Agent Memory Systems

Agent Memory Systems

Design and implement memory systems that give agents persistent knowledge and context.

When to Use

• Agents need to remember across sessions
• Multiple agents share knowledge
• Long-running tasks require state persistence
• Building agents that learn from experience

Memory Types

┌─────────────────────────────────────────────────────────────┐
│                    AGENT MEMORY TAXONOMY                     │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Working Memory (Active Context)                             │
│  └─ Current conversation, immediate task state               │
│                                                              │
│  Short-Term Memory (Session)                                 │
│  └─ Recent interactions, temporary facts                     │
│                                                              │
│  Long-Term Memory (Persistent)                               │
│  ├─ Episodic: Past events, experiences                       │
│  ├─ Semantic: Facts, knowledge, learned info                 │
│  └─ Procedural: How to do things, skills                     │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Working Memory

The agent's current context window.

interface WorkingMemory {
  // Current task context
  task: {
    description: string;
    requirements: string[];
    progress: string[];
  };

  // Active entities being discussed
  entities: Map;

  // Recent messages
  conversationWindow: Message[];

  // Scratchpad for reasoning
  scratchpad: string;
}

class WorkingMemoryManager {
  private memory: WorkingMemory;
  private MAX_MESSAGES = 20;

  addMessage(message: Message) {
    this.memory.conversationWindow.push(message);

    // Evict old messages
    if (this.memory.conversationWindow.length > this.MAX_MESSAGES) {
      const evicted = this.memory.conversationWindow.shift();
      // Optionally summarize and store
      this.summarizeToShortTerm(evicted);
    }
  }

  updateEntity(id: string, entity: Entity) {
    this.memory.entities.set(id, entity);
  }

  getContext(): string {
    return `
Task: ${this.memory.task.description}
Progress: ${this.memory.task.progress.join(', ')}
Key Entities: ${[...this.memory.entities.values()].map(e => e.summary).join('\n')}
Recent Discussion: ${this.memory.conversationWindow.slice(-5).map(m => m.content).join('\n')}
    `;
  }
}

Short-Term Memory

Session-scoped, expires after inactivity.

interface ShortTermMemory {
  sessionId: string;
  startedAt: Date;
  lastAccessedAt: Date;
  expiresAt: Date;

  // Conversation summary
  summary: string;

  // Key facts learned this session
  facts: Fact[];

  // Decisions made
  decisions: Decision[];

  // Files accessed
  touchedFiles: string[];
}

class ShortTermStore {
  private store = new Map();
  private TTL_MS = 30 * 60 * 1000; // 30 minutes

  async get(sessionId: string): Promise {
    const memory = this.store.get(sessionId);

    if (!memory) return null;
    if (Date.now() > memory.expiresAt.getTime()) {
      // Expired - archive to long-term
      await this.archiveToLongTerm(memory);
      this.store.delete(sessionId);
      return null;
    }

    // Touch
    memory.lastAccessedAt = new Date();
    memory.expiresAt = new Date(Date.now() + this.TTL_MS);

    return memory;
  }

  async addFact(sessionId: string, fact: Fact) {
    const memory = await this.getOrCreate(sessionId);
    memory.facts.push(fact);

    // Check if fact should be promoted to long-term
    if (this.shouldPromote(fact)) {
      await this.promoteToLongTerm(fact);
    }
  }
}

Long-Term Memory

Episodic Memory (Events/Experiences)

interface Episode {
  id: string;
  timestamp: Date;
  type: 'task_completed' | 'error_resolved' | 'user_feedback' | 'learning';

  // What happened
  description: string;
  context: Record;

  // Outcome
  outcome: 'success' | 'failure' | 'partial';
  lessons: string[];

  // For retrieval
  embedding: number[];
  tags: string[];
}

class EpisodicMemory {
  private vectorStore: VectorStore;

  async remember(episode: Episode): Promise {
    // Generate embedding for semantic search
    episode.embedding = await this.embed(
      `${episode.description} ${episode.lessons.join(' ')}`
    );

    await this.vectorStore.insert(episode);
  }

  async recall(query: string, limit: number = 5): Promise {
    const queryEmbedding = await this.embed(query);

    return this.vectorStore.similaritySearch(queryEmbedding, {
      limit,
      threshold: 0.7
    });
  }

  async recallByType(type: Episode['type'], limit: number = 10): Promise {
    return this.vectorStore.filter({ type }, { limit, orderBy: 'timestamp DESC' });
  }
}

Semantic Memory (Facts/Knowledge)

interface Fact {
  id: string;
  subject: string;
  predicate: string;
  object: string;
  confidence: number;
  source: string;
  learnedAt: Date;
  lastVerified: Date;
  embedding: number[];
}

class SemanticMemory {
  private facts: VectorStore;

  async learn(fact: Omit): Promise {
    // Check for conflicts
    const existing = await this.findRelated(fact.subject, fact.predicate);

    if (existing.length > 0) {
      // Handle contradiction or update
      await this.resolveConflict(existing, fact);
      return;
    }

    // Store new fact
    await this.facts.insert({
      ...fact,
      id: generateId(),
      embedding: await this.embed(`${fact.subject} ${fact.predicate} ${fact.object}`)
    });
  }

  async query(question: string): Promise {
    // Semantic search for relevant facts
    const embedding = await this.embed(question);
    return this.facts.similaritySearch(embedding, { limit: 10 });
  }

  async getFactsAbout(subject: string): Promise {
    return this.facts.filter({ subject });
  }
}

Procedural Memory (Skills/How-To)

interface Procedure {
  id: string;
  name: string;
  description: string;
  trigger: string; // When to use this
  steps: string[];
  examples: Example[];
  successRate: number;
  usageCount: number;
}

class ProceduralMemory {
  private procedures: Map = new Map();

  async findProcedure(task: string): Promise {
    // Match task to known procedures
    const candidates = await this.matchProcedures(task);

    if (candidates.length === 0) return null;

    // Return best match by success rate and relevance
    return candidates.sort((a, b) =>
      (b.successRate * b.relevanceScore) - (a.successRate * a.relevanceScore)
    )[0];
  }

  async recordOutcome(procedureId: string, success: boolean): Promise {
    const proc = this.procedures.get(procedureId);
    if (!proc) return;

    // Update success rate with exponential moving average
    const alpha = 0.1;
    proc.successRate = alpha * (success ? 1 : 0) + (1 - alpha) * proc.successRate;
    proc.usageCount++;
  }

  async learnProcedure(
    name: string,
    steps: string[],
    fromEpisode: Episode
  ): Promise {
    // Create new procedure from successful episode
    this.procedures.set(generateId(), {
      id: generateId(),
      name,
      description: fromEpisode.description,
      trigger: this.extractTrigger(fromEpisode),
      steps,
      examples: [{ input: fromEpisode.context, output: fromEpisode.outcome }],
      successRate: 1.0,
      usageCount: 1
    });
  }
}

Shared Memory (Multi-Agent)

interface SharedMemory {
  // Namespace for isolation
  namespace: string;

  // Read/write with locking
  read(key: string): Promise;
  write(key: string, value: unknown): Promise;

  // Atomic operations
  compareAndSwap(key: string, expected: unknown, newValue: unknown): Promise;

  // Subscriptions
  subscribe(pattern: string, callback: (key: string, value: unknown) => void): void;
}

class RedisSharedMemory implements SharedMemory {
  constructor(
    private redis: Redis,
    public namespace: string
  ) {}

  private key(k: string): string {
    return `${this.namespace}:${k}`;
  }

  async read(key: string): Promise {
    const value = await this.redis.get(this.key(key));
    return value ? JSON.parse(value) : null;
  }

  async write(key: string, value: unknown): Promise {
    await this.redis.set(this.key(key), JSON.stringify(value));
    await this.redis.publish(`${this.namespace}:updates`, JSON.stringify({ key, value }));
  }

  subscribe(pattern: string, callback: (key: string, value: unknown) => void): void {
    const subscriber = this.redis.duplicate();
    subscriber.psubscribe(`${this.namespace}:${pattern}`);
    subscriber.on('pmessage', (_, channel, message) => {
      const { key, value } = JSON.parse(message);
      callback(key, value);
    });
  }
}

Memory Retrieval Strategies

Strategy 1: Recency-Weighted

function recencyWeightedRetrieval(
  memories: Memory[],
  query: string,
  recencyWeight: number = 0.3
): Memory[] {
  const now = Date.now();

  return memories
    .map(m => ({
      memory: m,
      score: (1 - recencyWeight) * m.relevanceScore +
             recencyWeight * Math.exp(-(now - m.timestamp.getTime()) / TIME_DECAY)
    }))
    .sort((a, b) => b.score - a.score)
    .map(x => x.memory);
}

Strategy 2: Importance-Based

function importanceBasedRetrieval(
  memories: Memory[],
  query: string
): Memory[] {
  return memories
    .map(m => ({
      memory: m,
      score: m.relevanceScore * m.importance * (m.accessCount / 10)
    }))
    .sort((a, b) => b.score - a.score)
    .map(x => x.memory);
}

Strategy 3: Contextual (RAG)

async function contextualRetrieval(
  query: string,
  currentContext: Context
): Promise {
  // Expand query with context
  const expandedQuery = `
    ${query}
    Current task: ${currentContext.task}
    Related entities: ${currentContext.entities.join(', ')}
  `;

  // Vector search
  const candidates = await vectorStore.search(expandedQuery, { limit: 20 });

  // Rerank with cross-encoder
  return reranker.rerank(query, candidates, { limit: 5 });
}

Memory Maintenance

class MemoryMaintenance {
  // Consolidate short-term to long-term
  async consolidate(): Promise {
    const sessions = await shortTermStore.getExpired();

    for (const session of sessions) {
      // Extract key learnings
      const learnings = await this.extractLearnings(session);

      // Store in appropriate long-term stores
      for (const learning of learnings) {
        if (learning.type === 'fact') {
          await semanticMemory.learn(learning);
        } else if (learning.type === 'procedure') {
          await proceduralMemory.learnProcedure(learning);
        } else {
          await episodicMemory.remember(learning);
        }
      }
    }
  }

  // Forget outdated/irrelevant memories
  async forget(): Promise {
    // Remove low-value memories
    await episodicMemory.prune({
      olderThan: days(90),
      accessCountBelow: 2,
      importanceBelow: 0.2
    });

    // Update fact confidence based on verification
    await semanticMemory.decayUnverified({
      olderThan: days(30),
      decayRate: 0.1
    });
  }
}

Best Practices

1. Separate concerns - Different memory types for different purposes
2. Index wisely - Use embeddings for semantic search
3. Expire aggressively - Don't let memory grow unbounded
4. Version memories - Track when facts were learned/updated
5. Handle conflicts - New information may contradict old
6. Secure sensitive data - Some memories shouldn't be shared

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: latestaiagents
• Source: latestaiagents/agent-skills
• License: MIT
• Homepage: https://latestaiagents.com

Install and usage instructions live in the source repository linked above.