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

Q: Can my CrewAI agents use Humanloop (LLM Prompt Management API) to optimize prompts?

Yes. An agent can call upsertprompt to apply changes after analyzing its own execution logs.

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Connect Humanloop (LLM Prompt Management API) MCP to CrewAI

Create your Vinkius account to connect Humanloop (LLM Prompt Management API) to CrewAI 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 CrewAI

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Perplexity

Autonomous prompt management for CrewAI

Let your crew manage their own instructions with `upsert_prompt`. Agents can update their own logic based on performance data. Use `list_prompts` to let a moderator agent audit the current registry.

Version control for agent teams

Use `list_prompt_versions` to track changes made by different agents. It keeps your development history transparent. Roll back to stable versions using `deploy_prompt` if a test agent identifies a regression.

Monitor agent performance with MCP

The monitor agent uses `update_monitoring` to toggle evaluators based on the current system load. It records everything with `log_to_prompt` so your research agent can analyze the logs later.

Setup guide

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

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Humanloop (LLM Prompt Management API) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Humanloop (LLM Prompt Management API) Analyst",
    goal="Access and analyze Humanloop (LLM Prompt Management API) data via MCP.",
    backstory="Expert analyst with direct Humanloop (LLM Prompt Management API) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Humanloop (LLM Prompt Management API) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

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

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Humanloop (LLM Prompt Management API) Analyst",
        goal="Access and analyze Humanloop (LLM Prompt Management API) data via MCP.",
        backstory="Expert analyst with direct Humanloop (LLM Prompt Management API) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Humanloop (LLM Prompt Management API) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 CrewAI

Add the MCP server URL to your agent configuration. Your agents will then see the prompt tools as available capabilities.

Yes. An agent can call `upsert_prompt` to apply changes after analyzing its own execution logs.

It works across any crew structure. You can restrict tool access to specific agents by using a tool filter.

The server stores your prompt definitions, version history, and execution logs. It does not store your private API keys or user credentials.

Access is restricted to your organization's workspace. Your data is never shared with other users or external models.

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