dsh-continual-harness
Run the following command in DeepSeek Harness:
dsh plugin install jasen215/dsh-continual-harness
Paste the following prompt into your AI chat to install this plugin:
In DeepSeek Harness, you can install this plugin by running the command `dsh plugin install jasen215/dsh-continual-harness`; the full source code address is https://github.com/jasen215/dsh-continual-harness.
About this plugin
Many AI agents suffer from amnesia across sessions, unable to retain and build upon past experiences, forcing them to start from scratch each time. The dsh-continual-harness plugin addresses this fundamental limitation by providing a comprehensive framework for continual learning. It endows agents with persistent memory, periodic automated review and refinement, and cross-session knowledge sharing, enabling them to learn and improve from experience much like humans do. At its core is a meticulously designed closed-loop process: from planning and validating changes to applying improvements, with automatic rollback mechanisms that ensure every evolutionary step is reliable and controllable.
Its capabilities go beyond simple memory storage. The plugin implements the Experience Solidification Protocol, structuring knowledge into version-controlled entries and using ranked injection to intelligently surface the most relevant information for the current task. Agents can not only optimize themselves in the background but are also provided with manual tools (like harness_refine) and commands, allowing developers to precisely guide their learning process. Crucially, it incorporates multi-layered governance such as impact minimization and legality guards to prevent erroneous modifications from breaking the agent's core functionality, ensuring every refinement is traceable and reversible.
This plugin is ideal for developers and researchers building complex, long-running AI systems that require self-evolution. If you are developing a smart assistant, an automation agent, or conducting autonomous AI research and wish for it to learn continuously from interactions, reduce repetitive errors, and maintain knowledge consistency, dsh-continual-harness provides the essential infrastructure. It transforms agents from stateless tools into memory-augmented collaborators, making it particularly suitable for scenarios like customer service, operations, and research analysis where accumulating domain knowledge is key.
Use Cases
- A long-running AI customer service agent needs to retain and optimize conversational knowledge.
- An automated operations assistant that must learn from past incident handling.
- A complex research analysis tool that requires accumulating domain insights across sessions.
Best For
- AI system developers and architects.
- Researchers in autonomous agents and intelligent assistants.
- Teams building AI applications that require long-term memory capabilities.
Related Plugins
Traceable, searchable cross-session memory for AI agents that turns conversation knowledge into a typed knowledge graph and recalls relevant subgraphs instead of replaying full history, natively integrated with DeepSeek Harness.
Gives DSH AI cross-session long-term memory, to-do and skill management, plus multi-session orchestration, external AI delegation, and an infinite canvas that grows with you.
dsh-mnemon is a three-tier, pluggable, Agent-driven memory system for DeepSeek Harness, combining Runtime memory, Project Documents, and replaceable Memory Spaces with nine long-term providers.
An opinionated, zero-infrastructure file-based memory protocol: human-readable markdown files plus a bounded always-loaded index, curation discipline, and no database or embeddings.