mirror of
https://github.com/langchain-ai/context_engineering.git
synced 2026-07-01 21:33:59 -04:00
fmt
This commit is contained in:
Lance Martin
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@@ -38,84 +38,10 @@
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Task supervisor with path ('__pregel_pull', 'supervisor') wrote to unknown channel is_last_step, ignoring it.\n",
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"Task supervisor with path ('__pregel_pull', 'supervisor') wrote to unknown channel remaining_steps, ignoring it.\n"
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]
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}
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],
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"source": [
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"from langgraph_supervisor import create_supervisor\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"# Augment the LLM with tools\n",
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"from langchain.chat_models import init_chat_model\n",
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"llm = init_chat_model(\"anthropic:claude-sonnet-4-20250514\", temperature=0)\n",
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"\n",
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"# Create specialized agents\n",
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"\n",
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"def add(a: float, b: float) -> float:\n",
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" \"\"\"Add two numbers.\"\"\"\n",
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" return a + b\n",
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"\n",
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"def multiply(a: float, b: float) -> float:\n",
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" \"\"\"Multiply two numbers.\"\"\"\n",
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" return a * b\n",
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"\n",
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"def web_search(query: str) -> str:\n",
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" \"\"\"Search the web for information.\"\"\"\n",
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" return (\n",
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" \"Here are the headcounts for each of the FAANG companies in 2024:\\n\"\n",
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" \"1. **Facebook (Meta)**: 67,317 employees.\\n\"\n",
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" \"2. **Apple**: 164,000 employees.\\n\"\n",
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" \"3. **Amazon**: 1,551,000 employees.\\n\"\n",
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" \"4. **Netflix**: 14,000 employees.\\n\"\n",
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" \"5. **Google (Alphabet)**: 181,269 employees.\"\n",
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" )\n",
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"\n",
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"math_agent = create_react_agent(\n",
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" model=llm,\n",
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" tools=[add, multiply],\n",
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" name=\"math_expert\",\n",
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" prompt=\"You are a math expert. Always use one tool at a time.\"\n",
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")\n",
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"\n",
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"research_agent = create_react_agent(\n",
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" model=llm,\n",
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" tools=[web_search],\n",
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" name=\"research_expert\",\n",
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" prompt=\"You are a world class researcher with access to web search. Do not do any math.\"\n",
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")\n",
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"\n",
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"# Create supervisor workflow\n",
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"workflow = create_supervisor(\n",
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" [research_agent, math_agent],\n",
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" model=llm,\n",
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" prompt=(\n",
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" \"You are a team supervisor managing a research expert and a math expert. \"\n",
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" \"For current events, use research_agent. \"\n",
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" \"For math problems, use math_agent.\"\n",
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" )\n",
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")\n",
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"\n",
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"# Compile and run\n",
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"app = workflow.compile()\n",
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"result = app.invoke({\n",
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" \"messages\": [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"what's the combined headcount of the FAANG companies in 2024?\"\n",
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" }\n",
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" ]\n",
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"})"
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]
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"outputs": [],
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"source": "from langchain.chat_models import init_chat_model\nfrom langgraph.prebuilt import create_react_agent\nfrom langgraph_supervisor import create_supervisor\n\n# Initialize the language model\nllm = init_chat_model(\"anthropic:claude-sonnet-4-20250514\", temperature=0)\n\n\ndef add(a: float, b: float) -> float:\n \"\"\"Add two numbers.\n \n Args:\n a: First number\n b: Second number\n \n Returns:\n Sum of a and b\n \"\"\"\n return a + b\n\n\ndef multiply(a: float, b: float) -> float:\n \"\"\"Multiply two numbers.\n \n Args:\n a: First number\n b: Second number\n \n Returns:\n Product of a and b\n \"\"\"\n return a * b\n\n\ndef web_search(query: str) -> str:\n \"\"\"Mock web search function that returns FAANG company headcounts.\n \n Args:\n query: Search query (unused in this mock)\n \n Returns:\n Static information about FAANG company headcounts\n \"\"\"\n return (\n \"Here are the headcounts for each of the FAANG companies in 2024:\\n\"\n \"1. **Facebook (Meta)**: 67,317 employees.\\n\"\n \"2. **Apple**: 164,000 employees.\\n\"\n \"3. **Amazon**: 1,551,000 employees.\\n\"\n \"4. **Netflix**: 14,000 employees.\\n\"\n \"5. **Google (Alphabet)**: 181,269 employees.\"\n )\n\n\n# Create specialized agents with isolated contexts\nmath_agent = create_react_agent(\n model=llm,\n tools=[add, multiply],\n name=\"math_expert\",\n prompt=\"You are a math expert. Always use one tool at a time.\"\n)\n\nresearch_agent = create_react_agent(\n model=llm,\n tools=[web_search],\n name=\"research_expert\",\n prompt=\"You are a world class researcher with access to web search. Do not do any math.\"\n)\n\n# Create supervisor workflow for coordinating agents\nworkflow = create_supervisor(\n [research_agent, math_agent],\n model=llm,\n prompt=(\n \"You are a team supervisor managing a research expert and a math expert. \"\n \"For current events, use research_agent. \"\n \"For math problems, use math_agent.\"\n )\n)\n\n# Compile the multi-agent application\napp = workflow.compile()\n\n# Execute the multi-agent workflow\nresult = app.invoke({\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"what's the combined headcount of the FAANG companies in 2024?\"\n }\n ]\n})"
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},
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{
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"cell_type": "code",
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@@ -336,118 +262,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a1e62a08",
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"metadata": {},
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"outputs": [
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"<span style=\"color: #000080; text-decoration-color: #000080\">│</span> what's 5 + 7? <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n",
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"<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span>\n",
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"<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span> Args: { <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span>\n",
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"<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span> \"code\": \"print(5 + 7)\" <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span>\n",
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"\u001b[37m│\u001b[0m I can calculate 5 + 7 for you using the Python code sandbox. \u001b[37m│\u001b[0m\n",
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"\u001b[37m│\u001b[0m \u001b[37m│\u001b[0m\n",
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"\u001b[33m│\u001b[0m https://cdn.jsdelivr.net/pyodide/v0.27.7/full/Didn't find package micropip-0.9.0-py3-none-any.whl locally, \u001b[33m│\u001b[0m\n",
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"\u001b[33m│\u001b[0m attempting to load from https://cdn.jsdelivr.net/pyodide/v0.27.7/full/Package packaging-24.2-py3-none-any.whl \u001b[33m│\u001b[0m\n",
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{
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"<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span> The answer to 5 + 7 is 12. <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\">│</span>\n",
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"\u001b[37m│\u001b[0m The answer to 5 + 7 is 12. \u001b[37m│\u001b[0m\n",
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from langgraph.prebuilt import create_react_agent\n",
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"from langchain_sandbox import PyodideSandboxTool\n",
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"\n",
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"tool = PyodideSandboxTool(\n",
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" # Allow Pyodide to install python packages that\n",
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" # might be required.\n",
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" allow_net=True\n",
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")\n",
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"agent = create_react_agent(\n",
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" \"anthropic:claude-3-7-sonnet-latest\",\n",
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" tools=[tool],\n",
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")\n",
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"result = await agent.ainvoke(\n",
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" {\"messages\": [{\"role\": \"user\", \"content\": \"what's 5 + 7?\"}]},\n",
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")\n",
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"\n",
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"format_messages(result['messages'])\n"
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]
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"outputs": [],
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"source": "from langchain_sandbox import PyodideSandboxTool\nfrom langgraph.prebuilt import create_react_agent\n\n# Create sandbox tool with network access for package installation\ntool = PyodideSandboxTool(\n # Allow Pyodide to install Python packages that might be required\n allow_net=True\n)\n\n# Create a React agent with the sandbox tool\nagent = create_react_agent(\n \"anthropic:claude-3-7-sonnet-latest\",\n tools=[tool],\n)\n\n# Execute a mathematical query using the sandbox\nresult = await agent.ainvoke(\n {\"messages\": [{\"role\": \"user\", \"content\": \"what's 5 + 7?\"}]},\n)\n\n# Format and display the results\nformat_messages(result['messages'])"
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},
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{
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"cell_type": "markdown",
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@@ -485,4 +303,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}
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