MARM-Systems

Turn AI into a persistent, memory-powered collaborator. Universal MCP Server (supports HTTP, STDIO, and WebSocket) enabling cross-platform AI memory, multi-agent coordination, and context sharing. Built with MARM protocol for structured reasoning that evolves with your work.

[{"step":"Install and configure MARM-Systems","action":"Download the Universal MCP Server from [official repository]. Run the server with your preferred protocol (e.g., `./marm-server --protocol websocket --port 8080`). Ensure the MARM protocol is initialized with your desired reasoning structure (e.g., `marm init --reasoning hed`).","tip":"Use Docker for cross-platform consistency: `docker run -p 8080:8080 marm-systems/mcp-server:latest --protocol websocket`"},{"step":"Define memory policies and context schema","action":"Create a memory configuration file (e.g., `memory.yaml`) specifying retention periods, priority flags, and schema for your data types. Load it with `marm config --file memory.yaml`. For multi-agent systems, define shared context keys (e.g., `project_objective`, `team_roles`).","tip":"Start with a 7-day retention window for development and extend to 30+ days for production. Use YAML anchors to avoid repetition in schema definitions."},{"step":"Integrate with your AI workflows","action":"Connect your AI agents/tools to the MCP server. For Python agents, use the `marm-client` library: `from marm import Client; client = Client('ws://localhost:8080'); client.store_context(key='current_task', value='Optimize neural network inference')`. For CLI tools, pipe inputs via STDIO: `echo '{\"task\":\"debug GPU memory leak\"}' | ./marm-server --protocol stdio`.","tip":"Add a `context_id` parameter to all operations to maintain traceability across sessions. Example: `client.query(task='debug', context_id='session_20240610')`."},{"step":"Validate persistence and coordination","action":"Test memory persistence by restarting the server and querying for previously stored context. For multi-agent systems, verify context sharing by having one agent store data and another retrieve it. Use the `marm audit` command to review memory operations.","tip":"Simulate agent handoffs by running separate instances of the MCP server on different ports (e.g., 8080 for Agent A, 8081 for Agent B) and configure them to sync via a shared memory backend (e.g., Redis)."},{"step":"Monitor and refine","action":"Use the MARM dashboard (available at `http://localhost:8080/dashboard`) to monitor memory usage, reasoning traces, and agent interactions. Adjust retention policies or schema based on observed patterns. For debugging, enable verbose logging with `marm server --log-level debug`.","tip":"Set up alerts for memory limits (e.g., 80% capacity) to prevent data loss. Use the `marm export` command to back up critical context before major updates."}]