeneous robot control stacks (JavaScript UI + Python learning)

Developers building cross-language robot state synchronization

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Robot state objects compatible with JSON or robotmem's native serialization format

Limitations

Serialization format determined by robotmem Python package; no customization from npm wrapper

Complex nested objects may lose type information during round-trip serialization

Binary data (images, sensor arrays) requires explicit encoding/decoding (base64, etc.)

What makes it unique

Transparently handles serialization boundary between JavaScript and Python without requiring developers to manually manage format conversions, delegating to robotmem's built-in serialization

vs alternatives

More convenient than manual JSON marshaling, but less efficient than native JavaScript state stores because every state operation incurs Python subprocess overhead

cli argument and environment configuration pass-through

Medium confidence

Accepts Node.js configuration objects and translates them into robotmem Python CLI arguments and environment variables, enabling programmatic control of robotmem behavior from JavaScript without hardcoding command-line strings. Supports passing options like storage backend, memory size limits, serialization format, and logging verbosity directly through the npm wrapper's API.

Solves for

I want to configure robotmem behavior from my Node.js application without shell scriptsI need to dynamically set robotmem options based on runtime conditionsI want to pass environment-specific configuration to the Python robotmem process

Best for

Node.js developers building configurable robotmem integrations

Teams deploying robotmem across multiple environments (dev, staging, prod)

Developers avoiding shell script complexity for robotmem invocation

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Knowledge of robotmem Python CLI options and environment variables

Limitations

Configuration schema must match robotmem Python CLI exactly; no validation or schema enforcement from npm wrapper

No type safety — invalid options silently passed to Python, causing runtime errors

Environment variables set per-process; no global configuration persistence

What makes it unique

Provides a thin JavaScript API for robotmem CLI configuration without adding abstraction layers, preserving direct access to all Python CLI options

vs alternatives

More flexible than hardcoded CLI invocations, but requires developers to understand robotmem's Python CLI interface directly

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓Node.js/TypeScript developers building AI agents or robotics systems
✓Teams with mixed JavaScript and Python codebases needing interop
✓MCP server implementations requiring Python-based memory backends
✓AI agent developers building Claude-powered robotics systems
✓Teams implementing persistent memory layers for embodied AI
✓MCP ecosystem participants needing memory/experience-replay backends
✓Robotics researchers implementing experience-replay learning algorithms
✓AI agent developers building long-horizon robot learning systems

Known Limitations

⚠Subprocess overhead adds latency compared to native Node.js implementation (~50-200ms per invocation)
⚠Requires Python runtime to be installed on the system despite npm packaging
⚠No built-in process pooling or connection reuse — spawns new Python process per call
⚠Error handling limited to Python exit codes and stderr capture; no structured exception propagation
⚠MCP protocol overhead adds serialization/deserialization latency
⚠Tool schema must be manually defined and kept in sync with robotmem CLI interface

Requirements

Node.js 14+ (for child_process and Promise support)Python 3.7+ installed and available in system PATHrobotmem Python package installed (pip install robotmem or equivalent)Node.js 14+Python 3.7+ with robotmem installedMCP client implementation (e.g., Claude desktop app, Cline, or custom MCP client)Understanding of MCP tool schema formatPersistent storage backend (file system or database) configured in robotmem Python package

Input / Output

Accepts: command-line arguments (strings), stdin text streams, environment variables, MCP tool call requests (JSON with tool name and arguments), structured memory queries (robot state, experience filters), robot state dictionaries (JSON-serializable), action records (strings or structured data), reward scalars (floats), query filters (state/action/time ranges), JavaScript objects (robot state dictionaries), JSON strings, typed arrays (for sensor data), JavaScript configuration objects, environment variable key-value pairs, CLI argument arrays

Produces: stdout text output, stderr error messages, exit codes (0 for success, non-zero for failure), MCP tool results (JSON responses), memory records (structured robot experience data), operation status (success/failure with details), experience records (structured trajectory data), query results (filtered memory subsets), persistence confirmations (success/failure), JSON-serialized state, Python-compatible object representations, deserialized JavaScript objects, robotmem process with applied configuration, confirmation of configuration acceptance

UnfragileRank

Adoption5%(25% weight)

Quality20%(25% weight)

Ecosystem50%(15% weight)

Match Graph25%(23% weight)

Freshness60%(12% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

5 capabilities

Visit @muscular/robotmem→

Package Details

npm

Registry

0.1.2

Version

Weekly Downloads

About

Thin npm wrapper for the robotmem Python CLI and MCP server.

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Capabilities5 decomposed

python cli wrapping via node.js subprocess bridge

Medium confidence

Solves for

Best for

Node.js/TypeScript developers building AI agents or robotics systems

Teams with mixed JavaScript and Python codebases needing interop

MCP server implementations requiring Python-based memory backends

Requires

Node.js 14+ (for child_process and Promise support)

Python 3.7+ installed and available in system PATH

robotmem Python package installed (pip install robotmem or equivalent)

Limitations

Subprocess overhead adds latency compared to native Node.js implementation (~50-200ms per invocation)

Requires Python runtime to be installed on the system despite npm packaging

No built-in process pooling or connection reuse — spawns new Python process per call

What makes it unique

Minimal wrapper design that delegates all robotmem logic to Python CLI rather than reimplementing in JavaScript, reducing maintenance burden and ensuring feature parity with Python version

vs alternatives

Simpler than native Node.js ports of robotmem because it reuses existing Python implementation, but introduces subprocess latency vs direct library binding

mcp server protocol bridging for robot memory operations

Medium confidence

Solves for

Best for

AI agent developers building Claude-powered robotics systems

Teams implementing persistent memory layers for embodied AI

MCP ecosystem participants needing memory/experience-replay backends

Requires

Node.js 14+

Python 3.7+ with robotmem installed

MCP client implementation (e.g., Claude desktop app, Cline, or custom MCP client)

Limitations

MCP protocol overhead adds serialization/deserialization latency

Tool schema must be manually defined and kept in sync with robotmem CLI interface

No built-in streaming support for large memory datasets — full responses buffered in memory

What makes it unique

Bridges MCP protocol semantics directly to robotmem CLI without intermediate abstraction layer, preserving robotmem's native command structure while exposing it as AI-callable tools

vs alternatives

More lightweight than building a full REST API wrapper, but less flexible than native MCP implementations because it depends on Python CLI stability and output format

experience replay data persistence and retrieval

Medium confidence

Solves for

Best for

Robotics researchers implementing experience-replay learning algorithms

AI agent developers building long-horizon robot learning systems

Teams needing persistent state management for embodied AI

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Persistent storage backend (file system or database) configured in robotmem Python package

Limitations

No built-in indexing — queries may be slow on large experience datasets (>100k records)

Storage format and schema determined by robotmem Python implementation; no customization via npm wrapper

No transaction support — concurrent writes from multiple processes may corrupt data

What makes it unique

Delegates experience-replay logic entirely to robotmem Python backend, avoiding reimplementation of complex state serialization and query logic in JavaScript

vs alternatives

Simpler integration than building custom experience-replay from scratch, but less performant than native Node.js memory stores because Python CLI overhead applies to every query

robot state serialization and deserialization

Medium confidence

Solves for

Best for

Node.js robotics frameworks integrating with Python-based memory systems

Teams with heterogeneous robot control stacks (JavaScript UI + Python learning)

Developers building cross-language robot state synchronization

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Robot state objects compatible with JSON or robotmem's native serialization format

Limitations

Serialization format determined by robotmem Python package; no customization from npm wrapper

Complex nested objects may lose type information during round-trip serialization

Binary data (images, sensor arrays) requires explicit encoding/decoding (base64, etc.)

What makes it unique

Transparently handles serialization boundary between JavaScript and Python without requiring developers to manually manage format conversions, delegating to robotmem's built-in serialization

vs alternatives

More convenient than manual JSON marshaling, but less efficient than native JavaScript state stores because every state operation incurs Python subprocess overhead

cli argument and environment configuration pass-through

Medium confidence

Solves for

Best for

Node.js developers building configurable robotmem integrations

Teams deploying robotmem across multiple environments (dev, staging, prod)

Developers avoiding shell script complexity for robotmem invocation

Requires

Node.js 14+

Python 3.7+ with robotmem installed

Knowledge of robotmem Python CLI options and environment variables

Limitations

Configuration schema must match robotmem Python CLI exactly; no validation or schema enforcement from npm wrapper

No type safety — invalid options silently passed to Python, causing runtime errors

Environment variables set per-process; no global configuration persistence

What makes it unique

Provides a thin JavaScript API for robotmem CLI configuration without adding abstraction layers, preserving direct access to all Python CLI options

vs alternatives

More flexible than hardcoded CLI invocations, but requires developers to understand robotmem's Python CLI interface directly

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@muscular/robotmem

Capabilities5 decomposed

python cli wrapping via node.js subprocess bridge

mcp server protocol bridging for robot memory operations

experience replay data persistence and retrieval

robot state serialization and deserialization

cli argument and environment configuration pass-through

Related Artifactssharing capabilities

ollama-mcp-bridge

Reloaderoo

MCP-Chatbot

mcp-chrome

mcp-server

mcp-smart-crawler

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

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Alternatives to @muscular/robotmem

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@muscular/robotmem

Capabilities5 decomposed

python cli wrapping via node.js subprocess bridge

mcp server protocol bridging for robot memory operations

experience replay data persistence and retrieval

robot state serialization and deserialization

cli argument and environment configuration pass-through

Related Artifactssharing capabilities

ollama-mcp-bridge

Reloaderoo

MCP-Chatbot

mcp-chrome

mcp-server

mcp-smart-crawler

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @muscular/robotmem

Are you the builder of @muscular/robotmem?

Get the weekly brief

Data Sources