> ## Documentation Index
> Fetch the complete documentation index at: https://docs.membase.so/llms.txt
> Use this file to discover all available pages before exploring further.
# Use Your Context in Membase
> How to interact with your stored knowledge: Chat in Dashboard, agent retrieval across memory and wiki, and dashboard exploration for both stores.
Once your context is in Membase, there are three ways to use it: **Chat in Dashboard** for direct conversations with your knowledge base, **Agent Retrieval** by your connected agents, and **Dashboard Exploration** in either the Memory or Wiki views.
## Chat in Dashboard
The most direct way to interact with your knowledge base. Chat lets you ask questions and get answers grounded in your stored context, without going through an external agent. When stored context is relevant, it can search **both** your memory and your wiki and weave results into a single answer.
* **Ask anything**: "What did we decide about the auth flow?", "What's my meeting schedule this week?", "What does our deployment runbook say about rollbacks?"
* **Citations**: Responses that use stored context link back to the exact memories and wiki documents they drew from
* **Graph panel**: See how referenced items connect to each other
* **Session history**: Pick up past conversations from the sidebar
Chat is available in [Chat](https://app.membase.so/chat). See [Chat in Dashboard](/features/chat) for details.
## Agent Retrieval
Your connected agents retrieve context from Membase when they need it. When past context or reference knowledge would improve the response, the agent can call `search_memory` (personal context) and `search_wiki` (factual knowledge) and combine the results before responding.
```text Your prompt theme={null}
Set up a new API route for user profiles.
```
```text What happens behind the scenes theme={null}
→ Agent calls search_memory: "project tech stack, API conventions"
Found: "Uses Next.js with TypeScript"
Found: "Prefers RESTful conventions with Zod validation"
Found: "Supabase for auth"
→ Agent calls search_wiki: "user profile API schema, auth middleware"
Found (doc): "User Profile Data Model" with fields, constraints, relations
Found (doc): "Auth Middleware" with how to require a session on a route
→ Agent generates the route using the right stack, schema, and middleware.
```
This works across all connected agents. Context stored by Cursor is available to Claude, ChatGPT, and every other agent on your account. The same is true for wiki documents.
You don't need to tell your agent to search. Agents call `search_memory` and `search_wiki` automatically whenever past context or reference knowledge would improve their response.
## Dashboard Exploration
You can explore your knowledge directly in the dashboard. Memory and Wiki are two separate tabs, each with its own views.
The **Memories** tab has two views for exploring personal context.
* **Graph View**: Explore your knowledge as an interactive network of entities (people, concepts, tools, projects) and relationships. Click any node to see connected entities and related episodes.
* **Table View**: Browse all memories in a structured list. Filter by source, project, and time period. Use the table search to text-filter the loaded memory list, then bulk-select rows to move them to a project or delete them.
For the full walkthrough, see [Memory](/features/memory).
The **Wiki** tab has two views for exploring factual knowledge.
* **Graph View**: A force-directed network of documents connected by `[[wikilinks]]`. Project styling helps related documents visually cluster.
* **Table View**: A sortable, filterable list of every document. Filter by Project or search query. Bulk-select rows to move or delete.
Projects in the Wiki navigation let you scope the list, graph, and search to a single group of documents. Documents without a Project live in Basic.
For the full walkthrough, see [Knowledge Wiki](/features/wiki).
## Other Dashboard Tabs
Beyond Chat, Memory, and Wiki, the dashboard has a few more tabs:
| Tab | Purpose |
| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Agents** | Manage connected AI agents: connection status, last activity, add new agents |
| **Sources** | Connect app integrations (Gmail, Calendar, Slack, Notion, and more), import Wiki files, and import past chat history |
| **Recipes** | Pre-built prompt templates that run directly in Chat, powered by your synced memories |
| **Settings** | Tabbed settings page with **Profile** (name, role, interests, custom instructions, timezone; shared with agents via `membase://profile`) and **Billing** (current plan, usage including wiki document quota, and invoices) |
## Next Steps
Import chat history and connect apps to enrich your knowledge base.
Explore and manage your memories.
Explore and manage your wiki documents.
Understand why shared memory across agents matters.
* **Ask anything**: "What did we decide about the auth flow?", "What's my meeting schedule this week?", "What does our deployment runbook say about rollbacks?"
* **Citations**: Responses that use stored context link back to the exact memories and wiki documents they drew from
* **Graph panel**: See how referenced items connect to each other
* **Session history**: Pick up past conversations from the sidebar
Chat is available in [Chat](https://app.membase.so/chat). See [Chat in Dashboard](/features/chat) for details.
## Agent Retrieval
Your connected agents retrieve context from Membase when they need it. When past context or reference knowledge would improve the response, the agent can call `search_memory` (personal context) and `search_wiki` (factual knowledge) and combine the results before responding.
```text Your prompt theme={null}
Set up a new API route for user profiles.
```
```text What happens behind the scenes theme={null}
→ Agent calls search_memory: "project tech stack, API conventions"
Found: "Uses Next.js with TypeScript"
Found: "Prefers RESTful conventions with Zod validation"
Found: "Supabase for auth"
→ Agent calls search_wiki: "user profile API schema, auth middleware"
Found (doc): "User Profile Data Model" with fields, constraints, relations
Found (doc): "Auth Middleware" with how to require a session on a route
→ Agent generates the route using the right stack, schema, and middleware.
```
This works across all connected agents. Context stored by Cursor is available to Claude, ChatGPT, and every other agent on your account. The same is true for wiki documents.