1// n8n Window Buffer Memory node
2// Keeps last N messages in context
3
4// Configuration:
5// Session Key: {{ $json.sessionId }}  
6// Context Window Length: 10
7// This preserves the last 10 messages
8
9// Example memory state:
10// [
11//   { role: "user", content: "What products do you have?" },
12//   { role: "assistant", content: "We have Product A, B, C..." },
13//   { role: "user", content: "Tell me about Product A" },
14//   { role: "assistant", content: "Product A is a..." }
15// ]

1// When conversation gets long, summarize instead of full buffer
2// n8n: Use Code node + OpenAI to compress history
3
4const summarizePrompt = `
5Summarize this conversation into key facts and decisions:
6
7${$json.conversationHistory}
8
9Summary (max 200 words):`;
10
11// Result: "User asked about products. Interested in Product A. 
12// Budget: under $500. Prefers blue color."

1// Save important interactions to vector store
2// Code node: Determine if worth saving
3function shouldSave(interaction) {
4  // Save if contains important information:
5  const importantTopics = ["preference", "decision", "problem", "feedback"];
6  return importantTopics.some(topic => 
7    interaction.toLowerCase().includes(topic)
8  );
9}
10
11if (shouldSave($json.userMessage + $json.aiResponse)) {
12  return {
13    json: {
14      content: `User: ${$json.userMessage}\nAssistant: ${$json.aiResponse}`,
15      metadata: {
16        sessionId: $json.sessionId,
17        userId: $json.userId,
18        timestamp: new Date().toISOString(),
19        topic: $json.detectedTopic
20      },
21      save: true
22    }
23  };
24}

1// Track entities mentioned in conversation
2// Code node: Entity extraction
3
4const entityPrompt = `
5Extract entities from this conversation:
6
7${$json.conversation}
8
9Return JSON:
10{
11  "people": [{"name": "...", "role": "...", "notes": "..."}],
12  "companies": [{"name": "...", "details": "..."}],
13  "products": [{"name": "...", "interest_level": "high/medium/low"}],
14  "preferences": [{"key": "...", "value": "..."}]
15}`;
16
17// Store entities in Redis or database per user
18// key: entity_${userId}
19// Retrieve entities on each new conversation

1// Code node: Build memory context
2const bufferHistory = $json.recentMessages; // Last 10
3const longTermResults = $json.vectorSearchResults; // Similar past
4const entities = $json.userEntities; // Known facts about user
5
6const memoryContext = `
7## Known about this user:
8${entities.map(e => `- ${e.key}: ${e.value}`).join('\n')}
9
10## Recent conversation:
11${bufferHistory.map(m => `${m.role}: ${m.content}`).join('\n')}
12
13## Relevant past interactions:
14${longTermResults.map(r => r.content).join('\n---\n')}
15`;
16
17return { json: { memoryContext } };

1// Redis for persistent session memory
2// n8n Redis Chat Memory node
3
4// Configuration:
5// - Redis URL: redis://localhost:6379
6// - Session Key: user_{{ $json.userId }}
7// - TTL: 86400 (24 hours)
8// - Max Messages: 50
9
10// Automatically handles:
11// - Persistent storage across n8n restarts
12// - Session isolation per user
13// - Auto-cleanup via TTL