# How to integrate Fibery MCP with Pydantic AI ```json { "title": "How to integrate Fibery MCP with Pydantic AI", "toolkit": "Fibery", "toolkit_slug": "fibery", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/fibery/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/fibery/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:11:15.959Z" } ``` ## Introduction This guide walks you through connecting Fibery to Pydantic AI using the Composio tool router. By the end, you'll have a working Fibery agent that can list all open tasks for your team, fetch details for project entity by id, delete file attachment from a task through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Fibery account through Composio's Fibery MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Fibery with - [OpenAI Agents SDK](https://composio.dev/toolkits/fibery/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fibery/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fibery/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fibery/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fibery/framework/codex) - [OpenClaw](https://composio.dev/toolkits/fibery/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fibery/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fibery/framework/cli) - [Google ADK](https://composio.dev/toolkits/fibery/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fibery/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fibery/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fibery/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fibery/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fibery/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Fibery - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Fibery workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## What is the Fibery MCP server, and what's possible with it? The Fibery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fibery account. It provides structured and secure access to your workspace data, so your agent can perform actions like querying entities, managing custom apps, running GraphQL queries, and organizing files—all with zero manual integration code. - Entity query and retrieval: Instantly fetch detailed information or lists of entities based on type, filters, and fields, making it easy to surface project or task data as needed. - Custom app and endpoint management: Let your agent list, inspect, or delete custom apps and endpoints, streamlining workspace configuration and app lifecycle management. - Flexible data manipulation with GraphQL: Execute custom GraphQL queries and mutations against your Fibery space to fetch, update, or manipulate structured data programmatically. - File and resource cleanup: Remove outdated files or entities efficiently, helping keep your workspace organized and clutter-free with automated deletions. - Authentication and workspace insights: Validate tokens securely and retrieve workspace or app metadata, ensuring your agent always operates with up-to-date context and permissions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FIBERY_DELETE_CUSTOM_APP_ENDPOINT` | Delete Custom App Endpoint | Tool to delete a specific custom app endpoint. Use after confirming the app and endpoint IDs to remove. | | `FIBERY_DELETE_ENTITY` | Delete Entity | Permanently delete a Fibery entity by its UUID and type. Use this action when you need to remove an entity from the workspace. Requires both the entity's UUID and its full qualified type name. WARNING: Deletion is irreversible. Example: Delete a task with entity_id='550e8400-e29b-41d4-a716-446655440000' and type='Tasks/Task'. | | `FIBERY_DELETE_FILE` | Delete File | Delete a file from Fibery storage using its secret identifier. Use this action to permanently remove an uploaded file. You must provide the file's secret (fibery/secret), not its ID (fibery/id). The secret is returned when you upload a file or can be queried via the commands API. | | `FIBERY_EXECUTE_GRAPH_QL_QUERY` | Execute GraphQL Query | Execute GraphQL queries or mutations against a Fibery workspace. Fibery organizes data into Spaces, each with its own GraphQL schema containing entity types and operations. This action automatically tries common space names if no space is specified, making it easy to use without prior knowledge. Best practices: - Start with introspection queries to discover schema: { __schema { types { name } } } - Use { __type(name: "Query") { fields { name } } } to see available queries - Space names typically match your workspace app/database names - The action returns both data and errors (GraphQL can return partial results) | | `FIBERY_GET_APP_INFO` | Get App Information | Tool to retrieve application information. Use when you need the version, name, description, authentication methods, and available data sources before further operations. | | `FIBERY_GET_CUSTOM_APP_ENDPOINTS` | Get Custom App Endpoints | Tool to list custom app endpoints. Use when you need the available custom endpoints for a given app before invoking them. | | `FIBERY_GET_CUSTOM_APPS` | Get Custom Apps | Tool to list all custom apps in the Fibery workspace. Use when you need the identifiers of all custom apps. | | `FIBERY_GET_FILE` | Get File | Download a file from Fibery by its secret or ID. Use this tool to retrieve file content from Fibery storage. The file secret is a UUID that uniquely identifies a file and is the preferred identifier. You can obtain the file secret: - From the 'fibery/secret' field when querying entities that have file fields - From the 'url' field in upload file response (extract the UUID from the URL) - From rich text content where files are embedded as /api/files/{secret} | | `FIBERY_GET_GRAPH_QL_SCHEMA` | Get GraphQL Schema | Retrieves the GraphQL schema for the Fibery workspace using standard GraphQL introspection. Returns the schema as a JSON string that includes all types, queries, mutations, and their fields. Use this to discover available GraphQL operations before executing queries. | | `FIBERY_GET_USER_PREFERENCES` | Get User Preferences | Tool to retrieve the current user's UI preferences. Use after authentication to tailor UI to user settings. | | `FIBERY_POST_AUTH_REFRESH_TOKEN` | Refresh access token | Tool to validate and refresh an access token. For Fibery's standard API, this validates the current token is still working (Fibery tokens don't expire). For OAuth2 integrations with third-party services, this could be used to refresh tokens through the validate endpoint. | | `FIBERY_POST_AUTH_TOKEN` | Validate Fibery authentication and get access token | Validates Fibery API authentication and returns the active access token. This action validates that your API token is working correctly by attempting to query the Fibery API. For standard Fibery workspaces, it validates the pre-configured API token from the Authorization header. Behavior: 1. First attempts OAuth2 password grant at /auth/token (rare, only custom installations) 2. If /auth/token returns 404 (standard case), validates existing token via /commands endpoint 3. Returns the validated token that can be used for subsequent API calls The returned access_token should be used in the header: `Authorization: Token ` Note: Most Fibery workspaces use pre-generated API tokens (created in workspace settings), not username/password authentication. The username/password parameters are only used if a custom OAuth2 endpoint exists. | | `FIBERY_POST_CREATE_ENTITY` | Create Entity | Tool to create a new Fibery entity. Use when you have prepared all necessary field values and need to persist a new record. Example: Create a 'Project/Task' with title and assignee. | | `FIBERY_POST_FETCH_DATA_COUNT` | Count Entities by Type | Count the total number of entities for a given Fibery type (database). This tool queries the Fibery workspace to return how many entities exist for the specified type. Use it to get totals like "how many users", "how many features", etc. Authentication: Requires a valid Fibery API token with read access. | | `FIBERY_POST_FETCH_DATA_LIST` | Fetch Datalist Options | Fetches one page of distinct values for a specific field from a Fibery entity type. Returns a list of unique options that can be used for filtering, dropdowns, or autocomplete. ONE Fibery API call is made per invocation; pagination is caller-driven via the `offset` and `limit` request fields and the `next_offset` response field. The action first attempts the `/datalist` endpoint and, if that endpoint is unavailable on the workspace (404/405/501), falls back to a single `fibery.entity/query` command via `/commands`. | | `FIBERY_POST_FETCH_SCHEMA` | Fetch Schema | Fetch the complete schema metadata for a Fibery workspace. Returns all types (entities) and their fields, including system types (fibery/user, fibery/app) and user-defined types (MySpace/Task, etc.). Use this to discover available types before querying data or to understand the structure of your workspace. | | `FIBERY_POST_OAUTH2_ACCESS_TOKEN` | Exchange OAuth2 authorization code | Exchange an OAuth2 authorization code for access and refresh tokens. This action is used during the final step of the OAuth2 authorization code flow when building Fibery custom integration apps. After a user authorizes your app on a third-party service and is redirected back with an authorization code, use this endpoint to exchange that code for access tokens. Important: This endpoint is typically implemented by YOUR custom integration app (connector), not by Fibery itself. The action probes multiple common endpoint paths across your app's base URL and Fibery's OAuth service to maximize compatibility. Typical flow: 1. User initiates OAuth authorization via /oauth2/v1/authorize 2. User approves on the third-party service 3. Third-party redirects to callback_uri with an authorization code 4. Call THIS endpoint with the code to get access/refresh tokens | | `FIBERY_POST_REVOKE_TOKEN` | Delete/Revoke Access Token | Delete/revoke an existing Fibery API access token by its ID. Uses the DELETE /api/tokens/:token_id endpoint to permanently remove an API token. Important: This endpoint typically requires cookie-based authentication (browser session). When using API token authentication, you may receive a 401 Unauthorized error. Use this when you need to invalidate a specific API token, such as during security rotation or when revoking access for a specific integration. | | `FIBERY_POST_VALIDATE_ACCOUNT` | Validate Fibery Workspace Credentials | Validates Fibery workspace credentials by performing a test API query to retrieve the authenticated user's name. This action verifies that the provided credentials (or existing metadata credentials) are valid and have access to the workspace. Use this to confirm authentication before executing other Fibery operations. Supports multiple authentication types: workspace tokens, API keys, basic auth, and OAuth2. | | `FIBERY_POST_VALIDATE_FILTER` | Validate Filter | Validates filter definitions before executing data queries. Use this tool to verify that a filter's structure and syntax are correct without actually fetching data. For Fibery workspaces: Validates by executing a safe test query with limit=1. For custom apps: Calls the app's POST /validate/filter endpoint if available. Returns validation result indicating whether the filter can be safely used. | | `FIBERY_UPDATE_ENTITY` | Update Entity | Update an existing Fibery entity's fields. Use this to modify text fields, numbers, single-select states, workflow states, or relation fields on an entity. Prerequisites: - You need the entity's UUID (fibery/id) - obtain via Get Entities or Create Entity. - You need the fully-qualified type name (e.g., 'Engineering/Task'). - For workflow/state fields, you need the state's UUID. Limitations: - Rich text fields cannot be updated via this command. - Entity collection fields should be updated after entity creation. | | `FIBERY_UPDATE_USER_PREFERENCES` | Update User Preferences | Tool to update the current user's preferences by using the Commands API. It fetches the current user id and preferences, merges the provided payload, and writes back the merged object into 'fibery/ui-preferences' of the current fibery/user. | | `FIBERY_UPLOAD_FILE` | Upload File | Upload a file to Fibery's file storage. Use this action to upload files that can later be attached to entities or used in documents. Returns file metadata including the file ID and secret needed for subsequent operations. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Fibery MCP server is an implementation of the Model Context Protocol that connects your AI agent to Fibery. It provides structured and secure access so your agent can perform Fibery 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 with an active API key - Basic familiarity with Python and async programming ### 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 Install the required libraries. What's happening: - composio connects your agent to external SaaS tools like Fibery - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Fibery - MCPServerStreamableHTTP connects to the Fibery MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Fibery tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Fibery composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["fibery"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Fibery endpoint - The agent uses GPT-5 to interpret user commands and perform Fibery operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent fibery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[fibery_mcp], instructions=( "You are a Fibery assistant. Use Fibery tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Fibery API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Fibery.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Fibery composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["fibery"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent fibery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[fibery_mcp], instructions=( "You are a Fibery assistant. Use Fibery tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Fibery.\n") while True: user_input = input("You: ").strip() if user_input.lower() in {"exit", "quit", "bye"}: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Fibery through Composio's Tool Router. With this setup, your agent can perform real Fibery actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Fibery for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Fibery MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/fibery/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fibery/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fibery/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fibery/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fibery/framework/codex) - [OpenClaw](https://composio.dev/toolkits/fibery/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fibery/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fibery/framework/cli) - [Google ADK](https://composio.dev/toolkits/fibery/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fibery/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fibery/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fibery/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fibery/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fibery/framework/crew-ai) ## 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. - [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. - [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. - [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. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page. - [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes. - [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights. - [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Fibery MCP? With a standalone Fibery MCP server, the agents and LLMs can only access a fixed set of Fibery tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Fibery and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Pydantic AI? Yes, you can. Pydantic AI 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 Fibery tools. ### Can I manage the permissions and scopes for Fibery while using Tool Router? Yes, absolutely. You can configure which Fibery 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 Fibery data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)