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183 lines
8.5 KiB
Plaintext
183 lines
8.5 KiB
Plaintext
---
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title: Memory
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description: >-
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Agents that remember who you are and how you work — transparent, structured,
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and fully under your control.
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tags:
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- LobeHub
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- Memory
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- Personalization
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- Memory Agent
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- Privacy
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- Context
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---
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# Memory
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Most AI tools treat every conversation like the first one — you repeat the same context, preferences, and background every time. With Memory, your Agents actually learn who you are. Key information is automatically extracted from your conversations, stored in a structured format, and applied in future interactions — making every session more useful than the last.
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This isn't a chat log. It's intelligent knowledge extraction: your role, work context, preferences, and habits — organized and ready to use.
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## Why Memory Matters
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Without Memory, every conversation starts from zero:
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- You repeat context constantly
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- Agents forget your preferences between sessions
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- Responses stay generic, unable to adapt to your situation
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With Memory, Agents build a clear, evolving picture of who you are and how you work. The more you use LobeHub, the better your Agents become.
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## How Memory Works
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Memory follows a four-step cycle:
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1. **Conversation analysis** — As you interact, the Memory Agent identifies insights: explicit preferences, implicit patterns, project context, communication style.
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2. **Memory extraction** — Key insights are extracted, categorized by type (preference, fact, context), and tagged with relevance and confidence.
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3. **Storage and indexing** — Memories are stored in a structured, searchable format. When new information conflicts with existing Memory, it updates automatically.
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4. **Contextual recall** — When relevant, Agents retrieve and apply the right memories — so responses fit your situation without you needing to re-explain.
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### White-Box Transparency
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LobeHub Memory is fully transparent. Unlike black-box AI memory, you can view every memory, edit any entry, delete what you don't want, and add memories manually at any time. Agents can't remember anything you haven't approved.
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## Types of Memory
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**Personal preferences** — How you like to work and communicate: response length, technical depth, tone, format preferences, working hours.
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**Personal context** — Who you are and what you're doing: role, organization, team, current projects, key stakeholders.
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**Skills and knowledge** — What you know and what you're learning. Helps Agents calibrate their explanations to your level.
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**Patterns and habits** — Recurring behaviors and workflows: weekly meetings, common requests, decision-making criteria, typical project workflows.
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**Historical context** — Past projects and decisions: lessons learned, reasoning behind past choices, key collaborators.
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## Understanding Agent Memory
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Agent Memory is a built-in plugin in LobeHub. It automatically identifies and extracts key information from your conversations, building a structured memory base. These memories are applied in future interactions, making communication more personalized and efficient. Instead of logging dialogue verbatim, it distills actionable insights — behavioral rules the Agent follows going forward.
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## Enabling Memory
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Memory is a built-in plugin and must be enabled for each Agent separately. You can enable it from the Agent Profile page or directly within a conversation.
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### Enable from Agent Profile
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Go to the Agent Profile page, click **+ Add Plugin**, and check **Memory** to enable it.
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### Enable in a Conversation
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Open a conversation, click the plugin icon below the chat input, and check **Memory** to activate it.
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## Managing Memory
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### View Memory
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Click the **Memory** icon at the bottom of the left sidebar to open the Memory panel. It displays all memories extracted from your conversations, organized by type. Each memory shows:
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- **Title** — A concise summary, e.g. "Dislikes coffee"
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- **Preference Weight** — How strongly the Agent prioritizes this memory. Higher weight = more influence in conversations.
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- **Created Time** — When the memory was extracted
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- **Memory Instruction** — The behavioral rule derived from the conversation, e.g. "Avoid recommending coffee unless the user explicitly requests it; suggest non-coffee alternatives."
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- **Possible Actions** — How the Agent might apply this memory, e.g. "Offer tea, hot chocolate, juice, or decaf options depending on context."
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- **Tags** — For categorization and search
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### Search Memory
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Use the search function to quickly find specific memories. You can also open the command menu at any time and search for memories there.
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### Edit Memory
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Modify any memory instruction to better reflect your actual needs and preferences.
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### Delete Memory
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Select a memory and click the delete button to remove it.
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## The Memory Agent
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The **Memory Agent** is a built-in Agent dedicated to managing your personal Memory:
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- **Extraction** — Analyzes conversations to identify valuable insights worth keeping
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- **Organization** — Categorizes and structures memories for easy retrieval
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- **Maintenance** — Identifies outdated or conflicting memories and suggests updates
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- **Application** — Determines when to inject relevant memories into an Agent's context
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Talk directly to the Memory Agent to add, update, or review memories:
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- "Remember that I prefer morning meetings over afternoon ones."
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- "What do you know about my current projects?"
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- "Update my role: I'm now VP of Product, not Senior PM."
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- "What do you remember about my communication preferences?"
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## Using Memory Effectively
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### Getting Started
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When you first use LobeHub, help Agents learn about you quickly:
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- Share your role, team, and current priorities
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- State preferences explicitly ("I prefer bullet points over paragraphs")
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- Add manual memories for important facts Agents should always know
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### Natural Conversation with Memory
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Once context is established, just work. Here's the difference Memory makes:
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**Without Memory:**
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- Agent asks: "What format would you like?" / "Who are the stakeholders?" / "What sections should I include?"
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**With Memory:**
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- Agent uses your preferred template automatically
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- Includes sections you always want (e.g., user research)
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- Lists your stakeholders without asking
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### Refining Over Time
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Memory improves as you work:
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- When an Agent gets something wrong, correct it: "Actually, I prefer X instead of Y." The Memory Agent updates immediately.
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- As your situation changes, tell Agents: "I'm now leading the mobile team in addition to web."
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- Review your Memory panel monthly — remove outdated entries, add new information.
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## Privacy and Control
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All memories belong to you:
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- Stored in your Workspace only
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- Never shared with other users
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- Not used to train models
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- Fully exportable (JSON, CSV, or Markdown)
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You can disable Memory extraction for specific conversations, mark sensitive topics as "don't remember", and set automatic expiration by time or project. Memory and conversation history are stored separately — deleting conversations doesn't affect Memory, and vice versa.
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## Best Practices
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- **Start with key context** — Have a conversation with the Memory Agent when you first start: "Let me tell you about myself and how I work." Cover your role, priorities, communication preferences, and common workflows.
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- **Be explicit about preferences** — "I always want PRDs to include a competitive analysis section" is memorable. "Add competitive analysis" is too vague.
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- **Correct mistakes immediately** — When an Agent misremembers, say: "That's not right. I prefer X instead of Y." The Memory Agent updates right away.
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- **Review monthly** — Remove outdated context, update changed preferences, add new important information.
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- **Don't store sensitive data** — Avoid passwords, financial details, or highly personal information. Memory works best for work preferences, professional context, and general patterns.
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<Cards>
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<Card href={'/docs/usage/getting-started/agent'} title={'Agent'} />
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<Card href={'/docs/usage/getting-started/resource'} title={'Resource Library'} />
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<Card href={'/docs/usage/agent/chain-of-thought'} title={'Chain of Thought'} />
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</Cards>
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