Workflow:Langchain ai Langgraph Functional API Workflow
| Knowledge Sources | |
|---|---|
| Domains | LLM_Ops, Agent_Orchestration, Functional_Programming |
| Last Updated | 2026-02-11 15:00 GMT |
Overview
End-to-end process for building LangGraph workflows using the functional API with `@entrypoint` and `@task` decorators as an alternative to the StateGraph builder.
Description
This workflow covers LangGraph's functional API, which provides a decorator-based approach to defining workflows. Instead of explicitly constructing a graph with nodes and edges, developers define an `@entrypoint` function that orchestrates `@task` functions. Tasks are units of work that are automatically checkpointed, retried, and can be executed concurrently. The entrypoint function controls the flow imperatively using standard Python control flow (if/else, loops, try/except) rather than declarative graph edges. This API is particularly suited for workflows that are naturally sequential or have complex branching logic that would be awkward to express as a graph.
Usage
Execute this workflow when you prefer an imperative, function-centric programming style over declarative graph construction. This is well-suited for workflows with complex conditional logic, loops, or dynamic task composition where the graph topology is not known ahead of time. Use it when standard Python control flow more naturally expresses your workflow than explicit edges.
Execution Steps
Step 1: Define Task Functions
Create individual processing steps as functions decorated with `@task`. Each task represents an atomic unit of work that will be automatically checkpointed after completion. Tasks can accept any parameters and return any serializable value. Tasks run within the execution context of an entrypoint and are managed by the LangGraph runtime.
Key considerations:
- Tasks are checkpointed after completion for durability
- Tasks can be retried automatically on failure via retry policies
- Each task execution is tracked independently in the checkpoint
- Tasks return `Future`-like objects when called from an entrypoint
- Use `task.result()` to await a task's completion synchronously
Step 2: Define the Entrypoint
Create the main workflow function decorated with `@entrypoint`. The entrypoint receives the initial input and a configuration object, orchestrates task execution using standard Python control flow, and returns the final result. The entrypoint function is the top-level coordinator that replaces the graph builder pattern.
Key considerations:
- The entrypoint function is the graph's equivalent of `compile()`
- Use standard Python if/else, for loops, and try/except for flow control
- Call task functions to dispatch work; they return futures
- The entrypoint has access to checkpointer, store, and config
- Multiple entrypoints can be defined for different workflows
Step 3: Compose Tasks with Control Flow
Within the entrypoint, orchestrate tasks using Python's native control flow. Call tasks sequentially for ordered processing, use list comprehensions or loops for parallel fan-out, and use conditionals for dynamic branching. The LangGraph runtime handles checkpointing, retries, and state management transparently.
Key considerations:
- Sequential calls: call tasks one after another for ordered execution
- Parallel execution: dispatch multiple tasks and collect results
- Conditional branching: use if/else to route based on intermediate results
- Loops: iterate over collections to process items dynamically
- Error handling: use try/except around task calls for graceful failures
Step 4: Configure and Compile
Configure the entrypoint with a checkpointer for persistence and a store for long-term memory, similar to the StateGraph API. The decorated entrypoint function behaves as a LangChain Runnable and supports `invoke()`, `stream()`, and their async variants.
Key considerations:
- Pass `checkpointer` to `@entrypoint(checkpointer=...)` for persistence
- The entrypoint supports the same config system as StateGraph
- Thread IDs and checkpoint namespaces work identically
- Interrupts via `interrupt()` are supported within entrypoints
Step 5: Execute the Workflow
Run the entrypoint using `invoke()` for a single result or `stream()` for incremental output. The runtime executes tasks according to the control flow defined in the entrypoint, checkpointing after each task completion. Provide a config with `thread_id` for persistent execution.
Key considerations:
- `invoke(input, config)` runs the full workflow and returns the result
- `stream(input, config)` yields updates as tasks complete
- Resumed executions skip already-completed tasks (via checkpoint)
- The functional API produces the same execution trace as the graph API