ReMe: Memory Management Kit for Agents¶

Remember Me, Refine Me.

ReMe provides AI agents with a unified memory system—enabling the ability to extract, reuse, and share memories across users, tasks, and agents.

Personal Memory + Task Memory + Tool Memory = Agent Memory

Personal memory helps “understand user preferences”, task memory helps agents “perform better”, and tool memory enables “smarter tool usage”.

Architecture Design¶

ReMe Logo

ReMe integrates three complementary memory capabilities:

Task Memory/Experience

Procedural knowledge reused across agents

Success Pattern Recognition: Identify effective strategies and understand their underlying principles
Failure Analysis Learning: Learn from mistakes and avoid repeating the same issues
Comparative Patterns: Different sampling trajectories provide more valuable memories through comparison
Validation Patterns: Confirm the effectiveness of extracted memories through validation modules

Learn more about how to use task memory from task memory

Personal Memory

Contextualized memory for specific users

Individual Preferences: User habits, preferences, and interaction styles
Contextual Adaptation: Intelligent memory management based on time and context
Progressive Learning: Gradually build deep understanding through long-term interaction
Time Awareness: Time sensitivity in both retrieval and integration

Learn more about how to use personal memory from personal memory

Tool Memory

Data-driven tool selection and usage optimization

Historical Performance Tracking: Success rates, execution times, and token costs from real usage
LLM-as-Judge Evaluation: Qualitative insights on why tools succeed or fail
Parameter Optimization: Learn optimal parameter configurations from successful calls
Dynamic Guidelines: Transform static tool descriptions into living, learned manuals

Learn more about how to use tool memory from tool memory

📦 Ready-to-Use Memories¶

ReMe provides pre-built memories that agents can immediately use with verified best practices:

Available Memories¶

appworld.jsonl: Memory library for Appworld agent interactions, covering complex task planning and execution patterns
bfcl_v3.jsonl: Working memory library for BFCL tool calls

Quick Usage¶

# Load pre-built memories
response = requests.post("http://localhost:8002/vector_store", json={
    "workspace_id": "appworld",
    "action": "load",
    "path": "./docs/library/"
})

# Query relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
    "workspace_id": "appworld",
    "query": "How to navigate to settings and update user profile?",
    "top_k": 1
})

📚 Resources¶

Installation Guide, Quick Start: Get started quickly with practical examples
Vector Storage Setup: Configure local/vector databases and usage
MCP Guide: Create MCP services
Personal Memory, Task Memory & Tool Memory: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines.
Example Collection: Real use cases and best practices

Citation¶

@software{AgentscopeReMe2025,
  title = {AgentscopeReMe: Memory Management Kit for Agents},
  author = {Li Yu, Jiaji Deng, Zouying Cao},
  url = {https://reme.agentscope.io},
  year = {2025}
}