Merge branch 'main' into vb/react-agent-prompt

.github/workflows/run_notebooks.yml

@@ -16,9 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        lib-version:
-          - "development"
-          - "latest"
+        lib-version: ${{ (github.event_name == 'workflow_dispatch' || github.event_name == 'schedule') && fromJSON('["development", "latest"]') || fromJSON('["development"]') }}
 
     steps:
       - uses: actions/checkout@v4

docs/docs/how-tos/react-agent-from-scratch.ipynb

@@ -6,37 +6,15 @@
    "source": [
     "# How to create a ReAct agent from scratch\n",
     "\n",
-    "<div class=\"admonition tip\">\n",
-    "    <p class=\"admonition-title\">Prerequisites</p>\n",
-    "    <p>\n",
-    "        This guide assumes familiarity with the following:\n",
-    "        <ul>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#tool-calling-agent\">\n",
-    "                    Tool calling agent\n",
-    "                </a>\n",
-    "            </li>                \n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/chat_models\">\n",
-    "                    Chat Models\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://python.langchain.com/docs/concepts/messages\">\n",
-    "                    Messages\n",
-    "                </a>\n",
-    "            </li>\n",
-    "            <li>\n",
-    "                <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/\">\n",
-    "                    LangGraph Glossary\n",
-    "                </a>\n",
-    "            </li>\n",
-    "        </ul>\n",
-    "    </p>\n",
-    "</div> \n",
-    "\n",
-    "\n",
-    "Using the prebuilt ReAct agent ([create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent)) is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
+    "!!! info \"Prerequisites\"\n",
+    "    This guide assumes familiarity with the following:\n",
+    "    \n",
+    "    - [Tool calling agent](../../concepts/agentic_concepts/#tool-calling-agent)\n",
+    "    - [Chat Models](https://python.langchain.com/docs/concepts/chat_models/)\n",
+    "    - [Messages](https://python.langchain.com/docs/concepts/messages/)\n",
+    "    - [LangGraph Glossary](../../concepts/low_level/)\n",
+    "\n",
+    "Using the prebuilt ReAct agent [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent] is a great way to get started, but sometimes you might want more control and customization. In those cases, you can create a custom ReAct agent. This guide shows how to implement ReAct agent from scratch using LangGraph.\n",
     "\n",
     "## Setup\n",
     "\n",

docs/docs/how-tos/tool-calling.ipynb

@@ -217,7 +217,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll be using a small chat model from Anthropic in our example. To use chat models with tool calling, we need to first ensure that the model is aware of the available tools. We do this by calling `.bind_tools` method on `ChatAnthropic` moodel"
+    "We'll be using a small chat model from Anthropic in our example. To use chat models with tool calling, we need to first ensure that the model is aware of the available tools. We do this by calling `.bind_tools` method on `ChatAnthropic` model"
    ]
   },
   {

docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb

@@ -130,36 +130,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "72d233ca-1dbf-4b43-b680-b3bf39e3691f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================\u001b[1m System Message \u001b[0m================================\n",
-      "\n",
-      "You are a helpful assistant.\n",
-      "\n",
-      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
-      "\n",
-      "\u001b[33;1m\u001b[1;3m{messages}\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain import hub\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
-    "# Get the prompt to use - you can modify this!\n",
-    "prompt = hub.pull(\"ih/ih-react-agent-executor\")\n",
-    "prompt.pretty_print()\n",
-    "\n",
     "# Choose the LLM that will drive the agent\n",
     "llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
+    "prompt = \"You are a helpful assistant.\"\n",
     "agent_executor = create_react_agent(llm, tools, state_modifier=prompt)"
    ]
   },
@@ -546,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

libs/langgraph/langgraph/func/__init__.py

@@ -3,7 +3,6 @@
 import functools
 import inspect
 import types
-from collections.abc import Iterator
 from dataclasses import dataclass
 from typing import (
     Any,
@@ -18,17 +17,12 @@
     overload,
 )
 
-from langchain_core.runnables.base import Runnable
-from langchain_core.runnables.config import RunnableConfig
-from langchain_core.runnables.graph import Graph, Node
-
 from langgraph.channels.ephemeral_value import EphemeralValue
 from langgraph.channels.last_value import LastValue
 from langgraph.checkpoint.base import BaseCheckpointSaver
 from langgraph.constants import END, PREVIOUS, START, TAG_HIDDEN
 from langgraph.pregel import Pregel
 from langgraph.pregel.call import P, T, call, get_runnable_for_entrypoint
-from langgraph.pregel.protocol import PregelProtocol
 from langgraph.pregel.read import PregelNode
 from langgraph.pregel.write import ChannelWrite, ChannelWriteEntry
 from langgraph.store.base import BaseStore
@@ -164,8 +158,10 @@ class entrypoint:
     to the function. This input parameter can be of any type. Use a dictionary
     to pass multiple parameters to the function.
 
-    The decorated function also has access to these optional parameters:
+    The decorated function can request access to additional parameters
+    that will be injected automatically at run time. These parameters include:
 
+    - `store`: An instance of [BaseStore][langgraph.store.base.BaseStore]. Useful for long-term memory.
     - `writer`: A `StreamWriter` instance for writing data to a stream.
     - `config`: A configuration object for accessing workflow settings.
     - `previous`: The previous return value for the given thread (available only when
@@ -255,7 +251,7 @@ def review_workflow(topic: str) -> dict:
 
         ```python
         from langgraph.checkpoint.memory import MemorySaver
-        from langgraph.func import entrypoint, task
+        from langgraph.func import entrypoint
 
         @entrypoint(checkpointer=MemorySaver())
         def my_workflow(input_data: str, previous: Optional[str] = None) -> str:
@@ -288,6 +284,29 @@ class final(Generic[R, S]):
 
         This primitive allows to save a value to the checkpointer distinct from the
         return value from the entrypoint.
+
+        Example: Decoupling the return value and the save value
+            ```python
+            from langgraph.checkpoint.memory import MemorySaver
+            from langgraph.func import entrypoint
+
+            @entrypoint(checkpointer=MemorySaver())
+            def my_workflow(number: int, *, previous: Any = None) -> entrypoint.final[int, int]:
+                previous = previous or 0
+                # This will return the previous value to the caller, saving
+                # 2 * number to the checkpoint, which will be used in the next invocation
+                # for the `previous` parameter.
+                return entrypoint.final(value=previous, save=2 * number)
+
+            config = {
+                "configurable": {
+                    "thread_id": "1"
+                }
+            }
+
+            my_workflow.invoke(3, config)  # 0 (previous was None)
+            my_workflow.invoke(1, config)  # 6 (previous was 3 * 2 from the previous invocation)
+            ```
         """
 
         value: R
@@ -498,7 +517,7 @@ def _pluck_save_value(value: Any) -> Any:
                 else:
                     output_type = save_type = sig.return_annotation
 
-        return EntrypointPregel(
+        return Pregel(
             nodes={
                 func.__name__: PregelNode(
                     bound=bound,
@@ -529,97 +548,3 @@ def _pluck_save_value(value: Any) -> Any:
             store=self.store,
             config_type=self.config_schema,
         )
-
-
-class EntrypointPregel(Pregel):
-    def get_graph(
-        self,
-        config: Optional[RunnableConfig] = None,
-        *,
-        xray: Union[int, bool] = False,
-    ) -> Graph:
-        name, entrypoint = next(iter(self.nodes.items()))
-        graph = Graph()
-        node = Node(f"__{name}", name, entrypoint.bound, None)
-        graph.nodes[node.id] = node
-        candidates: list[tuple[Node, Union[Callable, PregelProtocol]]] = [
-            *_find_children(entrypoint.bound, node)
-        ]
-        seen: set[Union[Callable, PregelProtocol]] = set()
-        for parent, child in candidates:
-            if child in seen:
-                continue
-            else:
-                seen.add(child)
-            if callable(child):
-                node = Node(f"__{child.__name__}", child.__name__, child, None)  # type: ignore[arg-type]
-                graph.nodes[node.id] = node
-                graph.add_edge(parent, node, conditional=True)
-                graph.add_edge(node, parent)
-                candidates.extend(_find_children(child, node))
-            elif isinstance(child, Runnable):
-                if xray > 0:
-                    graph = child.get_graph(config, xray=xray - 1 if xray else 0)
-                    graph.trim_first_node()
-                    graph.trim_last_node()
-                    s, e = graph.extend(graph, prefix=child.name or "")
-                    if s is None:
-                        raise ValueError(
-                            f"Could not extend subgraph '{child.name}' due to missing entrypoint"
-                        )
-                    else:
-                        graph.add_edge(parent, s, conditional=True)
-                    if e is not None:
-                        graph.add_edge(e, parent)
-                else:
-                    node = graph.add_node(child, child.name)
-                    graph.add_edge(parent, node, conditional=True)
-                    graph.add_edge(node, parent)
-        return graph
-
-
-def _find_children(
-    candidate: Union[Callable, Runnable], parent: Node
-) -> Iterator[tuple[Node, Union[Callable, PregelProtocol]]]:
-    from langchain_core.runnables.utils import get_function_nonlocals
-
-    from langgraph.utils.runnable import (
-        RunnableCallable,
-        RunnableLambda,
-        RunnableSeq,
-        RunnableSequence,
-    )
-
-    candidates: list[Union[Callable, Runnable]] = []
-    if callable(candidate) and getattr(candidate, "_is_pregel_task", False) is True:
-        candidates.extend(
-            nl.__self__ if hasattr(nl, "__self__") else nl
-            for nl in get_function_nonlocals(
-                candidate.__wrapped__
-                if hasattr(candidate, "__wrapped__") and callable(candidate.__wrapped__)
-                else candidate
-            )
-        )
-    else:
-        candidates.append(candidate)
-
-    for c in candidates:
-        if callable(c) and getattr(c, "_is_pregel_task", False) is True:
-            yield (parent, c)
-        elif isinstance(c, PregelProtocol):
-            yield (parent, c)
-        elif isinstance(c, RunnableSequence) or isinstance(c, RunnableSeq):
-            candidates.extend(c.steps)
-        elif isinstance(c, RunnableLambda):
-            candidates.extend(c.deps)
-        elif isinstance(c, RunnableCallable):
-            if c.func is not None:
-                candidates.extend(
-                    nl.__self__ if hasattr(nl, "__self__") else nl
-                    for nl in get_function_nonlocals(c.func)
-                )
-            elif c.afunc is not None:
-                candidates.extend(
-                    nl.__self__ if hasattr(nl, "__self__") else nl
-                    for nl in get_function_nonlocals(c.afunc)
-                )

libs/langgraph/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.2.66"
+version = "0.2.67"
 description = "Building stateful, multi-actor applications with LLMs"
 authors = []
 license = "MIT"