Memento-Teams/Memento

Health v2 · maintenance only

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2568 stars · 297 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Memento is a Python framework for building LLM agents that continually improve from experience via memory-based case-based reasoning, without fine-tuning model weights. It uses a planner-executor architecture with MCP tooling and episodic memory, achieving top results on the GAIA deep research benchmark.

Use cases

• build llm agents that learn from past tasks without fine-tuning
• deep research agent that answers complex multi-step questions
• memory-based continual learning for ai agents
• agent that retrieves relevant past cases to solve new tasks
• orchestrate mcp tools with a planner-executor agent
• improve agent accuracy on out-of-distribution tasks via episodic memory

When to choose

• you want adaptive agents without gradient updates or model weight changes
• you need a deep research agent with state-of-the-art GAIA performance
• you want case-based reasoning and memory augmentation in an agent loop
• you prefer MCP-based tool orchestration in Python

When to avoid

• you need a simple single-shot chatbot without memory or learning
• you require fine-tuning of model weights rather than memory-based adaptation
• you need a production enterprise agent platform with vendor support

Facets

framework · maturity active

agent-framework rag llm-inference mcp machine-learning artificial-intelligence large-language-models deep-learning python self-hosted case-based-reasoning memory-augmented-learning continual-learning planner-executor deep-research gaia-benchmark online-reinforcement-learning ai-agents retrieval-augmented-generation docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Memento-Teams/Memento")