How to Use the Humanloop (LLM Prompt Management API) MCP in AutoGen

Q: Can AutoGen agents deploy prompts using this Humanloop (LLM Prompt Management API) server?

Yes, your agents can use deployprompt to set the active prompt version for any target environment. This allows your multi-agent system to manage its own prompt updates based on consensus.

Q: How does this MCP Server handle prompt versioning in AutoGen?

The server uses listpromptversions and updatepromptversion to let your agents inspect and edit prompt history. This allows them to track changes and compare performance across different iterations.

Q: Can I stream prompt executions within an AutoGen conversation?

Yes, you can run callpromptstream to pipe the prompt output chunk-by-chunk. This keeps the multi-agent chat feed responsive as agents generate long responses.

Let your AutoGen agents debate, test, and update your Humanloop prompts in real-time via this MCP Server.

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Connect Humanloop (LLM Prompt Management API) MCP to AutoGen

Create your Vinkius account to connect Humanloop (LLM Prompt Management API) to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Humanloop (LLM Prompt Management API) with AutoGen

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Multi-agent prompt iteration and testing

Use `upsert_prompt` and `list_prompt_versions` to let your AutoGen agents collaboratively refine prompt templates. One agent can write a prompt, while another reviews it and calls the tool to save the new version. This setup turns prompt engineering into a collaborative, multi-agent conversation. You no longer have to manually copy-paste configurations between your editor and your testing environment.

Automate environment deployments in AutoGen

Run `deploy_prompt` to promote validated prompt configurations to staging or production targets. Your testing agents can run automated checks, and once they reach a consensus, trigger the deployment tool. If the agents detect a regression during live monitoring, they can run `remove_deployment` to instantly roll back the environment. This keeps your production systems safe from bad prompt updates.

Log agent debates with this AutoGen MCP Server

Run `log_to_prompt` to record the outputs of your multi-agent conversations directly against the active prompt configuration. This captures the full context of how your agents resolved complex tasks. You can also run `update_monitoring` to manage evaluators that run on these logs. It gives you a clean way to track agent performance and alignment over time.

Setup guide

Set up Humanloop (LLM Prompt Management API) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Humanloop (LLM Prompt Management API) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Humanloop (LLM Prompt Management API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Humanloop (LLM Prompt Management API) data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Humanloop (LLM Prompt Management API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Humanloop (LLM Prompt Management API) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Humanloop. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Humanloop (LLM Prompt Management API) MCP in AutoGen

Install the AutoGen extension package and use the server tools adapter with your server's connection URL. Pass the resulting tool list to your assistant agent constructor to grant access to the prompt registry.

Yes, your agents can use `deploy_prompt` to set the active prompt version for any target environment. This allows your multi-agent system to manage its own prompt updates based on consensus.

The server uses `list_prompt_versions` and `update_prompt_version` to let your agents inspect and edit prompt history. This allows them to track changes and compare performance across different iterations.

Yes, you can run `call_prompt_stream` to pipe the prompt output chunk-by-chunk. This keeps the multi-agent chat feed responsive as agents generate long responses.

All prompt configurations, version logs, and evaluator settings are routed directly to Humanloop via HTTPS. The Vinkius MCP server runtime acts as a zero-trust proxy, ensuring your data remains private and ephemeral.

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