├── .gitignore ├── README.md ├── ollama ├── .env.example ├── app.py ├── poetry.lock └── pyproject.toml └── openai_swarm_get_started.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .env 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OpenAI Swarm Tutorials 2 | -------------------------------------------------------------------------------- /ollama/.env.example: -------------------------------------------------------------------------------- 1 | OPENAI_API_KEY= -------------------------------------------------------------------------------- /ollama/app.py: -------------------------------------------------------------------------------- 1 | from swarm import Swarm, Agent 2 | from openai import OpenAI 3 | import json 4 | 5 | openai = OpenAI( 6 | api_key="sk-xxx", 7 | base_url="http://localhost:11434/v1" 8 | ) 9 | 10 | client = Swarm(client=openai) 11 | 12 | def get_weather(location, time="now"): 13 | """Get the current weather in a given location. Location MUST be a city.""" 14 | return json.dumps({"location": location, "temperature": "65", "time": time}) 15 | 16 | 17 | def send_email(recipient, subject, body): 18 | print("Sending email...") 19 | print(f"To: {recipient}") 20 | print(f"Subject: {subject}") 21 | print(f"Body: {body}") 22 | return "Sent!" 23 | 24 | 25 | weather_agent = Agent( 26 | name="Weather Agent", 27 | instructions="You are a helpful agent.", 28 | functions=[get_weather, send_email], 29 | ) 30 | 31 | response = client.run( 32 | agent=weather_agent, 33 | messages=[{"role": "user", "content": "Query the weather in Beijing today and email the results to me."}], 34 | model_override="llama3.1:8b" 35 | ) 36 | 37 | for message in response.messages: 38 | print(message) 39 | 40 | print(f"Response: {response.messages[-1]['content']}") 41 | -------------------------------------------------------------------------------- /ollama/poetry.lock: -------------------------------------------------------------------------------- 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[tool.poetry] 2 | name = "swarm-ollama" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["sugarforever "] 6 | readme = "README.md" 7 | 8 | [tool.poetry.dependencies] 9 | python = "^3.11" 10 | swarm = {git = "https://github.com/openai/swarm.git"} 11 | 12 | 13 | [build-system] 14 | requires = ["poetry-core"] 15 | build-backend = "poetry.core.masonry.api" 16 | -------------------------------------------------------------------------------- /openai_swarm_get_started.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyOsrAGopSaYGzQMH0YIdYgY", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "source": [ 32 | "# OpenAI Swarm Get Started\n", 33 | "\n", 34 | "OpenAI Swarm is an educational framework exploring ergonomic, lightweight multi-agent orchestration. It's managed by OpenAI Solution team.\n", 35 | "\n", 36 | "GitHub: https://github.com/openai/swarm\n", 37 | "\n", 38 | "In this get-started tutorial, we will showcase how multi-agents are coordinated by Swarm, and walk you through the basic components:\n", 39 | "- Swarm client\n", 40 | "- Agents\n", 41 | "- Functions\n", 42 | " - Basics\n", 43 | " - Schema\n", 44 | "\n", 45 | "## What is AI Agent?\n", 46 | "\n", 47 | "AI agents are autonomous intelligent systems performing specific tasks without human intervention." 48 | ], 49 | "metadata": { 50 | "id": "_zIxvC_-DQPl" 51 | } 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "source": [ 56 | "## Overview\n", 57 | "\n", 58 | "Let's take a quick overview with some examples on Swarm usage." 59 | ], 60 | "metadata": { 61 | "id": "g9bdZ6HrEXEB" 62 | } 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 38, 67 | "metadata": { 68 | "id": "lvqYVHrTumH1", 69 | "colab": { 70 | "base_uri": "https://localhost:8080/" 71 | }, 72 | "outputId": "19c96c49-e247-4727-af9e-9ecdc7945bd0" 73 | }, 74 | "outputs": [ 75 | { 76 | "output_type": "stream", 77 | "name": "stdout", 78 | "text": [ 79 | "Collecting git+https://github.com/openai/swarm.git\n", 80 | " Cloning https://github.com/openai/swarm.git to /tmp/pip-req-build-akbqp876\n", 81 | " Running command git clone --filter=blob:none --quiet https://github.com/openai/swarm.git /tmp/pip-req-build-akbqp876\n", 82 | " Resolved https://github.com/openai/swarm.git to commit 9db581cecaacea0d46a933d6453c312b034dbf47\n", 83 | " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", 84 | " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", 85 | " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 86 | "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from swarm==0.1.0) (1.26.4)\n", 87 | "Requirement already satisfied: openai>=1.33.0 in /usr/local/lib/python3.10/dist-packages (from swarm==0.1.0) 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yarl<2.0,>=1.12.0->aiohttp<4.0.0,>=3.9.1->instructor->swarm==0.1.0) (0.2.0)\n" 141 | ] 142 | } 143 | ], 144 | "source": [ 145 | "!pip install git+https://github.com/openai/swarm.git" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "source": [ 151 | "### Set up OpenAI execution environment\n", 152 | "\n", 153 | "Retrieve API key from Google Colab secrets and inject into enviroment variables" 154 | ], 155 | "metadata": { 156 | "id": "VR3yfpR1viZe" 157 | } 158 | }, 159 | { 160 | "cell_type": "code", 161 | "source": [ 162 | "import os\n", 163 | "from google.colab import userdata\n", 164 | "\n", 165 | "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')" 166 | ], 167 | "metadata": { 168 | "id": "1zP9k8SPvh8w" 169 | }, 170 | "execution_count": null, 171 | "outputs": [] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "source": [ 176 | "### A Basic Example\n", 177 | "\n", 178 | "Let's see how 2 agents are coordinated." 179 | ], 180 | "metadata": { 181 | "id": "buHcRSvP1o8b" 182 | } 183 | }, 184 | { 185 | "cell_type": "code", 186 | "source": [ 187 | "from swarm import Swarm, Agent\n", 188 | "\n", 189 | "client = Swarm()" 190 | ], 191 | "metadata": { 192 | "id": "S5ZJtLIavWD6" 193 | }, 194 | "execution_count": 45, 195 | "outputs": [] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "source": [ 200 | "def transfer_to_agent_b():\n", 201 | " return agent_b\n", 202 | "\n", 203 | "\n", 204 | "agent_a = Agent(\n", 205 | " name=\"Agent A\",\n", 206 | " instructions=\"You are a helpful agent.\",\n", 207 | " functions=[transfer_to_agent_b],\n", 208 | ")\n", 209 | "\n", 210 | "agent_b = Agent(\n", 211 | " name=\"Agent B\",\n", 212 | " instructions=\"Only speak in Chinese.\",\n", 213 | ")" 214 | ], 215 | "metadata": { 216 | "id": "1Jg8qguQvX4T" 217 | }, 218 | "execution_count": 46, 219 | "outputs": [] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "source": [ 224 | "response = client.run(\n", 225 | " agent=agent_a,\n", 226 | " messages=[{\"role\": \"user\", \"content\": \"I want to talk to agent B.\"}],\n", 227 | ")" 228 | ], 229 | "metadata": { 230 | "id": "bGq6KZ3Dvbvr" 231 | }, 232 | "execution_count": 47, 233 | "outputs": [] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "source": [ 238 | "response.messages" 239 | ], 240 | "metadata": { 241 | "id": "b4pXeaZIvdZC", 242 | "colab": { 243 | "base_uri": "https://localhost:8080/" 244 | }, 245 | "outputId": "231aaa72-1b0a-4b29-c357-d089a95653b3" 246 | }, 247 | "execution_count": 48, 248 | "outputs": [ 249 | { 250 | "output_type": "execute_result", 251 | "data": { 252 | "text/plain": [ 253 | "[{'content': None,\n", 254 | " 'refusal': None,\n", 255 | " 'role': 'assistant',\n", 256 | " 'audio': None,\n", 257 | " 'function_call': None,\n", 258 | " 'tool_calls': [{'id': 'call_Oep2ErkZAlQyo0GVlJrWu8SM',\n", 259 | " 'function': {'arguments': '{}', 'name': 'transfer_to_agent_b'},\n", 260 | " 'type': 'function'}],\n", 261 | " 'sender': 'Agent A'},\n", 262 | " {'role': 'tool',\n", 263 | " 'tool_call_id': 'call_Oep2ErkZAlQyo0GVlJrWu8SM',\n", 264 | " 'tool_name': 'transfer_to_agent_b',\n", 265 | " 'content': '{\"assistant\": \"Agent B\"}'},\n", 266 | " {'content': '抱歉，我无法连接到另一个代理。如果有任何问题或需要帮助，请告诉我，我会尽力协助你。',\n", 267 | " 'refusal': None,\n", 268 | " 'role': 'assistant',\n", 269 | " 'audio': None,\n", 270 | " 'function_call': None,\n", 271 | " 'tool_calls': None,\n", 272 | " 'sender': 'Agent B'}]" 273 | ] 274 | }, 275 | "metadata": {}, 276 | "execution_count": 48 277 | } 278 | ] 279 | }, 280 | { 281 | "cell_type": "code", 282 | "source": [ 283 | "response.messages[-1][\"content\"]" 284 | ], 285 | "metadata": { 286 | "id": "DHXjmp821VMZ", 287 | "colab": { 288 | "base_uri": "https://localhost:8080/", 289 | "height": 36 290 | }, 291 | "outputId": "f3a7dd9d-e7dc-4152-8bce-08d2472ae217" 292 | }, 293 | "execution_count": 44, 294 | "outputs": [ 295 | { 296 | "output_type": "execute_result", 297 | "data": { 298 | "text/plain": [ 299 | "'对不起，我不能直接将您转接到另一位助手，但您可以告诉我需要帮助的具体问题，我会尽力帮助您。'" 300 | ], 301 | "application/vnd.google.colaboratory.intrinsic+json": { 302 | "type": "string" 303 | } 304 | }, 305 | "metadata": {}, 306 | "execution_count": 44 307 | } 308 | ] 309 | }, 310 | { 311 | "cell_type": "markdown", 312 | "source": [ 313 | "### Example 2 - Weather Agent\n", 314 | "\n", 315 | "It's based on one of the examples OpenAI Swarm published - https://github.com/openai/swarm/tree/main/examples/weather_agent" 316 | ], 317 | "metadata": { 318 | "id": "IjVUNgnk10OJ" 319 | } 320 | }, 321 | { 322 | "cell_type": "code", 323 | "source": [ 324 | "import json\n", 325 | "\n", 326 | "def get_weather(location, time=\"now\"):\n", 327 | " \"\"\"Get the current weather in a given location. Location MUST be a city.\"\"\"\n", 328 | " return json.dumps({\"location\": location, \"temperature\": \"65\", \"time\": time})\n", 329 | "\n", 330 | "\n", 331 | "def send_email(recipient, subject, body):\n", 332 | " print(\"Sending email...\")\n", 333 | " print(f\"To: {recipient}\")\n", 334 | " print(f\"Subject: {subject}\")\n", 335 | " print(f\"Body: {body}\")\n", 336 | " return \"Sent!\"\n", 337 | "\n", 338 | "\n", 339 | "weather_agent = Agent(\n", 340 | " name=\"Weather Agent\",\n", 341 | " instructions=\"You are a helpful agent.\",\n", 342 | " functions=[get_weather, send_email],\n", 343 | ")" 344 | ], 345 | "metadata": { 346 | "id": "XZ1ikTHa2Jkt" 347 | }, 348 | "execution_count": null, 349 | "outputs": [] 350 | }, 351 | { 352 | "cell_type": "code", 353 | "source": [ 354 | "response = client.run(\n", 355 | " agent=weather_agent,\n", 356 | " messages=[{\"role\": \"user\", \"content\": \"Send me the weather in Beijing today.\"}],\n", 357 | ")" 358 | ], 359 | "metadata": { 360 | "id": "nSKX2kMV2oIE" 361 | }, 362 | "execution_count": 49, 363 | "outputs": [] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "source": [ 368 | "response.messages" 369 | ], 370 | "metadata": { 371 | "id": "TiauwOTn29Qd", 372 | "colab": { 373 | "base_uri": "https://localhost:8080/" 374 | }, 375 | "outputId": "0da58b48-335c-4842-b829-7c955e4bf994" 376 | }, 377 | "execution_count": 50, 378 | "outputs": [ 379 | { 380 | "output_type": "execute_result", 381 | "data": { 382 | "text/plain": [ 383 | "[{'content': None,\n", 384 | " 'refusal': None,\n", 385 | " 'role': 'assistant',\n", 386 | " 'audio': None,\n", 387 | " 'function_call': None,\n", 388 | " 'tool_calls': [{'id': 'call_yGlAzBk4xGqi3w5MIcQsPBSn',\n", 389 | " 'function': {'arguments': '{\"location\":\"Beijing\"}', 'name': 'get_weather'},\n", 390 | " 'type': 'function'}],\n", 391 | " 'sender': 'Weather Agent'},\n", 392 | " {'role': 'tool',\n", 393 | " 'tool_call_id': 'call_yGlAzBk4xGqi3w5MIcQsPBSn',\n", 394 | " 'tool_name': 'get_weather',\n", 395 | " 'content': '{\"location\": \"Beijing\", \"temperature\": \"65\", \"time\": \"now\"}'},\n", 396 | " {'content': 'The current temperature in Beijing is 65°F.',\n", 397 | " 'refusal': None,\n", 398 | " 'role': 'assistant',\n", 399 | " 'audio': None,\n", 400 | " 'function_call': None,\n", 401 | " 'tool_calls': None,\n", 402 | " 'sender': 'Weather Agent'}]" 403 | ] 404 | }, 405 | "metadata": {}, 406 | "execution_count": 50 407 | } 408 | ] 409 | }, 410 | { 411 | "cell_type": "markdown", 412 | "source": [ 413 | "## Components Introduction" 414 | ], 415 | "metadata": { 416 | "id": "AAgqmo12Ekks" 417 | } 418 | }, 419 | { 420 | "cell_type": "markdown", 421 | "source": [ 422 | "### Swarm Client\n", 423 | "\n", 424 | "Swarm client internally just instantiates an OpenAI client." 425 | ], 426 | "metadata": { 427 | "id": "Upq59ULpEp-T" 428 | } 429 | }, 430 | { 431 | "cell_type": "code", 432 | "source": [ 433 | "from swarm import Swarm\n", 434 | "from openai import OpenAI\n", 435 | "\n", 436 | "client = OpenAI(\n", 437 | " api_key=os.environ[\"OPENAI_API_KEY\"],\n", 438 | " base_url=\"https://api.openai.com/v1\"\n", 439 | ")\n", 440 | "\n", 441 | "swarm = Swarm(client=client)" 442 | ], 443 | "metadata": { 444 | "id": "Z5Z4HkoqE_-p" 445 | }, 446 | "execution_count": 51, 447 | "outputs": [] 448 | }, 449 | { 450 | "cell_type": "markdown", 451 | "source": [ 452 | "### Agents\n", 453 | "\n", 454 | "An Agent encapsulates a set of instructions with a set of functions (plus some additional settings below), and has the capability to hand off execution to another Agent." 455 | ], 456 | "metadata": { 457 | "id": "TXqfqWjJEs3l" 458 | } 459 | }, 460 | { 461 | "cell_type": "code", 462 | "source": [ 463 | "def instructions(context_variables):\n", 464 | " user_name = context_variables[\"user_name\"]\n", 465 | " return f\"You are the receptionist of company Tesla. {user_name} is coming to your reception. You should welcome with its name.\"\n", 466 | "\n", 467 | "agent = Agent(instructions=instructions)" 468 | ], 469 | "metadata": { 470 | "id": "mfetx01XFvBK" 471 | }, 472 | "execution_count": 54, 473 | "outputs": [] 474 | }, 475 | { 476 | "cell_type": "markdown", 477 | "source": [ 478 | "Swarm client runs with the agent." 479 | ], 480 | "metadata": { 481 | "id": "vywRKKeQF2FE" 482 | } 483 | }, 484 | { 485 | "cell_type": "code", 486 | "source": [ 487 | "response = swarm.run(\n", 488 | " agent=agent,\n", 489 | " messages=[{\"role\":\"user\", \"content\": \"Hi!\"}],\n", 490 | " context_variables={\n", 491 | " \"user_name\":\"VerySmallWoods\"\n", 492 | " }\n", 493 | ")\n", 494 | "print(response.messages[-1][\"content\"])" 495 | ], 496 | "metadata": { 497 | "id": "vIPa8uRdF5jI", 498 | "colab": { 499 | "base_uri": "https://localhost:8080/" 500 | }, 501 | "outputId": "5d0fcf32-887e-4970-ab17-3e9291e6c3cd" 502 | }, 503 | "execution_count": 55, 504 | "outputs": [ 505 | { 506 | "output_type": "stream", 507 | "name": "stdout", 508 | "text": [ 509 | "Hello, VerySmallWoods! Welcome to Tesla. How can I assist you today?\n" 510 | ] 511 | } 512 | ] 513 | }, 514 | { 515 | "cell_type": "markdown", 516 | "source": [ 517 | "### Functions\n", 518 | "\n", 519 | "The functions called by Swarm Agents should usually return a str (values will be attempted to be cast as a str), or an Agent.\n", 520 | "\n", 521 | "When an Agent is returned, the execution will be transferred to that one.\n", 522 | "\n", 523 | "If a function defines a context_variables parameter, it will be populated by the context_variables passed into\n", 524 | "`client.run()`.\n" 525 | ], 526 | "metadata": { 527 | "id": "DThMZEyJEvGK" 528 | } 529 | }, 530 | { 531 | "cell_type": "markdown", 532 | "source": [ 533 | "#### Handsoff and Context Variables Update\n", 534 | "\n", 535 | "An Agent can hand off to another Agent by returning it in a function, and update the context variables by returning a more complete `Result` object." 536 | ], 537 | "metadata": { 538 | "id": "4jQQSBoNKf0r" 539 | } 540 | }, 541 | { 542 | "cell_type": "markdown", 543 | "source": [ 544 | "**Handsoff to another agent**" 545 | ], 546 | "metadata": { 547 | "id": "_QOo5Q06K87Q" 548 | } 549 | }, 550 | { 551 | "cell_type": "code", 552 | "source": [ 553 | "sales_agent = Agent(name=\"Sales Agent\")\n", 554 | "\n", 555 | "def transfer_to_sales():\n", 556 | " return sales_agent\n", 557 | "\n", 558 | "agent = Agent(functions=[transfer_to_sales])\n", 559 | "\n", 560 | "response = swarm.run(agent, [{\"role\":\"user\", \"content\":\"Transfer me to sales.\"}])\n", 561 | "print(response.agent.name)" 562 | ], 563 | "metadata": { 564 | "id": "8ntiHb_vKDak", 565 | "colab": { 566 | "base_uri": "https://localhost:8080/" 567 | }, 568 | "outputId": "44222ea8-2fbe-40a9-e4ac-a1f64d00a533" 569 | }, 570 | "execution_count": 56, 571 | "outputs": [ 572 | { 573 | "output_type": "stream", 574 | "name": "stdout", 575 | "text": [ 576 | "Sales Agent\n" 577 | ] 578 | } 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "source": [ 584 | "response.messages" 585 | ], 586 | "metadata": { 587 | "colab": { 588 | "base_uri": "https://localhost:8080/" 589 | }, 590 | "id": "cBXj5-idyZtq", 591 | "outputId": "4be40b85-6869-49bb-ad75-56f397b8f58e" 592 | }, 593 | "execution_count": 57, 594 | "outputs": [ 595 | { 596 | "output_type": "execute_result", 597 | "data": { 598 | "text/plain": [ 599 | "[{'content': None,\n", 600 | " 'refusal': None,\n", 601 | " 'role': 'assistant',\n", 602 | " 'audio': None,\n", 603 | " 'function_call': None,\n", 604 | " 'tool_calls': [{'id': 'call_XnGD4raVQZz9CqWHyO3QF7FP',\n", 605 | " 'function': {'arguments': '{}', 'name': 'transfer_to_sales'},\n", 606 | " 'type': 'function'}],\n", 607 | " 'sender': 'Agent'},\n", 608 | " {'role': 'tool',\n", 609 | " 'tool_call_id': 'call_XnGD4raVQZz9CqWHyO3QF7FP',\n", 610 | " 'tool_name': 'transfer_to_sales',\n", 611 | " 'content': '{\"assistant\": \"Sales Agent\"}'},\n", 612 | " {'content': \"I've transferred you to the sales department. How can they assist you today?\",\n", 613 | " 'refusal': None,\n", 614 | " 'role': 'assistant',\n", 615 | " 'audio': None,\n", 616 | " 'function_call': None,\n", 617 | " 'tool_calls': None,\n", 618 | " 'sender': 'Sales Agent'}]" 619 | ] 620 | }, 621 | "metadata": {}, 622 | "execution_count": 57 623 | } 624 | ] 625 | }, 626 | { 627 | "cell_type": "markdown", 628 | "source": [ 629 | "**Update the context variables**" 630 | ], 631 | "metadata": { 632 | "id": "LxCxle6QLCNy" 633 | } 634 | }, 635 | { 636 | "cell_type": "code", 637 | "source": [ 638 | "sales_agent = Agent(name=\"Sales Agent\")\n", 639 | "\n", 640 | "def talk_to_sales():\n", 641 | " print(\"Hello, World!\")\n", 642 | " return Result(\n", 643 | " value=\"Done\",\n", 644 | " agent=sales_agent,\n", 645 | " context_variables={\"department\": \"sales\"}\n", 646 | " )\n", 647 | "\n", 648 | "agent = Agent(functions=[talk_to_sales])\n", 649 | "\n", 650 | "response = swarm.run(\n", 651 | " agent=agent,\n", 652 | " messages=[{\"role\": \"user\", \"content\": \"Transfer me to sales\"}],\n", 653 | " context_variables={\"user_name\": \"John\"}\n", 654 | ")\n", 655 | "print(response.agent.name)\n", 656 | "print(response.context_variables)" 657 | ], 658 | "metadata": { 659 | "id": "o3rHE8MgKE1w", 660 | "colab": { 661 | "base_uri": "https://localhost:8080/", 662 | "height": 392 663 | }, 664 | "outputId": "4c944821-d998-4ac3-e37c-e729ce5e6dbe" 665 | }, 666 | "execution_count": 58, 667 | "outputs": [ 668 | { 669 | "output_type": "stream", 670 | "name": "stdout", 671 | "text": [ 672 | "Hello, World!\n" 673 | ] 674 | }, 675 | { 676 | "output_type": "error", 677 | "ename": "NameError", 678 | "evalue": "name 'Result' is not defined", 679 | "traceback": [ 680 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 681 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 682 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0magent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAgent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtalk_to_sales\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m response = swarm.run(\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"role\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"user\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"content\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Transfer me to sales\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 683 | "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/swarm/core.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, agent, messages, context_variables, model_override, stream, debug, max_turns, execute_tools)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0;31m# handle function calls, updating context_variables, and switching agents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m partial_response = self.handle_tool_calls(\n\u001b[0m\u001b[1;32m 281\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtool_calls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactive_agent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext_variables\u001b[0m\u001b[0;34m,\u001b[0m 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