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‎examples/research/pyproject.toml‎

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@@ -9,6 +9,8 @@ readme = "README.md"
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license = { text = "MIT" }
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requires-python = ">=3.9"
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dependencies = [
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"httpx>=0.28.1",
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"markdownify>=1.1.0",
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"langchain-anthropic>=0.3.10",
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"langchain-mcp-adapters>=0.0.5",
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"langgraph>=0.3.21",

‎examples/research/src/agent/agent.ipynb‎

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@@ -10,26 +10,14 @@
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},
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"outputs": [],
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"source": [
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"! pip install langchain-mcp-adapters langgraph \"langchain[anthropic]\" langgraph-swarm"
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"! pip install langchain-mcp-adapters langgraph \"langchain[anthropic]\" langgraph-swarm httpx markdownify"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'langchain_anthropic'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Imports\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_anthropic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ChatAnthropic\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlangchain_mcp_adapters\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mclient\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m MultiServerMCPClient\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlanggraph\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mprebuilt\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m create_react_agent\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'langchain_anthropic'"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Imports\n",
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"from langchain_anthropic import ChatAnthropic\n",
@@ -40,29 +28,31 @@
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"\n",
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"planner_prompt = \"\"\"\n",
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"<Task>\n",
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"You will help plan the steps to implement a LangGraph application. \n",
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"You will help plan the steps to implement a LangGraph application based on the user's request. \n",
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"</Task>\n",
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"\n",
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"<Instructions>\n",
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"1. Reflect on the user's request. \n",
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"2. Use the list_doc_sources tool to fetch and the fetch_docs tool to read the llms.txt file.\n",
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"2. Use the fetch_doc tool to read this llms.txt file: {llms_txt}\n",
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"3. Identify documents that are relevant to the user's request.\n",
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"4. Ask follow-up questions to help refine the project scope and narrow the set of documents to be used for the project.\n",
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"5. When the project scope is clear produce a short description of the project with relevant URLs.\n",
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"6. Finally, transfer to transfer_to_researcher_agent.\n",
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"4. Ask any follow-up questions to help refine the project scope and narrow the set of documents to be used for the project.\n",
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"5. When the project scope is clear produce two things: \n",
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"- A short description that lays out the scope of the project \n",
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"- A list of relevant URLs to reference in order to implement the project\n",
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"6. Finally, transfer to the research agent using the transfer_to_researcher_agent tool.\n",
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"</Instructions>\n",
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"\"\"\"\n",
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"\n",
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"researcher_prompt = \"\"\"\n",
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"<Task>\n",
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"You will perform research on the project scope. \n",
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"You will perform research using provided URLs and use these to implement the solution to the user's request. \n",
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"</Task>\n",
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"\n",
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"<Instructions>\n",
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"1. Reflect on the project scope and provided URLs from the planner.\n",
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"2. Use the fetch_docs tool to fetch and read each URL.\n",
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"1. Reflect on the project scope and provided URLs.\n",
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"2. Use the fetch_doc tool to fetch and read each URL.\n",
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"3. Use the information in these URLs to implement the solution to the user's request.\n",
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"4. If you need further clarification or additional sources to implement the solution, transfer to transfer_to_planner_agent.\n",
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"4. If you need further clarification or additional sources to implement the solution, then transfer to transfer_to_planner_agent.\n",
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"</Instructions>\n",
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"\"\"\"\n",
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"\n",
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"# Handoff tools\n",
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"transfer_to_planner_agent = create_handoff_tool(\n",
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" agent_name=\"planner_agent\",\n",
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" description=\"Transfer user to the planner_agent to address clarifying questions or help them plan the steps to complete the user's request.\"\n",
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" description=\"Transfer the user to the planner_agent for clarifying questions related to the user's request.\"\n",
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")\n",
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"transfer_to_researcher_agent = create_handoff_tool(\n",
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" agent_name=\"researcher_agent\",\n",
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" description=\"Transfer user to researcher_agent to perform research on the user's request.\"\n",
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" description=\"Transfer the user to the researcher_agent to perform research and implement the solution to the user's request.\"\n",
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")\n",
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"\n",
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"# TODO: Move to configuration\n",
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"#configurable = Configuration.from_runnable_config(config)\n",
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"#llms_txt_urls = configurable.llms_txt\n",
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"llms_txt_urls = \"LangGraph:https://langchain-ai.github.io/langgraph/llms.txt\"\n"
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"# LLMS.txt\n",
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"llms_txt = \"LangGraph:https://langchain-ai.github.io/langgraph/llms.txt\"\n",
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"planner_prompt_formatted = planner_prompt.format(llms_txt=llms_txt)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import httpx\n",
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"from markdownify import markdownify\n",
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"\n",
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"httpx_client = httpx.Client(follow_redirects=False, timeout=10)\n",
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"\n",
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"def fetch_doc(url: str) -> str:\n",
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" \"\"\" Fetch a document from a URL and return the markdownified text. \n",
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" Args:\n",
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" url (str): The URL of the document to fetch.\n",
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" Returns:\n",
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" str: The markdownified text of the document.\n",
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" \"\"\"\n",
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" \n",
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" try:\n",
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" response = httpx_client.get(url, timeout=10)\n",
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" response.raise_for_status()\n",
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" return markdownify(response.text)\n",
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" except (httpx.HTTPStatusError, httpx.RequestError) as e:\n",
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" return f\"Encountered an HTTP error: {str(e)}\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langgraph.checkpoint.memory import InMemorySaver\n",
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"\n",
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"planner_agent = create_react_agent(model,\n",
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" prompt=planner_prompt_formatted, \n",
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" tools=[fetch_doc,transfer_to_researcher_agent],\n",
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" name=\"planner_agent\") \n",
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"\n",
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"# Researcher agent\n",
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"researcher_agent = create_react_agent(model, \n",
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" prompt=researcher_prompt, \n",
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" tools=[fetch_doc,transfer_to_researcher_agent],\n",
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" name=\"researcher_agent\") \n",
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"\n",
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"# Swarm\n",
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"checkpointer = InMemorySaver()\n",
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"agent_swarm = create_swarm([planner_agent, researcher_agent], default_active_agent=\"planner_agent\")\n",
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"app = agent_swarm.compile(checkpointer=checkpointer)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Input \n",
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"messages = {\"role\": \"user\", \"content\": \"Create a prompt chain that makes and improves a joke based on the user's input.\"}"
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"request = \"Create a LangGraph application that is a prompt chain: it takes a topic from a user, generates a joke, and checks if the joke has a punchline.\"\n",
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"messages = {\"role\": \"user\", \"content\": request}\n",
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"response = app.invoke({\"messages\": messages})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Research and planning tools \n",
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"async with MultiServerMCPClient(\n",
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" {\n",
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" \"research-server\": {\n",
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" \"command\": \"npx\",\n",
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" \"args\": [\"@playwright/mcp\"],\n",
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" \"transport\": \"stdio\",\n",
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" \"env\": {\n",
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" \"PATH\": \"/Users/rlm/.cursor/extensions/ms-python.python-2024.12.3-darwin-arm64/python_files/deactivate/zsh:/Users/rlm/Desktop/Code/langgraph-swarm/.venv/bin:/Users/rlm/.bun/bin:/Users/rlm/.poetry/bin:/Users/rlm/Library/Python/3.13/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/TeX/texbin:/Users/rlm/.cargo/bin:/Users/rlm/miniforge3/condabin:/Users/rlm/.local/bin\"\n",
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" }\n",
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" },\n",
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" \"planning-server\": {\n",
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" \"command\": \"uvx\",\n",
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" \"args\": [\n",
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"### Use MCP "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_mcp_adapters.client import StdioServerParameters\n",
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"\n",
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"server_params = StdioServerParameters(\n",
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" command=\"uvx\",\n",
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" args=[\n",
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" \"--from\",\n",
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" \"mcpdoc\",\n",
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" \"mcpdoc\",\n",
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" \"--urls\",\n",
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" llms_txt_urls,\n",
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" \"LangGraph:https://langchain-ai.github.io/langgraph/llms.txt\",\n",
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" \"--transport\",\n",
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" \"stdio\",\n",
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" \"--port\",\n",
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" \"8081\",\n",
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" \"--host\",\n",
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" \"localhost\"\n",
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" ],\n",
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" \"transport\": \"stdio\",\n",
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" }\n",
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" }\n",
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")\n",
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"\n",
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") as client:\n",
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" # Planner agent\n",
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" planner_agent = create_react_agent(model,\n",
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" prompt=planner_prompt, \n",
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" tools=client.server_name_to_tools[\"planning-server\"].append(transfer_to_researcher_agent),\n",
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" name=\"planner_agent\") \n",
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"from mcp import ClientSession, StdioServerParameters\n",
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"from mcp.client.stdio import stdio_client\n",
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"from langchain_mcp_adapters.tools import load_mcp_tools\n",
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"\n",
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" # Researcher agent\n",
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" researcher_agent = create_react_agent(model, \n",
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" prompt=researcher_prompt, \n",
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" tools=client.server_name_to_tools[\"research-server\"].append(transfer_to_planner_agent),\n",
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" name=\"researcher_agent\") \n",
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"async with stdio_client(server_params) as (read, write):\n",
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" async with ClientSession(read, write) as session:\n",
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" # Initialize the connection\n",
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" await session.initialize()\n",
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"\n",
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" # Swarm\n",
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" agent_swarm = create_swarm([planner_agent, researcher_agent], default_active_agent=\"planner_agent\")\n",
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" # Get tools\n",
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" tools = await load_mcp_tools(session)\n",
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"\n",
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" # app = agent_swarm.compile(config_schema=Configuration)\n",
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" agent = agent_swarm.compile()\n",
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" response = await agent.ainvoke({\"messages\": messages})\n"
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" planner_agent = create_react_agent(model,\n",
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" prompt=planner_prompt, \n",
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" tools=tools.append(transfer_to_researcher_agent),\n",
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" name=\"planner_agent\") \n",
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"\n",
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" # Researcher agent\n",
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" researcher_agent = create_react_agent(model, \n",
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" prompt=researcher_prompt, \n",
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" tools=tools.append(transfer_to_planner_agent),\n",
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" name=\"researcher_agent\") \n",
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"\n",
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" # Swarm\n",
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" agent_swarm = create_swarm([planner_agent, researcher_agent], default_active_agent=\"planner_agent\")\n",
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"\n",
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" # app = agent_swarm.compile(config_schema=Configuration)\n",
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" agent = agent_swarm.compile()\n",
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" response = await agent.ainvoke({\"messages\": messages})\n"
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]
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},
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{
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"\n",
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"# Research and planning tools \n",
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"async with MultiServerMCPClient(\n",
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" {\n",
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" \"planning-server\": {\n",
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" \"command\": \"uvx\",\n",
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" \"args\": [\n",
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" \"--from\",\n",
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" \"mcpdoc\",\n",
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" \"mcpdoc\",\n",
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" \"--urls\",\n",
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" llms_txt_urls,\n",
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" \"--transport\",\n",
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" \"stdio\",\n",
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" \"--port\",\n",
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" \"8081\",\n",
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" \"--host\",\n",
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" \"localhost\"\n",
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" ],\n",
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" \"transport\": \"stdio\",\n",
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" }\n",
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" }\n",
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"\n",
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") as client:\n",
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" # Planner agent\n",
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" planner_agent = create_react_agent(model,\n",
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" prompt=planner_prompt, \n",
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" tools=client.server_name_to_tools[\"planning-server\"].append(transfer_to_researcher_agent),\n",
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" name=\"planner_agent\") \n",
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"\n",
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" # Researcher agent\n",
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" researcher_agent = create_react_agent(model, \n",
281+
" prompt=researcher_prompt, \n",
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" tools=client.server_name_to_tools[\"planning-server\"].append(transfer_to_planner_agent),\n",
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" name=\"researcher_agent\") \n",
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"\n",
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" # Swarm\n",
286+
" agent_swarm = create_swarm([planner_agent, researcher_agent], default_active_agent=\"planner_agent\")\n",
287+
"\n",
288+
" # app = agent_swarm.compile(config_schema=Configuration)\n",
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" agent = agent_swarm.compile()\n",
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" response = await agent.ainvoke({\"messages\": messages})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"{\n",
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" \"research-server\": {\n",
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" \"command\": \"npx\",\n",
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" \"args\": [\"@playwright/mcp\"],\n",
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" \"transport\": \"stdio\",\n",
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" \"env\": {\n",
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" \"PATH\": \"/Users/rlm/.cursor/extensions/ms-python.python-2024.12.3-darwin-arm64/python_files/deactivate/zsh:/Users/rlm/Desktop/Code/langgraph-swarm/.venv/bin:/Users/rlm/.bun/bin:/Users/rlm/.poetry/bin:/Users/rlm/Library/Python/3.13/bin:/opt/homebrew/bin:/opt/homebrew/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/TeX/texbin:/Users/rlm/.cargo/bin:/Users/rlm/miniforge3/condabin:/Users/rlm/.local/bin\"\n",
306+
" }\n",
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" },\n",
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" \"planning-server\": {\n",
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" \"command\": \"uvx\",\n",
310+
" \"args\": [\n",
311+
" \"--from\",\n",
312+
" \"mcpdoc\",\n",
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" \"mcpdoc\",\n",
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" \"--urls\",\n",
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" llms_txt_urls,\n",
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" \"--transport\",\n",
317+
" \"stdio\",\n",
318+
" \"--port\",\n",
319+
" \"8081\",\n",
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" \"--host\",\n",
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" \"localhost\"\n",
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" ],\n",
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" \"transport\": \"stdio\",\n",
324+
" }\n",
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" }"
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]
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}
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],
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"metadata": {

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