# RocketRide Documentation

> Build, run, and ship data + AI pipelines with the RocketRide toolchain.

## Home

- [RocketRide Documentation](https://docs.rocketride.org/index.md): RocketRide is an open-source runtime for AI pipelines.

## Quickstart

- [Quickstart](https://docs.rocketride.org/quickstart.md): Pick the path that matches how you want to build.

## Evaluate

- [Security](https://docs.rocketride.org/evaluate/security.md): RocketRide is open-source and developed in the open with a documented security process.
- [Understanding RocketRide](https://docs.rocketride.org/evaluate/understanding.md): RocketRide has a small number of moving parts.
- [Use Cases](https://docs.rocketride.org/evaluate/use-cases.md): Two end-to-end walkthroughs: build a pipeline visually in your IDE, then integrate that pipeline into your own application with an SDK.
- [Why RocketRide](https://docs.rocketride.org/evaluate/why-rocketride.md): RocketRide is an open-source runtime for AI pipelines.

## Concepts

- [Advanced Agents](https://docs.rocketride.org/concepts/advanced-agents.md): This page builds on the foundations in Agents & Tools to cover multi-agent patterns, memory strategies, and safety techniques for production agent pipelines.
- [Agents & Tools](https://docs.rocketride.org/concepts/agents-tools-skills.md): Most nodes pass data along a lane and move on.
- [Best Practices](https://docs.rocketride.org/concepts/best-practices.md): Practical guidance for building reliable, maintainable RocketRide pipelines.
- [Error Handling](https://docs.rocketride.org/concepts/error-handling.md): Errors in RocketRide fall into two categories depending on when they occur and what they affect.
- [Execution Model](https://docs.rocketride.org/concepts/execution-model.md): A pipeline describes what to run; the execution model is how the engine runs it.
- [Nodes](https://docs.rocketride.org/concepts/nodes.md): A pipeline is a graph, and nodes are its vertices.
- [Performance](https://docs.rocketride.org/concepts/performance.md): Understanding how the RocketRide engine executes pipelines helps you make good decisions about node selection, chunk sizes, and deployment topology.
- [Pipelines](https://docs.rocketride.org/concepts/pipelines.md): A pipeline is the unit of work in RocketRide: a directed graph of components that move and transform data.
- [Runtime & Engine](https://docs.rocketride.org/concepts/runtime-engine.md): Pipelines don't run themselves.
- [Security Model](https://docs.rocketride.org/concepts/security-model.md): An overview of how RocketRide handles credentials, network exposure, and authentication across its components.

## Examples

- [Document Extraction](https://docs.rocketride.org/examples/document-extraction.md): Read files from the local file system, parse them into structured text, extract specific fields, and return structured JSON.
- [RAG Pipeline](https://docs.rocketride.org/examples/rag-pipeline.md): Retrieval-augmented generation (RAG) is the most common pattern in RocketRide: embed documents into a vector store, then answer questions by retrieving the relevant chunks and feeding them to an LLM.
- [Webhook Pipeline](https://docs.rocketride.org/examples/webhook-pipeline.md): The simplest useful pipeline: an HTTP endpoint that accepts a question, sends it to an LLM, and returns the answer.

## Protocols

- [MCP Server](https://docs.rocketride.org/protocols/mcp.md): Let AI assistants run your RocketRide pipelines via the Model Context Protocol.
- [WebSocket](https://docs.rocketride.org/protocols/websocket.md): The RocketRide engine speaks a native WebSocket protocol.
- [Crash reporting](https://docs.rocketride.org/protocols/websocket/crash-reporting.md): When the engine crashes it writes a minidump -- a compact snapshot of the process state at the moment of the fault.
- [Observability](https://docs.rocketride.org/protocols/websocket/observability.md): RocketRide exposes runtime observability (task lifecycle, periodic status, resource metrics, and per-component flow traces) as a live event stream over the same WebSocket the engine already speaks.

## Nodes

- [Overview](https://docs.rocketride.org/nodes.md)
- [Accessibility Describe](https://docs.rocketride.org/nodes/accessibility_describe.md): An accessibility-focused image analysis node that generates scene descriptions optimized for blind and visually impaired users.
- [CrewAI Agent](https://docs.rocketride.org/nodes/agent_crewai.md): Standalone single-agent CrewAI node.
- [CrewAI Agent](https://docs.rocketride.org/nodes/agent_crewai/crewai_agent.md): Standalone single-agent CrewAI node.
- [CrewAI Manager](https://docs.rocketride.org/nodes/agent_crewai/crewai_manager.md): Multi-agent manager using CrewAI hierarchical process.
- [CrewAI Subagent](https://docs.rocketride.org/nodes/agent_crewai/crewai_subagent.md): Managed CrewAI sub-agent.
- [Deep Agent](https://docs.rocketride.org/nodes/agent_deepagent.md): Single-agent execution using Deep Agents.
- [Deep Agent](https://docs.rocketride.org/nodes/agent_deepagent/deepagent_agent.md): Single-agent execution using Deep Agents.
- [DeepAgent Subagent](https://docs.rocketride.org/nodes/agent_deepagent/deepagent_subagent.md): Managed Deep Agent subagent.
- [LangChain](https://docs.rocketride.org/nodes/agent_langchain.md): Single-agent execution using LangChain.
- [LlamaIndex](https://docs.rocketride.org/nodes/agent_llamaindex.md): Single-agent execution using LlamaIndex's ReAct loop.
- [RocketRide Wave](https://docs.rocketride.org/nodes/agent_rocketride.md): Wave-planning agent built natively on the RocketRide architecture.
- [Anomaly Detector](https://docs.rocketride.org/nodes/anomaly_detector.md): A pipeline monitoring component that detects anomalies in numeric output values using statistical methods.
- [Anonymize](https://docs.rocketride.org/nodes/anonymize.md): A filter component that identifies and masks sensitive information in text data.
- [Answers to Documents](https://docs.rocketride.org/nodes/answer_documents.md): An adapter component that turns model/agent answers into embeddable documents.
- [Aparavi AQL](https://docs.rocketride.org/nodes/aparavi_aql.md): Queries the Aparavi data governance platform using AQL (Aparavi Query Language).
- [Player](https://docs.rocketride.org/nodes/audio_player.md): The Audio Player component plays audio through the system’s default audio output device, including the audio track from video content.
- [Transcribe](https://docs.rocketride.org/nodes/audio_transcribe.md): The Audio transcribe component recieves audio or video and transcribes into text.
- [Text To Speech](https://docs.rocketride.org/nodes/audio_tts.md): Converts incoming text into speech using Kokoro-82M (local KPipeline or --modelserver KokoroLoader).
- [Parse/Process/Embed](https://docs.rocketride.org/nodes/autopipe.md): This component combines document parsing, text preprocessing, and embedding generation in a single node.
- [Core](https://docs.rocketride.org/nodes/core.md): A combined configuration that bundles a preprocessor, embedding model, vector store, and LLM into a single selectable unit.
- [Fingerprinter](https://docs.rocketride.org/nodes/core/hash.md): A processing component that generates a unique fingerprint (hash) of a document's content.
- [Parser](https://docs.rocketride.org/nodes/core/parser.md): A document parsing component that extracts rich content from a wide variety of document types.
- [Currency Convert (Explicit)](https://docs.rocketride.org/nodes/currency_convert_explicit.md): An opt-in currency converter for the audit-grade financial extraction suite.
- [ClickHouse](https://docs.rocketride.org/nodes/db_clickhouse.md): A ClickHouse component that answers natural-language questions by translating them into SQL and executing them against the database, returning rows as a table, text, or structured answers.
- [MySQL](https://docs.rocketride.org/nodes/db_mysql.md): A processing component that takes structured table data and inserts it into a MySQL database.
- [PostgreSQL](https://docs.rocketride.org/nodes/db_postgres.md): A processing component that takes structured table data and inserts it into a PostgreSQL database.
- [Dictionary](https://docs.rocketride.org/nodes/dictionary.md): A processing component that analyzes documents to extract a dictionary of key terms and phrases.
- [Image](https://docs.rocketride.org/nodes/embedding_image.md): A processing component that generates vector embeddings from image content using advanced computer vision models.
- [OpenAI (Embedding)](https://docs.rocketride.org/nodes/embedding_openai.md): A component that transforms text into numerical vector representations using advanced embedding models.
- [Transformer](https://docs.rocketride.org/nodes/embedding_transformer.md): A component that transforms text into numerical vector representations using advanced embedding models.
- [Video](https://docs.rocketride.org/nodes/embedding_video.md): A processing component that generates vector embeddings from video content by extracting frames at configurable intervals and encoding them using vision models such as CLIP.
- [Data Extractor](https://docs.rocketride.org/nodes/extract_data.md): A component that processes unstructured or semi-structured text and extracts structured data in a tabular format.
- [Fact Extractor](https://docs.rocketride.org/nodes/extract_facts.md): The extraction brain.
- [Frame Grabber](https://docs.rocketride.org/nodes/frame_grabber.md): A component that extracts frames from video files and outputs them as image data.
- [ArangoDB](https://docs.rocketride.org/nodes/graph_arango.md): A processing component that connects to an ArangoDB multi-model database.
- [FalkorDB](https://docs.rocketride.org/nodes/graph_falkordb.md): A processing component that connects to a FalkorDB graph database.
- [HydraDB](https://docs.rocketride.org/nodes/graph_hydradb.md): A database/tool node for HydraDB, a managed graph + memory store.
- [Neo4J](https://docs.rocketride.org/nodes/graph_neo4j.md): A processing component that connects to a Neo4J graph database.
- [Guardrails](https://docs.rocketride.org/nodes/guardrails.md): A comprehensive input/output guardrails filter for AI safety.
- [Cleanup](https://docs.rocketride.org/nodes/image_cleanup.md): A component that processes an image, cleans it up for OCR tasks by converting to gray scale, removing noise, deskewing, and enhancing contrast.
- [LlamaParse](https://docs.rocketride.org/nodes/llamaparse.md): A document parsing component that uses LlamaParse to extract text and structured data from various document formats including PDFs, images, Word documents, Excel spreadsheets, and other formats.
- [Anthropic](https://docs.rocketride.org/nodes/llm_anthropic.md): A component that integrates with Anthropic's Claude models for natural language understanding and generation.
- [Baidu Qianfan](https://docs.rocketride.org/nodes/llm_baidu_qianfan.md): A component that connects to Baidu Qianfan ERNIE large language models through Qianfan's OpenAI-compatible chat-completions API.
- [Amazon Bedrock](https://docs.rocketride.org/nodes/llm_bedrock.md): A component that connects to Amazon Bedrock, providing access to a range of foundation models from leading AI providers through a unified AWS interface.
- [Deepseek](https://docs.rocketride.org/nodes/llm_deepseek.md): A component that connects to DeepSeek’s large language models for advanced natural language processing.
- [Gemini](https://docs.rocketride.org/nodes/llm_gemini.md): A component that connects to Gemini models for advanced natural language processing.
- [GMI Cloud](https://docs.rocketride.org/nodes/llm_gmi_cloud.md): A component that connects to GMI Cloud's large language models for advanced natural language processing.
- [IBM Watson](https://docs.rocketride.org/nodes/llm_ibm_watson.md)
- [Kimi (Moonshot)](https://docs.rocketride.org/nodes/llm_kimi.md): A component that connects to Moonshot AI's Kimi large language models for advanced natural language processing.
- [MiniMax](https://docs.rocketride.org/nodes/llm_minimax.md): A component that connects to MiniMax's large language models for advanced natural language processing.
- [Mistral AI](https://docs.rocketride.org/nodes/llm_mistral.md): A component that connects to Mistral AI's advanced language models for natural language processing.
- [Ollama](https://docs.rocketride.org/nodes/llm_ollama.md): A component that integrates with locally-hosted language models through Ollama.
- [OpenAI](https://docs.rocketride.org/nodes/llm_openai.md): A component that connects to OpenAI's latest GPT models for advanced natural language processing.
- [OpenAI-Compatible API](https://docs.rocketride.org/nodes/llm_openai_api.md): A component that connects to any OpenAI-compatible API endpoint for language model inference.
- [Perplexity](https://docs.rocketride.org/nodes/llm_perplexity.md): A component that connects to Perplexity AI's Sonar models for advanced natural language processing with real-time web search capabilities.
- [Qwen](https://docs.rocketride.org/nodes/llm_qwen.md): A component that connects to Alibaba Cloud's Qwen large language models via the DashScope API.
- [Gemini Vision](https://docs.rocketride.org/nodes/llm_vision_gemini.md): A component that connects to Google Gemini's vision-capable models for image analysis, OCR, visual understanding, and scene description.
- [Mistral Vision](https://docs.rocketride.org/nodes/llm_vision_mistral.md): A component that connects to Mistral AI's vision-capable models for image analysis, OCR, and visual understanding tasks.
- [Ollama Vision](https://docs.rocketride.org/nodes/llm_vision_ollama.md): A component that connects to locally-hosted open-source vision models through Ollama for image analysis, description, and visual understanding tasks.
- [OpenAI Vision](https://docs.rocketride.org/nodes/llm_vision_openai.md): A component that connects to OpenAI's vision-capable models for image analysis, OCR, visual understanding, and scene description.
- [xAI](https://docs.rocketride.org/nodes/llm_xai.md): A component that integrates with xAI's Grok language models for intelligent text generation and analysis.
- [Local Text Output](https://docs.rocketride.org/nodes/local_text_output.md): A target component that writes data to the file system.
- [Memory (Internal)](https://docs.rocketride.org/nodes/memory_internal.md): Run-scoped keyed memory store exposed as agent tools.
- [Persistent Memory](https://docs.rocketride.org/nodes/memory_persistent.md): A persistent cross-session memory node that retains data across pipeline invocations.
- [Named Entity Recognition](https://docs.rocketride.org/nodes/ner.md): A text processing component that identifies and extracts named entities from text using state-of-the-art transformer models.
- [Normalize Facts](https://docs.rocketride.org/nodes/normalize_facts.md): A deterministic fact normalizer for the audit-grade financial extraction suite.
- [OCR](https://docs.rocketride.org/nodes/ocr.md): A component that extracts machine-readable text from images and scanned documents using optical character recognition.
- [Code](https://docs.rocketride.org/nodes/preprocessor_code.md): A specialized component designed to parse and tokenize source code.
- [General Text](https://docs.rocketride.org/nodes/preprocessor_langchain.md): A preprocessing component that segments large bodies of text into intelligently sized chunks for downstream processing.
- [LLM](https://docs.rocketride.org/nodes/preprocessor_llm.md): A processing component that analyzes document content to extract concise summaries, key points, and named entities and to divide a document for storage into a vector database.
- [Prompt](https://docs.rocketride.org/nodes/prompt.md): A transformation component that takes multiple inputs and merges them into a single question with a configurable prompt.
- [Question](https://docs.rocketride.org/nodes/question.md): A transformation component that takes input text and encapsulates it as a Question object without modification.
- [Reducto](https://docs.rocketride.org/nodes/reducto.md): A parsing component that uses Reducto to extract text and structured data from various document formats including PDFs, images, and other document types.
- [Remote Processing](https://docs.rocketride.org/nodes/remote.md): A transport component that forwards data to a remote machine or processing node.
- [Cohere Rerank](https://docs.rocketride.org/nodes/rerank_cohere.md): A reranking component powered by Cohere's Rerank API that improves search quality by reordering retrieved documents based on their relevance to a given query.
- [Response](https://docs.rocketride.org/nodes/response.md): A component that returns processed answers back to the requesting client.
- [RocketRide Graph](https://docs.rocketride.org/nodes/rocketride_graph.md): A RocketRide-managed graph database backed by PostgreSQL + Apache AGE.
- [RocketRide SQL](https://docs.rocketride.org/nodes/rocketride_sql.md): A RocketRide-managed relational database.
- [RocketRide Vector](https://docs.rocketride.org/nodes/rocketride_vector.md): A RocketRide-managed vector store backed by PostgreSQL + pgvector.
- [Schema Validate](https://docs.rocketride.org/nodes/schema_validate.md): A guard node for the audit-grade financial extraction suite.
- [Exa Search](https://docs.rocketride.org/nodes/search_exa.md): A direct Exa web search node.
- [Astra DB](https://docs.rocketride.org/nodes/store_astra.md): A vector database component for Astra DB, enabling efficient storage and retrieval of vector embeddings.
- [MongoDB Atlas](https://docs.rocketride.org/nodes/store_atlas.md): A vector database component for MongoDB Atlas, enabling efficient storage and retrieval of vector embeddings using MongoDB's native vector search capabilities.
- [Chroma](https://docs.rocketride.org/nodes/store_chroma.md): A vector database component for Chroma, enabling efficient storage and retrieval of vector embeddings.
- [Elasticsearch](https://docs.rocketride.org/nodes/store_elasticsearch.md): A vector database component for Elasticsearch, enabling efficient storage and retrieval of vector embeddings.
- [Elasticsearch](https://docs.rocketride.org/nodes/store_elasticsearch/elasticsearch.md): A vector database component for Elasticsearch, enabling efficient storage and retrieval of vector embeddings.
- [OpenSearch](https://docs.rocketride.org/nodes/store_elasticsearch/opensearch.md): An OpenSearch node that supports classic BM25 search and vector search for ingestion and retrieval workflows.
- [Milvus](https://docs.rocketride.org/nodes/store_milvus.md): A vector database component for Milvus, enabling efficient storage, indexing, and retrieval of vector embeddings.
- [Pinecone](https://docs.rocketride.org/nodes/store_pinecone.md): A component that connects to the Pinecone vector database for storing and retrieving high-dimensional embeddings.
- [PostgreSQL (pgvector)](https://docs.rocketride.org/nodes/store_postgres.md): A component that enhances PostgreSQL with vector similarity search capabilities through the pgvector extension.
- [Qdrant](https://docs.rocketride.org/nodes/store_qdrant.md): A vector database component for Qdrant, enabling efficient storage and retrieval of vector embeddings.
- [Weaviate](https://docs.rocketride.org/nodes/store_weaviate.md): A component that stores vector embeddings in a Weaviate instance for semantic search and retrieval.
- [Summarization: LLM](https://docs.rocketride.org/nodes/summarization.md): A processing component that analyzes document content to extract concise summaries, key points, and named entities.
- [Telegram Bot](https://docs.rocketride.org/nodes/telegram.md): A Telegram Bot source node that receives messages from users via the Telegram Bot API.
- [Text Output](https://docs.rocketride.org/nodes/text_output.md): A target component that writes data to the file system.
- [Thumbnail](https://docs.rocketride.org/nodes/thumbnail.md): A processing component that creates thumbnails from input images.
- [Apify](https://docs.rocketride.org/nodes/tool_apify.md): Exposes Apify Actors as agent tools.
- [Bland AI](https://docs.rocketride.org/nodes/tool_bland_ai.md): Make and manage AI-powered phone calls via Bland AI.
- [Chart (Chart.js)](https://docs.rocketride.org/nodes/tool_chartjs.md): Generates Chart.js v4 chart configurations from data using the pipeline LLM.
- [Cognee](https://docs.rocketride.org/nodes/tool_cognee.md): Persistent semantic memory backed by a Cognee server, exposed as three agent tools.
- [Daytona](https://docs.rocketride.org/nodes/tool_daytona.md): Gives agents an isolated Daytona cloud sandbox for running code and shell commands.
- [DeepL](https://docs.rocketride.org/nodes/tool_deepl.md): Exposes DeepL translation and AI rephrasing as agent tools.
- [Exa Search](https://docs.rocketride.org/nodes/tool_exa_search.md): Exposes Exa semantic web search as an agent tool.
- [File System](https://docs.rocketride.org/nodes/tool_filesystem.md): File system node.
- [Firecrawl](https://docs.rocketride.org/nodes/tool_firecrawl.md): Exposes Firecrawl web-scraping operations as agent tools.
- [Git](https://docs.rocketride.org/nodes/tool_git.md): Exposes local Git repository operations as agent tools.
- [GitHub](https://docs.rocketride.org/nodes/tool_github.md): Exposes GitHub repository operations as agent tools.
- [Guild.ai](https://docs.rocketride.org/nodes/tool_guild.md): Run Guild.ai agents from a RocketRide pipeline or an agent.
- [HTTP Request](https://docs.rocketride.org/nodes/tool_http_request.md): Makes HTTP requests to any API endpoint, like curl for agents.
- [LaserData Memory](https://docs.rocketride.org/nodes/tool_laserdata_memory.md): Durable, shared agent memory exposed as tools, backed by LaserData (Apache Iggy) via the Laser SDK.
- [MCP Client](https://docs.rocketride.org/nodes/tool_mcp_client.md): Connects to the Butterbase MCP server and exposes its backend tools for agent tool-calling.
- [Oura](https://docs.rocketride.org/nodes/tool_oura.md): Exposes Oura Ring health data as read-only agent tools.
- [Pipeline Tool](https://docs.rocketride.org/nodes/tool_pipe.md): Exposes an inline pipeline as an agent tool.
- [Pipedrive](https://docs.rocketride.org/nodes/tool_pipedrive.md): Exposes the Pipedrive CRM REST API v1 as agent tools.
- [Python](https://docs.rocketride.org/nodes/tool_python.md): Executes Python code in a restricted in-process sandbox via exec().
- [Slack](https://docs.rocketride.org/nodes/tool_slack.md): Exposes Slack workspace operations as agent tools.
- [Tavily](https://docs.rocketride.org/nodes/tool_tavily.md): Exposes Tavily real-time web search as an agent tool.
- [v0 by Vercel](https://docs.rocketride.org/nodes/tool_v0.md): A component that connects to Vercel's v0 API to generate React + Tailwind CSS UI components from natural-language prompts.
- [xTrace Memory](https://docs.rocketride.org/nodes/tool_xtrace_memory.md): Long-term, shared agent memory exposed as tools, backed by xTrace Memory Manager.
- [TwelveLabs](https://docs.rocketride.org/nodes/twelvelabs.md): Sends a video to TwelveLabs along with instructions and returns the generated text response.
- [Vectorizer](https://docs.rocketride.org/nodes/vectorizer.md): An internal filter that chunks incoming text, computes embeddings via the configured embedding component, and writes the resulting documents to the vector store.
- [Webhook](https://docs.rocketride.org/nodes/webhook.md): A user interface component that provides a web-based chat experience.
- [Chat](https://docs.rocketride.org/nodes/webhook/chat.md): A user interface component that provides a web-based chat experience.
- [Drag & Drop](https://docs.rocketride.org/nodes/webhook/dropper.md): A user interface component that provides a web-based dropper experience.
- [Tools](https://docs.rocketride.org/nodes/webhook/tools.md): A source component that transfers no data: it exists to host tool nodes.
- [Webhook](https://docs.rocketride.org/nodes/webhook/webhook.md): A source component that listens for incoming HTTP requests and accepts uploaded documents or data from external systems or processes.

## Integrations

- [Anthropic](https://docs.rocketride.org/integrations/anthropic.md): RocketRide ships a native Anthropic node that connects Claude models directly to your pipelines — no adapter layer required.
- [Aparavi AQL](https://docs.rocketride.org/integrations/aparavi-aql.md): The aparaviaql node lets an AI agent query the Aparavi data-governance platform in plain English using AQL (Aparavi Query Language).
- [Firecrawl](https://docs.rocketride.org/integrations/firecrawl.md): The toolfirecrawl node exposes Firecrawl web-scraping operations to an AI agent.
- [Neo4j](https://docs.rocketride.org/integrations/neo4j.md): The graphneo4j node answers natural-language questions against a Neo4j graph database by translating them to Cypher with a connected LLM.
- [PostgreSQL](https://docs.rocketride.org/integrations/postgres.md): The dbpostgres node translates natural-language questions into SQL queries and executes them against a PostgreSQL database.
- [Qdrant](https://docs.rocketride.org/integrations/qdrant.md): Qdrant is a vector database for storing and querying embeddings.

## Develop

- [Python](https://docs.rocketride.org/develop/python.md): Build, run, and manage AI pipelines from Python.
- [Analytics / Telemetry Reporting](https://docs.rocketride.org/develop/python/analytics.md): - Overview - Import - API - Event Names - What This Module Is Not
- [Run Logs (client.log)](https://docs.rocketride.org/develop/python/log.md): Every task writes one continuous JSONL event log per projectId + source (+ scope) — a continuum in which individual runs are chapter markers.
- [TypeScript](https://docs.rocketride.org/develop/typescript.md): Build, run, and manage AI pipelines from Node.js or the browser.
- [Analytics / Telemetry Reporting](https://docs.rocketride.org/develop/typescript/analytics.md): - Overview - Import - API - Event Names - What This Module Is Not
- [database.sequelize()](https://docs.rocketride.org/develop/typescript/methods/database-sequelize.md): - Overview - Method Signature - Parameters - Prerequisites - Examples - Define a model and query rows - Transactions - Standalone factory (advanced) - Related Methods
- [Deploy](https://docs.rocketride.org/develop/typescript/methods/deploy.md): - Overview - Methods - The list envelope - Schedules - Record shapes - Usage Examples - Deployment States - Error Handling - API Endpoints - Related Methods
- [Get Task Status](https://docs.rocketride.org/develop/typescript/methods/get-task-status.md): - Overview - Method Signature - Parameters - Returns - Usage Examples - Task States - Response Format - Error Handling - API Endpoint - Related Methods
- [Run Log](https://docs.rocketride.org/develop/typescript/methods/log.md): Every task writes a run log: one continuous JSONL event stream per task identity (projectId + source, plus the scope: a teamId addresses that team's DEPLOY continuum — deploy runs log into the team's
- [Send / Send Files / Pipe](https://docs.rocketride.org/develop/typescript/methods/send.md): - Overview - send() - sendfiles() / sendFiles() - pipe() / DataPipe - Response Format - Error Handling - API Endpoint - Related Methods
- [Terminate](https://docs.rocketride.org/develop/typescript/methods/terminate.md): - Overview - Method Signature - Parameters - Returns - Usage Examples - Error Handling - API Endpoint - Related Methods
- [Use](https://docs.rocketride.org/develop/typescript/methods/use.md): - Overview - Method Signature - Parameters - Returns - Usage Examples - Pipeline Configuration Structure - Response Format - Error Handling - API Endpoint - Related Methods
- [Validate](https://docs.rocketride.org/develop/typescript/methods/validate.md): - Overview - Method Signature - Parameters - Returns - Usage Examples - Response Format - Error Handling - API Endpoint - Related Methods

## IDE Extensions

- [IDE Extensions](https://docs.rocketride.org/ide-extensions/overview.md): RocketRide ships a single extension that turns your editor into a visual pipeline builder: author .pipe files on a drag-and-drop canvas, deploy a runtime, run pipelines, and trace every call, without
- [Introduction](https://docs.rocketride.org/ide-extensions/vscode.md): Build, debug, and deploy AI pipelines - without leaving your IDE.
- [Deployment Webview Protocol](https://docs.rocketride.org/ide-extensions/vscode/deployment-webview.md): The deploy surfaces (the file view's DEPLOY page and the team-deployment record drawer) are rendered by shared UI components inside the Project webview; the extension host owns the SDK connection and
- [Google Account Sign-In](https://docs.rocketride.org/ide-extensions/vscode/google-oauth.md): Nodes that talk to Google services (for example the Gmail tool) offer a "Login with Google" button in the node config panel.
- [Installation](https://docs.rocketride.org/ide-extensions/vscode/installation.md): 1.
- [Usage Guide](https://docs.rocketride.org/ide-extensions/vscode/usage.md): 1.

## Pipeline JSON Reference

- [Pipeline JSON Reference](https://docs.rocketride.org/pipeline-reference.md): A .pipe file is JSON conforming to the interfaces below.

## CLI Reference

- [CLI Reference](https://docs.rocketride.org/cli.md): The rocketride command-line tool starts pipelines, streams files through them, and manages the engine's file store — the same operations the SDKs expose, from a terminal.

## Cloud

- [Cloud](https://docs.rocketride.org/cloud.md): RocketRide Cloud is a managed engine: the same runtime you can self-host, operated for you.

## Self-hosting

- [Self-hosting](https://docs.rocketride.org/self-hosting.md): Run the RocketRide engine on your own machine when you want full control over where data and model calls go.

## Troubleshooting

- [Troubleshooting](https://docs.rocketride.org/troubleshooting.md): Common issues when building and running pipelines, and how to fix them.

## Glossary

- [Glossary](https://docs.rocketride.org/glossary.md): Terms used across the RocketRide docs.