Shorten explanation of agents

make this shorter: Yeah, it is a bit more complex, I can go over what I did today, https://github.com/kliewerdaniel/agentsearch01/blob/master/agent_search.py So instead of just a prompt and response you are getting the following information passed and tracked between calls: response = { 'agent': self.name, 'original_query': query, 'refined_query': refined_query, 'search_results': search_results, 'analysis': analysis, 'discovered_topics': list(self.discovered_topics), 'confidence': analysis.get('confidence', 0.5), 'suggestions': analysis.get('suggestions', []), 'needs_collaboration': analysis.get('needs_collaboration', False) } So it is able to pass the results that one LLM call creates and alters or integrates the results of calling tools, like a basic search or what have you and incorporate that into the context. So one thing can simply be using a different speciality prompt for the LLM call depending on which agent you are using with something like: specialty_prompts = { 'contextualizer': f"Find background information, definitions, and context for: {query}", 'synthesizer': f"Summarize and synthesize information about: {query}", 'validator': f"Find evidence and validation for claims about: {query}", 'explorer': f"Discover related topics and connections to: {query}", 'temporal': f"Find chronological information and timelines about: {query}", 'technical': f"Find technical details, specifications, and implementations of: {query}" } Then you would use an orchestrator class like: class ResearchOrchestrator: def __init__(self, search_system: 'MarkdownSearchSystem', goal: str, max_iterations: int = 5): self.search_system = search_system self.goal = goal self.max_iterations = max_iterations self.agents = self._create_agents() self.iteration_count = 0 self.final_report = "" self.status = TaskStatus.PENDING self.conversation_log = [] def _create_agents(self) -> List[ResearchAgent]: """Create specialized research agents.""" agent_configs = [ ("ContextBot", "Information Contextualizer", "contextualizer"), ("SynthAI", "Content Synthesizer", "synthesizer"), ("ValidatorPro", "Information Validator", "validator"), ("ExplorerX", "Topic Explorer", "explorer"), ("ChronoAgent", "Temporal Analyzer", "temporal"), ("TechSpec", "Technical Specialist", "technical") ] Then you can create a graph with each agent as a node and use networkx and do more complex pathing using weights and such for directing a path from one agent to another. You would use other quantitative values that when the weight reaches a threshold would direct along the graph to the next agent to work on the task. So you can get really complex with it, this is all really basic compared to what you can do with a lot of data or by integrating databases or states from the frontend or any number of things.

Sure! Here's a shorter version of your explanation: --- Yeah, it's a bit more complex now. Here's what I worked on today: [agent_search.py](https://github.com/kliewerdaniel/agentsearch01/blob/master/agent_search.py) Instead of just sending a prompt and getting a response, each call tracks richer data: ```python response = { 'agent': self.name, 'original_query': query, 'refined_query': refined_query, 'search_results': search_results, 'analysis': analysis, 'discovered_topics': list(self.discovered_topics), 'confidence': analysis.get('confidence', 0.5), 'suggestions': analysis.get('suggestions', []), 'needs_collaboration': analysis.get('needs_collaboration', False) } ``` This allows one LLM call to influence the next by integrating tool outputs (like search results) into its context. Each agent has a specialized prompt: ```python specialty_prompts = { 'contextualizer': f"Find background info for: {query}", 'synthesizer': f"Summarize: {query}", 'validator': f"Validate claims about: {query}", 'explorer': f"Find related topics to: {query}", 'temporal': f"Get timelines for: {query}", 'technical': f"Find technical details on: {query}" } ``` These agents are managed by a `ResearchOrchestrator`, which coordinates tasks across multiple agents and tracks iterations. You can take it further with networkx graphs where each agent is a node, and paths are weighted based on confidence or other metrics, letting the system dynamically decide the next best agent to handle a task. This is just the foundation—there’s a lot of potential for more advanced orchestration using databases, frontend state, or feedback loops. --- Let me know if you want this even more condensed.