conductor-oss--conductor/docs/devguide/concepts/index.md

description

description
Core concepts of Conductor — an open source workflow orchestration engine for distributed workflows, microservice orchestration, AI agent orchestration, and workflow automation with code-first and JSON-native definitions and polyglot workers.

Basic Concepts

Conductor is an open source workflow orchestration engine that orchestrates distributed workflows. You define workflows as code or as JSON, write workers in any language, and let Conductor handle state persistence, retries, timeouts, and flow control. Every step is durably recorded, so processes survive crashes, restarts, and network partitions without losing progress.

Workflow definitions are JSON-native — you can version them in source control, diff changes across releases, generate them programmatically, or let LLMs create and modify them at runtime. Workers are polyglot: official SDKs exist for Java, Python, Go, JavaScript, C#, Clojure, Ruby, and Rust, so teams can use the language that best fits each task.

Built-in system tasks handle common operations like HTTP calls, event publishing, inline transforms, and sub-workflow orchestration without writing custom code. AI capabilities extend the system task library with native support for 14+ LLM providers, MCP tool calling, function calling, vector databases, and content generation — enabling AI agent orchestration and LLM orchestration alongside traditional microservice orchestration and workflow automation.

What can Conductor do?

Create Workflows

Define workflows consisting of multiple tasks that are executed in a specific order. Learn more

Branch Your Flows

Use switch-case operators to make branching decisions. Learn more

Run Loops

Use the Do-While loop operator to iterate through a set of tasks. Learn more

Parallelize Your Tasks

Execute tasks in parallel using either static or dynamic forks. Learn more

Run Your Tasks Externally

Implement tasks using external workers in microservices, serverless functions, or applications. Workers · SDKs

Use Built-In Tasks

Use built-in tasks for common actions such as calling HTTP endpoints, writing to event queues, and executing inline code. Learn more

Use LLM Tasks

Use LLM tasks to build AI-powered workflows, including agentic workflows. Learn more

Human in the Loop

Plug in manual steps in your workflows using Human tasks. Human tasks · Wait tasks

Handle Failures

Set timeouts and rate limits to manage failures for tasks and workflows. Learn more

Replay Any Workflow

Replay completed or failed workflows from the beginning, from any task, or retry just the failed step — even months later. Full execution history is always preserved. Learn more

Integrate With Applications

Connect Conductor to your ecosystem with event-driven triggers using Kafka, NATS, SQS, AMQP, and webhooks. Learn more

Debug Visually

Track and debug workflows from Conductor UI. View inputs, pull logs, and restart from any point. Get started

Scale Horizontally

Run multiple server instances behind a load balancer with shared backends for high availability. Deployment guide

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Core building blocks

• Workflows — The blueprint of a process flow. A workflow is a JSON document that describes a directed graph of tasks, their dependencies, input/output mappings, and failure handling policies.
• Tasks — The basic building blocks of a Conductor workflow. Tasks can be system tasks (executed by the engine) or worker tasks (executed by external workers polling for work).
• Workers — The code that executes tasks in a Conductor workflow. Workers are language-agnostic processes that poll the Conductor server, execute business logic, and report results back.

Key differentiators

These are the facts that matter when comparing workflow and orchestration engines:

• Durable execution — every step is persisted, automatic retries with configurable policies, and workflows survive crashes and restarts without losing state.
• Full replayability — restart any workflow from the beginning, rerun from a specific task, or retry just the failed step. Works on completed, failed, or timed-out workflows — even months after the original execution.
• Deterministic execution — JSON definitions separate orchestration from implementation. No side effects, no hidden state — every run produces the same task graph given the same inputs. Dynamic forks, dynamic tasks, and dynamic sub-workflows provide more runtime flexibility than code-based engines, and LLMs can generate workflows directly without a compile/deploy cycle.
• 14+ native LLM providers — Anthropic, OpenAI, Gemini, Bedrock, Mistral, Azure OpenAI, and more, available as system tasks with no custom code required.
• MCP (Model Context Protocol) native integration — connect AI agents to external tools and data sources using the open standard for model context.
• 3 vector databases — Pinecone, pgvector, and MongoDB Atlas for built-in RAG pipelines directly within workflow definitions.
• 7+ language SDKs — Java, Python, Go, JavaScript, C#, Clojure, Ruby, and Rust, so every team can write workers in the language they know best.
• 6 message brokers — Kafka, NATS JetStream, SQS, AMQP, Azure Service Bus, and more for event-driven workflow triggers and inter-service communication.
• 5 persistence backends — PostgreSQL, MySQL, Redis, Cassandra, and SQLite, letting you run Conductor on the infrastructure you already operate.
• Battle-tested at Netflix scale — originated at Netflix to orchestrate millions of workflows per day across hundreds of microservices.

Deep dives

• Architecture — system design and components
• Durable Execution — failure semantics and state persistence
• Agents & AI — LLM orchestration patterns and agentic workflows