Mem AI (Knowledge Workspace) MCP Server for OpenAI Agents SDK 12 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Mem AI (Knowledge Workspace) through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Mem AI (Knowledge Workspace) Assistant",
instructions=(
"You help users interact with Mem AI (Knowledge Workspace). "
"You have access to 12 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Mem AI (Knowledge Workspace)"
)
print(result.final_output)
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.
The OpenAI Agents SDK auto-discovers all 12 tools from Mem AI (Knowledge Workspace) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Mem AI (Knowledge Workspace), another analyzes results, and a third generates reports, all orchestrated through Vinkius.
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 OpenAI Agents SDK 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 OpenAI Agents SDK via MCP
Follow these steps to integrate the Mem AI (Knowledge Workspace) MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 12 tools from Mem AI (Knowledge Workspace)
Why Use OpenAI Agents SDK with the Mem AI (Knowledge Workspace) MCP Server
OpenAI Agents SDK provides unique advantages when paired with Mem AI (Knowledge Workspace) through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Mem AI (Knowledge Workspace) + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Mem AI (Knowledge Workspace) MCP Server delivers measurable value.
Automated workflows: build agents that query Mem AI (Knowledge Workspace), process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Mem AI (Knowledge Workspace), another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Mem AI (Knowledge Workspace) tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Mem AI (Knowledge Workspace) to resolve tickets, look up records, and update statuses without human intervention
Mem AI (Knowledge Workspace) MCP Tools for OpenAI Agents SDK (12)
These 12 tools become available when you connect Mem AI (Knowledge Workspace) to OpenAI Agents SDK 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 OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK 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 OpenAI Agents SDK
Common issues when connecting Mem AI (Knowledge Workspace) to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Mem AI (Knowledge Workspace) + OpenAI Agents SDK FAQ
Common questions about integrating Mem AI (Knowledge Workspace) MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
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 OpenAI Agents SDK
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.