# How to integrate Vectorshift MCP with CrewAI ```json { "title": "How to integrate Vectorshift MCP with CrewAI", "toolkit": "Vectorshift", "toolkit_slug": "vectorshift", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/vectorshift/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/vectorshift/framework/crew-ai.md", "updated_at": "2026-03-29T06:54:33.673Z" } ``` ## Introduction This guide walks you through connecting Vectorshift to CrewAI using the Composio tool router. By the end, you'll have a working Vectorshift agent that can trigger the lead qualification chatbot workflow, get status of the sales pipeline automation, update knowledge base with latest product faq through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Vectorshift account through Composio's Vectorshift MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Vectorshift with - [OpenAI Agents SDK](https://composio.dev/toolkits/vectorshift/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/vectorshift/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/vectorshift/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/vectorshift/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/vectorshift/framework/codex) - [OpenClaw](https://composio.dev/toolkits/vectorshift/framework/openclaw) - [Hermes](https://composio.dev/toolkits/vectorshift/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/vectorshift/framework/cli) - [Google ADK](https://composio.dev/toolkits/vectorshift/framework/google-adk) - [LangChain](https://composio.dev/toolkits/vectorshift/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/vectorshift/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/vectorshift/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/vectorshift/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Vectorshift connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Vectorshift - Build a conversational loop where your agent can execute Vectorshift 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 Vectorshift MCP server, and what's possible with it? The Vectorshift MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Vectorshift account. It provides structured and secure access so your agent can perform Vectorshift operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `VECTORSHIFT_CREATE_CHATBOT` | Create Chatbot | Tool to create a new chatbot. Chatbots are conversational AI interfaces built on pipelines. Use when you need to create a new chatbot with a specific pipeline configuration. | | `VECTORSHIFT_DELETE_CHATBOT` | Delete Chatbot | Tool to delete a chatbot by its ID. Permanently removes the chatbot from the account. Use when you need to remove a chatbot that is no longer needed. | | `VECTORSHIFT_GET_CHATBOT` | Get Chatbot | Tool to fetch an existing chatbot by its ID or name. Returns chatbot configuration and metadata. Use when you need to retrieve details about a specific chatbot. Either chatbot ID or name must be provided. | | `VECTORSHIFT_GET_KNOWLEDGE_BASE` | Get Knowledge Base | Tool to fetch an existing knowledge base by its ID or name. Returns knowledge base configuration and metadata. Use when you need to retrieve details about a specific knowledge base. | | `VECTORSHIFT_GET_PIPELINE` | Get Pipeline | Tool to fetch an existing pipeline by its ID or name. Returns pipeline configuration and metadata. Use when you need to retrieve a specific pipeline's details, configuration, or metadata. | | `VECTORSHIFT_LIST_CHATBOTS` | List Chatbots | Tool to list all available chatbots in the account. Use when you need to retrieve chatbot IDs or full chatbot details. | | `VECTORSHIFT_LIST_KNOWLEDGE_BASES` | List Knowledge Bases | Tool to list all available knowledge bases in your VectorShift account. Use when you need to retrieve knowledge base information by id or name. | | `VECTORSHIFT_LIST_PIPELINES` | List Pipelines | Tool to list all available pipelines in the VectorShift account. Use when you need to retrieve the catalog of pipelines. Supports filtering for shared pipelines and verbose output with full pipeline details. | | `VECTORSHIFT_LIST_TRANSFORMATIONS` | List Transformations | Tool to list all available transformations in the account. Use when you need to retrieve transformation IDs or complete transformation objects. | | `VECTORSHIFT_RUN_PIPELINE` | Run Pipeline | Tool to run a VectorShift pipeline with the given inputs. Use when you need to execute a pipeline and get its results or run_id for asynchronous execution. Returns the pipeline execution status, run_id, and outputs if execution completed synchronously. | | `VECTORSHIFT_RUN_PIPELINE_IN_BULK` | Run Pipeline in Bulk | Tool to run a VectorShift pipeline in bulk with multiple sets of inputs. Use when you need to batch process multiple pipeline executions in a single API call. Returns the overall status and an array of outputs with run_id for each execution. | | `VECTORSHIFT_TERMINATE_PIPELINE_EXECUTION` | Terminate Pipeline Execution | Tool to terminate a running pipeline execution. Use when you need to stop a pipeline run by its run_id. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Vectorshift MCP server is an implementation of the Model Context Protocol that connects your AI agent to Vectorshift. It provides structured and secure access so your agent can perform Vectorshift 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 Vectorshift 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 Vectorshift 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 Vectorshift 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 Vectorshift **What's happening:** - You create a Vectorshift only session through Composio - Composio returns an MCP HTTP URL that exposes Vectorshift tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["vectorshift"]) 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=["vectorshift"], ) 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 Vectorshift through Composio's Tool Router. The agent can perform Vectorshift 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 Vectorshift MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/vectorshift/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/vectorshift/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/vectorshift/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/vectorshift/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/vectorshift/framework/codex) - [OpenClaw](https://composio.dev/toolkits/vectorshift/framework/openclaw) - [Hermes](https://composio.dev/toolkits/vectorshift/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/vectorshift/framework/cli) - [Google ADK](https://composio.dev/toolkits/vectorshift/framework/google-adk) - [LangChain](https://composio.dev/toolkits/vectorshift/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/vectorshift/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/vectorshift/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/vectorshift/framework/llama-index) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Vectorshift MCP? With a standalone Vectorshift MCP server, the agents and LLMs can only access a fixed set of Vectorshift tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Vectorshift 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 Vectorshift tools. ### Can I manage the permissions and scopes for Vectorshift while using Tool Router? Yes, absolutely. You can configure which Vectorshift 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 Vectorshift data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)