Weights & Biases MCP Server for AutoGen 6 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Weights & Biases as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="weights_biases_agent",
tools=tools,
system_message=(
"You help users with Weights & Biases. "
"6 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
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 Weights & Biases MCP Server
Connect your Weights & Biases (WandB) account to any AI agent and manage your machine learning experiments through natural conversation.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Weights & Biases tools. Connect 6 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Project Management — List all projects within your WandB entity (user or team) to browse your experiment folders
- Run Monitoring — List and track individual experiment runs within a project to monitor real-time activity
- Deep Run Analysis — Retrieve full details for any run, including latest accuracies, losses, and hyperparameters
- Artifact Management — List versioned datasets, models, and other artifacts to track data lineage and dependencies
- Sweep Tracking — Monitor automated hyperparameter search sweeps to see optimization progress
- Reports & Collaboration — List saved analysis reports and dashboards to access collaborative documentation
The Weights & Biases MCP Server exposes 6 tools through the Vinkius. Connect it to AutoGen 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 Weights & Biases to AutoGen via MCP
Follow these steps to integrate the Weights & Biases MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 6 tools from Weights & Biases automatically
Why Use AutoGen with the Weights & Biases MCP Server
AutoGen provides unique advantages when paired with Weights & Biases through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Weights & Biases tools to solve complex tasks
Role-based architecture lets you assign Weights & Biases tool access to specific agents — a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Weights & Biases tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Weights & Biases tool responses in an isolated environment
Weights & Biases + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Weights & Biases MCP Server delivers measurable value.
Collaborative analysis: one agent queries Weights & Biases while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Weights & Biases, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Weights & Biases data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Weights & Biases responses in a sandboxed execution environment
Weights & Biases MCP Tools for AutoGen (6)
These 6 tools become available when you connect Weights & Biases to AutoGen via MCP:
get_run_details
Retrieves full details for a specific W&B run, including summary metrics and config
list_project_artifacts
Lists all artifacts (datasets, models, etc.) in a project
list_project_reports
Lists all saved analysis reports in a project
list_project_runs
Lists all experiment runs within a specific W&B project
list_project_sweeps
Lists hyperparameter search sweeps within a project
list_wandb_projects
Lists all projects within a Weights & Biases entity (user or team)
Example Prompts for Weights & Biases in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Weights & Biases immediately.
"List all runs in my 'transformer-nmt' project for entity 'ai-team'."
"Get the final accuracy and config for run ID 'vibrant-sweep-1'."
"What artifacts are available in the 'resnet-training' project?"
Troubleshooting Weights & Biases MCP Server with AutoGen
Common issues when connecting Weights & Biases to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Weights & Biases + AutoGen FAQ
Common questions about integrating Weights & Biases MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
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Connect Weights & Biases to AutoGen
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.