From ccd24f3a61982cce7727361e50c2aabb40be33ae Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Sun, 31 Aug 2025 14:18:27 -0700 Subject: [PATCH 1/5] [DOCS][Web scraper agents] --- docs/developer_guides/web_scraper.md | 84 +++ docs/mkdocs.yml | 1 + .../batched_scraper_agent.py | 25 + .../web_scraper_agents/web_scraper_agent.py | 17 + examples/mcp/mcp_agent_tool.py | 7 +- swarms/structs/hiearchical_swarm.py | 116 ++-- swarms/structs/multi_agent_exec.py | 4 - swarms/structs/multi_agent_router.py | 16 +- tests/structs/test_support_mcp.py | 547 ++++++++++++------ 9 files changed, 569 insertions(+), 248 deletions(-) create mode 100644 docs/developer_guides/web_scraper.md create mode 100644 examples/guides/web_scraper_agents/batched_scraper_agent.py create mode 100644 examples/guides/web_scraper_agents/web_scraper_agent.py diff --git a/docs/developer_guides/web_scraper.md b/docs/developer_guides/web_scraper.md new file mode 100644 index 00000000..a835004d --- /dev/null +++ b/docs/developer_guides/web_scraper.md @@ -0,0 +1,84 @@ +# Web Scraper Agents + +Web scraper agents are specialized AI agents that can automatically extract and process information from websites. These agents combine the power of large language models with web scraping tools to intelligently gather, analyze, and structure data from the web. + +Web scraper agents are AI-powered tools that can: + +| Capability | Description | +|----------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------| +| **Automatically navigate websites** | Extract relevant information from web pages | +| **Parse and structure data** | Convert HTML content into readable, structured formats | +| **Handle dynamic content** | Process JavaScript-rendered pages and dynamic website elements | +| **Provide intelligent summaries and analysis** | Generate summaries and analyze the scraped content | +| **Scale to multiple websites simultaneously** | Scrape and process data from several websites at once for comprehensive research | + + +## Install + +```bash +pip3 install -U swarms swarms-tools +``` + +## Environment Setup + +```bash +OPENAI_API_KEY="your_openai_api_key_here" +``` + +## Basic Usage + +Here's a simple example of how to create a web scraper agent: + +```python +from swarms import Agent +from swarms_tools import scrape_and_format_sync + +agent = Agent( + agent_name="Web Scraper Agent", + model_name="gpt-4o-mini", + tools=[scrape_and_format_sync], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt="You are a web scraper agent. You are given a URL and you need to scrape the website and return the data in a structured format. The format type should be full", +) + +out = agent.run( + "Scrape swarms.ai website and provide a full report of the company does. The format type should be full." +) +print(out) +``` + +## Scraping Multiple Sites + +For comprehensive research, you can scrape multiple websites simultaneously using batch execution: + +```python +from swarms.structs.multi_agent_exec import batched_grid_agent_execution +from swarms_tools import scrape_and_format_sync +from swarms import Agent + +agent = Agent( + agent_name="Web Scraper Agent", + model_name="gpt-4o-mini", + tools=[scrape_and_format_sync], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt="You are a web scraper agent. You are given a URL and you need to scrape the website and return the data in a structured format. The format type should be full", +) + +out = batched_grid_agent_execution( + agents=[agent, agent], + tasks=[ + "Scrape swarms.ai website and provide a full report of the company's mission, products, and team. The format type should be full.", + "Scrape langchain.com website and provide a full report of the company's mission, products, and team. The format type should be full.", + ], +) + +print(out) +``` + +## Conclusion + +Web scraper agents combine AI with advanced automation to efficiently gather and process web data at scale. As you master the basics, explore features like batch processing and custom tools to unlock the full power of AI-driven web scraping. diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 0e2cfa4c..1effe52d 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -435,6 +435,7 @@ nav: - RAG with Qdrant: "swarms/RAG/qdrant_rag.md" - Apps: + - Web Scraper Agents: "developer_guides/web_scraper.md" - Smart Database: "examples/smart_database.md" diff --git a/examples/guides/web_scraper_agents/batched_scraper_agent.py b/examples/guides/web_scraper_agents/batched_scraper_agent.py new file mode 100644 index 00000000..6253ce05 --- /dev/null +++ b/examples/guides/web_scraper_agents/batched_scraper_agent.py @@ -0,0 +1,25 @@ +from swarms.structs.multi_agent_exec import ( + batched_grid_agent_execution, +) +from swarms_tools import scrape_and_format_sync +from swarms import Agent + +agent = Agent( + agent_name="Web Scraper Agent", + model_name="gpt-4o-mini", + tools=[scrape_and_format_sync], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt="You are a web scraper agent. You are given a URL and you need to scrape the website and return the data in a structured format. The format type should be full", +) + +out = batched_grid_agent_execution( + agents=[agent, agent], + tasks=[ + "Scrape swarms.ai website and provide a full report of the company's mission, products, and team. The format type should be full.", + "Scrape langchain.com website and provide a full report of the company's mission, products, and team. The format type should be full.", + ], +) + +print(out) diff --git a/examples/guides/web_scraper_agents/web_scraper_agent.py b/examples/guides/web_scraper_agents/web_scraper_agent.py new file mode 100644 index 00000000..8b67321b --- /dev/null +++ b/examples/guides/web_scraper_agents/web_scraper_agent.py @@ -0,0 +1,17 @@ +from swarms import Agent +from swarms_tools import scrape_and_format_sync + +agent = Agent( + agent_name="Web Scraper Agent", + model_name="gpt-4o-mini", + tools=[scrape_and_format_sync], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt="You are a web scraper agent. You are given a URL and you need to scrape the website and return the data in a structured format. The format type should be full", +) + +out = agent.run( + "Scrape swarms.ai website and provide a full report of the company does. The format type should be full." +) +print(out) diff --git a/examples/mcp/mcp_agent_tool.py b/examples/mcp/mcp_agent_tool.py index 5a07f7fd..a0489cb8 100644 --- a/examples/mcp/mcp_agent_tool.py +++ b/examples/mcp/mcp_agent_tool.py @@ -4,11 +4,14 @@ from swarms import Agent mcp = FastMCP("MCPAgentTool") + @mcp.tool( name="create_agent", description="Create an agent with the specified name, system prompt, and model, then run a task.", ) -def create_agent(agent_name: str, system_prompt: str, model_name: str, task: str) -> str: +def create_agent( + agent_name: str, system_prompt: str, model_name: str, task: str +) -> str: """ Create an agent with the given parameters and execute the specified task. @@ -30,4 +33,4 @@ def create_agent(agent_name: str, system_prompt: str, model_name: str, task: str if __name__ == "__main__": - mcp.run() \ No newline at end of file + mcp.run() diff --git a/swarms/structs/hiearchical_swarm.py b/swarms/structs/hiearchical_swarm.py index 7f918274..5e840d03 100644 --- a/swarms/structs/hiearchical_swarm.py +++ b/swarms/structs/hiearchical_swarm.py @@ -51,6 +51,7 @@ from swarms.prompts.reasoning_prompt import INTERNAL_MONOLGUE_PROMPT from swarms.structs.agent import Agent from swarms.structs.conversation import Conversation from swarms.structs.ma_utils import list_all_agents +from swarms.structs.omni_agent_types import AgentListType from swarms.tools.base_tool import BaseTool from swarms.utils.history_output_formatter import ( history_output_formatter, @@ -672,7 +673,7 @@ class HierarchicalSwarm: name: str = "HierarchicalAgentSwarm", description: str = "Distributed task swarm", director: Optional[Union[Agent, Callable, Any]] = None, - agents: List[Union[Agent, Callable, Any]] = None, + agents: AgentListType = None, max_loops: int = 1, output_type: OutputType = "dict-all-except-first", feedback_director_model_name: str = "gpt-4o-mini", @@ -822,7 +823,7 @@ class HierarchicalSwarm: # Initialize logger only if verbose is enabled if self.verbose: logger.info( - f"šŸš€ Initializing HierarchicalSwarm: {self.name}" + f"[INIT] Initializing HierarchicalSwarm: {self.name}" ) self.conversation = Conversation(time_enabled=False) @@ -847,7 +848,7 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… HierarchicalSwarm: {self.name} initialized successfully." + f"[SUCCESS] HierarchicalSwarm: {self.name} initialized successfully." ) if self.multi_agent_prompt_improvements: @@ -867,7 +868,7 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info("šŸ“ Adding agent context to director") + logger.info("[INFO] Adding agent context to director") list_all_agents( agents=self.agents, @@ -878,15 +879,15 @@ class HierarchicalSwarm: if self.verbose: logger.success( - "āœ… Agent context added to director successfully" + "[SUCCESS] Agent context added to director successfully" ) except Exception as e: error_msg = ( - f"āŒ Failed to add context to director: {str(e)}" + f"[ERROR] Failed to add context to director: {str(e)}" ) logger.error( - f"{error_msg}\nšŸ” Traceback: {traceback.format_exc()}" + f"{error_msg}\n[TRACE] Traceback: {traceback.format_exc()}" ) def setup_director(self): @@ -904,12 +905,12 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info("šŸŽÆ Setting up director agent") + logger.info("[SETUP] Setting up director agent") schema = BaseTool().base_model_to_dict(SwarmSpec) if self.verbose: - logger.debug(f"šŸ“‹ Director schema: {schema}") + logger.debug(f"[SCHEMA] Director schema: {schema}") return Agent( agent_name=self.director_name, @@ -923,7 +924,7 @@ class HierarchicalSwarm: ) except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def reliability_checks(self): @@ -963,7 +964,7 @@ class HierarchicalSwarm: ) except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def agents_no_print(self): @@ -995,7 +996,9 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info(f"šŸŽÆ Running director with task: {task}") + logger.info( + f"[RUN] Running director with task: {task}" + ) if self.planning_director_agent is not None: plan = self.planning_director_agent.run( @@ -1022,15 +1025,17 @@ class HierarchicalSwarm: ) if self.verbose: - logger.success("āœ… Director execution completed") + logger.success( + "[SUCCESS] Director execution completed" + ) logger.debug( - f"šŸ“‹ Director output type: {type(function_call)}" + f"[OUTPUT] Director output type: {type(function_call)}" ) return function_call except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) raise e @@ -1059,7 +1064,7 @@ class HierarchicalSwarm: try: if self.verbose: logger.info( - f"šŸ‘£ Executing single step for task: {task}" + f"[STEP] Executing single step for task: {task}" ) # Update dashboard for director execution @@ -1073,7 +1078,7 @@ class HierarchicalSwarm: if self.verbose: logger.info( - f"šŸ“‹ Parsed plan and {len(orders)} orders" + f"[PARSE] Parsed plan and {len(orders)} orders" ) # Update dashboard with plan and orders information @@ -1094,7 +1099,7 @@ class HierarchicalSwarm: outputs = self.execute_orders(orders) if self.verbose: - logger.info(f"āš” Executed {len(outputs)} orders") + logger.info(f"[EXEC] Executed {len(outputs)} orders") if self.director_feedback_on is True: feedback = self.feedback_director(outputs) @@ -1102,12 +1107,14 @@ class HierarchicalSwarm: feedback = outputs if self.verbose: - logger.success("āœ… Step completed successfully") + logger.success( + "[SUCCESS] Step completed successfully" + ) return feedback except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def run( @@ -1146,7 +1153,6 @@ class HierarchicalSwarm: if task is None and self.interactive: task = self._get_interactive_task() - current_loop = 0 last_output = None @@ -1157,16 +1163,16 @@ class HierarchicalSwarm: if self.verbose: logger.info( - f"šŸš€ Starting hierarchical swarm run: {self.name}" + f"[START] Starting hierarchical swarm run: {self.name}" ) logger.info( - f"šŸ“Š Configuration - Max loops: {self.max_loops}" + f"[CONFIG] Configuration - Max loops: {self.max_loops}" ) while current_loop < self.max_loops: if self.verbose: logger.info( - f"šŸ”„ Loop {current_loop + 1}/{self.max_loops} - Processing task" + f"[LOOP] Loop {current_loop + 1}/{self.max_loops} - Processing task" ) # Update dashboard loop counter @@ -1196,11 +1202,11 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… Loop {current_loop + 1} completed successfully" + f"[SUCCESS] Loop {current_loop + 1} completed successfully" ) except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) current_loop += 1 @@ -1218,10 +1224,10 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"šŸŽ‰ Hierarchical swarm run completed: {self.name}" + f"[COMPLETE] Hierarchical swarm run completed: {self.name}" ) logger.info( - f"šŸ“Š Total loops executed: {current_loop}" + f"[STATS] Total loops executed: {current_loop}" ) return history_output_formatter( @@ -1234,7 +1240,7 @@ class HierarchicalSwarm: self.dashboard.update_director_status("ERROR") self.dashboard.stop() - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def _get_interactive_task(self) -> str: @@ -1275,7 +1281,7 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info("šŸ“ Generating director feedback") + logger.info("[FEEDBACK] Generating director feedback") task = f"History: {self.conversation.get_str()} \n\n" @@ -1302,13 +1308,13 @@ class HierarchicalSwarm: if self.verbose: logger.success( - "āœ… Director feedback generated successfully" + "[SUCCESS] Director feedback generated successfully" ) return output except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def call_single_agent( @@ -1336,7 +1342,7 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info(f"šŸ“ž Calling agent: {agent_name}") + logger.info(f"[CALL] Calling agent: {agent_name}") # Find agent by name agent = None @@ -1373,7 +1379,7 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… Agent {agent_name} completed task successfully" + f"[SUCCESS] Agent {agent_name} completed task successfully" ) return output @@ -1385,7 +1391,7 @@ class HierarchicalSwarm: agent_name, "ERROR", task, f"Error: {str(e)}" ) - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def parse_orders(self, output): @@ -1409,8 +1415,8 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info("šŸ“‹ Parsing director orders") - logger.debug(f"šŸ“Š Output type: {type(output)}") + logger.info("[PARSE] Parsing director orders") + logger.debug(f"[TYPE] Output type: {type(output)}") import json @@ -1454,7 +1460,7 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… Successfully parsed plan and {len(orders)} orders" + f"[SUCCESS] Successfully parsed plan and {len(orders)} orders" ) return plan, orders @@ -1463,7 +1469,7 @@ class HierarchicalSwarm: ) as json_err: if self.verbose: logger.warning( - f"āš ļø JSON decode error: {json_err}" + f"[WARN] JSON decode error: {json_err}" ) pass # Check if it's a direct function call format @@ -1488,7 +1494,7 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… Successfully parsed plan and {len(orders)} orders" + f"[SUCCESS] Successfully parsed plan and {len(orders)} orders" ) return plan, orders @@ -1497,7 +1503,7 @@ class HierarchicalSwarm: ) as json_err: if self.verbose: logger.warning( - f"āš ļø JSON decode error: {json_err}" + f"[WARN] JSON decode error: {json_err}" ) pass # If no function call found, raise error @@ -1515,7 +1521,7 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… Successfully parsed plan and {len(orders)} orders" + f"[SUCCESS] Successfully parsed plan and {len(orders)} orders" ) return plan, orders @@ -1529,7 +1535,7 @@ class HierarchicalSwarm: ) except Exception as e: - error_msg = f"āŒ Failed to parse orders: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to parse orders: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) raise e @@ -1552,13 +1558,13 @@ class HierarchicalSwarm: """ try: if self.verbose: - logger.info(f"āš” Executing {len(orders)} orders") + logger.info(f"[EXEC] Executing {len(orders)} orders") outputs = [] for i, order in enumerate(orders): if self.verbose: logger.info( - f"šŸ“‹ Executing order {i+1}/{len(orders)}: {order.agent_name}" + f"[ORDER] Executing order {i+1}/{len(orders)}: {order.agent_name}" ) # Update dashboard for agent execution @@ -1590,13 +1596,13 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"āœ… All {len(orders)} orders executed successfully" + f"[SUCCESS] All {len(orders)} orders executed successfully" ) return outputs except Exception as e: - error_msg = f"āŒ Failed to setup director: {str(e)}\nšŸ” Traceback: {traceback.format_exc()}\nšŸ› If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" + error_msg = f"[ERROR] Failed to setup director: {str(e)}\n[TRACE] Traceback: {traceback.format_exc()}\n[BUG] If this issue persists, please report it at: https://github.com/kyegomez/swarms/issues" logger.error(error_msg) def batched_run( @@ -1624,10 +1630,10 @@ class HierarchicalSwarm: try: if self.verbose: logger.info( - f"šŸš€ Starting batched hierarchical swarm run: {self.name}" + f"[START] Starting batched hierarchical swarm run: {self.name}" ) logger.info( - f"šŸ“Š Configuration - Max loops: {self.max_loops}" + f"[CONFIG] Configuration - Max loops: {self.max_loops}" ) # Initialize a list to store the results @@ -1640,18 +1646,18 @@ class HierarchicalSwarm: if self.verbose: logger.success( - f"šŸŽ‰ Batched hierarchical swarm run completed: {self.name}" + f"[COMPLETE] Batched hierarchical swarm run completed: {self.name}" + ) + logger.info( + f"[STATS] Total tasks processed: {len(tasks)}" ) - logger.info(f"šŸ“Š Total tasks processed: {len(tasks)}") return results except Exception as e: - error_msg = ( - f"āŒ Batched hierarchical swarm run failed: {str(e)}" - ) + error_msg = f"[ERROR] Batched hierarchical swarm run failed: {str(e)}" if self.verbose: logger.error(error_msg) logger.error( - f"šŸ” Traceback: {traceback.format_exc()}" + f"[TRACE] Traceback: {traceback.format_exc()}" ) diff --git a/swarms/structs/multi_agent_exec.py b/swarms/structs/multi_agent_exec.py index 7c764b36..d4119a7a 100644 --- a/swarms/structs/multi_agent_exec.py +++ b/swarms/structs/multi_agent_exec.py @@ -144,7 +144,6 @@ def run_agents_concurrently_multiprocess( def batched_grid_agent_execution( agents: List[AgentType], tasks: List[str], - max_workers: int = None, ) -> List[Any]: """ Run multiple agents with different tasks concurrently. @@ -158,9 +157,6 @@ def batched_grid_agent_execution( "The number of agents must match the number of tasks." ) - if max_workers is None: - max_workers = os.cpu_count() - results = [] for agent, task in zip(agents, tasks): diff --git a/swarms/structs/multi_agent_router.py b/swarms/structs/multi_agent_router.py index c8a71b16..ca719603 100644 --- a/swarms/structs/multi_agent_router.py +++ b/swarms/structs/multi_agent_router.py @@ -1,20 +1,9 @@ -""" -Todo: - -- Add multi-agent selection for a task and then run them automatically -- Add shared memory for large instances of agents - - - -""" - import os from typing import List, Optional from loguru import logger from pydantic import BaseModel, Field from swarms.utils.function_caller_model import OpenAIFunctionCaller -from swarms.structs.agent import Agent from swarms.structs.conversation import Conversation from swarms.utils.output_types import OutputType from swarms.utils.any_to_str import any_to_str @@ -22,7 +11,7 @@ from swarms.utils.history_output_formatter import ( history_output_formatter, ) from swarms.utils.formatter import formatter -from typing import Callable, Union +from swarms.structs.omni_agent_types import AgentListType class AgentResponse(BaseModel): @@ -60,7 +49,7 @@ class MultiAgentRouter: self, name: str = "swarm-router", description: str = "Routes tasks to specialized agents based on their capabilities", - agents: List[Union[Agent, Callable]] = [], + agents: AgentListType = None, model: str = "gpt-4o-mini", temperature: float = 0.1, shared_memory_system: callable = None, @@ -86,7 +75,6 @@ class MultiAgentRouter: self.model = model self.temperature = temperature self.if_print = if_print - # Initialize Agents self.agents = {agent.name: agent for agent in agents} self.conversation = Conversation() diff --git a/tests/structs/test_support_mcp.py b/tests/structs/test_support_mcp.py index 4d46baff..084be573 100644 --- a/tests/structs/test_support_mcp.py +++ b/tests/structs/test_support_mcp.py @@ -19,7 +19,7 @@ from swarms.tools.mcp_client_call import ( execute_multiple_tools_on_multiple_mcp_servers_sync, execute_tool_call_simple, get_mcp_tools_sync, - get_tools_for_multiple_mcp_servers, + get_tools_for_multiple_mcp_servers, transform_mcp_tool_to_openai_tool, transform_openai_tool_call_request_to_mcp_tool_call_request, ) @@ -31,23 +31,26 @@ logger.add("test_results.log", rotation="10 MB", level="DEBUG") TEST_CONFIG = { "server_url": "http://localhost:8080/mcp", "transport": "streamable_http", - "timeout": 10 + "timeout": 10, } # Test results storage test_results = [] -def log_test_result(test_name: str, status: str, message: str = "", error: str = ""): + +def log_test_result( + test_name: str, status: str, message: str = "", error: str = "" +): """Log test result and add to results list""" result = { "test_name": test_name, "status": status, "message": message, "error": error, - "timestamp": datetime.now().isoformat() + "timestamp": datetime.now().isoformat(), } test_results.append(result) - + if status == "PASS": logger.success(f"āœ“ {test_name}: {message}") elif status == "FAIL": @@ -55,10 +58,11 @@ def log_test_result(test_name: str, status: str, message: str = "", error: str = else: logger.info(f"~ {test_name}: {message}") + def test_transform_mcp_tool_to_openai_tool(): """Test MCP tool to OpenAI tool transformation""" test_name = "test_transform_mcp_tool_to_openai_tool" - + try: # Create mock MCP tool class MockMCPTool: @@ -66,403 +70,577 @@ def test_transform_mcp_tool_to_openai_tool(): self.name = name self.description = description self.inputSchema = input_schema - + mock_tool = MockMCPTool( name="test_function", description="Test function description", - input_schema={"type": "object", "properties": {"param1": {"type": "string"}}} + input_schema={ + "type": "object", + "properties": {"param1": {"type": "string"}}, + }, ) - + result = transform_mcp_tool_to_openai_tool(mock_tool) - + # Validate result structure assert result["type"] == "function" assert result["function"]["name"] == "test_function" - assert result["function"]["description"] == "Test function description" + assert ( + result["function"]["description"] + == "Test function description" + ) assert result["function"]["parameters"]["type"] == "object" - - log_test_result(test_name, "PASS", "Successfully transformed MCP tool to OpenAI format") - + + log_test_result( + test_name, + "PASS", + "Successfully transformed MCP tool to OpenAI format", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to transform tool: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to transform tool: {str(e)}", + ) + def test_get_function_arguments(): """Test function argument extraction""" test_name = "test_get_function_arguments" - + try: # Test with dict arguments - function_def = {"arguments": {"param1": "value1", "param2": "value2"}} + function_def = { + "arguments": {"param1": "value1", "param2": "value2"} + } result = _get_function_arguments(function_def) assert isinstance(result, dict) assert result["param1"] == "value1" - + # Test with string arguments - function_def_str = {"arguments": '{"param1": "value1", "param2": "value2"}'} + function_def_str = { + "arguments": '{"param1": "value1", "param2": "value2"}' + } result_str = _get_function_arguments(function_def_str) assert isinstance(result_str, dict) assert result_str["param1"] == "value1" - + # Test with empty arguments function_def_empty = {} result_empty = _get_function_arguments(function_def_empty) assert result_empty == {} - - log_test_result(test_name, "PASS", "Successfully extracted function arguments in all formats") - + + log_test_result( + test_name, + "PASS", + "Successfully extracted function arguments in all formats", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to extract arguments: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to extract arguments: {str(e)}", + ) + def test_transform_openai_tool_call_request_to_mcp_tool_call_request(): """Test OpenAI tool call to MCP tool call transformation""" test_name = "test_transform_openai_tool_call_request_to_mcp_tool_call_request" - + try: openai_tool = { "function": { "name": "test_function", - "arguments": {"param1": "value1", "param2": "value2"} + "arguments": {"param1": "value1", "param2": "value2"}, } } - - result = transform_openai_tool_call_request_to_mcp_tool_call_request(openai_tool) - + + result = transform_openai_tool_call_request_to_mcp_tool_call_request( + openai_tool + ) + assert result.name == "test_function" assert result.arguments["param1"] == "value1" assert result.arguments["param2"] == "value2" - - log_test_result(test_name, "PASS", "Successfully transformed OpenAI tool call to MCP format") - + + log_test_result( + test_name, + "PASS", + "Successfully transformed OpenAI tool call to MCP format", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to transform tool call: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to transform tool call: {str(e)}", + ) + def test_auto_detect_transport(): """Test transport auto-detection""" test_name = "test_auto_detect_transport" - + try: # Test HTTP URL http_url = "http://localhost:8080/mcp" transport = auto_detect_transport(http_url) assert transport == "streamable_http" - + # Test HTTPS URL https_url = "https://example.com/mcp" transport = auto_detect_transport(https_url) assert transport == "streamable_http" - + # Test WebSocket URL ws_url = "ws://localhost:8080/mcp" transport = auto_detect_transport(ws_url) assert transport == "sse" - + # Test stdio stdio_url = "stdio://local" transport = auto_detect_transport(stdio_url) assert transport == "stdio" - + # Test unknown scheme unknown_url = "unknown://test" transport = auto_detect_transport(unknown_url) assert transport == "sse" # Default - - log_test_result(test_name, "PASS", "Successfully auto-detected all transport types") - + + log_test_result( + test_name, + "PASS", + "Successfully auto-detected all transport types", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to auto-detect transport: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to auto-detect transport: {str(e)}", + ) + def test_connect_to_mcp_server(): """Test MCP server connection configuration""" test_name = "test_connect_to_mcp_server" - + try: from swarms.schemas.mcp_schemas import MCPConnection - + # Create connection object connection = MCPConnection( url="http://localhost:8080/mcp", transport="streamable_http", timeout=10, headers={"Content-Type": "application/json"}, - authorization_token="test_token" + authorization_token="test_token", + ) + + headers, timeout, transport, url = connect_to_mcp_server( + connection ) - - headers, timeout, transport, url = connect_to_mcp_server(connection) - + assert url == "http://localhost:8080/mcp" assert transport == "streamable_http" assert timeout == 10 assert "Authorization" in headers assert headers["Authorization"] == "Bearer test_token" - - log_test_result(test_name, "PASS", "Successfully configured MCP server connection") - + + log_test_result( + test_name, + "PASS", + "Successfully configured MCP server connection", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to configure connection: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to configure connection: {str(e)}", + ) + async def test_aget_mcp_tools(): """Test async MCP tools fetching""" test_name = "test_aget_mcp_tools" - + try: # This will attempt to connect to the actual server tools = await aget_mcp_tools( server_path=TEST_CONFIG["server_url"], format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + assert isinstance(tools, list) - log_test_result(test_name, "PASS", f"Successfully fetched {len(tools)} tools from server") - + log_test_result( + test_name, + "PASS", + f"Successfully fetched {len(tools)} tools from server", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to fetch tools: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to fetch tools: {str(e)}", + ) + def test_get_mcp_tools_sync(): """Test synchronous MCP tools fetching""" test_name = "test_get_mcp_tools_sync" - + try: tools = get_mcp_tools_sync( server_path=TEST_CONFIG["server_url"], format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + assert isinstance(tools, list) - log_test_result(test_name, "PASS", f"Successfully fetched {len(tools)} tools synchronously") - + log_test_result( + test_name, + "PASS", + f"Successfully fetched {len(tools)} tools synchronously", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to fetch tools sync: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to fetch tools sync: {str(e)}", + ) + def test_fetch_tools_for_server(): """Test fetching tools for a single server""" test_name = "test_fetch_tools_for_server" - + try: tools = _fetch_tools_for_server( url=TEST_CONFIG["server_url"], format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + assert isinstance(tools, list) - log_test_result(test_name, "PASS", f"Successfully fetched tools for single server: {len(tools)} tools") - + log_test_result( + test_name, + "PASS", + f"Successfully fetched tools for single server: {len(tools)} tools", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to fetch tools for server: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to fetch tools for server: {str(e)}", + ) + def test_get_tools_for_multiple_mcp_servers(): """Test fetching tools from multiple servers""" test_name = "test_get_tools_for_multiple_mcp_servers" - + try: - urls = [TEST_CONFIG["server_url"]] # Using single server for testing - + urls = [ + TEST_CONFIG["server_url"] + ] # Using single server for testing + tools = get_tools_for_multiple_mcp_servers( urls=urls, format="openai", transport=TEST_CONFIG["transport"], - max_workers=2 + max_workers=2, ) - + assert isinstance(tools, list) - log_test_result(test_name, "PASS", f"Successfully fetched tools from multiple servers: {len(tools)} tools") - + log_test_result( + test_name, + "PASS", + f"Successfully fetched tools from multiple servers: {len(tools)} tools", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to fetch tools from multiple servers: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to fetch tools from multiple servers: {str(e)}", + ) + async def test_execute_tool_call_simple(): """Test simple tool execution""" test_name = "test_execute_tool_call_simple" - + try: # First try to get available tools try: tools = await aget_mcp_tools( server_path=TEST_CONFIG["server_url"], format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + if not tools: - log_test_result(test_name, "SKIP", "No tools available for testing") + log_test_result( + test_name, + "SKIP", + "No tools available for testing", + ) return - + # Use the first available tool for testing first_tool = tools[0] tool_name = first_tool["function"]["name"] - + # Create a basic tool call request tool_call_request = { "function": { "name": tool_name, - "arguments": {} # Basic empty arguments + "arguments": {}, # Basic empty arguments } } - + result = await execute_tool_call_simple( response=tool_call_request, server_path=TEST_CONFIG["server_url"], transport=TEST_CONFIG["transport"], - output_type="str" + output_type="str", ) - + assert result is not None - log_test_result(test_name, "PASS", f"Successfully executed tool call for {tool_name}") - + log_test_result( + test_name, + "PASS", + f"Successfully executed tool call for {tool_name}", + ) + except MCPConnectionError: - log_test_result(test_name, "SKIP", "Server not available for tool execution test") - + log_test_result( + test_name, + "SKIP", + "Server not available for tool execution test", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to execute tool call: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to execute tool call: {str(e)}", + ) + async def test_create_server_tool_mapping(): """Test server tool mapping creation""" test_name = "test_create_server_tool_mapping" - + try: urls = [TEST_CONFIG["server_url"]] - + mapping = await _create_server_tool_mapping_async( urls=urls, format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + assert isinstance(mapping, dict) - log_test_result(test_name, "PASS", f"Successfully created server tool mapping with {len(mapping)} functions") - + log_test_result( + test_name, + "PASS", + f"Successfully created server tool mapping with {len(mapping)} functions", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to create server tool mapping: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to create server tool mapping: {str(e)}", + ) + async def test_execute_multiple_tools_on_multiple_servers(): """Test executing multiple tools across servers""" test_name = "test_execute_multiple_tools_on_multiple_servers" - + try: urls = [TEST_CONFIG["server_url"]] - + # First get available tools try: tools = await aget_mcp_tools( server_path=TEST_CONFIG["server_url"], format="openai", - transport=TEST_CONFIG["transport"] + transport=TEST_CONFIG["transport"], ) - + if not tools: - log_test_result(test_name, "SKIP", "No tools available for testing") + log_test_result( + test_name, + "SKIP", + "No tools available for testing", + ) return - + # Create test requests using available tools responses = [] for tool in tools[:2]: # Test with first 2 tools tool_call = { "function": { "name": tool["function"]["name"], - "arguments": {} + "arguments": {}, } } responses.append(tool_call) - + if not responses: - log_test_result(test_name, "SKIP", "No suitable tools found for testing") + log_test_result( + test_name, + "SKIP", + "No suitable tools found for testing", + ) return - - results = await execute_multiple_tools_on_multiple_mcp_servers( - responses=responses, - urls=urls, - transport=TEST_CONFIG["transport"], - max_concurrent=2 + + results = ( + await execute_multiple_tools_on_multiple_mcp_servers( + responses=responses, + urls=urls, + transport=TEST_CONFIG["transport"], + max_concurrent=2, + ) ) - + assert isinstance(results, list) - log_test_result(test_name, "PASS", f"Successfully executed {len(results)} tool calls") - + log_test_result( + test_name, + "PASS", + f"Successfully executed {len(results)} tool calls", + ) + except MCPConnectionError: - log_test_result(test_name, "SKIP", "Server not available for multiple tool execution test") - + log_test_result( + test_name, + "SKIP", + "Server not available for multiple tool execution test", + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed to execute multiple tools: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed to execute multiple tools: {str(e)}", + ) + def test_execute_multiple_tools_sync(): """Test synchronous multiple tool execution""" test_name = "test_execute_multiple_tools_sync" - + try: urls = [TEST_CONFIG["server_url"]] - + # Create minimal test requests responses = [ { "function": { "name": "test_function", # This will likely fail but tests the sync wrapper - "arguments": {} + "arguments": {}, } } ] - + results = execute_multiple_tools_on_multiple_mcp_servers_sync( responses=responses, urls=urls, transport=TEST_CONFIG["transport"], - max_concurrent=1 + max_concurrent=1, ) - + assert isinstance(results, list) - log_test_result(test_name, "PASS", f"Successfully ran sync multiple tools execution (got {len(results)} results)") - + log_test_result( + test_name, + "PASS", + f"Successfully ran sync multiple tools execution (got {len(results)} results)", + ) + except MCPConnectionError as e: - log_test_result(test_name, "SKIP", f"Server not available: {str(e)}") + log_test_result( + test_name, "SKIP", f"Server not available: {str(e)}" + ) except Exception as e: - log_test_result(test_name, "FAIL", error=f"Failed sync multiple tools execution: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Failed sync multiple tools execution: {str(e)}", + ) + def test_error_handling(): """Test error handling for various scenarios""" test_name = "test_error_handling" - + try: # Test invalid server URL try: get_mcp_tools_sync( server_path="http://invalid-url:99999/mcp", - transport="streamable_http" + transport="streamable_http", ) assert False, "Should have raised an exception" except MCPConnectionError: pass # Expected - + # Test invalid connection object try: connect_to_mcp_server("invalid_connection") assert False, "Should have raised an exception" except MCPValidationError: pass # Expected - + # Test invalid transport detection transport = auto_detect_transport("") assert transport == "sse" # Should default to sse - - log_test_result(test_name, "PASS", "All error handling tests passed") - + + log_test_result( + test_name, "PASS", "All error handling tests passed" + ) + except Exception as e: - log_test_result(test_name, "FAIL", error=f"Error handling test failed: {str(e)}") + log_test_result( + test_name, + "FAIL", + error=f"Error handling test failed: {str(e)}", + ) + async def run_all_tests(): """Run all test functions""" logger.info("Starting MCP unit tests...") - + # Synchronous tests test_transform_mcp_tool_to_openai_tool() test_get_function_arguments() @@ -474,22 +652,25 @@ async def run_all_tests(): test_get_tools_for_multiple_mcp_servers() test_execute_multiple_tools_sync() test_error_handling() - + # Asynchronous tests await test_aget_mcp_tools() await test_execute_tool_call_simple() await test_create_server_tool_mapping() await test_execute_multiple_tools_on_multiple_servers() - - logger.info(f"Completed all tests. Total tests run: {len(test_results)}") + + logger.info( + f"Completed all tests. Total tests run: {len(test_results)}" + ) + def generate_markdown_report(): """Generate markdown report of test results""" - + passed_tests = [r for r in test_results if r["status"] == "PASS"] failed_tests = [r for r in test_results if r["status"] == "FAIL"] skipped_tests = [r for r in test_results if r["status"] == "SKIP"] - + markdown_content = f"""# MCP Unit Test Results ## Summary @@ -508,32 +689,44 @@ def generate_markdown_report(): ### āœ… Passed Tests ({len(passed_tests)}) """ - + for test in passed_tests: - markdown_content += f"- **{test['test_name']}**: {test['message']}\n" - + markdown_content += ( + f"- **{test['test_name']}**: {test['message']}\n" + ) + if failed_tests: - markdown_content += f"\n### āŒ Failed Tests ({len(failed_tests)})\n" + markdown_content += ( + f"\n### āŒ Failed Tests ({len(failed_tests)})\n" + ) for test in failed_tests: - markdown_content += f"- **{test['test_name']}**: {test['error']}\n" - + markdown_content += ( + f"- **{test['test_name']}**: {test['error']}\n" + ) + if skipped_tests: - markdown_content += f"\n### ā­ļø Skipped Tests ({len(skipped_tests)})\n" + markdown_content += ( + f"\n### ā­ļø Skipped Tests ({len(skipped_tests)})\n" + ) for test in skipped_tests: - markdown_content += f"- **{test['test_name']}**: {test['message']}\n" - + markdown_content += ( + f"- **{test['test_name']}**: {test['message']}\n" + ) + markdown_content += """ ## Detailed Results | Test Name | Status | Message/Error | Timestamp | |-----------|---------|---------------|-----------| """ - + for test in test_results: - status_emoji = {"PASS": "āœ…", "FAIL": "āŒ", "SKIP": "ā­ļø"}.get(test["status"], "ā“") + status_emoji = {"PASS": "āœ…", "FAIL": "āŒ", "SKIP": "ā­ļø"}.get( + test["status"], "ā“" + ) message = test.get("message") or test.get("error", "") markdown_content += f"| {test['test_name']} | {status_emoji} {test['status']} | {message} | {test['timestamp']} |\n" - + markdown_content += f""" ## Notes - Tests marked as SKIP typically indicate the MCP server was not available at {TEST_CONFIG["server_url"]} @@ -542,27 +735,34 @@ def generate_markdown_report(): Generated at: {datetime.now().isoformat()} """ - + return markdown_content + async def main(): """Main test runner""" try: await run_all_tests() - + # Generate and save markdown report markdown_report = generate_markdown_report() - + with open("mcp_test_results.md", "w") as f: f.write(markdown_report) - + logger.info("Test results saved to mcp_test_results.md") - + # Print summary - passed = len([r for r in test_results if r["status"] == "PASS"]) - failed = len([r for r in test_results if r["status"] == "FAIL"]) - skipped = len([r for r in test_results if r["status"] == "SKIP"]) - + passed = len( + [r for r in test_results if r["status"] == "PASS"] + ) + failed = len( + [r for r in test_results if r["status"] == "FAIL"] + ) + skipped = len( + [r for r in test_results if r["status"] == "SKIP"] + ) + print(f"\n{'='*50}") print("TEST SUMMARY") print(f"{'='*50}") @@ -572,10 +772,11 @@ async def main(): print(f"Skipped: {skipped}") print(f"Success Rate: {(passed/len(test_results)*100):.1f}%") print(f"{'='*50}") - + except Exception as e: logger.error(f"Error running tests: {str(e)}") logger.error(f"Traceback: {traceback.format_exc()}") + if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) From da7df166e70c49b70646d5f5b486db57f2f4472b Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Mon, 1 Sep 2025 11:52:35 -0700 Subject: [PATCH 2/5] [FEAT][Reasoning Integration] [Improvement][MajorityVoting Cleanup] [EXAMPLE][Firecrawl Agent] [FEAT][Streaming Callback for ConcurrentWorkflow] --- docs/developer_guides/firecrawl.md | 0 docs/swarms/structs/majorityvoting.md | 327 ++++--- .../img_gen_nano_banana.py | 23 + .../nano_banana_jarvis_agent/jarvis_agent.py | 2 + .../example_streaming_workflow.py | 62 ++ .../parallel_search_agents.py | 79 ++ examples/reasoning_agents/example_o3.py | 18 + examples/reasoning_agents/o3_agent.py | 23 + examples/tools/firecrawl_agents_example.py | 25 + .../b74ace74-8e49-42af-87ab-051d7fdab62a.png | Bin 0 -> 1765197 bytes swarms/structs/__init__.py | 6 - swarms/structs/agent.py | 49 +- swarms/structs/concurrent_workflow.py | 800 ++++-------------- swarms/structs/majority_voting.py | 156 +--- swarms/utils/formatter.py | 133 ++- swarms/utils/litellm_wrapper.py | 303 ++++--- .../test_reasoning_agent_router_all.py | 24 +- 17 files changed, 915 insertions(+), 1115 deletions(-) create mode 100644 docs/developer_guides/firecrawl.md create mode 100644 examples/guides/nano_banana_jarvis_agent/img_gen_nano_banana.py create mode 100644 examples/multi_agent/concurrent_examples/streaming_callback/example_streaming_workflow.py create mode 100644 examples/multi_agent/concurrent_examples/streaming_callback/parallel_search_agents.py create mode 100644 examples/reasoning_agents/example_o3.py create mode 100644 examples/reasoning_agents/o3_agent.py create mode 100644 examples/tools/firecrawl_agents_example.py create mode 100644 images/b74ace74-8e49-42af-87ab-051d7fdab62a.png diff --git a/docs/developer_guides/firecrawl.md b/docs/developer_guides/firecrawl.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/swarms/structs/majorityvoting.md b/docs/swarms/structs/majorityvoting.md index e44ccdd7..19a00385 100644 --- a/docs/swarms/structs/majorityvoting.md +++ b/docs/swarms/structs/majorityvoting.md @@ -12,206 +12,331 @@ graph TD D --> E[Single Task] D --> F[Batch Tasks] D --> G[Concurrent Tasks] - D --> H[Async Tasks] - E --> I[Run Agents] - F --> I - G --> I - H --> I - I --> J[Collect Responses] - J --> K[Consensus Analysis] - K --> L{Consensus Agent?} - L -->|Yes| M[Use Consensus Agent] - L -->|No| N[Use Last Agent] - M --> O[Final Output] - N --> O - O --> P[Save Conversation] + E --> H[Run Agents] + F --> H + G --> H + H --> I[Collect Responses] + I --> J[Consensus Analysis] + J --> K{Consensus Agent?} + K -->|Yes| L[Use Consensus Agent] + K -->|No| M[Use Last Agent] + L --> N[Final Output] + M --> N + N --> O[Return Result] ``` ### Key Concepts - **Majority Voting**: A method to determine the most common response from a set of answers. - **Agents**: Entities (e.g., models, algorithms) that provide responses to tasks or queries. -- **Output Parser**: A function that processes the responses from the agents before performing the majority voting. - **Consensus Agent**: An optional agent that analyzes the responses from all agents to determine the final consensus. - **Conversation History**: A record of all agent interactions and responses during the voting process. +- **Output Types**: Support for different output formats (string, dictionary, list). ## Class Definition: `MajorityVoting` -### Parameters +```python +class MajorityVoting: + def __init__( + self, + id: str = swarm_id(), + name: str = "MajorityVoting", + description: str = "A majority voting system for agents", + agents: List[Agent] = None, + consensus_agent: Optional[Agent] = None, + autosave: bool = False, + verbose: bool = False, + max_loops: int = 1, + output_type: OutputType = "dict", + *args, + **kwargs, + ): +``` -| Parameter | Type | Description | -|------------------|----------------|-----------------------------------------------------------------------------| -| `name` | `str` | Name of the majority voting system. Default is "MajorityVoting". | -| `description` | `str` | Description of the system. Default is "A majority voting system for agents". | -| `agents` | `List[Agent]` | A list of agents to be used in the majority voting system. | -| `output_parser` | `Callable` | Function to parse agent outputs. Default is `majority_voting` function. | -| `consensus_agent`| `Agent` | Optional agent for analyzing consensus among responses. | -| `autosave` | `bool` | Whether to autosave conversations. Default is `False`. | -| `verbose` | `bool` | Whether to enable verbose logging. Default is `False`. | -| `max_loops` | `int` | Maximum number of voting loops. Default is 1. | +### Constructor Parameters + +| Parameter | Type | Default | Description | +|------------------|-------------------|---------------|-----------------------------------------------------------------------------| +| `id` | `str` | `swarm_id()` | Unique identifier for the majority voting system. | +| `name` | `str` | `"MajorityVoting"` | Name of the majority voting system. | +| `description` | `str` | `"A majority voting system for agents"` | Description of the system. | +| `agents` | `List[Agent]` | `None` | A list of agents to be used in the majority voting system. Required. | +| `consensus_agent`| `Optional[Agent]` | `None` | Optional agent for analyzing consensus among responses. If None, uses last agent. | +| `autosave` | `bool` | `False` | Whether to autosave conversations. | +| `verbose` | `bool` | `False` | Whether to enable verbose logging. | +| `max_loops` | `int` | `1` | Maximum number of voting loops. | +| `output_type` | `OutputType` | `"dict"` | Output format: "str", "dict", "list", or other. | +| `*args` | `Any` | - | Variable length argument list passed to Conversation. | +| `**kwargs` | `Any` | - | Arbitrary keyword arguments passed to Conversation. | ### Methods -#### `run(task: str, correct_answer: str, *args, **kwargs) -> List[Any]` +#### `run(task: str, *args, **kwargs) -> Any` -Runs the majority voting system for a single task. +Runs the majority voting system for a single task and returns the consensus result. **Parameters:** -- `task` (str): The task to be performed by the agents -- `correct_answer` (str): The correct answer for evaluation -- `*args`, `**kwargs`: Additional arguments + +- `task` (`str`): The task to be performed by the agents +- `*args`: Variable length argument list passed to agents +- `**kwargs`: Arbitrary keyword arguments passed to agents **Returns:** -- List[Any]: The conversation history as a string, including the majority vote + +- `Any`: The consensus result in the specified output format (string, dict, or list) + +**Raises:** + +- `ValueError`: If agents list is empty #### `batch_run(tasks: List[str], *args, **kwargs) -> List[Any]` -Runs multiple tasks in sequence. +Runs the majority voting system for multiple tasks in sequence. **Parameters:** -- `tasks` (List[str]): List of tasks to be performed -- `*args`, `**kwargs`: Additional arguments + +- `tasks` (`List[str]`): List of tasks to be performed by the agents +- `*args`: Variable length argument list passed to each run +- `**kwargs`: Arbitrary keyword arguments passed to each run **Returns:** -- List[Any]: List of majority votes for each task + +- `List[Any]`: List of consensus results for each task #### `run_concurrently(tasks: List[str], *args, **kwargs) -> List[Any]` -Runs multiple tasks concurrently using thread pooling. +Runs the majority voting system for multiple tasks concurrently using thread pooling. **Parameters:** -- `tasks` (List[str]): List of tasks to be performed -- `*args`, `**kwargs`: Additional arguments -**Returns:** -- List[Any]: List of majority votes for each task - -#### `run_async(tasks: List[str], *args, **kwargs) -> List[Any]` +- `tasks` (`List[str]`): List of tasks to be performed by the agents +- `*args`: Variable length argument list passed to each run +- `**kwargs`: Arbitrary keyword arguments passed to each run -Runs multiple tasks asynchronously using asyncio. +**Returns:** -**Parameters:** -- `tasks` (List[str]): List of tasks to be performed -- `*args`, `**kwargs`: Additional arguments +- `List[Any]`: List of consensus results for each task, in completion order -**Returns:** -- List[Any]: List of majority votes for each task +**Note:** Uses `os.cpu_count()` workers for optimal performance. ## Usage Examples -### Example 1: Basic Single Task Execution with Modern LLMs +### Example 1: Investment Analysis with Consensus Agent + +This example demonstrates using MajorityVoting for financial analysis with specialized agents and a dedicated consensus agent. ```python from swarms import Agent, MajorityVoting -# Initialize multiple agents with different specialties +# Initialize multiple specialized agents agents = [ Agent( - agent_name="Financial-Analysis-Agent", - agent_description="Personal finance advisor focused on market analysis", - system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.", + agent_name="Market-Analysis-Agent", + agent_description="Market trend analyst", + system_prompt="You are a market analyst specializing in identifying growth opportunities and market trends.", max_loops=1, model_name="gpt-4o" ), Agent( - agent_name="Risk-Assessment-Agent", - agent_description="Risk analysis and portfolio management expert", - system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.", + agent_name="Risk-Assessment-Agent", + agent_description="Risk analysis expert", + system_prompt="You are a risk assessment expert focused on evaluating investment risks and volatility.", max_loops=1, model_name="gpt-4o" ), Agent( - agent_name="Tech-Investment-Agent", - agent_description="Technology sector investment specialist", - system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.", + agent_name="Portfolio-Strategy-Agent", + agent_description="Portfolio optimization specialist", + system_prompt="You are a portfolio strategist focused on diversification and long-term growth strategies.", max_loops=1, model_name="gpt-4o" ) ] - +# Dedicated consensus agent for final analysis consensus_agent = Agent( - agent_name="Consensus-Agent", - agent_description="Consensus agent focused on analyzing investment advice", - system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.", + agent_name="Investment-Consensus-Agent", + agent_description="Investment consensus analyzer", + system_prompt="You are an expert at synthesizing investment advice from multiple perspectives into a coherent recommendation.", max_loops=1, model_name="gpt-4o" ) # Create majority voting system -majority_voting = MajorityVoting( - name="Investment-Advisory-System", - description="Multi-agent system for investment advice", +investment_system = MajorityVoting( + name="Investment-Analysis-System", + description="Multi-agent investment analysis with consensus evaluation", agents=agents, + consensus_agent=consensus_agent, verbose=True, - consensus_agent=consensus_agent + output_type="dict" ) -# Run the analysis with majority voting -result = majority_voting.run( - task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.", - correct_answer="" # Optional evaluation metric +# Execute investment analysis +result = investment_system.run( + task="""Analyze the following investment scenario and provide recommendations: + - Budget: $50,000 + - Risk tolerance: Moderate + - Time horizon: 5-7 years + - Focus areas: Technology, Healthcare, Renewable Energy + Provide specific ETF/index fund recommendations with allocation percentages.""" ) +print("Investment Analysis Results:") print(result) - ``` -## Batch Execution +### Example 2: Content Creation with Batch Processing + +This example shows how to use batch processing for content creation tasks with multiple writing styles. ```python from swarms import Agent, MajorityVoting -# Initialize multiple agents with different specialties -agents = [ +# Initialize content creation agents with different styles +content_agents = [ + Agent( + agent_name="Creative-Writer", + agent_description="Creative content specialist", + system_prompt="You are a creative writer who produces engaging, story-driven content with vivid descriptions.", + max_loops=1, + model_name="gpt-4o" + ), Agent( - agent_name="Financial-Analysis-Agent", - agent_description="Personal finance advisor focused on market analysis", - system_prompt="You are a financial advisor specializing in market analysis and investment opportunities.", + agent_name="Technical-Writer", + agent_description="Technical content specialist", + system_prompt="You are a technical writer who focuses on clarity, accuracy, and structured information.", max_loops=1, model_name="gpt-4o" ), Agent( - agent_name="Risk-Assessment-Agent", - agent_description="Risk analysis and portfolio management expert", - system_prompt="You are a risk assessment expert focused on evaluating investment risks and portfolio diversification.", + agent_name="SEO-Optimized-Writer", + agent_description="SEO content specialist", + system_prompt="You are an SEO specialist who creates content optimized for search engines while maintaining quality.", max_loops=1, model_name="gpt-4o" ), Agent( - agent_name="Tech-Investment-Agent", - agent_description="Technology sector investment specialist", - system_prompt="You are a technology investment specialist focused on AI, emerging tech, and growth opportunities.", + agent_name="Conversational-Writer", + agent_description="Conversational content specialist", + system_prompt="You are a conversational writer who creates relatable, engaging content that connects with readers.", max_loops=1, model_name="gpt-4o" ) ] +# Create majority voting system without dedicated consensus agent +content_system = MajorityVoting( + name="Content-Creation-System", + description="Multi-style content creation with majority voting", + agents=content_agents, + verbose=True, + output_type="str" +) + +# Define multiple content tasks +content_tasks = [ + "Write a blog post about the benefits of renewable energy adoption", + "Create social media content for a new fitness app launch", + "Develop a product description for eco-friendly water bottles", + "Write an email newsletter about artificial intelligence trends" +] + +# Execute batch processing +batch_results = content_system.batch_run(content_tasks) -consensus_agent = Agent( - agent_name="Consensus-Agent", - agent_description="Consensus agent focused on analyzing investment advice", - system_prompt="You are a consensus agent focused on analyzing investment advice and providing a final answer.", +print("Batch Content Creation Results:") +for i, result in enumerate(batch_results, 1): + print(f"\nTask {i} Result:") + print(result[:500] + "..." if len(str(result)) > 500 else result) +``` + +### Example 3: Research Analysis with Concurrent Processing + +This example demonstrates concurrent processing for research analysis with multiple research perspectives. + +```python +from swarms import Agent, MajorityVoting + +# Initialize research agents with different methodologies +research_agents = [ + Agent( + agent_name="Quantitative-Researcher", + agent_description="Quantitative research specialist", + system_prompt="You are a quantitative researcher who analyzes data, statistics, and numerical evidence.", + max_loops=1, + model_name="gpt-4o" + ), + Agent( + agent_name="Qualitative-Researcher", + agent_description="Qualitative research specialist", + system_prompt="You are a qualitative researcher who focuses on patterns, themes, and contextual understanding.", + max_loops=1, + model_name="gpt-4o" + ), + Agent( + agent_name="Literature-Review-Specialist", + agent_description="Literature review expert", + system_prompt="You are a literature review specialist who synthesizes existing research and identifies knowledge gaps.", + max_loops=1, + model_name="gpt-4o" + ), + Agent( + agent_name="Methodology-Expert", + agent_description="Research methodology specialist", + system_prompt="You are a methodology expert who evaluates research design, validity, and reliability.", + max_loops=1, + model_name="gpt-4o" + ), + Agent( + agent_name="Ethics-Reviewer", + agent_description="Research ethics specialist", + system_prompt="You are an ethics reviewer who ensures research practices are responsible and unbiased.", + max_loops=1, + model_name="gpt-4o" + ) +] + +# Consensus agent for synthesizing research findings +research_consensus = Agent( + agent_name="Research-Synthesis-Agent", + agent_description="Research synthesis specialist", + system_prompt="You are an expert at synthesizing diverse research perspectives into comprehensive, well-supported conclusions.", max_loops=1, model_name="gpt-4o" ) -# Create majority voting system -majority_voting = MajorityVoting( - name="Investment-Advisory-System", - description="Multi-agent system for investment advice", - agents=agents, +# Create majority voting system for research +research_system = MajorityVoting( + name="Research-Analysis-System", + description="Concurrent multi-perspective research analysis", + agents=research_agents, + consensus_agent=research_consensus, verbose=True, - consensus_agent=consensus_agent + output_type="list" ) -# Run the analysis with majority voting -result = majority_voting.batch_run( - task="Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.", - correct_answer="" # Optional evaluation metric -) +# Define research questions for concurrent analysis +research_questions = [ + "What are the environmental impacts of electric vehicle adoption?", + "How does remote work affect employee productivity and well-being?", + "What are the economic implications of universal basic income?", + "How can AI be used to improve healthcare outcomes?", + "What are the social effects of social media on mental health?" +] -print(result) +# Execute concurrent research analysis +concurrent_results = research_system.run_concurrently(research_questions) +print("Concurrent Research Analysis Results:") +print(f"Total questions analyzed: {len(concurrent_results)}") +for i, result in enumerate(concurrent_results, 1): + print(f"\nResearch Question {i}:") + if isinstance(result, list) and len(result) > 0: + print(f"Analysis length: {len(str(result))} characters") + print(f"Sample output: {str(result)[:300]}...") + else: + print(f"Result: {result}") + +``` -``` \ No newline at end of file diff --git a/examples/guides/nano_banana_jarvis_agent/img_gen_nano_banana.py b/examples/guides/nano_banana_jarvis_agent/img_gen_nano_banana.py new file mode 100644 index 00000000..9a137332 --- /dev/null +++ b/examples/guides/nano_banana_jarvis_agent/img_gen_nano_banana.py @@ -0,0 +1,23 @@ +from swarms import Agent + +IMAGE_GEN_SYSTEM_PROMPT = ( + "You are an advanced image generation agent. Given a textual description, generate a high-quality, photorealistic image that matches the prompt. " + "Return only the generated image." +) + +image_gen_agent = Agent( + agent_name="Image-Generation-Agent", + agent_description="Agent specialized in generating high-quality, photorealistic images from textual prompts.", + model_name="gemini/gemini-2.5-flash-image-preview", # Replace with your preferred image generation model if available + dynamic_temperature_enabled=True, + max_loops=1, + dynamic_context_window=True, + retry_interval=1, +) + +image_gen_out = image_gen_agent.run( + task=f"{IMAGE_GEN_SYSTEM_PROMPT} \n\n Generate a photorealistic image of a futuristic city skyline at sunset.", +) + +print("Image Generation Output:") +print(image_gen_out) diff --git a/examples/guides/nano_banana_jarvis_agent/jarvis_agent.py b/examples/guides/nano_banana_jarvis_agent/jarvis_agent.py index 7e71cd71..ad0c1981 100644 --- a/examples/guides/nano_banana_jarvis_agent/jarvis_agent.py +++ b/examples/guides/nano_banana_jarvis_agent/jarvis_agent.py @@ -20,6 +20,7 @@ SYSTEM_PROMPT = ( "Return the image only." ) +# Agent for AR annotation agent = Agent( agent_name="Tactical-Strategist-Agent", agent_description="Agent specialized in tactical strategy, scenario analysis, and actionable recommendations for complex situations.", @@ -35,4 +36,5 @@ out = agent.run( img="hk.jpg", ) +print("AR Annotation Output:") print(out) diff --git a/examples/multi_agent/concurrent_examples/streaming_callback/example_streaming_workflow.py b/examples/multi_agent/concurrent_examples/streaming_callback/example_streaming_workflow.py new file mode 100644 index 00000000..0993ba8b --- /dev/null +++ b/examples/multi_agent/concurrent_examples/streaming_callback/example_streaming_workflow.py @@ -0,0 +1,62 @@ +from swarms import Agent, ConcurrentWorkflow + + +def streaming_callback( + agent_name: str, chunk: str, is_complete: bool +): + """ + Print streaming output from each agent as it arrives. + """ + if chunk: + print(f"[{agent_name}] {chunk}", end="", flush=True) + if is_complete: + print(" [DONE]") + + +def main(): + """ + Run a simple concurrent workflow with streaming output. + """ + agents = [ + Agent( + agent_name="Financial", + system_prompt="Financial analysis.", + model_name="groq/moonshotai/kimi-k2-instruct", + max_loops=1, + print_on=False, + ), + Agent( + agent_name="Legal", + system_prompt="Legal analysis.", + model_name="groq/moonshotai/kimi-k2-instruct", + max_loops=1, + print_on=False, + ), + ] + + workflow = ConcurrentWorkflow( + name="Simple-Streaming", + agents=agents, + show_dashboard=False, + output_type="dict-all-except-first", + ) + + task = "Give a short analysis of the risks and opportunities for a SaaS startup expanding to Europe." + + print("Streaming output:\n") + result = workflow.run( + task=task, streaming_callback=streaming_callback + ) + print("\n\nFinal results:") + # Convert list of messages to dict with agent names as keys + agent_outputs = {} + for message in result: + if message["role"] != "User": # Skip the user message + agent_outputs[message["role"]] = message["content"] + + for agent, output in agent_outputs.items(): + print(f"{agent}: {output}") + + +if __name__ == "__main__": + main() diff --git a/examples/multi_agent/concurrent_examples/streaming_callback/parallel_search_agents.py b/examples/multi_agent/concurrent_examples/streaming_callback/parallel_search_agents.py new file mode 100644 index 00000000..415270da --- /dev/null +++ b/examples/multi_agent/concurrent_examples/streaming_callback/parallel_search_agents.py @@ -0,0 +1,79 @@ +from swarms import Agent +from swarms_tools import scrape_and_format_sync +from swarms.structs.concurrent_workflow import ConcurrentWorkflow +import time + +agent = Agent( + agent_name="Web Scraper Agent", + model_name="groq/moonshotai/kimi-k2-instruct", + system_prompt="You are a web scraper agent. You are given a URL and you need to scrape the website and return the data in a structured format. The format type should be full", + tools=[scrape_and_format_sync], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + streaming_on=True, # Enable streaming mode + print_on=False, # Prevent direct printing (let callback handle it) +) + + +def streaming_callback(agent_name: str, chunk: str, is_final: bool): + """ + Callback function to handle streaming output from agents. + + Args: + agent_name (str): Name of the agent producing the output + chunk (str): Chunk of output text + is_final (bool): Whether this is the final chunk (completion signal) + """ + if is_final: + print(f"\n[{agent_name}] - COMPLETED") + return + + if chunk: + # Print timestamp with agent name for each chunk + timestamp = time.strftime("%H:%M:%S", time.localtime()) + print( + f"[{timestamp}] [{agent_name}] {chunk}", + end="", + flush=True, + ) + else: + # Debug: print when chunk is empty but not final + print(f"[{agent_name}] - EMPTY CHUNK", end="", flush=True) + + +# Alternative simple callback (uncomment to use instead): +# def simple_streaming_callback(agent_name: str, chunk: str, is_final: bool): +# """Simple callback that just prints agent output without timestamps.""" +# if is_final: +# print(f"\n--- {agent_name} FINISHED ---") +# elif chunk: +# print(f"[{agent_name}] {chunk}", end="", flush=True) + + +# For saving to file (uncomment to use): +# import os +# def file_streaming_callback(agent_name: str, chunk: str, is_final: bool): +# """Callback that saves streaming output to separate files per agent.""" +# if not os.path.exists('agent_outputs'): +# os.makedirs('agent_outputs') +# +# filename = f"agent_outputs/{agent_name.replace(' ', '_')}.txt" +# +# with open(filename, 'a', encoding='utf-8') as f: +# if is_final: +# f.write(f"\n--- COMPLETED ---\n") +# elif chunk: +# f.write(chunk) + + +swarm = ConcurrentWorkflow( + agents=[agent, agent], + name="Web Scraper Swarm", + description="This swarm is used to scrape the web and return the data in a structured format.", +) + +swarm.run( + task="Scrape swarms.ai website and provide a full report of the company does. The format type should be full.", + streaming_callback=streaming_callback, +) diff --git a/examples/reasoning_agents/example_o3.py b/examples/reasoning_agents/example_o3.py new file mode 100644 index 00000000..48e01870 --- /dev/null +++ b/examples/reasoning_agents/example_o3.py @@ -0,0 +1,18 @@ +from swarms.utils.litellm_wrapper import LiteLLM + +# Initialize the LiteLLM wrapper with reasoning support +llm = LiteLLM( + model_name="claude-sonnet-4-20250514", # OpenAI o3 model with reasoning + reasoning_effort="low", # Enable reasoning with high effort + temperature=1, + max_tokens=2000, + stream=False, + thinking_tokens=1024, +) + +# Example task that would benefit from reasoning +task = "Solve this step-by-step: A farmer has 17 sheep and all but 9 die. How many sheep does he have left?" + +print("=== Running reasoning model ===") +response = llm.run(task) +print(response) diff --git a/examples/reasoning_agents/o3_agent.py b/examples/reasoning_agents/o3_agent.py new file mode 100644 index 00000000..e5d4b7b0 --- /dev/null +++ b/examples/reasoning_agents/o3_agent.py @@ -0,0 +1,23 @@ +from swarms import Agent + +# Initialize the LiteLLM wrapper with reasoning support +agent = Agent( + model_name="claude-sonnet-4-20250514", # OpenAI o3 model with reasoning + reasoning_effort="low", # Enable reasoning with high effort + temperature=1, + max_tokens=2000, + stream=False, + thinking_tokens=1024, + top_p=0.95, + streaming_on=True, + print_on=False, +) + + +out = agent.run( + task="Solve this step-by-step: A farmer has 17 sheep and all but 9 die. How many sheep does he have left?", +) + +for chunk in out: + # Flush + print(chunk, end="", flush=True) diff --git a/examples/tools/firecrawl_agents_example.py b/examples/tools/firecrawl_agents_example.py new file mode 100644 index 00000000..67ef6c97 --- /dev/null +++ b/examples/tools/firecrawl_agents_example.py @@ -0,0 +1,25 @@ +from swarms_tools import crawl_entire_site_firecrawl + +from swarms import Agent + +agent = Agent( + agent_name="Marketing Copy Improver", + model_name="gpt-4.1", + tools=[crawl_entire_site_firecrawl], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt=( + "You are a world-class marketing copy improver. " + "Given a website URL, your job is to crawl the entire site, analyze all marketing copy, " + "and rewrite it to maximize clarity, engagement, and conversion. " + "Return the improved marketing copy in a structured, easy-to-read format. " + "Be concise, persuasive, and ensure the tone matches the brand. " + "Highlight key value propositions and calls to action." + ), +) + +out = agent.run( + "Crawl 2-3 pages of swarms.ai and improve the marketing copy found on those pages. 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zf=^%?MC@kH5D^jSO*InllYL-Ujo~e+1A@aX_Nm2D3{mXKlnf$abBo-fcqw zz4~%-@48(3}@BNkk{>8kxUXI`U(fhym^>^-; zR!c2X&o`AFrnY7n8 z4ABInV=knH35rre4+#urP-ZqLk=f=H?o`#a_qF$)A0aXbFwkZK8?msk)H+SX3=|Mt zyZ3I^*X8K0v2=-u5gSWJi5P|xN}hp$Fb$>PcTl~+zouju?LQF9>r;F@^wP;mAT;4b zrPNX@GqaSWzbpj@B`?W=0y{iD@%KmyB_QbVjIBEq5}^Knmt3UIR_XB!%_xaQKJ>K+uZM|{EPrbC)z zl*2{Loa#pIs?01559%Ic>I}*ic@xFqBqT#TM%NraL;J(yN&~|YSC7*`9!`CADq}Dn zY-pBxOAmY&r_*syv0hZa0iv8PP>dET=YHfxkQ53mfVWXSB-4E;rI@3nA_2AWWab1B zw}l~MFOVQ{{_sQyKr=-g9aLilGJX#7HZmy;5*X7ZptEVsSNQ$}9sTq+HZufONVpT20V9oN>^_0i*p z=et?EZnjgM%H96>;ge_XEF!|B&4lFge9MK7`vt@!eUep}H4y*+1prAzK~zZu4(&P% zKRDao-S6+#by1s2+3hw@pT2aG*qh;?wRa{q_fiWSg_w!I{jG2B_wDjxTST_o?X=yz z_x?xs`~7Zv`T5U(_W%BO{?0rVfQ51Q^8PRU@-P3w&;Rs)_MiSI`*p9);BH;5_b>nS zXMXwTf9n7F|NYHpw|ASfGjJguaU?N9g9zmDk&z*`brR1-YH`d0rbV?dJ5leZ?%oZt z?oya2)(KfDq1zL3ugu53GLv;n;YB!+gq^IZz4qwQ`Q-sj1hf0@c7Izy^IWH?K6r3m z3e_T0;fu|5ezv*0xji0^UHevIE*iYp09^6vs4E-{AzO?Ykzf#Hu9;<8jVBLb zt}L9q3W94Echjye5^x&RSnCU?h|)rG_H>H-0b*50+*IyPoEWTVf?zamKn`LH%tWQg z&D}i_)iRNgJ56(l+BN8PP8uhb*yOG$sz!j;@oXoruP`_?fP(PvQph5uWbIIM33xxy zn-m-;RuQ7g!1H5iW$d-6%Eb(j;^Y}v54j5&HBCIM8#VQS;L0gbOR z)XE+coSVZUz9d7Ja?DU4c$X8Cn|D=p8(?sFZ4$+xro*)M+j^Pl|0vnMbA=o{a9 z_v!VjHcz`E^=!K-?55t`G*oaR0L*;t%`C<9s@j{beSs<1nWdJ%I61oEY(L_#Q67NA z72_k$<~Kz>aqrVOit%Rxm(PX;W7-}b5_d&&)vkRtUE!UW;>c!pM&gddrLfdeE7u7K zUAuZ;&6;a>)!bc=FcT4FPE}kGeZEMQPmAEN8bC4p4XjIj1DhR_-J}gvAQSES_DiX`)3G zRT?l%jM%B87W#>Y$Mat!dN%%LKv;D0@#bV2r;G`f1ZEvl y5in)GA^9`JyWy{fqE|5l@G(hw0MZ<}cmIEJT+-Jy7Jjh+0000>> from swarms import Agent, ConcurrentWorkflow - >>> - >>> # Create specialized agents - >>> financial_agent = Agent( - ... agent_name="Financial-Analyst", - ... system_prompt="You are a financial analysis expert...", - ... model_name="gpt-4" - ... ) - >>> legal_agent = Agent( - ... agent_name="Legal-Advisor", - ... system_prompt="You are a legal expert...", - ... model_name="gpt-4" - ... ) - >>> - >>> # Create workflow - >>> workflow = ConcurrentWorkflow( - ... name="Multi-Expert-Analysis", - ... agents=[financial_agent, legal_agent], - ... show_dashboard=True, - ... auto_save=True - ... ) - >>> - >>> # Execute analysis - >>> task = "Analyze the risks of investing in cryptocurrency" - >>> results = workflow.run(task) - >>> print(f"Analysis complete with {len(results)} perspectives") - - Batch processing example: - - >>> tasks = [ - ... "Analyze Q1 financial performance", - ... "Review Q2 market trends", - ... "Forecast Q3 projections" - ... ] - >>> batch_results = workflow.batch_run(tasks) - >>> print(f"Processed {len(batch_results)} quarterly reports") - - Note: - - Agents are executed using ThreadPoolExecutor with 95% of available CPU cores - - Each agent runs independently and cannot communicate with others during execution - - The workflow maintains conversation history across all runs for context - - Dashboard mode disables individual agent printing to prevent output conflicts - - Error handling ensures partial results are available even if some agents fail - """ + """Concurrent workflow for running multiple agents simultaneously.""" def __init__( self, + id: str = swarm_id(), name: str = "ConcurrentWorkflow", description: str = "Execution of multiple agents concurrently", agents: List[Union[Agent, Callable]] = None, - metadata_output_path: str = "agent_metadata.json", auto_save: bool = True, output_type: str = "dict-all-except-first", max_loops: int = 1, @@ -149,17 +33,6 @@ class ConcurrentWorkflow(BaseSwarm): *args, **kwargs, ): - """ - Initialize the ConcurrentWorkflow with configuration parameters. - - Performs initialization, validation, and setup of internal components including - conversation management, agent status tracking, and dashboard configuration. - - Note: - The constructor automatically performs reliability checks and configures - agents for dashboard mode if enabled. Agent print outputs are disabled - when dashboard mode is active to prevent display conflicts. - """ super().__init__( name=name, description=description, @@ -170,7 +43,9 @@ class ConcurrentWorkflow(BaseSwarm): self.name = name self.description = description self.agents = agents - self.metadata_output_path = metadata_output_path + self.metadata_output_path = ( + f"concurrent_workflow_name_{name}_id_{id}.json" + ) self.auto_save = auto_save self.max_loops = max_loops self.auto_generate_prompts = auto_generate_prompts @@ -188,58 +63,14 @@ class ConcurrentWorkflow(BaseSwarm): self.agents = self.fix_agents() def fix_agents(self): - """ - Configure agents for dashboard mode by disabling individual print outputs. - - When dashboard mode is enabled, individual agent print outputs can interfere - with the dashboard display. This method disables print_on for all agents - to ensure clean dashboard rendering. - - Returns: - List[Agent]: The modified list of agents with print_on disabled. - - Note: - This method only modifies agents when show_dashboard is True. - Agent functionality is not affected, only their output display behavior. - - Example: - >>> workflow = ConcurrentWorkflow(show_dashboard=True, agents=[agent1, agent2]) - >>> # Agents automatically configured for dashboard mode - >>> all(not agent.print_on for agent in workflow.agents) - True - """ + """Configure agents for dashboard mode.""" if self.show_dashboard is True: for agent in self.agents: agent.print_on = False return self.agents def reliability_check(self): - """ - Perform comprehensive validation of workflow configuration and agents. - - Validates that the workflow is properly configured with valid agents and - provides warnings for suboptimal configurations. This method is called - automatically during initialization. - - Validates: - - Agents list is not None or empty - - At least one agent is provided - - Warns if only one agent is provided (suboptimal for concurrent execution) - - Raises: - ValueError: If agents list is None or empty. - - Logs: - Warning: If only one agent is provided (concurrent execution not beneficial). - Error: If validation fails with detailed error information. - - Example: - >>> workflow = ConcurrentWorkflow(agents=[]) - ValueError: ConcurrentWorkflow: No agents provided - - >>> workflow = ConcurrentWorkflow(agents=[single_agent]) - # Logs warning about single agent usage - """ + """Validate workflow configuration.""" try: if self.agents is None: raise ValueError( @@ -253,7 +84,7 @@ class ConcurrentWorkflow(BaseSwarm): if len(self.agents) == 1: logger.warning( - "ConcurrentWorkflow: Only one agent provided. With ConcurrentWorkflow, you should use at least 2+ agents." + "ConcurrentWorkflow: Only one agent provided." ) except Exception as e: logger.error( @@ -262,33 +93,7 @@ class ConcurrentWorkflow(BaseSwarm): raise def activate_auto_prompt_engineering(self): - """ - Enable automatic prompt engineering for all agents in the workflow. - - When activated, each agent will automatically enhance and optimize their - system prompts based on the task context and their previous interactions. - This can improve response quality but may increase execution time. - - Side Effects: - - Sets auto_generate_prompt=True for all agents in the workflow - - Affects subsequent agent.run() calls, not retroactively - - May increase latency due to prompt optimization overhead - - Note: - This method can be called at any time, but changes only affect - future agent executions. Already running agents are not affected. - - Example: - >>> workflow = ConcurrentWorkflow(agents=[agent1, agent2]) - >>> workflow.activate_auto_prompt_engineering() - >>> # All agents will now auto-generate optimized prompts - >>> result = workflow.run("Complex analysis task") - >>> # Agents used enhanced prompts for better performance - - See Also: - - Agent.auto_generate_prompt: Individual agent prompt engineering - - auto_generate_prompts: Constructor parameter for default behavior - """ + """Enable automatic prompt engineering.""" if self.auto_generate_prompts is True: for agent in self.agents: agent.auto_generate_prompt = True @@ -297,48 +102,8 @@ class ConcurrentWorkflow(BaseSwarm): self, title: str = "ConcurrentWorkflow Dashboard", is_final: bool = False, - ) -> None: - """ - Display a real-time dashboard showing the current status of all agents. - - Renders a formatted dashboard with agent names, execution status, and - output previews. The dashboard is updated in real-time during workflow - execution to provide visibility into agent progress and results. - - Args: - title (str, optional): The dashboard title to display at the top. - Defaults to "šŸ¤– ConcurrentWorkflow Dashboard". - is_final (bool, optional): Whether this is the final dashboard display - after all agents have completed. Changes formatting and styling. - Defaults to False. - - Side Effects: - - Prints formatted dashboard to console - - Updates display in real-time during execution - - May clear previous dashboard content for clean updates - - Note: - This method is automatically called during workflow execution when - show_dashboard=True. Manual calls are supported for custom monitoring. - - Dashboard Status Values: - - "pending": Agent queued but not yet started - - "running": Agent currently executing task - - "completed": Agent finished successfully - - "error": Agent execution failed with error - - Example: - >>> workflow = ConcurrentWorkflow(agents=[agent1, agent2], show_dashboard=True) - >>> workflow.display_agent_dashboard("Custom Dashboard Title") - # Displays: - # šŸ¤– Custom Dashboard Title - # ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¬ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” - # ā”‚ Agent Name ā”‚ Status ā”‚ Output Preview ā”‚ - # ā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¼ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¼ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤ - # ā”‚ Financial-Agent ā”‚ running ā”‚ Analyzing data...ā”‚ - # ā”‚ Legal-Agent ā”‚ pending ā”‚ ā”‚ - # ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ - """ + ): + """Display real-time dashboard.""" agents_data = [ { "name": agent.agent_name, @@ -358,69 +123,11 @@ class ConcurrentWorkflow(BaseSwarm): task: str, img: Optional[str] = None, imgs: Optional[List[str]] = None, + streaming_callback: Optional[ + Callable[[str, str, bool], None] + ] = None, ): - """ - Execute all agents concurrently with real-time dashboard monitoring. - - This method provides the same concurrent execution as _run() but with - enhanced real-time monitoring through a visual dashboard. Agent status - updates are displayed in real-time, showing progress, completion, and - any errors that occur during execution. - - Args: - task (str): The task description or prompt to be executed by all agents. - This will be passed to each agent's run() method. - img (Optional[str], optional): Path to a single image file for agents - that support multimodal input. Defaults to None. - imgs (Optional[List[str]], optional): List of image file paths for - agents that support multiple image inputs. Defaults to None. - - Returns: - Any: Formatted output based on the configured output_type setting. - The return format depends on the output_type parameter set during - workflow initialization. - - Raises: - Exception: Re-raises any exceptions from agent execution after - updating the dashboard with error status. - - Side Effects: - - Displays initial dashboard showing all agents as "pending" - - Updates dashboard in real-time as agents start, run, and complete - - Displays final dashboard with all results when execution completes - - Adds all task inputs and agent outputs to conversation history - - Automatically cleans up dashboard display resources - - Dashboard Flow: - 1. Initial dashboard shows all agents as "pending" - 2. As agents start, status updates to "running" - 3. As agents complete, status updates to "completed" with output preview - 4. Final dashboard shows complete results summary - 5. Dashboard resources are cleaned up automatically - - Performance: - - Uses 95% of available CPU cores for optimal concurrency - - ThreadPoolExecutor manages thread lifecycle automatically - - Real-time updates have minimal performance overhead - - Example: - >>> workflow = ConcurrentWorkflow( - ... agents=[financial_agent, legal_agent], - ... show_dashboard=True - ... ) - >>> result = workflow.run_with_dashboard( - ... task="Analyze the merger proposal", - ... img="company_financials.png" - ... ) - # Dashboard shows real-time progress: - # Agent-1: pending -> running -> completed - # Agent-2: pending -> running -> completed - >>> print("Analysis complete:", result) - - Note: - This method is automatically called when show_dashboard=True. - For headless execution without dashboard, use _run() directly. - """ + """Execute agents with dashboard monitoring.""" try: self.conversation.add(role="User", content=task) @@ -431,61 +138,61 @@ class ConcurrentWorkflow(BaseSwarm): "output": "", } - # Display initial dashboard if enabled if self.show_dashboard: self.display_agent_dashboard() - # Use 95% of available CPU cores for optimal performance max_workers = int(get_cpu_cores() * 0.95) - - # Create a list to store all futures and their results futures = [] results = [] def run_agent_with_status(agent, task, img, imgs): try: - # Update status to running self.agent_statuses[agent.agent_name][ "status" ] = "running" if self.show_dashboard: self.display_agent_dashboard() - # Create a streaming callback for this agent with throttling - last_update_time = [ - 0 - ] # Use list to allow modification in nested function - update_interval = 0.1 # Update dashboard every 100ms for smooth streaming - - def streaming_callback(chunk: str): - """Update dashboard with streaming content""" - if self.show_dashboard: - # Append the chunk to the agent's current output - current_output = self.agent_statuses[ - agent.agent_name - ]["output"] - self.agent_statuses[agent.agent_name][ - "output" - ] = (current_output + chunk) - - # Throttle dashboard updates for better performance - current_time = time.time() + last_update_time = [0] + update_interval = 0.1 + + def agent_streaming_callback(chunk: str): + try: + if self.show_dashboard and chunk: + current_output = self.agent_statuses[ + agent.agent_name + ]["output"] + self.agent_statuses[agent.agent_name][ + "output" + ] = (current_output + chunk) + + current_time = time.time() + if ( + current_time - last_update_time[0] + >= update_interval + ): + self.display_agent_dashboard() + last_update_time[0] = current_time + if ( - current_time - last_update_time[0] - >= update_interval + streaming_callback + and chunk is not None ): - self.display_agent_dashboard() - last_update_time[0] = current_time + streaming_callback( + agent.agent_name, chunk, False + ) + except Exception as callback_error: + logger.warning( + f"Dashboard streaming callback failed for {agent.agent_name}: {str(callback_error)}" + ) - # Run the agent with streaming callback output = agent.run( task=task, img=img, imgs=imgs, - streaming_callback=streaming_callback, + streaming_callback=agent_streaming_callback, ) - # Update status to completed self.agent_statuses[agent.agent_name][ "status" ] = "completed" @@ -495,9 +202,11 @@ class ConcurrentWorkflow(BaseSwarm): if self.show_dashboard: self.display_agent_dashboard() + if streaming_callback: + streaming_callback(agent.agent_name, "", True) + return output except Exception as e: - # Update status to error self.agent_statuses[agent.agent_name][ "status" ] = "error" @@ -506,24 +215,25 @@ class ConcurrentWorkflow(BaseSwarm): ] = f"Error: {str(e)}" if self.show_dashboard: self.display_agent_dashboard() + + if streaming_callback: + streaming_callback( + agent.agent_name, f"Error: {str(e)}", True + ) + raise - # Run agents concurrently using ThreadPoolExecutor with concurrent.futures.ThreadPoolExecutor( max_workers=max_workers ) as executor: - # Submit all agent tasks futures = [ executor.submit( run_agent_with_status, agent, task, img, imgs ) for agent in self.agents ] - - # Wait for all futures to complete concurrent.futures.wait(futures) - # Process results in order of completion for future, agent in zip(futures, self.agents): try: output = future.result() @@ -536,11 +246,9 @@ class ConcurrentWorkflow(BaseSwarm): (agent.agent_name, f"Error: {str(e)}") ) - # Add all results to conversation for agent_name, output in results: self.conversation.add(role=agent_name, content=output) - # Display final dashboard if enabled if self.show_dashboard: self.display_agent_dashboard( "Final ConcurrentWorkflow Dashboard", @@ -548,11 +256,9 @@ class ConcurrentWorkflow(BaseSwarm): ) return history_output_formatter( - conversation=self.conversation, - type=self.output_type, + conversation=self.conversation, type=self.output_type ) finally: - # Always clean up the dashboard display if self.show_dashboard: formatter.stop_dashboard() @@ -561,316 +267,160 @@ class ConcurrentWorkflow(BaseSwarm): task: str, img: Optional[str] = None, imgs: Optional[List[str]] = None, + streaming_callback: Optional[ + Callable[[str, str, bool], None] + ] = None, ): - """ - Execute all agents concurrently without dashboard monitoring (headless mode). - - This is the core execution method that runs all agents simultaneously using - ThreadPoolExecutor for optimal performance. Results are collected and - formatted according to the configured output_type. - - Args: - task (str): The task description or prompt to be executed by all agents. - Each agent receives the same task input for independent processing. - img (Optional[str], optional): Path to a single image file for multimodal - agents. The same image is provided to all agents. Defaults to None. - imgs (Optional[List[str]], optional): List of image file paths for agents - that support multiple image inputs. All agents receive the same image list. - Defaults to None. - - Returns: - Any: Formatted output according to the output_type configuration: - - "dict-all-except-first": Dict with all agent outputs except first - - "dict": Dict with all agent outputs keyed by agent name - - "str": Concatenated string of all agent outputs - - "list": List of individual agent outputs in completion order - - Side Effects: - - Adds the user task to conversation history - - Adds each agent's output to conversation history upon completion - - No visual output or dashboard updates (headless execution) - - Performance Characteristics: - - Uses ThreadPoolExecutor with 95% of available CPU cores - - Agents execute truly concurrently, not sequentially - - Results are collected in completion order, not submission order - - Memory efficient for large numbers of agents - - Error Handling: - - Individual agent failures don't stop other agents - - Failed agents have their exceptions logged but execution continues - - Partial results are still returned for successful agents - - Thread Safety: - - Conversation object handles concurrent access safely - - Agent status is not tracked in this method (dashboard-free) - - Each agent runs in isolation without shared state - - Example: - >>> workflow = ConcurrentWorkflow( - ... agents=[agent1, agent2, agent3], - ... output_type="dict" - ... ) - >>> result = workflow._run( - ... task="Analyze market trends for Q4", - ... img="market_chart.png" - ... ) - >>> print(f"Received {len(result)} agent analyses") - >>> # Result format: {"agent1": "analysis1", "agent2": "analysis2", ...} - - Note: - This method is called automatically by run() when show_dashboard=False. - For monitoring and real-time updates, use run_with_dashboard() instead. - """ + """Execute agents concurrently without dashboard.""" self.conversation.add(role="User", content=task) - # Use 95% of available CPU cores for optimal performance max_workers = int(get_cpu_cores() * 0.95) - # Run agents concurrently using ThreadPoolExecutor with concurrent.futures.ThreadPoolExecutor( max_workers=max_workers ) as executor: - # Submit all agent tasks and store with their index future_to_agent = { executor.submit( - agent.run, task=task, img=img, imgs=imgs + self._run_agent_with_streaming, + agent, + task, + img, + imgs, + streaming_callback, ): agent for agent in self.agents } - # Collect results and add to conversation in completion order for future in concurrent.futures.as_completed( future_to_agent ): agent = future_to_agent[future] output = future.result() - self.conversation.add(role=agent.name, content=output) + self.conversation.add( + role=agent.agent_name, content=output + ) return history_output_formatter( - conversation=self.conversation, - type=self.output_type, + conversation=self.conversation, type=self.output_type ) + def _run_agent_with_streaming( + self, + agent: Union[Agent, Callable], + task: str, + img: Optional[str] = None, + imgs: Optional[List[str]] = None, + streaming_callback: Optional[ + Callable[[str, str, bool], None] + ] = None, + ): + """Run single agent with streaming support.""" + if streaming_callback is None: + return agent.run(task=task, img=img, imgs=imgs) + + def agent_streaming_callback(chunk: str): + try: + # Safely call the streaming callback + if streaming_callback and chunk is not None: + streaming_callback(agent.agent_name, chunk, False) + except Exception as callback_error: + logger.warning( + f"Streaming callback failed for {agent.agent_name}: {str(callback_error)}" + ) + + try: + output = agent.run( + task=task, + img=img, + imgs=imgs, + streaming_callback=agent_streaming_callback, + ) + # Ensure completion callback is called even if there were issues + try: + streaming_callback(agent.agent_name, "", True) + except Exception as callback_error: + logger.warning( + f"Completion callback failed for {agent.agent_name}: {str(callback_error)}" + ) + return output + except Exception as e: + error_msg = f"Agent {agent.agent_name} failed: {str(e)}" + logger.error(error_msg) + # Try to send error through callback + try: + streaming_callback( + agent.agent_name, f"Error: {str(e)}", True + ) + except Exception as callback_error: + logger.warning( + f"Error callback failed for {agent.agent_name}: {str(callback_error)}" + ) + raise + + def cleanup(self): + """Clean up resources and connections.""" + try: + # Reset agent statuses + for agent in self.agents: + if hasattr(agent, "cleanup"): + try: + agent.cleanup() + except Exception as e: + logger.warning( + f"Failed to cleanup agent {agent.agent_name}: {str(e)}" + ) + + # Clear conversation if needed + if hasattr(self, "conversation"): + # Keep the conversation for result formatting but reset for next run + pass + + except Exception as e: + logger.error(f"Cleanup failed: {str(e)}") + def run( self, task: str, img: Optional[str] = None, imgs: Optional[List[str]] = None, + streaming_callback: Optional[ + Callable[[str, str, bool], None] + ] = None, ): - """ - Execute all agents concurrently on the given task with optional dashboard monitoring. - - This is the main entry point for workflow execution. Automatically selects - between dashboard-enabled execution (run_with_dashboard) or headless execution - (_run) based on the show_dashboard configuration. - - Args: - task (str): The task description, prompt, or instruction to be executed - by all agents concurrently. Each agent processes the same task - independently. - img (Optional[str], optional): Path to a single image file for agents - that support multimodal (text + image) input. Defaults to None. - imgs (Optional[List[str]], optional): List of image file paths for - agents that support multiple image inputs. Defaults to None. - - Returns: - Any: Aggregated results from all agents formatted according to output_type: - - "dict-all-except-first": Dictionary excluding first agent's output - - "dict": Complete dictionary with all agent outputs - - "str": Concatenated string of all outputs - - "list": List of individual agent outputs - - Execution Modes: - - Dashboard Mode (show_dashboard=True): Provides real-time visual monitoring - with status updates, progress tracking, and error visibility - - Headless Mode (show_dashboard=False): Silent execution with no visual output, - optimized for performance and automation scenarios - - Concurrent Execution: - - All agents run simultaneously using ThreadPoolExecutor - - Utilizes 95% of available CPU cores for optimal performance - - Thread-safe conversation history management - - Independent agent execution without inter-agent communication - - Error Resilience: - - Individual agent failures don't halt the entire workflow - - Partial results are returned for successful agents - - Comprehensive error logging for debugging - - Graceful degradation under failure conditions - - Example: - Basic concurrent execution: - - >>> workflow = ConcurrentWorkflow(agents=[analyst, reviewer, summarizer]) - >>> result = workflow.run("Evaluate the new product proposal") - >>> print(f"Received insights from {len(workflow.agents)} experts") - - With dashboard monitoring: - - >>> workflow = ConcurrentWorkflow( - ... agents=[financial_agent, legal_agent], - ... show_dashboard=True - ... ) - >>> result = workflow.run("Review merger agreement") - # Real-time dashboard shows progress and completion status - - Multimodal analysis: - - >>> result = workflow.run( - ... task="Analyze this chart and provide insights", - ... img="quarterly_results.png" - ... ) - - Performance Tips: - - Use 2+ agents to benefit from concurrent execution - - Dashboard mode adds minimal overhead for monitoring - - Larger agent counts scale well with available CPU cores - - Consider batch_run() for multiple related tasks - - See Also: - - batch_run(): For processing multiple tasks sequentially - - run_with_dashboard(): For direct dashboard execution - - _run(): For direct headless execution - """ - if self.show_dashboard: - return self.run_with_dashboard(task, img, imgs) - else: - return self._run(task, img, imgs) + """Execute all agents concurrently.""" + try: + if self.show_dashboard: + result = self.run_with_dashboard( + task, img, imgs, streaming_callback + ) + else: + result = self._run( + task, img, imgs, streaming_callback + ) + return result + finally: + # Always cleanup resources + self.cleanup() def batch_run( self, tasks: List[str], imgs: Optional[List[str]] = None, + streaming_callback: Optional[ + Callable[[str, str, bool], None] + ] = None, ): - """ - Execute the workflow on multiple tasks sequentially with concurrent agent processing. - - This method processes a list of tasks one by one, where each task is executed - by all agents concurrently. This is ideal for batch processing scenarios where - you have multiple related tasks that need the same multi-agent analysis. - - Args: - tasks (List[str]): List of task descriptions, prompts, or instructions. - Each task will be processed by all agents concurrently before - moving to the next task. Tasks are processed sequentially. - imgs (Optional[List[str]], optional): List of image file paths corresponding - to each task. If provided, imgs[i] will be used for tasks[i]. - If fewer images than tasks are provided, remaining tasks will - execute without images. Defaults to None. - - Returns: - List[Any]: List of results, one for each task. Each result is formatted - according to the workflow's output_type configuration. The length - of the returned list equals the length of the input tasks list. - - Processing Flow: - 1. Tasks are processed sequentially (not concurrently with each other) - 2. For each task, all agents execute concurrently - 3. Results are collected and formatted for each task - 4. Conversation history accumulates across all tasks - 5. Final result list contains aggregated outputs for each task - - Image Handling: - - If imgs is None: All tasks execute without images - - If imgs has fewer items than tasks: Extra tasks execute without images - - If imgs has more items than tasks: Extra images are ignored - - Each task gets at most one corresponding image - - Dashboard Behavior: - - If show_dashboard=True, dashboard resets and updates for each task - - Progress is shown separately for each task's agent execution - - Final dashboard shows results from the last task only - - Memory and Performance: - - Conversation history grows with each task (cumulative) - - Memory usage scales with number of tasks and agent outputs - - CPU utilization is optimal during each task's concurrent execution - - Consider clearing conversation history for very large batch jobs - - Example: - Financial analysis across quarters: - - >>> workflow = ConcurrentWorkflow(agents=[analyst1, analyst2, analyst3]) - >>> quarterly_tasks = [ - ... "Analyze Q1 financial performance and market position", - ... "Analyze Q2 financial performance and market position", - ... "Analyze Q3 financial performance and market position", - ... "Analyze Q4 financial performance and market position" - ... ] - >>> results = workflow.batch_run(quarterly_tasks) - >>> print(f"Completed {len(results)} quarterly analyses") - >>> # Each result contains insights from all 3 analysts for that quarter - - With corresponding images: - - >>> tasks = ["Analyze chart trends", "Review performance metrics"] - >>> charts = ["q1_chart.png", "q2_chart.png"] - >>> results = workflow.batch_run(tasks, imgs=charts) - >>> # Task 0 uses q1_chart.png, Task 1 uses q2_chart.png - - Batch processing with dashboard: - - >>> workflow = ConcurrentWorkflow( - ... agents=[agent1, agent2], - ... show_dashboard=True - ... ) - >>> results = workflow.batch_run([ - ... "Process document batch 1", - ... "Process document batch 2", - ... "Process document batch 3" - ... ]) - # Dashboard shows progress for each batch separately - - Use Cases: - - Multi-period financial analysis (quarterly/yearly reports) - - Batch document processing with multiple expert reviews - - A/B testing across different scenarios or datasets - - Systematic evaluation of multiple proposals or options - - Comparative analysis across time periods or categories - - Note: - Tasks are intentionally processed sequentially to maintain result - order and prevent resource contention. For truly concurrent task - processing, create separate workflow instances. - - See Also: - - run(): For single task execution - - ConcurrentWorkflow: For configuration options - """ + """Execute workflow on multiple tasks sequentially.""" results = [] for idx, task in enumerate(tasks): img = None - if imgs is not None: - # Use the img at the same index if available, else None - if idx < len(imgs): - img = imgs[idx] - results.append(self.run(task=task, img=img)) + if imgs is not None and idx < len(imgs): + img = imgs[idx] + results.append( + self.run( + task=task, + img=img, + streaming_callback=streaming_callback, + ) + ) return results - - -# if __name__ == "__main__": -# # Assuming you've already initialized some agents outside of this class -# agents = [ -# Agent( -# agent_name=f"Financial-Analysis-Agent-{i}", -# system_prompt=FINANCIAL_AGENT_SYS_PROMPT, -# model_name="gpt-4o", -# max_loops=1, -# ) -# for i in range(3) # Adjust number of agents as needed -# ] - -# # Initialize the workflow with the list of agents -# workflow = ConcurrentWorkflow( -# agents=agents, -# metadata_output_path="agent_metadata_4.json", -# return_str_on=True, -# ) - -# # Define the task for all agents -# task = "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?" - -# # Run the workflow and save metadata -# metadata = workflow.run(task) -# print(metadata) diff --git a/swarms/structs/majority_voting.py b/swarms/structs/majority_voting.py index 09c12520..1875e8db 100644 --- a/swarms/structs/majority_voting.py +++ b/swarms/structs/majority_voting.py @@ -1,149 +1,27 @@ -import asyncio import concurrent.futures import os -import re -from collections import Counter from concurrent.futures import ThreadPoolExecutor -from typing import Any, Callable, List, Optional +from typing import Any, List, Optional from swarms.structs.agent import Agent from swarms.structs.conversation import Conversation from swarms.structs.multi_agent_exec import run_agents_concurrently -from swarms.utils.output_types import OutputType +from swarms.structs.swarm_id import swarm_id from swarms.utils.formatter import formatter from swarms.utils.loguru_logger import initialize_logger +from swarms.utils.output_types import OutputType logger = initialize_logger(log_folder="majority_voting") -def extract_last_python_code_block(text): - """ - Extracts the last Python code block from the given text. - - Args: - text (str): The text to search for Python code blocks. - - Returns: - str or None: The last Python code block found in the text, or None if no code block is found. - """ - # The regular expression pattern for Python code blocks - pattern = r"```[pP]ython(.*?)```" - - # Find all matches in the text - matches = re.findall(pattern, text, re.DOTALL) - - # If there are matches, return the last one - if matches: - return matches[-1].strip() - else: - return None - - -def parse_code_completion(agent_response, question): - """ - Parses the code completion response from the agent and extracts the last Python code block. - - Args: - agent_response (str): The response from the agent. - question (str): The original question. - - Returns: - tuple: A tuple containing the parsed Python code and a boolean indicating success. - """ - python_code = extract_last_python_code_block(agent_response) - if python_code is None: - if agent_response.count("impl]") == 0: - python_code = agent_response - else: - python_code_lines = agent_response.split("\n") - python_code = "" - in_func = False - for line in python_code_lines: - if in_func: - python_code += line + "\n" - if "impl]" in line: - in_func = True - if python_code.count("def") == 0: - python_code = question + python_code - return python_code, True - - -def most_frequent( - clist: list, - cmp_func: callable = None, -): - """ - Finds the most frequent element in a list based on a comparison function. - - Args: - clist (list): The list of elements to search. - cmp_func (function, optional): The comparison function used to determine the frequency of elements. - If not provided, the default comparison function is used. - - Returns: - tuple: A tuple containing the most frequent element and its frequency. - """ - counter = 0 - num = clist[0] - - for i in clist: - current_frequency = sum(cmp_func(i, item) for item in clist) - if current_frequency > counter: - counter = current_frequency - num = i - - return num, counter - - -def majority_voting(answers: List[str]): - """ - Performs majority voting on a list of answers and returns the most common answer. - - Args: - answers (list): A list of answers. - - Returns: - The most common answer in the list. - """ - counter = Counter(answers) - if counter: - answer = counter.most_common(1)[0][0] - else: - answer = "I don't know" - - return answer - - class MajorityVoting: - """ - Class representing a majority voting system for agents. - - Args: - agents (list): A list of agents to be used in the majority voting system. - output_parser (function, optional): A function used to parse the output of the agents. - If not provided, the default majority voting function is used. - autosave (bool, optional): A boolean indicating whether to autosave the conversation to a file. - verbose (bool, optional): A boolean indicating whether to enable verbose logging. - Examples: - >>> from swarms.structs.agent import Agent - >>> from swarms.structs.majority_voting import MajorityVoting - >>> agents = [ - ... Agent("GPT-3"), - ... Agent("Codex"), - ... Agent("Tabnine"), - ... ] - >>> majority_voting = MajorityVoting(agents) - >>> majority_voting.run("What is the capital of France?") - 'Paris' - - """ def __init__( self, + id: str = swarm_id(), name: str = "MajorityVoting", description: str = "A majority voting system for agents", - agents: List[Agent] = [], - output_parser: Optional[Callable] = majority_voting, + agents: List[Agent] = None, consensus_agent: Optional[Agent] = None, autosave: bool = False, verbose: bool = False, @@ -152,10 +30,10 @@ class MajorityVoting: *args, **kwargs, ): + self.id = id self.name = name self.description = description self.agents = agents - self.output_parser = output_parser self.consensus_agent = consensus_agent self.autosave = autosave self.verbose = verbose @@ -179,6 +57,10 @@ class MajorityVoting: title="Majority Voting", ) + if self.consensus_agent is None: + # if no consensus agent is provided, use the last agent + self.consensus_agent = self.agents[-1] + def run(self, task: str, *args, **kwargs) -> List[Any]: """ Runs the majority voting system and returns the majority vote. @@ -290,21 +172,3 @@ class MajorityVoting: future.result() for future in concurrent.futures.as_completed(futures) ] - - async def run_async( - self, tasks: List[str], *args, **kwargs - ) -> List[Any]: - """ - Runs the majority voting system concurrently using asyncio. - - Args: - tasks (List[str]): List of tasks to be performed by the agents. - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - List[Any]: List of majority votes for each task. - """ - return await asyncio.gather( - *[self.run(task, *args, **kwargs) for task in tasks] - ) diff --git a/swarms/utils/formatter.py b/swarms/utils/formatter.py index e2d53aaa..108332b3 100644 --- a/swarms/utils/formatter.py +++ b/swarms/utils/formatter.py @@ -1,4 +1,3 @@ -import threading import time import re from typing import Any, Callable, Dict, List, Optional @@ -17,13 +16,6 @@ from rich.spinner import Spinner from rich.markdown import Markdown -# Global lock to ensure only a single Rich Live context is active at any moment. -# Rich's Live render is **not** thread-safe; concurrent Live contexts on the same -# console raise runtime errors. Using a module-level lock serialises access and -# prevents crashes when multiple agents stream simultaneously in different -# threads (e.g., in ConcurrentWorkflow). -live_render_lock = threading.Lock() - # Global Live display for the dashboard dashboard_live = None @@ -490,62 +482,56 @@ class Formatter: complete_response = "" chunks_collected = [] - # Acquire the lock so that only one Live panel is active at a time. - # Other threads will wait here until the current streaming completes, - # avoiding Rich.Live concurrency errors. - with live_render_lock: - # TRUE streaming with Rich's automatic text wrapping - with Live( - create_streaming_panel(streaming_text), - console=self.console, - refresh_per_second=20, - ) as live: - try: - for part in streaming_response: - if ( - hasattr(part, "choices") - and part.choices - and part.choices[0].delta.content - ): - # Add ONLY the new chunk to the Text object with random color style - chunk = part.choices[0].delta.content - streaming_text.append( - chunk, style=text_style - ) - complete_response += chunk - - # Collect chunks if requested - if collect_chunks: - chunks_collected.append(chunk) - - # Call chunk callback if provided - if on_chunk_callback: - on_chunk_callback(chunk) - - # Update display with new text - Rich handles all wrapping automatically - live.update( - create_streaming_panel( - streaming_text, is_complete=False - ) + # TRUE streaming with Rich's automatic text wrapping + with Live( + create_streaming_panel(streaming_text), + console=self.console, + refresh_per_second=20, + ) as live: + try: + for part in streaming_response: + if ( + hasattr(part, "choices") + and part.choices + and part.choices[0].delta.content + ): + # Add ONLY the new chunk to the Text object with random color style + chunk = part.choices[0].delta.content + streaming_text.append(chunk, style=text_style) + complete_response += chunk + + # Collect chunks if requested + if collect_chunks: + chunks_collected.append(chunk) + + # Call chunk callback if provided + if on_chunk_callback: + on_chunk_callback(chunk) + + # Update display with new text - Rich handles all wrapping automatically + live.update( + create_streaming_panel( + streaming_text, is_complete=False ) - - # Final update to show completion - live.update( - create_streaming_panel( - streaming_text, is_complete=True ) - ) - except Exception as e: - # Handle any streaming errors gracefully - streaming_text.append( - f"\n[Error: {str(e)}]", style="bold red" + # Final update to show completion + live.update( + create_streaming_panel( + streaming_text, is_complete=True ) - live.update( - create_streaming_panel( - streaming_text, is_complete=True - ) + ) + + except Exception as e: + # Handle any streaming errors gracefully + streaming_text.append( + f"\n[Error: {str(e)}]", style="bold red" + ) + live.update( + create_streaming_panel( + streaming_text, is_complete=True ) + ) return complete_response @@ -611,21 +597,20 @@ class Formatter: title (str): The title of the dashboard. is_final (bool): Whether this is the final update of the dashboard. """ - with live_render_lock: - if self._dashboard_live is None: - # Create new Live display if none exists - self._dashboard_live = Live( - self._create_dashboard_table(agents_data, title), - console=self.console, - refresh_per_second=10, # Increased refresh rate - transient=False, # Make display persistent - ) - self._dashboard_live.start() - else: - # Update existing Live display - self._dashboard_live.update( - self._create_dashboard_table(agents_data, title) - ) + if self._dashboard_live is None: + # Create new Live display if none exists + self._dashboard_live = Live( + self._create_dashboard_table(agents_data, title), + console=self.console, + refresh_per_second=10, # Increased refresh rate + transient=False, # Make display persistent + ) + self._dashboard_live.start() + else: + # Update existing Live display + self._dashboard_live.update( + self._create_dashboard_table(agents_data, title) + ) # If this is the final update, add a newline to separate from future output if is_final: diff --git a/swarms/utils/litellm_wrapper.py b/swarms/utils/litellm_wrapper.py index ff0ae728..884c3af8 100644 --- a/swarms/utils/litellm_wrapper.py +++ b/swarms/utils/litellm_wrapper.py @@ -7,7 +7,7 @@ from typing import List, Optional import litellm import requests -from litellm import acompletion, completion, supports_vision +from litellm import completion, supports_vision from loguru import logger from pydantic import BaseModel @@ -231,6 +231,10 @@ class LiteLLM: base_url: str = None, api_key: str = None, api_version: str = None, + reasoning_effort: str = None, + drop_params: bool = True, + thinking_tokens: int = None, + reasoning_enabled: bool = True, *args, **kwargs, ): @@ -284,6 +288,9 @@ class LiteLLM: self.base_url = base_url self.api_key = api_key self.api_version = api_version + self.reasoning_effort = reasoning_effort + self.thinking_tokens = thinking_tokens + self.reasoning_enabled = reasoning_enabled self.modalities = [] self.messages = [] # Initialize messages list @@ -296,7 +303,7 @@ class LiteLLM: retries # Add retries for better reliability ) - litellm.drop_params = True + litellm.drop_params = drop_params # Add system prompt if present if self.system_prompt is not None: @@ -308,6 +315,63 @@ class LiteLLM: self.init_args = args self.init_kwargs = kwargs + self.reasoning_check() + + def reasoning_check(self): + """ + Check if reasoning is enabled and supported by the model, and adjust temperature accordingly. + + If reasoning is enabled and the model supports reasoning, set temperature to 1 for optimal reasoning. + Also logs information or warnings based on the model's reasoning support and configuration. + """ + if ( + self.reasoning_enabled is True + and litellm.supports_reasoning(model=self.model_name) + is True + ): + logger.info( + f"Model {self.model_name} supports reasoning and reasoning enabled is set to {self.reasoning_enabled}. Temperature will be set to 1 for better reasoning as some models may not work with low temperature." + ) + self.temperature = 1 + else: + logger.warning( + f"Model {self.model_name} does not support reasoning and reasoning enabled is set to {self.reasoning_enabled}. Temperature will not be set to 1." + ) + + if ( + self.reasoning_enabled is True + and litellm.supports_reasoning(model=self.model_name) + is False + ): + logger.warning( + f"Model {self.model_name} may or may not support reasoning and reasoning enabled is set to {self.reasoning_enabled}" + ) + + if ( + self.reasoning_enabled is True + and self.check_if_model_name_uses_anthropic( + model_name=self.model_name + ) + is True + ): + if self.thinking_tokens is None: + logger.info( + f"Model {self.model_name} is an Anthropic model and reasoning enabled is set to {self.reasoning_enabled}. Thinking tokens is mandatory for Anthropic models." + ) + self.thinking_tokens = self.max_tokens / 4 + + if ( + self.reasoning_enabled is True + and self.check_if_model_name_uses_anthropic( + model_name=self.model_name + ) + is True + ): + logger.info( + "top_p must be greater than 0.95 for Anthropic models with reasoning enabled" + ) + self.top_p = 0.95 + def _process_additional_args( self, completion_params: dict, runtime_args: tuple ): @@ -357,6 +421,61 @@ class LiteLLM: out = out.model_dump() return out + def output_for_reasoning(self, response: any): + """ + Handle output for reasoning models, formatting reasoning content and thinking blocks. + + Args: + response: The response object from the LLM API call + + Returns: + str: Formatted string containing reasoning content, thinking blocks, and main content + """ + output_parts = [] + + # Check if reasoning content is available + if ( + hasattr(response.choices[0].message, "reasoning_content") + and response.choices[0].message.reasoning_content + ): + output_parts.append( + f"Reasoning Content:\n{response.choices[0].message.reasoning_content}\n" + ) + + # Check if thinking blocks are available (Anthropic models) + if ( + hasattr(response.choices[0].message, "thinking_blocks") + and response.choices[0].message.thinking_blocks + ): + output_parts.append("Thinking Blocks:") + for i, block in enumerate( + response.choices[0].message.thinking_blocks, 1 + ): + block_type = block.get("type", "") + thinking = block.get("thinking", "") + output_parts.append( + f"Block {i} (Type: {block_type}):" + ) + output_parts.append(f" Thinking: {thinking}") + output_parts.append("") + + # Include tools if available + if ( + hasattr(response.choices[0].message, "tool_calls") + and response.choices[0].message.tool_calls + ): + output_parts.append( + f"Tools:\n{self.output_for_tools(response)}\n" + ) + + # Always include the main content + content = response.choices[0].message.content + if content: + output_parts.append(f"Content:\n{content}") + + # Join all parts into a single string + return "\n".join(output_parts) + def _prepare_messages( self, task: Optional[str] = None, @@ -648,6 +767,26 @@ class LiteLLM: f"Model {self.model_name} does not support vision" ) + @staticmethod + def check_if_model_name_uses_anthropic(model_name: str): + """ + Check if the model name uses Anthropic. + """ + if "anthropic" in model_name.lower(): + return True + else: + return False + + @staticmethod + def check_if_model_name_uses_openai(model_name: str): + """ + Check if the model name uses OpenAI. + """ + if "openai" in model_name.lower(): + return True + else: + return False + def run( self, task: str, @@ -737,6 +876,29 @@ class LiteLLM: if self.modalities and len(self.modalities) >= 2: completion_params["modalities"] = self.modalities + if ( + self.reasoning_effort is not None + and litellm.supports_reasoning(model=self.model_name) + is True + ): + completion_params["reasoning_effort"] = ( + self.reasoning_effort + ) + else: + logger.warning( + f"Model {self.model_name} may or may not support reasoning" + ) + + if ( + self.reasoning_enabled is True + and self.thinking_tokens is not None + ): + thinking = { + "type": "enabled", + "budget_tokens": self.thinking_tokens, + } + completion_params["thinking"] = thinking + # Process additional args if any self._process_additional_args(completion_params, args) @@ -747,6 +909,13 @@ class LiteLLM: if self.stream: return response # Return the streaming generator directly + # Handle reasoning model output + elif ( + self.reasoning_enabled + and self.reasoning_effort is not None + ): + return self.output_for_reasoning(response) + # Handle tool-based response elif self.tools_list_dictionary is not None: return self.output_for_tools(response) @@ -783,118 +952,6 @@ class LiteLLM: """ return self.run(task, *args, **kwargs) - async def arun(self, task: str, *args, **kwargs): - """ - Run the LLM model asynchronously for the given task. - - Args: - task (str): The task to run the model for. - *args: Additional positional arguments. - **kwargs: Additional keyword arguments. - - Returns: - str: The content of the response from the model. - """ - try: - # Extract image parameter from kwargs if present - img = kwargs.pop("img", None) if "img" in kwargs else None - messages = self._prepare_messages(task=task, img=img) - - # Prepare common completion parameters - completion_params = { - "model": self.model_name, - "messages": messages, - "stream": self.stream, - "temperature": self.temperature, - "max_tokens": self.max_tokens, - } - - # Merge initialization kwargs first (lower priority) - if self.init_kwargs: - completion_params.update(self.init_kwargs) - - # Merge runtime kwargs (higher priority - overrides init kwargs) - if kwargs: - completion_params.update(kwargs) - - # Handle tool-based completion - if self.tools_list_dictionary is not None: - completion_params.update( - { - "tools": self.tools_list_dictionary, - "tool_choice": self.tool_choice, - "parallel_tool_calls": self.parallel_tool_calls, - } - ) - - # Process additional args if any - self._process_additional_args(completion_params, args) - - # Make the completion call - response = await acompletion(**completion_params) - - # Handle tool-based response - if self.tools_list_dictionary is not None: - return ( - response.choices[0] - .message.tool_calls[0] - .function.arguments - ) - elif self.return_all is True: - return response.model_dump() - elif "gemini" in self.model_name.lower(): - return gemini_output_img_handler(response) - else: - # For non-Gemini models, return the content directly - return response.choices[0].message.content - - except Exception as error: - logger.error(f"Error in LiteLLM arun: {str(error)}") - # if "rate_limit" in str(error).lower(): - # logger.warning( - # "Rate limit hit, retrying with exponential backoff..." - # ) - # await asyncio.sleep(2) # Use async sleep - # return await self.arun(task, *args, **kwargs) - raise error - - async def _process_batch( - self, tasks: List[str], batch_size: int = 10 - ): - """ - Process a batch of tasks asynchronously. - - Args: - tasks (List[str]): List of tasks to process. - batch_size (int): Size of each batch. - - Returns: - List[str]: List of responses. - """ - results = [] - for i in range(0, len(tasks), batch_size): - batch = tasks[i : i + batch_size] - batch_results = await asyncio.gather( - *[self.arun(task) for task in batch], - return_exceptions=True, - ) - - # Handle any exceptions in the batch - for result in batch_results: - if isinstance(result, Exception): - logger.error( - f"Error in batch processing: {str(result)}" - ) - results.append(str(result)) - else: - results.append(result) - - # Add a small delay between batches to avoid rate limits - if i + batch_size < len(tasks): - await asyncio.sleep(0.5) - - return results - def batched_run(self, tasks: List[str], batch_size: int = 10): """ Run multiple tasks in batches synchronously. @@ -910,21 +967,3 @@ class LiteLLM: f"Running {len(tasks)} tasks in batches of {batch_size}" ) return asyncio.run(self._process_batch(tasks, batch_size)) - - async def batched_arun( - self, tasks: List[str], batch_size: int = 10 - ): - """ - Run multiple tasks in batches asynchronously. - - Args: - tasks (List[str]): List of tasks to process. - batch_size (int): Size of each batch. - - Returns: - List[str]: List of responses. - """ - logger.info( - f"Running {len(tasks)} tasks asynchronously in batches of {batch_size}" - ) - return await self._process_batch(tasks, batch_size) diff --git a/tests/structs/test_reasoning_agent_router_all.py b/tests/structs/test_reasoning_agent_router_all.py index 931f62e7..4376075c 100644 --- a/tests/structs/test_reasoning_agent_router_all.py +++ b/tests/structs/test_reasoning_agent_router_all.py @@ -131,9 +131,7 @@ def test_router_description(report): """Test ReasoningAgentRouter with custom description (only change description param)""" start_time = time.time() try: - result = test_agents_swarm( - description="Test description for router" - ) + test_agents_swarm(description="Test description for router") # Check if the description was set correctly router = ReasoningAgentRouter( description="Test description for router" @@ -164,7 +162,7 @@ def test_router_model_name(report): """Test ReasoningAgentRouter with custom model_name (only change model_name param)""" start_time = time.time() try: - result = test_agents_swarm(model_name="gpt-4") + test_agents_swarm(model_name="gpt-4") router = ReasoningAgentRouter(model_name="gpt-4") if router.model_name == "gpt-4": report.add_result( @@ -192,9 +190,7 @@ def test_router_system_prompt(report): """Test ReasoningAgentRouter with custom system_prompt (only change system_prompt param)""" start_time = time.time() try: - result = test_agents_swarm( - system_prompt="You are a test router." - ) + test_agents_swarm(system_prompt="You are a test router.") router = ReasoningAgentRouter( system_prompt="You are a test router." ) @@ -224,7 +220,7 @@ def test_router_max_loops(report): """Test ReasoningAgentRouter with custom max_loops (only change max_loops param)""" start_time = time.time() try: - result = test_agents_swarm(max_loops=5) + test_agents_swarm(max_loops=5) router = ReasoningAgentRouter(max_loops=5) if router.max_loops == 5: report.add_result( @@ -252,7 +248,7 @@ def test_router_swarm_type(report): """Test ReasoningAgentRouter with custom swarm_type (only change swarm_type param)""" start_time = time.time() try: - result = test_agents_swarm(swarm_type="reasoning-agent") + test_agents_swarm(swarm_type="reasoning-agent") router = ReasoningAgentRouter(swarm_type="reasoning-agent") if router.swarm_type == "reasoning-agent": report.add_result( @@ -281,7 +277,7 @@ def test_router_num_samples(report): start_time = time.time() try: router = ReasoningAgentRouter(num_samples=3) - output = router.run("How many samples do you use?") + router.run("How many samples do you use?") if router.num_samples == 3: report.add_result( "Parameter: num_samples", @@ -389,7 +385,7 @@ def test_router_eval(report): """Test ReasoningAgentRouter with eval enabled (only change eval param)""" start_time = time.time() try: - result = test_agents_swarm(eval=True) + test_agents_swarm(eval=True) router = ReasoningAgentRouter(eval=True) if router.eval is True: report.add_result( @@ -417,7 +413,7 @@ def test_router_random_models_on(report): """Test ReasoningAgentRouter with random_models_on enabled (only change random_models_on param)""" start_time = time.time() try: - result = test_agents_swarm(random_models_on=True) + test_agents_swarm(random_models_on=True) router = ReasoningAgentRouter(random_models_on=True) if router.random_models_on is True: report.add_result( @@ -445,7 +441,7 @@ def test_router_majority_voting_prompt(report): """Test ReasoningAgentRouter with custom majority_voting_prompt (only change majority_voting_prompt param)""" start_time = time.time() try: - result = test_agents_swarm( + test_agents_swarm( majority_voting_prompt="Vote for the best answer." ) router = ReasoningAgentRouter( @@ -526,7 +522,7 @@ def test_router_select_swarm(report): majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT, ) # Run the method to test - result = router.select_swarm() + router.select_swarm() # Determine if the result is as expected (not raising error is enough for this test) report.add_result( "Method: select_swarm()", From 97655835bc11ff6195ee9de9383223176a393205 Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Mon, 1 Sep 2025 11:56:30 -0700 Subject: [PATCH 3/5] [Updated docs for concurrent workflow] --- docs/swarms/structs/concurrentworkflow.md | 456 ++++++++++++++++------ 1 file changed, 341 insertions(+), 115 deletions(-) diff --git a/docs/swarms/structs/concurrentworkflow.md b/docs/swarms/structs/concurrentworkflow.md index 48297ed3..142502da 100644 --- a/docs/swarms/structs/concurrentworkflow.md +++ b/docs/swarms/structs/concurrentworkflow.md @@ -1,20 +1,21 @@ # ConcurrentWorkflow Documentation -## Overview +The `ConcurrentWorkflow` class is designed to facilitate the concurrent execution of multiple agents, each tasked with solving a specific query or problem. This class is particularly useful in scenarios where multiple agents need to work in parallel, allowing for efficient resource utilization and faster completion of tasks. The workflow manages the execution, handles streaming callbacks, and provides optional dashboard monitoring for real-time progress tracking. -The `ConcurrentWorkflow` class is designed to facilitate the concurrent execution of multiple agents, each tasked with solving a specific query or problem. This class is particularly useful in scenarios where multiple agents need to work in parallel, allowing for efficient resource utilization and faster completion of tasks. The workflow manages the execution, collects metadata, and optionally saves the results in a structured format. +Full Path: `swarms.structs.concurrent_workflow` ### Key Features -- **Concurrent Execution**: Runs multiple agents simultaneously using Python's `ThreadPoolExecutor` -- **Interactive Mode**: Supports interactive task modification and execution -- **Caching System**: Implements LRU caching for repeated prompts -- **Progress Tracking**: Optional progress bar for task execution -- **Enhanced Error Handling**: Implements retry mechanism with exponential backoff -- **Input Validation**: Validates task inputs before execution -- **Batch Processing**: Supports running tasks in batches -- **Metadata Collection**: Gathers detailed metadata about each agent's execution -- **Customizable Output**: Allows saving metadata to file or returning as string/dictionary +| Feature | Description | +|---------------------------|-----------------------------------------------------------------------------------------------| +| Concurrent Execution | Runs multiple agents simultaneously using Python's `ThreadPoolExecutor` | +| Dashboard Monitoring | Optional real-time dashboard for tracking agent status and progress | +| Streaming Support | Full support for streaming callbacks during agent execution | +| Error Handling | Comprehensive error handling with logging and status tracking | +| Batch Processing | Supports running multiple tasks sequentially | +| Resource Management | Automatic cleanup of resources and connections | +| Flexible Output Types | Multiple output format options for conversation history | +| Agent Status Tracking | Real-time tracking of agent execution states (pending, running, completed, error) | ## Class Definition @@ -40,7 +41,7 @@ The `ConcurrentWorkflow` class is designed to facilitate the concurrent executio | `_cache` | `dict` | The cache for storing agent outputs. | | `_progress_bar` | `tqdm` | The progress bar for tracking execution. | -## Methods +## Constructor ### ConcurrentWorkflow.\_\_init\_\_ @@ -48,120 +49,203 @@ Initializes the `ConcurrentWorkflow` class with the provided parameters. #### Parameters -| Parameter | Type | Default Value | Description | -|-----------------------|----------------|----------------------------------------|-----------------------------------------------------------| -| `name` | `str` | `"ConcurrentWorkflow"` | The name of the workflow. | -| `description` | `str` | `"Execution of multiple agents concurrently"` | A brief description of the workflow. | -| `agents` | `List[Agent]` | `[]` | A list of agents to be executed concurrently. | -| `metadata_output_path`| `str` | `"agent_metadata.json"` | Path to save the metadata output. | -| `auto_save` | `bool` | `True` | Flag indicating whether to automatically save the metadata. | -| `output_type` | `str` | `"dict"` | The type of output format. | -| `max_loops` | `int` | `1` | Maximum number of loops for each agent. | -| `return_str_on` | `bool` | `False` | Flag to return output as string. | -| `auto_generate_prompts`| `bool` | `False` | Flag indicating whether to auto-generate prompts for agents. | -| `return_entire_history`| `bool` | `False` | Flag to return entire conversation history. | -| `interactive` | `bool` | `False` | Flag indicating whether to enable interactive mode. | -| `cache_size` | `int` | `100` | The size of the cache. | -| `max_retries` | `int` | `3` | The maximum number of retry attempts. | -| `retry_delay` | `float` | `1.0` | The delay between retry attempts in seconds. | -| `show_progress` | `bool` | `False` | Flag indicating whether to show progress. | +| Parameter | Type | Default Value | Description | +|-----------------------|-------------------------------|----------------------------------------|-----------------------------------------------------------| +| `id` | `str` | `swarm_id()` | Unique identifier for the workflow instance. | +| `name` | `str` | `"ConcurrentWorkflow"` | The name of the workflow. | +| `description` | `str` | `"Execution of multiple agents concurrently"` | A brief description of the workflow. | +| `agents` | `List[Union[Agent, Callable]]`| `None` | A list of agents or callables to be executed concurrently. | +| `auto_save` | `bool` | `True` | Flag indicating whether to automatically save metadata. | +| `output_type` | `str` | `"dict-all-except-first"` | The type of output format. | +| `max_loops` | `int` | `1` | Maximum number of loops for each agent. | +| `auto_generate_prompts`| `bool` | `False` | Flag indicating whether to auto-generate prompts for agents. | +| `show_dashboard` | `bool` | `False` | Flag indicating whether to show real-time dashboard. | #### Raises -- `ValueError`: If the list of agents is empty or if the description is empty. +- `ValueError`: If no agents are provided or if the agents list is empty. + +## Methods + +### ConcurrentWorkflow.fix_agents + +Configures agents for dashboard mode by disabling print statements when dashboard is enabled. + +#### Returns + +- `List[Union[Agent, Callable]]`: The configured list of agents. -### ConcurrentWorkflow.disable_agent_prints +```python +agents = workflow.fix_agents() +``` + +### ConcurrentWorkflow.reliability_check + +Validates workflow configuration and ensures agents are properly set up. -Disables print statements for all agents in the workflow. +#### Raises + +- `ValueError`: If no agents are provided or if the agents list is empty. ```python -workflow.disable_agent_prints() +workflow.reliability_check() ``` ### ConcurrentWorkflow.activate_auto_prompt_engineering -Activates the auto-generate prompts feature for all agents in the workflow. +Enables automatic prompt generation for all agents in the workflow. ```python workflow.activate_auto_prompt_engineering() ``` -### ConcurrentWorkflow.enable_progress_bar +### ConcurrentWorkflow.display_agent_dashboard + +Displays real-time dashboard showing agent status and progress. + +#### Parameters -Enables the progress bar display for task execution. +| Parameter | Type | Default Value | Description | +|-------------|---------|----------------------------|-----------------------------------------------------------| +| `title` | `str` | `"ConcurrentWorkflow Dashboard"` | Title for the dashboard. | +| `is_final` | `bool` | `False` | Whether this is the final dashboard display. | ```python -workflow.enable_progress_bar() +workflow.display_agent_dashboard("Execution Progress", is_final=False) ``` -### ConcurrentWorkflow.disable_progress_bar +### ConcurrentWorkflow.run_with_dashboard + +Executes agents with real-time dashboard monitoring and streaming support. + +#### Parameters + +| Parameter | Type | Description | +|-----------------------|-----------------------------------|-----------------------------------------------------------| +| `task` | `str` | The task to execute. | +| `img` | `Optional[str]` | Optional image for processing. | +| `imgs` | `Optional[List[str]]` | Optional list of images for processing. | +| `streaming_callback` | `Optional[Callable[[str, str, bool], None]]` | Callback for streaming agent outputs. | + +#### Returns -Disables the progress bar display. +- `Any`: The formatted conversation history based on output_type. ```python -workflow.disable_progress_bar() +result = workflow.run_with_dashboard( + task="Analyze this data", + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) ``` -### ConcurrentWorkflow.clear_cache +### ConcurrentWorkflow._run -Clears the task cache. +Executes agents concurrently without dashboard monitoring. + +#### Parameters + +| Parameter | Type | Description | +|-----------------------|-----------------------------------|-----------------------------------------------------------| +| `task` | `str` | The task to execute. | +| `img` | `Optional[str]` | Optional image for processing. | +| `imgs` | `Optional[List[str]]` | Optional list of images for processing. | +| `streaming_callback` | `Optional[Callable[[str, str, bool], None]]` | Callback for streaming agent outputs. | + +#### Returns + +- `Any`: The formatted conversation history based on output_type. ```python -workflow.clear_cache() +result = workflow._run( + task="Process this task", + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) ``` -### ConcurrentWorkflow.get_cache_stats +### ConcurrentWorkflow._run_agent_with_streaming -Gets cache statistics. +Runs a single agent with streaming callback support. + +#### Parameters + +| Parameter | Type | Description | +|-----------------------|-----------------------------------|-----------------------------------------------------------| +| `agent` | `Union[Agent, Callable]` | The agent or callable to execute. | +| `task` | `str` | The task to execute. | +| `img` | `Optional[str]` | Optional image for processing. | +| `imgs` | `Optional[List[str]]` | Optional list of images for processing. | +| `streaming_callback` | `Optional[Callable[[str, str, bool], None]]` | Callback for streaming outputs. | #### Returns -- `Dict[str, int]`: A dictionary containing cache statistics. +- `str`: The output from the agent execution. + +```python +output = workflow._run_agent_with_streaming( + agent=my_agent, + task="Analyze data", + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) +``` + +### ConcurrentWorkflow.cleanup + +Cleans up resources and connections used by the workflow. ```python -stats = workflow.get_cache_stats() -print(stats) # {'cache_size': 5, 'max_cache_size': 100} +workflow.cleanup() ``` ### ConcurrentWorkflow.run -Executes the workflow for the provided task. +Main execution method that runs all agents concurrently. #### Parameters -| Parameter | Type | Description | -|-------------|---------------------|-----------------------------------------------------------| -| `task` | `Optional[str]` | The task or query to give to all agents. | -| `img` | `Optional[str]` | The image to be processed by the agents. | -| `*args` | `tuple` | Additional positional arguments. | -| `**kwargs` | `dict` | Additional keyword arguments. | +| Parameter | Type | Description | +|-----------------------|-----------------------------------|-----------------------------------------------------------| +| `task` | `str` | The task to execute. | +| `img` | `Optional[str]` | Optional image for processing. | +| `imgs` | `Optional[List[str]]` | Optional list of images for processing. | +| `streaming_callback` | `Optional[Callable[[str, str, bool], None]]` | Callback for streaming agent outputs. | #### Returns -- `Any`: The result of the execution, format depends on output_type and return_entire_history settings. +- `Any`: The formatted conversation history based on output_type. -#### Raises - -- `ValueError`: If an invalid device is specified. -- `Exception`: If any other error occurs during execution. +```python +result = workflow.run( + task="What are the benefits of renewable energy?", + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) +``` -### ConcurrentWorkflow.run_batched +### ConcurrentWorkflow.batch_run -Runs the workflow for a batch of tasks. +Executes the workflow on multiple tasks sequentially. #### Parameters -| Parameter | Type | Description | -|-------------|--------------|-----------------------------------------------------------| -| `tasks` | `List[str]` | A list of tasks or queries to give to all agents. | +| Parameter | Type | Description | +|-----------------------|-----------------------------------|-----------------------------------------------------------| +| `tasks` | `List[str]` | List of tasks to execute. | +| `imgs` | `Optional[List[str]]` | Optional list of images corresponding to tasks. | +| `streaming_callback` | `Optional[Callable[[str, str, bool], None]]` | Callback for streaming outputs. | #### Returns -- `List[Any]`: A list of results for each task. +- `List[Any]`: List of results for each task. + +```python +results = workflow.batch_run( + tasks=["Task 1", "Task 2", "Task 3"], + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) +``` ## Usage Examples -### Example 1: Basic Usage with Interactive Mode +### Example 1: Basic Concurrent Execution ```python from swarms import Agent, ConcurrentWorkflow @@ -169,59 +253,136 @@ from swarms import Agent, ConcurrentWorkflow # Initialize agents agents = [ Agent( - agent_name=f"Agent-{i}", - system_prompt="You are a helpful assistant.", + agent_name="Research-Agent", + system_prompt="You are a research specialist focused on gathering information.", + model_name="gpt-4", + max_loops=1, + ), + Agent( + agent_name="Analysis-Agent", + system_prompt="You are an analysis expert who synthesizes information.", + model_name="gpt-4", + max_loops=1, + ), + Agent( + agent_name="Summary-Agent", + system_prompt="You are a summarization expert who creates concise reports.", model_name="gpt-4", max_loops=1, ) - for i in range(3) ] -# Initialize workflow with interactive mode +# Initialize workflow workflow = ConcurrentWorkflow( - name="Interactive Workflow", + name="Research Analysis Workflow", + description="Concurrent execution of research, analysis, and summarization tasks", agents=agents, - interactive=True, - show_progress=True, - cache_size=100, - max_retries=3, - retry_delay=1.0 + auto_save=True, + output_type="dict-all-except-first", + show_dashboard=False ) # Run workflow -task = "What are the benefits of using Python for data analysis?" +task = "What are the environmental impacts of electric vehicles?" result = workflow.run(task) print(result) ``` -### Example 2: Batch Processing with Progress Bar +### Example 2: Dashboard Monitoring with Streaming ```python -# Initialize workflow +import time + +def streaming_callback(agent_name: str, chunk: str, is_final: bool): + """Handle streaming output from agents.""" + if chunk: + print(f"[{agent_name}] {chunk}", end="", flush=True) + if is_final: + print(f"\n[{agent_name}] Completed\n") + +# Initialize workflow with dashboard workflow = ConcurrentWorkflow( - name="Batch Processing Workflow", + name="Monitored Workflow", agents=agents, - show_progress=True, - auto_save=True + show_dashboard=True, # Enable real-time dashboard + output_type="dict-all-except-first" ) -# Define tasks +# Run with streaming and dashboard +task = "Analyze the future of artificial intelligence in healthcare" +result = workflow.run( + task=task, + streaming_callback=streaming_callback +) + +print("Final Result:", result) +``` + +### Example 3: Batch Processing Multiple Tasks + +```python +# Define multiple tasks tasks = [ - "Analyze the impact of climate change on agriculture", - "Evaluate renewable energy solutions", - "Assess water conservation strategies" + "What are the benefits of renewable energy adoption?", + "How does blockchain technology impact supply chains?", + "What are the challenges of implementing remote work policies?", + "Analyze the growth of e-commerce in developing countries" +] + +# Initialize workflow for batch processing +workflow = ConcurrentWorkflow( + name="Batch Analysis Workflow", + agents=agents, + output_type="dict-all-except-first", + show_dashboard=False +) + +# Process all tasks +results = workflow.batch_run(tasks=tasks) + +# Display results +for i, (task, result) in enumerate(zip(tasks, results)): + print(f"\n{'='*50}") + print(f"Task {i+1}: {task}") + print(f"{'='*50}") + print(f"Result: {result}") +``` + +### Example 4: Auto-Prompt Engineering + +```python +# Initialize agents without specific prompts +agents = [ + Agent( + agent_name="Creative-Agent", + model_name="gpt-4", + max_loops=1, + ), + Agent( + agent_name="Technical-Agent", + model_name="gpt-4", + max_loops=1, + ) ] -# Run batch processing -results = workflow.run_batched(tasks) +# Initialize workflow with auto-prompt engineering +workflow = ConcurrentWorkflow( + name="Auto-Prompt Workflow", + agents=agents, + auto_generate_prompts=True, # Enable auto-prompt generation + output_type="dict-all-except-first" +) + +# Activate auto-prompt engineering (can also be done in init) +workflow.activate_auto_prompt_engineering() -# Process results -for task, result in zip(tasks, results): - print(f"Task: {task}") - print(f"Result: {result}\n") +# Run workflow +task = "Design a mobile app for fitness tracking" +result = workflow.run(task) +print(result) ``` -### Example 3: Error Handling and Retries +### Example 5: Error Handling and Cleanup ```python import logging @@ -229,38 +390,103 @@ import logging # Set up logging logging.basicConfig(level=logging.INFO) -# Initialize workflow with retry settings +# Initialize workflow workflow = ConcurrentWorkflow( name="Reliable Workflow", agents=agents, - max_retries=3, - retry_delay=1.0, - show_progress=True + output_type="dict-all-except-first" ) -# Run workflow with error handling +# Run workflow with proper error handling try: - task = "Generate a comprehensive market analysis report" + task = "Generate a comprehensive report on quantum computing applications" result = workflow.run(task) + print("Workflow completed successfully!") print(result) except Exception as e: - logging.error(f"An error occurred: {str(e)}") + logging.error(f"Workflow failed: {str(e)}") +finally: + # Always cleanup resources + workflow.cleanup() + print("Resources cleaned up") ``` -## Tips and Best Practices +### Example 6: Working with Images -- **Agent Initialization**: Ensure all agents are correctly initialized with required configurations. -- **Interactive Mode**: Use interactive mode for tasks requiring user input or modification. -- **Caching**: Utilize the caching system for repeated tasks to improve performance. -- **Progress Tracking**: Enable progress bar for long-running tasks to monitor execution. -- **Error Handling**: Implement proper error handling and use retry mechanism for reliability. -- **Resource Management**: Monitor cache size and clear when necessary. -- **Batch Processing**: Use batch processing for multiple related tasks. -- **Logging**: Implement detailed logging for debugging and monitoring. +```python +# Initialize agents capable of image processing +vision_agents = [ + Agent( + agent_name="Image-Analysis-Agent", + system_prompt="You are an expert at analyzing images and extracting insights.", + model_name="gpt-4-vision-preview", + max_loops=1, + ), + Agent( + agent_name="Content-Description-Agent", + system_prompt="You specialize in creating detailed descriptions of visual content.", + model_name="gpt-4-vision-preview", + max_loops=1, + ) +] + +# Initialize workflow for image processing +workflow = ConcurrentWorkflow( + name="Image Analysis Workflow", + agents=vision_agents, + output_type="dict-all-except-first", + show_dashboard=True +) + +# Run with image input +task = "Analyze this image and provide insights about its content" +image_path = "/path/to/image.jpg" + +result = workflow.run( + task=task, + img=image_path, + streaming_callback=lambda agent, chunk, done: print(f"{agent}: {chunk}") +) -## References and Resources +print(result) +``` -- [Python's ThreadPoolExecutor Documentation](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor) -- [tqdm Progress Bar Documentation](https://tqdm.github.io/) -- [Python's functools.lru_cache Documentation](https://docs.python.org/3/library/functools.html#functools.lru_cache) -- [Loguru for Logging in Python](https://loguru.readthedocs.io/en/stable/) \ No newline at end of file +### Example 7: Custom Callable Agents + +```python +from typing import Optional + +def custom_analysis_agent(task: str, img: Optional[str] = None, **kwargs) -> str: + """Custom analysis function that can be used as an agent.""" + # Custom logic here + return f"Custom analysis result for: {task}" + +def sentiment_analysis_agent(task: str, img: Optional[str] = None, **kwargs) -> str: + """Sentiment analysis function.""" + # Custom sentiment analysis logic + return f"Sentiment analysis for: {task}" + +# Mix of Agent objects and callable functions +mixed_agents = [ + Agent( + agent_name="GPT-Agent", + system_prompt="You are a helpful assistant.", + model_name="gpt-4", + max_loops=1, + ), + custom_analysis_agent, # Callable function + sentiment_analysis_agent # Another callable function +] + +# Initialize workflow with mixed agent types +workflow = ConcurrentWorkflow( + name="Mixed Agents Workflow", + agents=mixed_agents, + output_type="dict-all-except-first" +) + +# Run workflow +task = "Analyze customer feedback and provide insights" +result = workflow.run(task) +print(result) +``` \ No newline at end of file From 9daee6d1dd764a9129feda2a3b9b194ad55a3838 Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Mon, 1 Sep 2025 12:18:58 -0700 Subject: [PATCH 4/5] [DOCS][Firecrawl Guides] --- docs/developer_guides/firecrawl.md | 65 ++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/docs/developer_guides/firecrawl.md b/docs/developer_guides/firecrawl.md index e69de29b..bca015ac 100644 --- a/docs/developer_guides/firecrawl.md +++ b/docs/developer_guides/firecrawl.md @@ -0,0 +1,65 @@ +# Firecrawl Tool + + +The Firecrawl tool is a powerful web crawling utility that integrates seamlessly with Swarms agents to extract, analyze, and process content from websites. It leverages the Firecrawl API to crawl entire websites, extract structured data, and provide comprehensive content analysis for various use cases including marketing, research, content creation, and data analysis. + +### Key Features + +| Feature | Description | +|-----------------------------|-----------------------------------------------------------------------------------------------| +| **Complete Site Crawling** | Crawl entire websites and extract content from multiple pages | +| **Structured Data Extraction** | Automatically parse and structure web content | +| **Agent Integration** | Works seamlessly with Swarms agents for intelligent content processing | +| **Marketing Copy Analysis** | Specialized for analyzing and improving marketing content | +| **Content Optimization** | Identify and enhance key value propositions and calls-to-action | + +## Prerequisites + +Before getting started, you'll need: + +1. **Python 3.8+** installed on your system +2. **Firecrawl API Key** from [firecrawl.dev/app](https://www.firecrawl.dev/app) +3. **OpenAI API Key** for agent functionality + +## Install + +```bash +pip3 install -U swarms swarms-tools +``` + +## ENV + +```txt +FIRECRAWL_API_KEY="" +OPENAI_API_KEY="" +``` + +## Usage + +```python +from swarms_tools import crawl_entire_site_firecrawl + +from swarms import Agent + +agent = Agent( + agent_name="Marketing Copy Improver", + model_name="gpt-4.1", + tools=[crawl_entire_site_firecrawl], + dynamic_context_window=True, + dynamic_temperature_enabled=True, + max_loops=1, + system_prompt=( + "You are a world-class marketing copy improver. " + "Given a website URL, your job is to crawl the entire site, analyze all marketing copy, " + "and rewrite it to maximize clarity, engagement, and conversion. " + "Return the improved marketing copy in a structured, easy-to-read format. " + "Be concise, persuasive, and ensure the tone matches the brand. " + "Highlight key value propositions and calls to action." + ), +) + +out = agent.run( + "Crawl 2-3 pages of swarms.ai and improve the marketing copy found on those pages. Return the improved copy in a structured format." +) +print(out) +``` \ No newline at end of file From c86869b6298a27cf4b16214d624ce8d37f2cc1db Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Mon, 1 Sep 2025 12:19:30 -0700 Subject: [PATCH 5/5] [MKDOCS][Firecrawl] --- docs/mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 1effe52d..fa7049ab 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -430,6 +430,7 @@ nav: - Advanced Research: "examples/av.md" - Browser Use: "examples/browser_use.md" - Yahoo Finance: "swarms/examples/yahoo_finance.md" + - Firecrawl: "developer_guides/firecrawl.md" - RAG: - RAG with Qdrant: "swarms/RAG/qdrant_rag.md"