# How to integrate Parallel MCP with CrewAI ```json { "title": "How to integrate Parallel MCP with CrewAI", "toolkit": "Parallel", "toolkit_slug": "parallel", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/parallel/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/parallel/framework/crew-ai.md", "updated_at": "2026-05-12T10:21:17.787Z" } ``` ## Introduction This guide walks you through connecting Parallel to CrewAI using the Composio tool router. By the end, you'll have a working Parallel agent that can find top articles on generative ai trends, summarize recent news about electric vehicles, batch search for competitors' product launches through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Parallel account through Composio's Parallel MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Parallel with - [OpenAI Agents SDK](https://composio.dev/toolkits/parallel/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/parallel/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/parallel/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/parallel/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/parallel/framework/codex) - [OpenClaw](https://composio.dev/toolkits/parallel/framework/openclaw) - [Hermes](https://composio.dev/toolkits/parallel/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/parallel/framework/cli) - [Google ADK](https://composio.dev/toolkits/parallel/framework/google-adk) - [LangChain](https://composio.dev/toolkits/parallel/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/parallel/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/parallel/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/parallel/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Parallel connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Parallel - Build a conversational loop where your agent can execute Parallel 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 Parallel MCP server, and what's possible with it? The Parallel MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Parallel account. It provides structured and secure access to advanced web research automation, so your agent can perform actions like launching batch research tasks, running semantic searches, monitoring task progress, and generating research suggestions on your behalf. - Automated web research task creation: Instantly create structured research tasks or batch multiple queries for parallel execution, saving time and effort. - Semantic search across multiple topics: Direct your agent to run parallel semantic searches and retrieve top-matching documents or data for several queries at once. - Real-time task group monitoring: Let your agent stream live updates about the progress, completion, or status of ongoing research task groups. - Context-driven research suggestions: Have the agent suggest the next best research tasks based on your project or intent, keeping your workflow efficient and on track. - Task group retrieval and management: Fetch detailed information about specific research task groups to review results or track progress seamlessly. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `PARALLEL_ADD_ENRICHMENT_TO_FIND_ALL_RUN` | Add Enrichment to FindAll Run | Tool to add an enrichment to a FindAll run. Use when you need to enrich existing FindAll run results with additional structured data fields. Enrichments define what information to extract from matched entities using a JSON schema. | | `PARALLEL_ADD_RUNS_TO_TASK_GROUP` | Add Runs to Task Group | Tool to initiate multiple task runs within a TaskGroup. Use when you need to execute multiple tasks in parallel within an existing task group. | | `PARALLEL_CANCEL_FIND_ALL_RUN` | Cancel FindAll Run | Tool to cancel an active FindAll run by findall_id. Use when you need to stop a running FindAll operation before it completes. Cannot cancel runs that have already terminated. | | `PARALLEL_CREATE_CHAT_COMPLETIONS` | Create Chat Completions | Tool to get realtime chat completions from Parallel AI. Use when you need conversational AI responses or structured outputs via chat interface. Can be combined with Task API processors for research-grade structured outputs with citations and reasoning. | | `PARALLEL_CREATE_MONITOR` | Create Monitor | Tool to create a web monitor that periodically runs the specified query. The monitor runs once at creation and then continues according to the specified cadence (hourly, daily, weekly, or every two weeks). Use when you need to track changes or developments for a specific search query over time. | | `PARALLEL_CREATE_TASK_GROUP` | Create Task Group | Tool to create a new task group. Use when batching multiple tasks for parallel execution. Task Groups enable grouping and tracking of multiple task runs within a single manageable unit. | | `PARALLEL_CREATE_TASK_RUN` | Create Task Run | Tool to create and initiate a task run. Returns immediately with a run object in status 'queued'. Use when you need to execute tasks asynchronously with Parallel AI processors. | | `PARALLEL_DELETE_MONITOR` | Delete Monitor | Tool to delete a monitor, stopping all future executions. Use when you need to permanently remove a monitor. Deleted monitors can no longer be updated or retrieved. | | `PARALLEL_EXTEND_FIND_ALL_RUN` | Extend FindAll Run | Tool to extend a FindAll run by adding additional matches to the current match limit. Use when you need to increase the number of matches for an existing FindAll run that is still active or has completed. | | `PARALLEL_EXTRACT` | Extract Content from URLs | Tool to extract relevant content from specific web URLs. Use when you need to fetch and extract content from known URLs with optional focusing on specific objectives or search queries. | | `PARALLEL_FETCH_TASK_GROUP_RUNS` | Fetch Task Group Runs | Tool to retrieve task runs from a Task Group as a resumable stream. Use when you need to fetch all runs within a group, optionally including their inputs and outputs. The stream can be resumed using the event_id as a cursor. | | `PARALLEL_FIND_ALL` | Start FindAll Run | Tool to start a FindAll run. Use when you need to discover and match entities based on natural-language objectives. Supports custom conditions, exclusion lists, and webhook callbacks. | | `PARALLEL_GET_FIND_ALL_RUN_RESULT` | Get FindAll Run Result | Tool to fetch the final (or latest available) FindAll candidates and result payload for a run. Use when you need to retrieve matched/unmatched candidates after a FindAll run has progressed or completed. | | `PARALLEL_GET_FIND_ALL_RUN_SCHEMA` | Get FindAll Run Schema | Tool to retrieve the schema configuration of a FindAll run by findall_id. Use when you need to inspect the objective, entity type, match conditions, and other schema details for a previously created FindAll run. | | `PARALLEL_INGEST_FIND_ALL_RUN` | Ingest FindAll Run | Tool to transform a natural language search objective into a structured FindAll specification. Use when you need to generate a FindAll run spec from a user's natural language description. The generated specification serves as a suggested starting point and can be further customized. | | `PARALLEL_LIST_MONITOR_EVENTS` | List Monitor Events | Tool to list events for a monitor from up to the last 300 event groups. Retrieves events including errors and material changes in reverse chronological order. | | `PARALLEL_LIST_MONITORS` | List Monitors | Tool to list active monitors for the user. Returns all monitors regardless of status with their configuration and current state. Supports cursor-based pagination using monitor_id and limit parameters. | | `PARALLEL_RETRIEVE_EVENT_GROUP` | Retrieve Event Group | Tool to retrieve an event group for a monitor. Use when you have a valid monitor ID and event group ID and want to view the execution history. | | `PARALLEL_RETRIEVE_FIND_ALL_RUN_STATUS` | Retrieve FindAll Run Status | Tool to retrieve status and metadata for a FindAll run by findall_id. Use when you need to poll or check the progress of a FindAll run that was previously created. | | `PARALLEL_RETRIEVE_MONITOR` | Retrieve Monitor | Tool to retrieve a specific monitor by ID. Returns the monitor configuration including status, cadence, query, and webhook settings. | | `PARALLEL_RETRIEVE_TASK_GROUP` | Retrieve Task Group | Tool to retrieve details of a specific task group. Use when you have a valid task group ID and want to view its details. | | `PARALLEL_RETRIEVE_TASK_GROUP_RUN` | Retrieve Task Group Run | Tool to retrieve run status by run_id for a task group. Use when you need to check the status of a specific task group run or poll for completion. | | `PARALLEL_RETRIEVE_TASK_RUN` | Retrieve Task Run | Tool to retrieve run status by run_id. Use when you need to check the status or details of a specific task run. The run result is available from the /result endpoint. | | `PARALLEL_RETRIEVE_TASK_RUN_INPUT` | Retrieve Task Run Input | Tool to retrieve the input data of a specific task run by run_id. Use when you need to view the original input parameters that were provided to a task run. | | `PARALLEL_RETRIEVE_TASK_RUN_RESULT` | Retrieve Task Run Result | Tool to retrieve the result of a task run by run_id, blocking until the run completes. Use when you need to wait for and fetch the final output of a previously initiated task run. The request will block until the run completes or the timeout is reached. | | `PARALLEL_PARALLEL_SEARCH` | Parallel Search | Tool to perform parallel semantic search. Use when you need to retrieve top matching documents for multiple queries in a single call. | | `PARALLEL_SIMULATE_EVENT` | Simulate Event | Tool to simulate sending an event for a monitor. Use when testing monitor webhooks or validating monitor configurations. Simulates sending an event of the specified type (defaults to monitor.event.detected). | | `PARALLEL_STREAM_FIND_ALL_EVENTS` | Stream FindAll Events | Tool to stream events from a FindAll run. Use when you need real-time updates on candidate discovery, matching progress, and run status. | | `PARALLEL_STREAM_TASK_GROUP_EVENTS` | Stream Task Group Events | Tool to stream events for a Task Group. Use when you want real-time updates of group status and run completions. | | `PARALLEL_STREAM_TASK_RUN_EVENTS` | Stream Task Run Events | Tool to stream events for a Task Run. Returns progress updates and state changes for the task run. For runs without enable_events=true, event frequency is reduced. | | `PARALLEL_SUGGEST_TASK` | Suggest Task | Tool to suggest tasks based on user intent. Use when you need task specifications generated from a natural language description of what you want to accomplish. | | `PARALLEL_UPDATE_MONITOR` | Update Monitor | Tool to update a monitor's configuration. Use when you need to modify an existing monitor's cadence, query, metadata, or webhook settings. At least one field must be non-null to apply an update. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Parallel MCP server is an implementation of the Model Context Protocol that connects your AI agent to Parallel. It provides structured and secure access so your agent can perform Parallel 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 Parallel 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 Parallel 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 Parallel 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 Parallel **What's happening:** - You create a Parallel only session through Composio - Composio returns an MCP HTTP URL that exposes Parallel tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["parallel"]) 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=["parallel"], ) 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 Parallel through Composio's Tool Router. The agent can perform Parallel 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 Parallel MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/parallel/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/parallel/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/parallel/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/parallel/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/parallel/framework/codex) - [OpenClaw](https://composio.dev/toolkits/parallel/framework/openclaw) - [Hermes](https://composio.dev/toolkits/parallel/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/parallel/framework/cli) - [Google ADK](https://composio.dev/toolkits/parallel/framework/google-adk) - [LangChain](https://composio.dev/toolkits/parallel/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/parallel/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/parallel/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/parallel/framework/llama-index) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [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. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. - [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows. - [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches. - [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand. - [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance. - [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows. - [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Parallel MCP? With a standalone Parallel MCP server, the agents and LLMs can only access a fixed set of Parallel tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Parallel 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 Parallel tools. ### Can I manage the permissions and scopes for Parallel while using Tool Router? Yes, absolutely. You can configure which Parallel 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 Parallel data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)