Mem AI (Knowledge Workspace) MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mem AI (Knowledge Workspace) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Mem AI (Knowledge Workspace). "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Mem AI (Knowledge Workspace)?"
)
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 Mem AI (Knowledge Workspace) MCP Server
Connect your Mem.ai workspace to any AI agent and take full control of your personal and team knowledge through natural conversation.
LlamaIndex agents combine Mem AI (Knowledge Workspace) tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through 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
- Knowledge Orchestration — Create new mems (notes) using Markdown directly from your agent, instantly transforming textual ideas into indexed knowledge vectors
- AI Semantic Search — Leverage dense semantic similarity to find notes across your entire workspace, identifying relevant information based on meaning rather than explicit keywords
- Deep Content Retrieval — Extract the full scalar text body and context metadata for specific mems to retrieve precise project details securely
- Collection Management — Establish thematic groupings (Collections) and attach live mems structurally to maintain organized project boundaries natively
- Quick Capture (Mem It) — Trigger rapid capture blocks for links, snippets, or raw thoughts, allowing your agent to log ideas without manual dashboard navigation
- Contextual Updates — Mutate existing mem content to keep project logs and meeting notes up-to-date while preserving historical knowledge mappings
- Resource Inventory — List all available mems or explore specific collections to understand your knowledge distribution and team documentation footprint
The Mem AI (Knowledge Workspace) MCP Server exposes 12 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 Mem AI (Knowledge Workspace) to LlamaIndex via MCP
Follow these steps to integrate the Mem AI (Knowledge Workspace) 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 12 tools from Mem AI (Knowledge Workspace)
Why Use LlamaIndex with the Mem AI (Knowledge Workspace) MCP Server
LlamaIndex provides unique advantages when paired with Mem AI (Knowledge Workspace) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mem AI (Knowledge Workspace) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mem AI (Knowledge Workspace) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mem AI (Knowledge Workspace), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mem AI (Knowledge Workspace) tools were called, what data was returned, and how it influenced the final answer
Mem AI (Knowledge Workspace) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mem AI (Knowledge Workspace) MCP Server delivers measurable value.
Hybrid search: combine Mem AI (Knowledge Workspace) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mem AI (Knowledge Workspace) 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 Mem AI (Knowledge Workspace) for fresh data
Analytical workflows: chain Mem AI (Knowledge Workspace) queries with LlamaIndex's data connectors to build multi-source analytical reports
Mem AI (Knowledge Workspace) MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Mem AI (Knowledge Workspace) to LlamaIndex via MCP:
add_mem_to_collection
Attach live Mems structurally inside explicitly mapped Collections
create_collection
Establish new logical thematic groupings mapping notes
create_mem
ai. Converts plain textual knowledge to indexed vectors immediately mapped implicitly via AI. Create a new mem (note) in Mem.ai using Markdown
delete_mem
No recovery is possible via API. Irreversibly vaporize a mem document globally
get_collection
Inspect specific Collection metadata elements
get_mem
Retrieve explicit full context metadata by target Mem ID
list_collection_mems
Query ALL explicit Mem bodies inside specific Collections
list_collections
Query explicitly tracked thematic Collections arrays
list_mems
Returns identifiers and raw bodies. Careful, this returns heavy payloads. List all raw mems across the global workspace
mem_it
Quick capture shortcut generating automated blocks
search_mems
AI semantic search looking into all indexed knowledge
update_mem
Replaces absolute text values so ensure `get_mem` was run to append rather than destroy inadvertently. Update pre-existing mem content natively swapping strings
Example Prompts for Mem AI (Knowledge Workspace) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mem AI (Knowledge Workspace) immediately.
"Search my mems for anything related to 'quarterly business review'"
"Create a new mem with today's standup notes in Markdown"
"List all my thematic collections in Mem"
Troubleshooting Mem AI (Knowledge Workspace) MCP Server with LlamaIndex
Common issues when connecting Mem AI (Knowledge Workspace) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMem AI (Knowledge Workspace) + LlamaIndex FAQ
Common questions about integrating Mem AI (Knowledge Workspace) 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 Mem AI (Knowledge Workspace) 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 Mem AI (Knowledge Workspace) to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.