LangChain
LangChain adapter for Flyte.
Bring your own LangChain agent and run it durably on Flyte. The adapter provides:
flyteplugins.agents.langchain.tool— turn a Flyte@env.taskinto a LangChainStructuredTool(aBaseTool) that executes as a durable child action (own container/GPU, retries, caching).flyteplugins.agents.langchain.run_agent— run the LangChain agent (a compiledcreate_agentgraph) inside your task and return the final answer. Either pass a pre-builtagentor let it build one fromtools+model+instructions.
Each tool call runs as a durable Flyte child action, and the run timeline is rendered into the Flyte task report.
Directory
Methods
| Method | Description |
|---|---|
run_agent() |
Run a LangChain agent with the given tools and prompt; return the final text. |
run_agent_sync() |
Synchronous variant of run_agent for use in sync tasks; runs the async implementation on a dedicated event loop. |
tool() |
Convert a Flyte task (or plain callable) into a LangChain StructuredTool. |
Methods
run_agent()
def run_agent(
input: str,
tools: typing.Sequence[typing.Any] = (),
model: typing.Any = None,
instructions: str | None = None,
agent: typing.Any = None,
name: str = 'langchain-agent',
durable: bool = True,
observability: bool = True,
memory_key: str | None = None,
config: dict[str, typing.Any] | None = None,
**agent_kwargs: typing.Any,
) -> strRun a LangChain agent with the given tools and prompt; return the final text.
Await this from an async task as await run_agent(...); from a sync task
use flyteplugins.agents.langchain.run_agent_sync instead.
Call this from inside an @env.task — that task is the durable parent.
Within it, each tool call runs as a durable Flyte child action. Give the
enclosing task retries=... for self-healing and report=True to see
the agent timeline.
Provide either a pre-built agent (a compiled graph from create_agent)
or tools + model to have one built for you.
| Parameter | Type | Description |
|---|---|---|
input |
str |
The user prompt. |
tools |
typing.Sequence[typing.Any] |
tool-wrapped tools or bare @env.task templates. |
model |
typing.Any |
A LangChain-compatible chat model (e.g. ChatOpenAI(model="gpt-4o")). Required when agent is not given — one is built using this model. |
instructions |
str | None |
System prompt for the built agent. |
agent |
typing.Any |
A pre-built LangChain agent (a compiled create_agent graph). Mutually exclusive with tools. |
name |
str |
Agent name (for debugging/observability). |
durable |
bool |
Record/replay each model turn via flyte.trace. Applies when a model is being built (tools + model, or a caller-passed BaseChatModel) — the model is wrapped in DurableChatModel. A fully pre-built compiled agent cannot be rewrapped, so its model turns are not made durable (its tool calls remain durable regardless). |
observability |
bool |
Render the run timeline into the Flyte task report. |
memory_key |
str | None |
Stable id (e.g. a user/thread id) for cross-run memory. When set, conversation history is persisted to a keyed MemoryStore and resumed on a later run with the same key. |
config |
dict[str, typing.Any] | None |
A LangChain RunnableConfig forwarded to the graph’s ainvoke — your own callbacks, tags, or metadata. Any registered instrumentor is offered this config, so an observability handler is appended to your callbacks rather than replacing them. |
**agent_kwargs |
typing.Any |
Returns: The agent’s final output as a string.
run_agent_sync()
def run_agent_sync(
input: str,
tools: typing.Sequence[typing.Any] = (),
model: typing.Any = None,
instructions: str | None = None,
agent: typing.Any = None,
name: str = 'langchain-agent',
durable: bool = True,
observability: bool = True,
memory_key: str | None = None,
config: dict[str, typing.Any] | None = None,
**agent_kwargs: typing.Any,
) -> strSynchronous variant of run_agent for use in sync tasks; runs the async implementation on a dedicated event loop.
Run a LangChain agent with the given tools and prompt; return the final text.
Await this from an async task as await run_agent(...); from a sync task
use flyteplugins.agents.langchain.run_agent_sync instead.
Call this from inside an @env.task — that task is the durable parent.
Within it, each tool call runs as a durable Flyte child action. Give the
enclosing task retries=... for self-healing and report=True to see
the agent timeline.
Provide either a pre-built agent (a compiled graph from create_agent)
or tools + model to have one built for you.
| Parameter | Type | Description |
|---|---|---|
input |
str |
The user prompt. |
tools |
typing.Sequence[typing.Any] |
tool-wrapped tools or bare @env.task templates. |
model |
typing.Any |
A LangChain-compatible chat model (e.g. ChatOpenAI(model="gpt-4o")). Required when agent is not given — one is built using this model. |
instructions |
str | None |
System prompt for the built agent. |
agent |
typing.Any |
A pre-built LangChain agent (a compiled create_agent graph). Mutually exclusive with tools. |
name |
str |
Agent name (for debugging/observability). |
durable |
bool |
Record/replay each model turn via flyte.trace. Applies when a model is being built (tools + model, or a caller-passed BaseChatModel) — the model is wrapped in DurableChatModel. A fully pre-built compiled agent cannot be rewrapped, so its model turns are not made durable (its tool calls remain durable regardless). |
observability |
bool |
Render the run timeline into the Flyte task report. |
memory_key |
str | None |
Stable id (e.g. a user/thread id) for cross-run memory. When set, conversation history is persisted to a keyed MemoryStore and resumed on a later run with the same key. |
config |
dict[str, typing.Any] | None |
A LangChain RunnableConfig forwarded to the graph’s ainvoke — your own callbacks, tags, or metadata. Any registered instrumentor is offered this config, so an observability handler is appended to your callbacks rather than replacing them. |
**agent_kwargs |
typing.Any |
Returns
The agent’s final output as a string.
tool()
def tool(
func: AsyncFunctionTaskTemplate | typing.Callable | None = None,
name: str | None = None,
description: str | None = None,
) -> typing.AnyConvert a Flyte task (or plain callable) into a LangChain StructuredTool.
- For an
@env.task: returns aStructuredToolwhose async coroutine runs the task as a durable Flyte child action when the agent invokes it. The input schema is derived from the task’s typed signature. The backing task is wired toflyteplugins.agents.core.ToolTaskResolverand exposed via__wrapped_task__so it resolves to itself on the worker (no recursion). - For a plain (async) callable: returns a
StructuredToolthat runs it inline.
Usable bare, parametrized, or as a direct call:
@tool
@env.task
async def get_weather(city: str) -> str: ...| Parameter | Type | Description |
|---|---|---|
func |
AsyncFunctionTaskTemplate | typing.Callable | None |
|
name |
str | None |
|
description |
str | None |