Mem0 MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mem0 as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Mem0. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Mem0?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Mem0 MCP Server
Connect your AI agent to Mem0 — the industry-standard memory layer that enables agents to remember, learn, and personalize across conversations.
LlamaIndex agents combine Mem0 tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Add Memories — Store facts, preferences, and context from conversations. Mem0 AI automatically extracts key information and structures it as searchable memories
- Semantic Search — Find relevant memories using natural language queries. Ask 'What does the user prefer?' and get ranked results by relevance
- List Memories — View all stored memories for a user to build comprehensive profiles and understand accumulated context
- Delete Memories — Remove outdated or incorrect memories to keep the knowledge base clean
The Mem0 MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Mem0 to LlamaIndex via MCP
Follow these steps to integrate the Mem0 MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from Mem0
Why Use LlamaIndex with the Mem0 MCP Server
LlamaIndex provides unique advantages when paired with Mem0 through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mem0 tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mem0 tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mem0, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mem0 tools were called, what data was returned, and how it influenced the final answer
Mem0 + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mem0 MCP Server delivers measurable value.
Hybrid search: combine Mem0 real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mem0 to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Mem0 for fresh data
Analytical workflows: chain Mem0 queries with LlamaIndex's data connectors to build multi-source analytical reports
Mem0 MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Mem0 to LlamaIndex via MCP:
add_memory
The system automatically extracts structured facts from the provided content and stores them as searchable, persistent memories associated with the given user ID. Store a new memory for a user. The AI extracts key facts and preferences from the content and stores them as persistent memories
delete_memory
Use with caution — this action cannot be undone. Delete a specific memory by its ID
get_memories
Useful for reviewing what the agent knows about a user or for building a user profile. List all stored memories for a specific user
search_memories
Returns results ranked by relevance score, enabling the agent to recall past preferences, facts, and context. Semantically search stored memories for a specific user. Returns the most relevant memories matching your query
Example Prompts for Mem0 in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mem0 immediately.
"Remember that I prefer dark mode, use VS Code, and my favorite language is TypeScript."
"What do you remember about my coding preferences?"
"Show me all the memories you have stored for my user profile."
Troubleshooting Mem0 MCP Server with LlamaIndex
Common issues when connecting Mem0 to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMem0 + LlamaIndex FAQ
Common questions about integrating Mem0 MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Mem0 with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Mem0 to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.