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docs: update other frameworks how-to (#3264)

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Vadym Barda
2025-01-31 20:23:41 +00:00
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docs/docs/how-tos/autogen-integration-functional.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "100c0c81-6a9f-4ba1-b1a8-42aae82b7172",
"metadata": {},
"source": [
"# How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks\n",
"\n",
"LangGraph is a framework for building agentic and multi-agent applications. LangGraph can be easily integrated with other agent frameworks. \n",
"\n",
"The primary reasons you might want to integrate LangGraph with other agent frameworks:\n",
"\n",
"- create [multi-agent systems](../../concepts/multi_agent) where individual agents are built with different frameworks\n",
"- leverage LangGraph to add features like [persistence](../../concepts/persistence), [streaming](../../concepts/streaming), [short and long-term memory](../../concepts/memory) and more\n",
"\n",
"The simplest way to integrate agents from other frameworks is by calling those agents inside a LangGraph [node](../../concepts/low_level/#nodes):\n",
"\n",
"```python\n",
"import autogen\n",
"from langgraph.func import entrypoint, task\n",
"\n",
"autogen_agent = autogen.AssistantAgent(name=\"assistant\", ...)\n",
"user_proxy = autogen.UserProxyAgent(name=\"user_proxy\", ...)\n",
"\n",
"@task\n",
"def call_autogen_agent(messages):\n",
" response = user_proxy.initiate_chat(\n",
" autogen_agent,\n",
" message=messages[-1],\n",
" ...\n",
" )\n",
" ...\n",
"\n",
"\n",
"@entrypoint()\n",
"def workflow(messages):\n",
" response = call_autogen_agent(messages).result()\n",
" return response\n",
"\n",
"\n",
"workflow.invoke(\n",
" [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n",
" }\n",
" ]\n",
")\n",
"```\n",
"\n",
"In this guide we show how to build a LangGraph chatbot that integrates with AutoGen, but you can follow the same approach with other frameworks."
]
},
{
"cell_type": "markdown",
"id": "b189ceb2-132b-4c7b-81b4-c7b8b062f833",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "62417d3a-94f9-4a52-9962-12639d714966",
"metadata": {},
"outputs": [],
"source": [
"%pip install autogen langgraph"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d46da41d-0a71-4654-aec8-9e6ad8765236",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"OPENAI_API_KEY: ········\n"
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"\n",
"def _set_env(var: str):\n",
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "1926bbc3-6b06-41e0-9604-860a2bbf8fa3",
"metadata": {},
"source": [
"## Define AutoGen agent\n",
"\n",
"Here we define our AutoGen agent. Adapted from official tutorial [here](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "524de117-ff09-4b26-bfe8-a9f85a46ffd5",
"metadata": {},
"outputs": [],
"source": [
"import autogen\n",
"import os\n",
"\n",
"config_list = [{\"model\": \"gpt-4o\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n",
"\n",
"llm_config = {\n",
" \"timeout\": 600,\n",
" \"cache_seed\": 42,\n",
" \"config_list\": config_list,\n",
" \"temperature\": 0,\n",
"}\n",
"\n",
"autogen_agent = autogen.AssistantAgent(\n",
" name=\"assistant\",\n",
" llm_config=llm_config,\n",
")\n",
"\n",
"user_proxy = autogen.UserProxyAgent(\n",
" name=\"user_proxy\",\n",
" human_input_mode=\"NEVER\",\n",
" max_consecutive_auto_reply=10,\n",
" is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
" code_execution_config={\n",
" \"work_dir\": \"web\",\n",
" \"use_docker\": False,\n",
" }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
" llm_config=llm_config,\n",
" system_message=\"Reply TERMINATE if the task has been solved at full satisfaction. Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8aa858e2-4acb-4f75-be20-b9ccbbcb5073",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "dcc478f5-4a35-43f8-bf59-9cb71289cd00",
"metadata": {},
"source": [
"## Create the workflow\n",
"\n",
"We will now create a LangGraph chatbot graph that calls AutoGen agent."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d129e4e1-3766-429a-b806-cde3d8bc0469",
"metadata": {},
"outputs": [],
"source": [
"from typing import Literal, TypedDict\n",
"\n",
"from langchain_core.messages import convert_to_openai_messages, BaseMessage\n",
"from langgraph.func import entrypoint, task\n",
"from langgraph.graph import add_messages\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"\n",
"\n",
"@task\n",
"def call_autogen_agent(messages: list[BaseMessage]):\n",
" # convert to openai-style messages\n",
" messages = convert_to_openai_messages(messages)\n",
" response = user_proxy.initiate_chat(\n",
" autogen_agent,\n",
" message=messages[-1],\n",
" # pass previous message history as context\n",
" carryover=messages[:-1],\n",
" )\n",
" # get the final response from the agent\n",
" content = response.chat_history[-1][\"content\"]\n",
" return {\"role\": \"assistant\", \"content\": content}\n",
"\n",
"\n",
"# add short-term memory for storing conversation history\n",
"checkpointer = MemorySaver()\n",
"\n",
"\n",
"@entrypoint(checkpointer=checkpointer)\n",
"def workflow(messages: list[BaseMessage], previous: list[BaseMessage]):\n",
" messages = add_messages(previous or [], messages)\n",
" response = call_autogen_agent(messages).result()\n",
" return entrypoint.final(value=response, save=add_messages(messages, response))"
]
},
{
"cell_type": "markdown",
"id": "23d629c3-1d6b-40af-adf6-915e15657566",
"metadata": {},
"source": [
"## Run the graph\n",
"\n",
"We can now run the graph."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a279b667-0f5d-4008-8d43-c806a3f379c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
"\n",
"Find numbers between 10 and 30 in fibonacci sequence\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
"\n",
"To find numbers between 10 and 30 in the Fibonacci sequence, we can generate the Fibonacci sequence and check which numbers fall within this range. Here's a plan:\n",
"\n",
"1. Generate Fibonacci numbers starting from 0.\n",
"2. Continue generating until the numbers exceed 30.\n",
"3. Collect and print the numbers that are between 10 and 30.\n",
"\n",
"Let's implement this in Python:\n",
"\n",
"```python\n",
"# filename: fibonacci_range.py\n",
"\n",
"def fibonacci_sequence():\n",
" a, b = 0, 1\n",
" while a <= 30:\n",
" if 10 <= a <= 30:\n",
" print(a)\n",
" a, b = b, a + b\n",
"\n",
"fibonacci_sequence()\n",
"```\n",
"\n",
"This script will print the Fibonacci numbers between 10 and 30. Please execute the code to see the result.\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[31m\n",
">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
"\n",
"exitcode: 0 (execution succeeded)\n",
"Code output: \n",
"13\n",
"21\n",
"\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
"\n",
"The Fibonacci numbers between 10 and 30 are 13 and 21. \n",
"\n",
"These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n",
"\n",
"The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n",
"\n",
"As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n",
"\n",
"TERMINATE\n",
"\n",
"--------------------------------------------------------------------------------\n",
"{'call_autogen_agent': {'role': 'assistant', 'content': 'The Fibonacci numbers between 10 and 30 are 13 and 21. \\n\\nThese numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \\n\\nThe sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\\n\\nAs you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\\n\\nTERMINATE'}}\n",
"{'workflow': {'role': 'assistant', 'content': 'The Fibonacci numbers between 10 and 30 are 13 and 21. \\n\\nThese numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \\n\\nThe sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\\n\\nAs you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\\n\\nTERMINATE'}}\n"
]
}
],
"source": [
"# pass the thread ID to persist agent outputs for future interactions\n",
"# highlight-next-line\n",
"config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
"\n",
"for chunk in workflow.stream(\n",
" [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Find numbers between 10 and 30 in fibonacci sequence\",\n",
" }\n",
" ],\n",
" # highlight-next-line\n",
" config,\n",
"):\n",
" print(chunk)"
]
},
{
"cell_type": "markdown",
"id": "c6cd57b4-d4ee-49f6-be12-318613849669",
"metadata": {},
"source": [
"Since we're leveraging LangGraph's [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/) features we can now continue the conversation using the same thread ID -- LangGraph will automatically pass previous history to the AutoGen agent:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e68811a7-962e-4fe3-9f45-9b99ebbe04e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33muser_proxy\u001b[0m (to assistant):\n",
"\n",
"Multiply the last number by 3\n",
"Context: \n",
"Find numbers between 10 and 30 in fibonacci sequence\n",
"The Fibonacci numbers between 10 and 30 are 13 and 21. \n",
"\n",
"These numbers are part of the Fibonacci sequence, which is generated by adding the two preceding numbers to get the next number, starting from 0 and 1. \n",
"\n",
"The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...\n",
"\n",
"As you can see, 13 and 21 are the only numbers in this sequence that fall between 10 and 30.\n",
"\n",
"TERMINATE\n",
"\n",
"--------------------------------------------------------------------------------\n",
"\u001b[33massistant\u001b[0m (to user_proxy):\n",
"\n",
"The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\n",
"\n",
"21 * 3 = 63\n",
"\n",
"TERMINATE\n",
"\n",
"--------------------------------------------------------------------------------\n",
"{'call_autogen_agent': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\\n\\n21 * 3 = 63\\n\\nTERMINATE'}}\n",
"{'workflow': {'role': 'assistant', 'content': 'The last number in the Fibonacci sequence between 10 and 30 is 21. Multiplying 21 by 3 gives:\\n\\n21 * 3 = 63\\n\\nTERMINATE'}}\n"
]
}
],
"source": [
"for chunk in workflow.stream(\n",
" [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Multiply the last number by 3\",\n",
" }\n",
" ],\n",
" # highlight-next-line\n",
" config,\n",
"):\n",
" print(chunk)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/docs/how-tos/autogen-integration.ipynb
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docs/docs/how-tos/index.md
+6 -1
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@@ -122,7 +122,7 @@ These how-to guides show common patterns for tool calling with LangGraph:
See the [multi-agent tutorials](../tutorials/index.md#multi-agent-systems) for implementations of other multi-agent architectures.
See the below guides for how-to implement multi-agent workflows with the (beta)
See the below guides for how to implement multi-agent workflows with the (beta)
[Functional API](../concepts/functional_api.md):
- [How to build a multi-agent network (functional API)](multi-agent-network-functional.ipynb)
@@ -145,6 +145,11 @@ See the below guides for how-to implement multi-agent workflows with the (beta)
- [How to return state before hitting recursion limit](return-when-recursion-limit-hits.ipynb)
- [How to integrate LangGraph with AutoGen, CrewAI, and other frameworks](autogen-integration.ipynb)
See the below guide for how to integrate with other frameworks using the (beta)
[Functional API](../concepts/functional_api.md):
- [How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks](autogen-integration-functional.ipynb)
### Prebuilt ReAct Agent
The LangGraph [prebuilt ReAct agent](../reference/prebuilt.md#langgraph.prebuilt.chat_agent_executor.create_react_agent) is pre-built implementation of a [tool calling agent](../concepts/agentic_concepts.md#tool-calling-agent).
docs/mkdocs.yml
+3
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@@ -187,6 +187,8 @@ nav:
- how-tos/react-agent-structured-output.ipynb
- how-tos/run-id-langsmith.ipynb
- how-tos/return-when-recursion-limit-hits.ipynb
- how-tos/autogen-integration.ipynb
- how-tos/autogen-integration-functional.ipynb
- Prebuilt ReAct Agent:
- Prebuilt ReAct Agent: how-tos#prebuilt-react-agent
- how-tos/create-react-agent.ipynb
@@ -207,6 +209,7 @@ nav:
- cloud/deployment/custom_docker.md
- cloud/deployment/test_locally.md
- cloud/deployment/graph_rebuild.md
- how-tos/autogen-langgraph-platform.ipynb
- Deployment:
- Deployment: how-tos#deployment
- cloud/deployment/cloud.md