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Add Lab 3.6 release notes (#1426)
* new: Add Lab 3.6 release notes * Apply suggestions from code review --------- Co-authored-by: Toni Lastre <[email protected]> Co-authored-by: Matea Pesic <[email protected]>
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next-env.d.ts

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/// <reference types="next" />
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/// <reference types="next/image-types/global" />
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/// <reference path="./.next/types/routes.d.ts" />
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// NOTE: This file should not be edited
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// see https://nextjs.org/docs/pages/api-reference/config/typescript for more information.

pages/memgraph-lab/features/graphchat.mdx

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2. [**Start chatting**](#chat-interface): Once connected, open the chat
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interface. You can create multiple threads to organize conversations by topic,
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question type, or model comparison. Each questionanswer pair is stored as an
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question type, or model comparison. Each question - answer pair is stored as an
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exchange, which can be reused as context in future prompts.
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3. **Let GraphChat handle the rest**: GraphChat automatically selects the most
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appropriate tool—whether generating a Cypher query, running an algorithm, or
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inspecting metadata. You can review, adjust, or expand any answer to inspect the
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LLM's reasoning process and control the prompt context.
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3. **Let GraphChat handle the rest**: GraphChat
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automatically selects the most appropriate tool - whether it’s a built-in tool,
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a custom tool, or even a remote MCP server - when generating a Cypher query,
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running an algorithm, or inspecting metadata. You can review, adjust, or expand
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any answer to inspect the LLM's reasoning process and control the prompt context.
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<Callout type="info">
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From Memgraph 2.16, GraphChat doesn't require MAGE to be installed. For schema
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can take when using tools to answer a question. More steps enable deeper
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problem-solving but may increase latency and usage costs.
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- **LLM permissions** - Limit the changes GraphChat can make to your database.
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- **Additional context** – Include extra contextual information as needed.
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- **System instructions** – Define the assistant's role, tone, and behavior. These
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instructions are always included at the start of the prompt.
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- **System additional context** – Select predefined modules (graph schema, Cypher
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query specifics, various constraints) to enrich the assistant's context. These
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are appended to the system instructions.
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You can also create multiple configurations for the same model to suit different use cases.
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exchanges directly from the conversation view for even more customization.
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</Callout>
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To generate responses, GraphChat leverages [prompt context](#prompt-context) and
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[built-in tools](#built-in-tools). [Exploring exchanges](#explore-exchanges)
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To generate responses, GraphChat leverages:
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- [Prompt context](#prompt-context) - GraphChat constructs a detailed prompt that defines the assistant’s role, tone, and permissions, optionally including schema and Cypher-specific guidance to ensure accurate and context-aware responses.
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- [Tools](#tools) - A collection of built-in and custom Cypher-backed tools that let the LLM query data, analyze graphs, and interact directly with the Memgraph database.
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- [MCP servers](#mcp-servers) - External tool integrations that expand GraphChat’s capabilities by connecting to third-party or custom MCP servers through configurable connections.
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- [Exploring exchanges](#explore-exchanges) - Lets you inspect the LLM’s reasoning process, view which tools were used, and examine the full context and schema involved in generating each response.
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gives you deeper insight into the LLM's reasoning process.
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### Prompt context
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When you pose a question, GraphChat builds a **prompt context** for the LLM that
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includes:
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When you ask a question, GraphChat constructs a **prompt context** for the
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LLM that includes:
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* **Introduction** - Define the assistant’s role, tone, and behavior.
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These instructions are always included at the start of the prompt.
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You can edit the default settings by adding new rules or completely
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redefining the role, tone, and behavior of the assistant.
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* **Graph schema (*Optional*)** - If selected, Lab ensures that each LLM
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interaction has access to the graph schema. Without it, the LLM will
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attempt to infer the schema on its own.
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* **Query permissions (*Optional*)** - If enabled, Lab updates the prompt
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context with query constraints, specifying whether the assistant can
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read, update, insert, and/or delete data in Memgraph.
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* **Cypher-specific notes (*Optional*)** - Provides rules and guidance
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where Memgraph's Cypher syntax differs from other Cypher-based
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databases.
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* Introduction
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* Schema
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* Constraints
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* Cypher-specific notes
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* Additional (user-provided) context
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<Callout type="info">
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**Note**: Large graph schemas can consume significant tokens in the LLM's
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context window. In such cases, consider disabling automatic inclusion of
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the graph schema to optimize cost and performance.
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</Callout>
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![graphchat-model-prompt-edit](/pages/data-visualization/features/graphchat/graphchat-model-prompt.png)
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### Tools
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### Built-in tools
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GraphChat includes a set of **built-in tools** and supports
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creating **custom Cypher-backed tools**.
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{<h4 className="custom-header">Built-in tools</h4>}
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Built-in tools cover a variety of tasks such as querying data,
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retrieving database information, running graph algorithms,
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checking indexes, managing triggers, and more:
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GraphChat gives models access to built-in tools that allow them to query your
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data, inspect the database, and run algorithms:
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* `run-cypher-query`: Generate and execute a Cypher query.
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* `run-page-rank`: Identify the most connected and impactful node using PageRank.
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* `run-betweenness-centrality`: Determine which nodes serve as critical bridges in the graph.
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* `show-config`: List all Memgraph database configuration parameters.
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* `show-schema-info`: Display the full database schema (requires `--schema-info-enabled` flag on startup).
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* `show-storage-info`: View memory usage, available memory, disk usage, and counts of nodes and relationships.
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* `run-page-rank`: Identify the most connected and impactful
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node using PageRank.
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* `run-betweenness-centrality`: Determine which nodes serve
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as critical bridges in the graph.
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* `show-config`: List all Memgraph database configuration
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parameters.
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* `show-schema-info`: Display the full database schema
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(requires `--schema-info-enabled` flag on startup).
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* `show-storage-info`: View memory usage, available memory,
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disk usage, and counts of nodes and relationships.
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* `show-indexes`: List all database indexes.
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* `show-constraints`: List all defined constraints.
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* `show-triggers`: List all active triggers.
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{<h4 className="custom-header">Tool usage before Lab 3.3</h4>}
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![graphchat-toolkit-builtin](/pages/data-visualization/features/graphchat/toolkit-builtin.png)
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<Callout type="info">
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**Tool usage before Lab 3.3**
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In earlier versions, GraphChat only used the `run-cypher-query` tool. This tool:
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* Generates and runs Cypher queries from LLM prompts
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* **Automatically retries invalid queries** up to the retry limit defined in the model configuration
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* **Automatically retries invalid queries** up to the retry limit defined in
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the model configuration
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If you want to replicate this behavior, disable all other tools in the
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configuration.
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</Callout>
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{<h4 className="custom-header">Cypher-backed tool</h4>}
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From Lab 3.3 onward, you can also define your own custom
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tools by providing:
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* A tool name
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* A description
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* A parameterized Cypher query to execute on call
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![graphchat-toolkit-custom](/pages/data-visualization/features/graphchat/toolkit-custom.png)
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Make sure each custom tool has a unique name and clear description
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so the LLM can accurately select the appropriate tool when responding.
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If you want to replicate this behavior, disable all other tools in the configuration.
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### MCP servers
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{<h4 className="custom-header">Enhanced tool support from Lab 3.3 onward</h4>}
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Starting with Lab 3.6, you can integrate tools from MCP servers,
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where Lab acts as an MCP client. This greatly expands the
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capabilities of the assistant in GraphChat, allowing it to
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leverage external tools beyond the built-in and custom ones.
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Starting with Lab 3.3, GraphChat can use all built-in tools automatically. The
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LLM decides which tool to use based on the context of the question.
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![mcp-servers-settings](/pages/data-visualization/features/graphchat/mcp-servers-settings.png)
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You can also define your own **custom tools** by providing:
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* A **tool name**
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* A **description**
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* A **parameterized Cypher query**
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{<h4 className="custom-header">Setting up the connection</h4>}
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Ensure each custom tool has a distinct name and description so the LLM can
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choose the right one.
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In Settings/LLM tab, you can create and manage MCP connections.
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Lab currently supports a streamable HTTP transport layer. To
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set up a connection:
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* Enter the **URL** where your MCP server is listening.
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* (Optional) Add an **access token** - it will be sent as an
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Authorization header.
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* (Optional) Add any **custom headers** required by your MCP server.
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Upon a successful connection, Lab retrieves a list of tools provided
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by the MCP server. You can then select which of these tools you
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want to enable in GraphChat.
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GraphChat automatically resyncs tools and MCP connections each
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time you open a new GraphChat view. If there's an issue with a
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connection, the affected tools will be unavailable, and you'll
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see the error response that explains why the connection
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failed - helping you troubleshoot and resolve the issue.
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{<h4 className="custom-header">Managing MCP tools in GraphChat</h4>}
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Once an MCP connection is set up in Lab, you can manage its
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tools directly within GraphChat. When you open the tools list
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from the input box in GraphChat, you'll see:
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* All your active MCP servers
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* The tools each server provides
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![toolkit-mcp](/pages/data-visualization/features/graphchat/toolkit-mcp.png)
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You can choose which tools to enable for the current GraphChat
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conversation. Additionally, you can quickly enable or disable
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all tools from a specific MCP server with a single action - making
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it easy to tailor the toolset to your workflow.
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{<h4 className="custom-header">Examples</h4>}
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Tavily Integration:
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* Enter `https://mcp.tavily.com/mcp/` in the URL field.
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* Register on Tavily to obtain an access token and add it
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in the Access Token field.
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![mcp-create-tavily](/pages/data-visualization/features/graphchat/mcp-create-tavily.png)
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Memgraph MCP Integration:
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* Pull the [ai-toolkit/memgraph](https://github.com/memgraph/ai-toolkit/tree/main/integrations/mcp-memgraph) repository.
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* Build the provided Dockerfile to start the MCP server.
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* Enter `https://localhost:8000/mcp/` in the URL field.
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![mcp-create-memgraph](/pages/data-visualization/features/graphchat/mcp-create-memgraph.png)
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### Explore exchanges
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You can also explore:
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* **Number of previous exchanges**, **tokens**, and **tools used**
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* **Schema used** – Click to visualize the included schema
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* **Schema used** – Click to visualize the included schema if schema was
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generated for the prompt by the Lab
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* **Context used** – Click to view the full prompt context sent to the LLM
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## Resources
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- [Talking to Your Graph Database with LLMs Using GraphChat](https://memgraph.com/blog/talking-to-graph-database-llms-graphchat-webinar-recap)

pages/memgraph-lab/querying.mdx

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![](/pages/data-visualization/lab-user-manual/graph-results.png)
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Moreover, you can use the search bar above the graph to quickly find nodes by name.
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As you type, matching nodes appear in the results list, and hovering over or selecting one
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will highlight it directly on the graph.
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![](/pages/data-visualization/lab-user-manual/graph-search-bar.png)
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{<h4 className="custom-header">Graph customization</h4>}
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To customize the appearance of the graph you can update the GSS in the [Graph

pages/release-notes.mdx

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### Lab v3.6.0 - October 8th, 2025
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<LabReleasesClient version="3.6.0" />
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## Previous releases
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### Memgraph v3.5.2 - September 24th, 2025
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