docs/usage/getting-started/memory.mdx
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.
This isn't a chat log. It's intelligent knowledge extraction: your role, work context, preferences, and habits — organized and ready to use.
Without Memory, every conversation starts from zero:
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.
Memory follows a four-step cycle:
<Steps> ### Conversation AnalysisAs you interact, the Memory Agent analyzes conversations for insights: explicit preferences, implicit patterns, project context, and communication style.
Key insights are extracted, categorized by type (preference, fact, context), and tagged with relevance and confidence. Each memory becomes a behavioral rule the Agent follows going forward.
Memories are stored in a structured, searchable format. When new information conflicts with existing Memory, it updates automatically.
When relevant, Agents retrieve and apply the right memories — so responses fit your situation without you needing to re-explain. </Steps>
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.
How you like to work and communicate:
Communication Style:
- Preference: Concise responses
- Detail level: Technical, skip basics
- Tone: Professional but conversational
- Format: Bullet points preferred over paragraphs
Work Preferences:
- Work hours: 9 AM - 6 PM EST
- Decision-making: Data-driven, wants multiple options
- Feedback: Direct and actionable
Who you are and what you're doing: role, organization, team, current projects, key stakeholders.
Professional Context:
- Role: Senior Product Manager
- Company: B2B SaaS startup, 50 employees
- Team: 3 engineers, 1 designer, 1 analyst
- Current project: Mobile app redesign, Q2 launch
What you know and what you're learning. Helps Agents calibrate their explanations to your level.
Recurring behaviors and workflows: weekly meetings, common requests, decision-making criteria, typical project workflows.
Past projects and decisions: lessons learned, reasoning behind past choices, key collaborators.
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.
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.
Go to the Agent Profile page, click + Add Plugin, and check Memory to enable it.
Open a conversation, click the plugin icon below the chat input, and check Memory to activate it.
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:
Use the search function to quickly find specific memories. You can also open the command menu at any time and search for memories there. Filter by keyword, category, date range, or confidence level.
Modify any memory instruction to better reflect your actual needs and preferences.
Select a memory and click the delete button to remove it. You can also bulk delete by category or clear all memories.
The Memory Agent is a built-in Agent dedicated to managing your personal Memory:
Talk directly to the Memory Agent to add, update, or review memories:
Organize memories by category:
Each memory has a confidence score that affects how agents use it:
<AccordionGroup> <Accordion title="High Confidence (90–100%)"> **Source**: Explicit statements, repeated patterns**Example**: "I always need data to back up product decisions"
**Usage**: Agents apply these memories automatically
**Example**: User often asks for competitive analysis
**Usage**: Agents consider but may verify
**Example**: User mentioned interest in a topic once
**Usage**: Agents note but don't heavily rely on
Memories can be linked to each other, providing richer context when recalled. For example, your role links to your current project, which links to your timeline and stakeholders.
Find specific memories by:
When you first use LobeHub, help Agents learn about you quickly:
<Steps> ### Share Professional ContextTell agents about your role, team, organization, and current projects.
Be clear about how you like to communicate, what level of detail you need, and your preferred formats.
Create memories for important facts agents should always know, recurring contexts, and guidelines for agent behavior. </Steps>
Once context is established, just work. Here's the difference Memory makes:
Without Memory:
With Memory:
Memory improves as you work:
All memories belong to you:
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.
**Solutions**: Make preference more explicit, manually add high-confidence memory, check memory settings.
<Card href={'/docs/usage/getting-started/resource'} title={'Resource Library'} />
<Card href={'/docs/usage/agent/chain-of-thought'} title={'Chain of Thought'} /> </Cards>