Memento-Teams/Memento-Skills

Memento-Skills: Let Agents Design Agents

What it solves

Memento-Skills provides a framework for AI agents to develop and persist reusable capabilities. Instead of relying solely on model weights, it allows agents to author, repair, and store skills in an external memory, enabling them to improve their performance on complex tasks over time through a "Read-Execute-Reflect-Write" lifecycle.

How it works

The system uses a 4-stage ReAct architecture consisting of Intent, Planning, Execution, and Reflection. When an agent encounters a task, it routes to existing skills using a hybrid approach (keywords or LLM-based routing). If a skill fails or is missing, the agent can reflect on the execution trace to author a new skill or repair an existing one. These skills are stored in a pluggable storage system (database, file, or vector storage) and can be evolved in the background via a "Dream Daemon" that consolidates experiences into long-term memory.

Who it’s for

It is designed for developers building autonomous agents that need to be extensible and capable of self-improvement without constant fine-tuning. It is also suitable for users who want a deployable agent with a GUI, CLI, and integration into IM platforms like Feishu, DingTalk, and WeChat.

Highlights

  • Skill-Centric Runtime: A complete orchestration system including routing, execution, and reflection.
  • Self-Evolving Capabilities: Ability to automatically create and repair skills based on failed execution trajectories.
  • Multi-Platform Deployment: Native support for desktop apps (Windows/macOS), CLI, GUI, and various IM gateways.
  • Robot-like Learning: Uses a research-backed approach to grow a skill library from atomic tools into complex learned behaviors.
  • Extensive Tooling: Includes a unified tool registry with atomic tools (bash, python_repl, web) and MCP client integration.

Related

  • Project
    ReflexioAI/reflexio

    An AI agent self-improvement harness that turns user corrections and successful interactions into persisted user profiles and reusable playbooks to improve agent performance over time.

  • Project
    TencentCloud/TencentDB-Agent-Memory

    A shared memory server and management hub that allows AI agents to persist and share experience, skills, and project knowledge across different sessions and frameworks.

  • Project
    NousResearch/hermes-agent

    A self-improving AI agent with a built-in learning loop that creates and refines skills from experience and maintains persistent user memory across sessions.

  • Project
    fuyuxiang/echo-agent

    A self-hosted, long-running AI agent featuring a four-layer cognitive memory system and a self-evolution engine to improve skills from real-world usage.

  • Project
    agentspan-ai/agentspan

    A durable runtime for AI agents that prevents execution loss during process crashes and supports long-running, event-driven workflows with human-in-the-loop approvals.