agent-memory-framework

name

agent-memory-framework

description

Design and implement memory-augmented AI agents using modular architecture (extraction, update, retrieval, response). Inspired by MemFactory (arxiv:2603.29493) - unified training/inference framework for agent memory with RL-driven policy optimization (GRPO). Use when building long-term AI agents, memory management systems, or implementing Memory-R1/RMM/MemAgent paradigms. Keywords: agent memory, memory-augmented LLM, MemFactory, Memory-R1, memory lifecycle, GRPO, memory extraction, memory retrieval.

Agent Memory Framework

Design memory-augmented AI agents with modular, RL-optimized memory management.

Core Concepts

Memory Lifecycle (6 Stages)

Conversation → [Extraction] → [Update Decision] → [Storage] → [Organization] → [Retrieval] → [Response] → Answer

Stage	Function	Implementation
Extraction	Extract key info from conversation	LLM-based extraction
Update Decision	ADD/UPDATE/DELETE/NOOP	RL policy (GRPO)
Storage	Store memory entries	Vector DB / Knowledge Graph
Organization	Structure memory	Hierarchical / Temporal
Retrieval	Find relevant memories	Semantic search
Response	Generate answer	LLM reasoning

Memory Operations

Operation	Trigger	Example
ADD	New information	User: "My name is Alice" → ADD name=Alice
UPDATE	Information change	User: "I moved to NYC" → UPDATE location=NYC
DELETE	Outdated info	Time-based expiration → DELETE old entries
NOOP	No new info	Irrelevant conversation → NOOP

RL Policy Optimization (GRPO)

Group Relative Policy Optimization:

• Fine-tune memory management policies
• Multi-dimensional rewards:
  • Answer quality
  • Memory efficiency
  • Conversation coherence

Architecture Patterns

Pattern 1: Dual-Agent Architecture (Memory-R1)

Memory Manager Agent:
  Input: Conversation history
  Output: Memory operation sequence
  Training: RL (GRPO/PPO)

Answer Agent:
  Input: Question + Retrieved memories
  Output: Answer
  Training: RL + Supervised

Advantages:

• Specialization: Each agent focuses on its task
• Scalability: Can train agents separately
• Efficiency: 152 QA pairs sufficient for training

Pattern 2: Modular Memory Pipeline

class MemoryPipeline:
    def __init__(self):
        self.extractor = MemoryExtractor()
        self.updater = MemoryUpdateDecision()
        self.storage = MemoryStorage()
        self.retriever = MemoryRetriever()
        self.responder = ResponseGenerator()
    
    def process(self, conversation, query):
        # 1. Extract from conversation
        new_info = self.extractor.extract(conversation)
        
        # 2. Decide memory operations
        operations = self.updater.decide(self.storage, new_info)
        
        # 3. Execute operations
        self.storage.apply(operations)
        
        # 4. Retrieve relevant memories
        memories = self.retriever.retrieve(query, self.storage)
        
        # 5. Generate response
        return self.responder.generate(query, memories)

Pattern 3: RL Training Loop

# GRPO training for memory management
def train_memory_policy(agent, episodes):
    for episode in episodes:
        # Simulate conversation
        conversation = simulate_dialogue()
        
        # Get memory operations
        operations = agent.get_operations(conversation)
        
        # Execute and evaluate
        outcome = execute_operations(operations)
        reward = compute_reward(outcome)
        
        # Update policy
        agent.policy.update(operations, reward)

Implementation Guide

Step 1: Design Memory Schema

# Memory entry structure
memory_entry = {
    "id": "mem_001",
    "content": "User preference: dark mode",
    "type": "preference",  # fact, preference, event, context
    "timestamp": 1703275200,
    "importance": 0.8,
    "source": "conversation_123",
    "metadata": {"category": "ui_settings"}
}

Step 2: Implement Extraction

def extract_memory(conversation: str) -> List[MemoryEntry]:
    """Extract key information from conversation."""
    # LLM-based extraction
    prompt = f"""
    Extract key facts, preferences, and events from this conversation.
    Return as structured JSON.
    
    Conversation: {conversation}
    """
    extracted = llm.generate(prompt)
    return parse_to_memories(extracted)

Step 3: Implement Update Decision

class MemoryUpdatePolicy:
    def decide(self, storage: MemoryStorage, new_info: List) -> List[Operation]:
        """Decide which memory operations to perform."""
        operations = []
        for info in new_info:
            existing = storage.search(info.content)
            if not existing:
                operations.append(Operation("ADD", info))
            elif info.is_update(existing):
                operations.append(Operation("UPDATE", existing.id, info))
            # ... check for DELETE, NOOP
        return operations

Step 4: Implement Retrieval

def retrieve_memories(query: str, storage: MemoryStorage, k: int = 60) -> List:
    """Retrieve top-k relevant memories."""
    # Semantic search
    candidates = storage.vector_search(query, k)
    
    # Filter and rank
    relevant = filter_by_relevance(candidates, query)
    
    # Return subset (Memory-R1: up to 60 candidates, distilled to subset)
    return select_top_subset(relevant, threshold=0.7)

Step 5: RL Training

# Reward function for memory policy
def compute_reward(outcome: Outcome) -> float:
    reward = 0.0
    reward += outcome.answer_quality * 0.5
    reward += outcome.memory_efficiency * 0.3
    reward += outcome.conversation_coherence * 0.2
    return reward

Supported Paradigms

Memory-R1 (arxiv:2508.19828)

• Dual-agent architecture
• Operations: ADD, UPDATE, DELETE, NOOP
• Training: PPO/GRPO, 152 QA pairs
• Benchmarks: LoCoMo, MSC, LongMemEval

RMM (Retrieval-Augmented Memory Management)

• Focus: Memory retrieval optimization
• RL-based retrieval policy
• Semantic + temporal retrieval

MemAgent

• Focus: Long-context handling
• Memory for extended conversations
• Context compression and summarization

Best Practices

1. Modular Design

• Each memory stage as independent module
• Plug-and-play architecture
• "Lego-like" composition

2. RL-Driven Policies

• Learn when to add/update/delete memories
• Multi-dimensional rewards
• Minimal supervision (152 pairs sufficient)

3. Specialization

• Separate memory management from answer generation
• Different agents for different tasks
• Targeted training

4. Evaluation

• Cross-benchmark testing
• Generalization across diverse questions
• Multiple model scales (3B-14B)

Tools Used

• read: Load conversation history, existing memories
• write: Create/update memory entries
• edit: Modify memory content
• exec: Run RL training, vector search
• sqlite3: Memory storage (kg.db pattern)

Activation Keywords

• agent memory
• memory-augmented LLM
• MemFactory
• Memory-R1
• memory lifecycle
• GRPO
• memory extraction
• memory retrieval
• memory management
• long-term AI agent

Related Skills

• memory-retrieval: Memory search and retrieval
• indexed-memory: Indexed memory management
• chat-history-lancedb: LanceDB for chat history
• knowledge-graph: Knowledge graph integration

References

• MemFactory (arxiv:2603.29493): Unified framework
• Memory-R1 (arxiv:2508.19828): RL-driven memory management
• LLaMA-Factory: Inspiration for modular design
• ReContext (arxiv:2607.02509): references/recontext-associative-memory-pattern.md — neuroscience-inspired associative memory mapping for LLM retrieval systems

GitHub

• https://github.com/MemTensor/MemFactory
• https://github.com/Valsure/MemFactory

Notes

• Modular design enables easy customization
• RL training requires minimal data (152 pairs)
• Performance gains up to 14.8% over base models
• Dual-agent architecture separates concerns effectively