# How to integrate Seqera MCP with CrewAI ```json { "title": "How to integrate Seqera MCP with CrewAI", "toolkit": "Seqera", "toolkit_slug": "seqera", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/seqera/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/seqera/framework/crew-ai.md", "updated_at": "2026-05-12T10:25:32.226Z" } ``` ## Introduction This guide walks you through connecting Seqera to CrewAI using the Composio tool router. By the end, you'll have a working Seqera agent that can launch a new nextflow pipeline run, monitor status of active pipeline jobs, list recent workflow execution logs through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Seqera account through Composio's Seqera MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Seqera with - [OpenAI Agents SDK](https://composio.dev/toolkits/seqera/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/seqera/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/seqera/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/seqera/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/seqera/framework/codex) - [OpenClaw](https://composio.dev/toolkits/seqera/framework/openclaw) - [Hermes](https://composio.dev/toolkits/seqera/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/seqera/framework/cli) - [Google ADK](https://composio.dev/toolkits/seqera/framework/google-adk) - [LangChain](https://composio.dev/toolkits/seqera/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/seqera/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/seqera/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/seqera/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Seqera connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Seqera - Build a conversational loop where your agent can execute Seqera operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## What is the Seqera MCP server, and what's possible with it? The Seqera MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Seqera account. It provides structured and secure access to your workflow orchestration environment, so your agent can perform actions like running pipelines, monitoring jobs, managing compute resources, and automating Nextflow processes on your behalf. - Pipeline orchestration and execution: Launch, schedule, and track complex Nextflow pipelines across cloud and on-prem infrastructure without manual intervention. - Job monitoring and status updates: Let your agent check the progress, status, and logs of running or completed workflows to keep you updated in real time. - Compute environment management: Automatically provision, scale, or tear down compute environments to match your workflow needs. - Resource usage and cost tracking: Retrieve detailed metrics on resource consumption and workflow costs to optimize performance and spending. - Error detection and alerting: Quickly identify failed jobs or bottlenecks and trigger alerts or automated troubleshooting steps to maintain workflow reliability. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SEQERA_CREATE_WAVE_CONTAINER` | Create Wave Container | Tool to submit a request to Wave for accessing a private container registry or building a container image on-the-fly. Use when you need to provision containers via Wave with Dockerfile, Conda recipe, or package specifications. Returns the name of the container made available by Wave. | | `SEQERA_GET_ORGANIZATION_DETAILS` | Get Organization Details | Tool to retrieve detailed information about a specific organization. | | `SEQERA_GET_SERVICE_INFO` | Get Service Info | Tool to retrieve information about the Seqera API service. Use when you need to check the service version and commit ID for troubleshooting. | | `SEQERA_GET_USER_INFO` | Get User Info | Tool to retrieve information about the authenticated user. Use when you need to fetch details of the current user after authentication. | | `SEQERA_GET_WAVE_BUILD_LOGS` | Get Wave Build Logs | Tool to get logs for a container build by build ID. Use when you need to troubleshoot or inspect the build process of a Wave container. | | `SEQERA_GET_WAVE_BUILD_STATUS` | Get Wave Build Status | Tool to retrieve the status of a Wave container build by build ID. Use when you need to check if a container build is completed and whether it succeeded. | | `SEQERA_INSPECT_WAVE_CONTAINER` | Inspect Wave Container | Tool to inspect container image metadata via Wave. Use when you need registry, manifest, digest, configuration, and OCI compatibility information about a container image. | | `SEQERA_LIST_ACTION_EVENT_TYPES` | List Action Event Types | Tool to list supported action event types. Use when you need to discover available triggers for pipelines, optionally filtered by workspace. | | `SEQERA_LIST_COMPUTE_ENVIRONMENTS` | List Compute Environments | Tool to retrieve a list of compute environments. Use when you need to page, sort, or filter compute environments accessible to your account. | | `SEQERA_LIST_ORGANIZATIONS` | List Organizations | Tool to list all organizations. Use after authentication to retrieve accessible Seqera organizations. | | `SEQERA_LIST_PIPELINES` | List Pipelines | Tool to list all pipelines accessible to the authenticated user. Use when you need to browse pipelines with optional pagination, sorting, or search filters. | | `SEQERA_LIST_WORKFLOWS` | List Workflows | Tool to list workflows. Use when you need to retrieve workflows filtered by workspace and paginated. | | `SEQERA_VALIDATE_ACTION_NAME` | Validate Action Name | Tool to validate action name. Use when you need to confirm if an action name is valid and available before creating an action. Returns validation status with details. | | `SEQERA_VALIDATE_PIPELINE_NAME` | Validate Pipeline Name | Tool to validate a pipeline name. Use when you need to check if a given pipeline name is valid and available before creating or updating one. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Seqera MCP server is an implementation of the Model Context Protocol that connects your AI agent to Seqera. It provides structured and secure access so your agent can perform Seqera operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Seqera connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies **What's happening:** - composio connects your agent to Seqera via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Seqera MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Seqera **What's happening:** - You create a Seqera only session through Composio - Composio returns an MCP HTTP URL that exposes Seqera tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["seqera"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["seqera"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Seqera through Composio's Tool Router. The agent can perform Seqera operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## How to build Seqera MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/seqera/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/seqera/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/seqera/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/seqera/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/seqera/framework/codex) - [OpenClaw](https://composio.dev/toolkits/seqera/framework/openclaw) - [Hermes](https://composio.dev/toolkits/seqera/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/seqera/framework/cli) - [Google ADK](https://composio.dev/toolkits/seqera/framework/google-adk) - [LangChain](https://composio.dev/toolkits/seqera/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/seqera/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/seqera/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/seqera/framework/llama-index) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Seqera MCP? With a standalone Seqera MCP server, the agents and LLMs can only access a fixed set of Seqera tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Seqera and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with CrewAI? Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Seqera tools. ### Can I manage the permissions and scopes for Seqera while using Tool Router? Yes, absolutely. You can configure which Seqera scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Seqera data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)