LangChain and LangGraph
View as MarkdownLangChain create_agent builds a tool-calling agent on LangGraph. The Dex adapter runs a create_agent agent or a custom LangGraph graph inside a durable session. It is available for Python.
When to use it
Use it when your agent already uses LangChain create_agent or a LangGraph graph. Keep the graph, its middleware, and its tools, and let Dex own the session.
How a session runs
DurableToolCallMiddleware stops each tool call with a LangGraph interrupt, so no tool runs inside RunModel. The RunModel Step runs the graph to its next tool calls, usually with one model call, and stores the graph checkpoint.
Each tool call runs in its own Step, in parallel: CallTool for a read or an idempotent write, CallSingleAttemptTool for any other write. A call that needs approval waits in AwaitToolApproval first. The tool Step validates the arguments and calls the tool once. The next RunModel restores the checkpoint and resumes the graph with the recorded results.
A custom graph builds its tool node with durable_tool_node instead of ToolNode. Its state must hold the conversation as LangChain messages.
A turn that looks up an order runs RunModel, CallTool, ResumeTurn, and RunModel.
Install
Install the adapter with its framework. Each adapter brings in the Dex Agents core.
pip install dex-agents-langgraph langchain-openai
Example: agents/python/langgraph/README.md
The example
The example is the order support agent that every framework page uses.
The Flow type is LangChainOrderSupport. The full example is under examples/python/agent-harnesses.
Agent and tools
The tools are LangChain tools. A factory builds the agent with DurableToolCallMiddleware. Only a Worker calls the factory, so a Client process needs no provider key. The tool policies name each tool.
def build_langchain_order_support_flow(
order_desk: OrderDesk,
model: str | BaseChatModel,
slack_connection: slack.Connection | None,
) -> AgentSessionFlow:
"""Builds the session Flow; ``model`` is a LangChain model name, such as "openai:gpt-5.2"."""
# The tools are closures, so they call the OrderDesk passed in here.
@tool
def look_up_order(order_id: str) -> str:
"""Look up one order by its order ID and return its items and status."""
return order_desk.describe_order(order_id)
@tool
def cancel_order(order_id: str, reason: str) -> str:
"""Cancel one order that has not shipped yet."""
return order_desk.cancel_order(order_id, reason)
# Only a Worker calls the factory, so a Client process needs no provider key.
def build_order_support_agent() -> Pregel[Any, Any, Any, Any]:
return create_agent(
model,
tools=[look_up_order, cancel_order],
system_prompt=ORDER_SUPPORT_INSTRUCTIONS,
middleware=[DurableToolCallMiddleware()],
)
return langchain_agent_session_flow(
build_order_support_agent,
flow_type=LANGCHAIN_ORDER_SUPPORT_FLOW_TYPE,
tool_sources=build_slack_tool_sources(slack_connection),
tool_policies={
"look_up_order": ToolPolicy(ToolEffect.READ),
"cancel_order": ToolPolicy(ToolEffect.NON_IDEMPOTENT_WRITE),
},
)
Example: examples/python/agent-harnesses/agent_harness_examples/langchain_order_support.py
Worker
One Worker serves the session Flows of every harness in the example. Agent session Steps are async, so the Python Registry allows async handlers. Build the same session Flow in every process that starts sessions or calls their RPCs.
# Agent session Steps are async, so the Registry allows async handlers.
self.registry = Registry(
tuple(example.session_flow for example in self.examples), allow_async_handlers=True
)
config.blob_cache_dir.mkdir(parents=True, exist_ok=True)
self.blob_cache = open_blob_cache(BlobCacheConfig(str(config.blob_cache_dir), 1 << 30))
self.worker = AsyncWorker(
self.registry,
self.blob_cache,
WorkerOptions(
server_address=config.server_address,
pull=PullWorkerOptions(max_concurrent_tasks=MAX_CONCURRENT_TASKS),
),
)
self.client = AsyncClient(
self.registry, self.blob_cache, ClientOptions(server_address=config.server_address)
)
# ...
def _new_langchain_example(
config: AgentHarnessExamplesConfig, slack_connection: slack.Connection | None
) -> AgentHarnessExample:
order_desk = OrderDesk()
model = _langchain_model(config.langchain_model)
flow = build_langchain_order_support_flow(order_desk, model, slack_connection)
return AgentHarnessExample("langchain", flow, order_desk)
# ...
def _langchain_model(model: str) -> str | BaseChatModel:
return LangChainDemoChatModel() if is_demo_model(model) else model
Example: examples/python/agent-harnesses/agent_harness_examples/app.py
Session routes
Every harness serves these same routes. Session routes explains them and the session token.
@blueprint.post("/sessions")
async def start_session() -> Response:
# The server picks the session ID, so a caller cannot claim an existing session.
session_id = f"{harness_name}-order-support-{uuid.uuid4()}"
await sessions.start_session(session_id)
session_token = sign_session_id(app_state.config.session_key, session_id)
return jsonify(session_id=session_id, session_token=session_token)
@blueprint.post("/messages")
async def send_user_message() -> Response:
body, session_id = await session_request()
accepted = await sessions.send_user_message(session_id, _required_string(body, "text"))
return jsonify(accepted=accepted)
@blueprint.post("/interrupts/resolve")
async def resolve_interrupt() -> Response:
body, session_id = await session_request()
interrupt_id = _required_string(body, "interrupt_id")
if not isinstance(approved := body.get("approved"), bool):
raise BadRequest("approved must be a boolean")
rejection_message = body.get("rejection_message")
if rejection_message is not None and not isinstance(rejection_message, str):
raise BadRequest("rejection_message must be a string")
accepted = await sessions.resolve_interrupt(
session_id, interrupt_id, approved=approved, rejection_message=rejection_message
)
return jsonify(accepted=accepted)
@blueprint.get("/snapshot")
async def get_agent_snapshot() -> Response:
session_id = request.args.get("session_id", "")
if not session_id:
raise BadRequest("session_id is required")
authorize_session(session_id)
try:
snapshot = await read_agent_event_stream_snapshot(
app_state.client, example.session_flow, session_id
)
except FlowNotActiveOrNotFoundError as error:
raise NotFound("the agent session is missing or no longer retained") from error
return jsonify(snapshot.to_json())
Example: examples/python/agent-harnesses/agent_harness_examples/http_routes.py
Run it locally
Start Dex and the example
In a first terminal, render the session graph, then start Dex. Dex Web reads the graph when it starts, to show the session and its approval controls.
cd examples/python/agent-harnesses
uv sync --locked
mkdir -p definitions
uv run --frozen dex-agents-session-graph --flow-type LangChainOrderSupport \
--permission order-support.operate --out definitions/langchain-langgraph.json
dexcli dev --flow-rendering-dir ./definitions
dexcli dev stays in the foreground. In a second terminal, start the example:
cd examples/python/agent-harnesses
uv run --frozen python -m agent_harness_examples
The app serves on 127.0.0.1:8081. Dex Web is at http://127.0.0.1:8802.
Without DEX_LANGCHAIN_MODEL, the agent uses mock/order-support, a local demo model that needs no key. It calls look_up_order for "look up order 1001" and cancel_order for "cancel order 1002".
| Variable | Default | Meaning |
|---|---|---|
| DEX_LANGCHAIN_MODEL | mock/order-support | A LangChain model name, such as openai:gpt-5.2. |
| OPENAI_API_KEY | none | The provider key, for an OpenAI model. |
| DEX_AGENT_SESSION_KEY | a random key | The key that signs session tokens. |
Call the session
Start a session, ask the agent to cancel order 1002, and approve the call:
base=http://127.0.0.1:8081/products/agent-harnesses/langchain
session=$(curl -s -X POST "$base/sessions" -H 'Content-Type: application/json' -d '{}')
id=$(echo "$session" | jq -r .session_id)
token=$(echo "$session" | jq -r .session_token)
curl -s -X POST "$base/messages" -H 'Content-Type: application/json' \
-H "X-Agent-Session-Token: $token" -d "{\"session_id\": \"$id\", \"text\": \"Cancel order 1002\"}"
until interrupt=$(curl -s "$base/snapshot?session_id=$id" -H "X-Agent-Session-Token: $token" \
| jq -re '.snapshot.pending_interrupts[0].interrupt_id'); do sleep 1; done
curl -s -X POST "$base/interrupts/resolve" -H 'Content-Type: application/json' \
-H "X-Agent-Session-Token: $token" \
-d "{\"session_id\": \"$id\", \"interrupt_id\": \"$interrupt\", \"approved\": true}"
The loop reads the snapshot until the agent asks to run cancel_order. That call waits for approval, then runs once. Read the snapshot again to see the agent's answer.
Approve in Dex Web
Instead of the last command, you can approve the call in Dex Web. Follow the steps in Approve in Dex Web.
Connect Connectors
The agent can call Connector operations as tools, as Connectors as tools describes. The example adds the Slack tool slack_post_thread_reply, which replies in a Slack thread.
Offer the tool
Install the connectors extra of dex-agents and the Connector package, such as dex-connector-slack. The install commands are in Connector tools.
The Worker opens the Slack connection support-slack from the project configuration. connector_tool turns the operation into a tool, and ConnectorToolSource holds it. Without the connection, the agent gets no Slack tool.
def build_slack_tool_sources(connection: slack.Connection | None) -> list[ToolSource]:
"""Returns the Slack tool source, or no source when Slack is not configured."""
if connection is None:
return []
return [
ConnectorToolSource([
# The manifest does not ask for approval; this agent asks before every post.
connector_tool(
connection.post_thread_reply(), connection.reference, requires_approval=True
),
]),
]
# ...
async def open_slack_project_connection(
environment: Mapping[str, str] = os.environ,
) -> SlackProjectConnection | None:
# ...
# An incomplete or invalid configuration raises here, so the Worker fails at startup.
try:
project = await load_from_environment(environment)
except FileNotFoundError:
_log_slack_not_set_up("the connector configuration file does not exist")
return None
# ...
try:
connection = slack.new_project_connection(project, SLACK_CONNECTION_NAME)
except BaseException:
await project.close()
raise
return SlackProjectConnection(project, connection)
Example: examples/python/agent-harnesses/agent_harness_examples/slack_connector_tools.py
The Worker opens the connection once, at startup, and every agent uses it:
async def main() -> None:
config = AgentHarnessExamplesConfig.from_env()
slack_setup = await open_slack_project_connection()
try:
app_state = AgentHarnessExamplesApp(config, slack_setup.connection if slack_setup else None)
# ...
finally:
if slack_setup is not None:
await slack_setup.aclose()
Example: examples/python/agent-harnesses/agent_harness_examples/__main__.py
The agent passes the tool source next to its own tools:
return langchain_agent_session_flow(
build_order_support_agent,
flow_type=LANGCHAIN_ORDER_SUPPORT_FLOW_TYPE,
tool_sources=build_slack_tool_sources(slack_connection),
# ...
)
Example: examples/python/agent-harnesses/agent_harness_examples/langchain_order_support.py
How a call runs
Each call of slack_post_thread_reply runs once, in its own CallSingleAttemptTool Step, after a person approves it. The Slack Connector tool explains why.
DurableToolCallMiddleware adds the Connector tools to every model request of a create_agent agent, after the agent's own tools. A custom graph binds get_current_tool_source_tools() next to its own tools in its model node, and durable_tool_node defers their calls like any other. The tool bodies never run inside the graph.
Configure the connection
Connections explains where the Worker finds the support-slack connection and what the Slack app needs.
To try it, start the example with DEX_CONNECTOR_CONFIG_FILE set. Then ask the agent to reply in a Slack thread, with the channel ID and the thread timestamp:
Reply in Slack thread C0123456789 1710000000.000100: Order 1002 ships on Thursday.
The post waits for approval, like cancel_order.
Limits
- A graph whose nodes make several model calls before a tool node makes them all in one RunModel. Summarization middleware and a planner node are examples. A retry repeats them all.
- Provider-run tools repeat when RunModel reruns.
- History is append-only. A graph that edits or removes earlier messages does not change the stored history.
- Only the message list survives between turns. Other state keys start from their defaults on every turn.
- Tools run outside the graph. They cannot use ToolRuntime, InjectedState, or InjectedStore, and cannot return a graph Command. InjectedToolCallId works.
- The graph may raise no interrupts of its own.
The adapter README lists every limit. See Limits for long sessions for the server limits.