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Clone the repo or pull the Docker image: docker pull ghcr.io/getmetal/motorhead:latest Start Redis and MotorHead with the included docker
compose.yml (set OPENAI_API_KEY for summarization) POST a message to /motorhead/v1/sessions/{session_id}/memory to start storing conversation history GET /motorhead/v1/sessions/{session_id}/memory to retrieve the context window plus summary Configure MOTORHEAD_MAX_WINDOW_SIZE to control how many recent messages to keep before summarizing
💡 Quick Start: Follow these 2 steps in order to get up and running with MotorHead quickly.
Explore the key features that make MotorHead powerful for ai memory & search workflows.
Maintains a configurable window of recent messages. When exceeded, older messages are compressed into a running summary rather than dropped. Default window is described as 12 messages in the supplied content and is configurable via environment variable.
A customer support chatbot keeps recent messages in full while preserving a summary of the earlier conversation for context, so the agent can avoid repeating questions already answered.
Updates the conversation summary as new messages arrive instead of regenerating the full transcript from scratch. The exact cost impact depends on prompt design, message volume, model pricing, and OpenAI API usage.
A long-running coaching or support chat updates its summary over time without needing to resend the entire conversation history on every turn.
All session data is described as stored in Redis with configurable TTL for automatic cleanup. Performance and reliability depend on the Redis deployment, network configuration, persistence settings, and operational monitoring.
A SaaS platform with an existing Redis deployment adds session memory without introducing a separate primary database for chat context.
The supplied content describes endpoints for posting messages to a session, getting context with recent messages plus summary, and deleting sessions. No framework dependency is required for basic HTTP integration.
A Go or Rust backend integrates chat memory without pulling in Python, LangChain, or another AI framework.
Available as a Docker image with Docker Compose configuration for the MotorHead and Redis stack. Actual setup time depends on the local environment, networking, environment variables, and deployment target.
A developer prototyping a chatbot deploys persistent memory locally or in a self-managed environment using Docker Compose.
The supplied content does not verify current maintenance status. Before adopting MotorHead for a new production project, check the GitHub repository directly for recent commits, releases, issue activity, and maintainer responses.
MotorHead is narrower and more infrastructure-focused. It stores conversation messages and supports automatic summarization of older context. Mem0 and Zep may be better fits when a project needs broader memory features such as semantic recall, richer user memory, or more productized memory workflows.
The supplied setup notes reference OPENAI_API_KEY for summarization, so OpenAI is the verified provider in this record. Teams should check the current repository documentation before assuming support for other providers.
MotorHead is built as backend infrastructure using Rust and Redis, which are both commonly used in production systems. However, this record does not verify benchmark results, concurrency limits, service-level guarantees, or managed scaling, so teams should load test their own deployment before relying on it for critical workloads.
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Tutorial updated March 2026