Composio

Deprecated: use check active connections instead for bulk operations. check active connection status for a toolkit or specific connected account id. returns connection details if active, or required parameters for establishing connection if none exists. active connections enable agent actions on the toolkit.

Check active connection status for multiple toolkits or specific connected account ids. returns connection details if active, or required parameters for establishing connection if none exists. active connections enable agent actions on toolkits.

This is a workflow builder that ensures the LLM produces a complete, step-by-step plan for any use case. WHEN TO CALL: - Call this tool based on COMPOSIO_SEARCH_TOOLS output. If search tools response indicates create_plan should be called and the usecase is not easy, call it. - Use this tool after COMPOSIO_SEARCH_TOOLS or COMPOSIO_MANAGE_CONNECTIONS to generate an execution plan for the user's use case. - USE for medium or hard tasks — skip it for easy ones. - If the user switches to a new use case in the same chat and COMPOSIO_SEARCH_TOOLS again instructs you to call the planner, you MUST call this tool again for that new use case. Memory Integration: - You can choose to add the memory received from the search tool into the known_fields parameter of the plan function to enhance planning with discovered relationships and information. Outputs a complete plan with sections such as "workflow_steps", "complexity_assessment", "decision_matrix", "failure_handling" "output_format", and more as needed. If you skip this step for non-easy tasks, workflows will likely be incomplete, or fail during execution for complex tasks. Calling it guarantees reliable, accurate, and end-to-end workflows aligned with the available tools and connections.

Download a file from a public s3 (or r2) url to a local path.

Enable a specific trigger for the authenticated user.

Execute a tool using the composio api.

Get the dependency graph for a given tool, showing related parent tools that might be useful. this action calls the composio labs dependency graph api to retrieve tools that are commonly used together with or before the specified tool. this helps discover related tools and understand common workflows.

Gets the required parameters for connecting to a toolkit via initiate connection. returns the exact parameter names and types needed for initiate connection's parameters field. supports api keys, oauth credentials, connection fields, and hybrid authentication scenarios. if has default credentials is true, you can call initiate connection with empty parameters for seamless oauth flow.

Retrieves the response schema for a specified composio tool. this action fetches the complete response schema definition for any valid composio tool, returning it as a dictionary that describes the expected response structure.

Initiate a connection to a toolkit with comprehensive authentication support. supports all authentication scenarios: 1. composio default oauth (no parameters needed) 2. custom oauth (user's client id/client secret) 3. api key/bearer token authentication 4. basic auth (username/password) 5. hybrid scenarios (oauth + connection fields like site name) 6. connection-only fields (subdomain, api key at connection level) 7. no authentication required automatically detects and validates auth config vs connection fields, provides helpful error messages for missing parameters.

List all the available toolkits on composio with filtering options.

List available triggers and their configuration schemas.

Create or manage connections to user's apps. Returns a branded authentication link that works for OAuth, API keys, and all other auth types. Call policy: - First call COMPOSIO_SEARCH_TOOLS for the user's query. - If COMPOSIO_SEARCH_TOOLS indicates there is no active connection for a toolkit, call COMPOSIO_MANAGE_CONNECTIONS with the exact toolkit name(s) returned. - Use exact toolkit slugs returned by COMPOSIO_SEARCH_TOOLS; never invent toolkit names. - NEVER execute any toolkit tool without an ACTIVE connection. Tool Behavior: - If a connection is Active, the tool returns the connection details. Always use this to verify connection status and fetch metadata. - If a connection is not Active, returns a authentication link (redirect_url) to create new connection. - If reinitiate_all is true, the tool forces reconnections for all toolkits, even if they already have active connections. Workflow after initiating connection: - Always show the returned redirect_url as a FORMATTED MARKDOWN LINK to the user, and ask them to click on the link to finish authentication. - Begin executing tools only after the connection for that toolkit is confirmed Active.

Fast and parallel tool executor for tools discovered through COMPOSIO_SEARCH_TOOLS. Use this tool to execute up to 50 tools in parallel across apps only when they're logically independent (no ordering/output dependencies). Response contains structured outputs ready for immediate analysis - avoid reprocessing them via remote bash/workbench tools. Prerequisites: - Always use valid tool slugs and their arguments. NEVER invent tool slugs or argument fields. ALWAYS pass STRICTLY schema-compliant arguments with each tool execution. - Ensure an ACTIVE connection exists for the toolkits that are going to be executed. If none exists, MUST initiate one via COMPOSIO_MANAGE_CONNECTIONS before execution. - Only batch tools that are logically independent - no ordering, no output-to-input dependencies, and no intra-call chaining (tools in one call can't use each other's outputs). DO NOT pass dummy or placeholder inputs; always resolve required inputs using appropriate tools first. Usage guidelines: - If COMPOSIO_SEARCH_TOOLS returns a tool that can perform the task, prefer calling it via this executor. Do not write custom API calls or ad-hoc scripts for tasks that can be completed by available Composio tools. - Prefer parallel execution: group independent tools into a single multi-execute call where possible. - Predictively set sync_response_to_workbench=true if the response may be large or needed for later scripting. It still shows response inline; if the actual response data turns out small and easy to handle, keep everything inline and SKIP workbench usage. - Responses contain structured outputs for each tool. RULE: Small data - process yourself inline; large data - process in the workbench. - ALWAYS include inline references/links to sources in MARKDOWN format directly next to the relevant text. Eg provide slack thread links alongside with summary, render document links instead of raw IDs. Restrictions: Some tools or toolkits may be disabled in this environment. If the response indicates a restriction, inform the user and STOP execution immediately. Do NOT attempt workarounds or speculative actions. - CRITICAL: You MUST always include the 'memory' parameter - never omit it. Even if you think there's nothing to remember, include an empty object {} for memory. Memory Storage: - CRITICAL FORMAT: Memory must be a dictionary where keys are app names (strings) and values are arrays of strings. NEVER pass nested objects or dictionaries as values. - CORRECT format: {"slack": \["Channel general has ID C1234567"\], "gmail": \["John's email is john@example.com"\]} - Write memory entries in natural, descriptive language - NOT as key-value pairs. Use full sentences that clearly describe the relationship or information. - ONLY store information that will be valuable for future tool executions - focus on persistent data that saves API calls. - STORE: ID mappings, entity relationships, configs, stable identifiers. - DO NOT STORE: Action descriptions, temporary status updates, logs, or "sent/fetched" confirmations. - Examples of GOOD memory (store these): * "The important channel in Slack has ID C1234567 and is called #general" * "The team's main repository is owned by user 'teamlead' with ID 98765" * "The user prefers markdown docs with professional writing, no emojis" (user_preference) - Examples of BAD memory (DON'T store these): * "Successfully sent email to john@example.com with message hi" * "Fetching emails from last day (Sep 6, 2025) for analysis" - Do not repeat the memories stored or found previously.

Execute bash commands in a REMOTE sandbox for file operations, data processing, and system tasks. Essential for handling large tool responses saved to remote files. **Hard 3-minute (180s) execution limit** — break large tasks into smaller commands. PRIMARY USE CASES: - Process large tool responses saved by COMPOSIO_MULTI_EXECUTE_TOOL to remote sandbox - File system operations, extract specific information from JSON with shell tools like jq, awk, sed, grep, etc. - Commands run from /home/user directory by default

Process **REMOTE FILES** or script BULK TOOL EXECUTIONS using Python code IN A REMOTE SANDBOX. If you can see the data in chat, DON'T USE THIS TOOL. **ONLY** use this when processing **data stored in a remote file** or when scripting bulk tool executions. DO NOT USE - When the complete response is already inline/in-memory, or you only need quick parsing, summarization, or basic math. USE IF - To parse/analyze tool outputs saved by COMPOSIO_MULTI_EXECUTE_TOOL to a remote file in the sandbox or to script multi-tool chains there. - For bulk or repeated executions of known Composio tools (e.g., add a label to 100 emails). - To call APIs via proxy_execute when no Composio tool exists for that API. OUTPUTS - Returns a compact result or, if too long, artifacts under `/mnt/files/.composio/output` (cloud-backed FUSE mount, persisted across sandbox restarts). IMPORTANT CODING RULES: 1. Stepwise Execution: Split work into small steps. Save intermediate outputs to `/mnt/files/` (cloud-backed, persisted across failures/timeouts) or variables. Call COMPOSIO_REMOTE_WORKBENCH again for the next step. 2. Notebook Persistence: This is a persistent Jupyter notebook cell: variables, functions, imports, and in-memory state persist across executions. Helper functions are preloaded. 3. Top-level cells: Do not use `return`; Jupyter only allows it inside functions. For final values, end with `output` or `print(output)`, not `return output`. 4. Parallelism & Timeout (CRITICAL): There is a **hard 3-minute (180s) execution limit** per cell. Always prioritize PARALLEL execution using ThreadPoolExecutor for bulk operations - e.g., call run_composio_tool or invoke_llm across rows. If data is large, split it into smaller batches across cells. 5. Checkpoints: Save checkpoints to `/mnt/files/` so that long runs can be resumed from the last completed step, even after a timeout or sandbox restart. 6. Schema Safety: Never assume the response schema for run_composio_tool if not known already from previous tools. To inspect schema, either run a simple request **outside** the workbench or use invoke_llm helper. 7. LLM Helpers: Always use invoke_llm helper for summary, analysis, or field extraction on results; prefer it for much better results over ad hoc filtering. 8. Avoid Meta Loops: Do not use run_composio_tool to call COMPOSIO_* meta tools. Only use it for app tools. 9. Pagination: Use when data spans multiple pages. Continue fetching pages with the returned next_page_token or cursor until none remains. Parallelize page fetches when the tool supports page_number. 10. No Hardcoding: Never hardcode data. Load it from files or tool responses, iterating to construct intermediate or final inputs/outputs. 11. If the final output is in a workbench file, use upload_local_file to download it - never expose the raw workbench file path to the user. Prefer to download useful artifacts after task is complete. ENV & HELPERS: - Home directory: `/home/user`. - NOTE: Helper functions already initialized in the workbench - DO NOT import or redeclare them: - `run_composio_tool(tool_slug: str, arguments: dict) -> tuple\[Dict\[str, Any\], str\]`: Execute a known Composio **app** tool. Do not invent names; match the tool input schema. Use for loops/parallel/bulk calls. i) run_composio_tool returns JSON with top-level "data". Parse carefully—structure may be nested. - `invoke_llm(query: str) -> tuple\[str, str\]`: Invoke an LLM for semantic tasks. Pass MAX 200k characters. i) NOTE Prompting guidance: When building prompts for invoke_llm, prefer f-strings (or concatenation) so literal braces stay intact. If using str.format, escape braces by doubling them ({{ }}). ii) Define the exact JSON schema you want and batch items into smaller groups to stay within token limit. - `upload_local_file(*file_paths) -> tuple\[Dict\[str, Any\], str\]`: Upload sandbox files to Composio S3/R2 storage for user-downloadable artifacts. - `proxy_execute(method, endpoint, toolkit, query_params=None, body=None, headers=None) -> tuple\[Any, str\]`: Call a toolkit API directly when no Composio tool exists. Only one toolkit can be invoked with proxy_execute per workbench call - `web_search(query: str) -> tuple\[str, str\]`: Search the web for information. - `smart_file_extract(sandbox_file_path: str, show_preview: bool = True) -> tuple\[str, str\]`: Extracts text from files in the sandbox (e.g., PDF, image). - Workbench comes with comprehensive Image Processing (PIL/Pillow, OpenCV, scikit-image), PyTorch ML libraries, Document and Report handling tools (pandoc, python-docx, pdfplumber, reportlab), and standard Data Analysis tools (pandas, numpy, matplotlib) for advanced visual, analytical, and AI tasks. All helper functions return a tuple (result, error). Always check error before using result.