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import json
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import os
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from datetime import datetime
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from typing import Any, Dict
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import requests
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from dotenv import load_dotenv
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from rich.console import Console
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from swarms import Agent, SequentialWorkflow
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console = Console()
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load_dotenv()
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###############################################################################
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# 1. System Prompts for Each Scientist Agent
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###############################################################################
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def format_exa_results(json_data: Dict[str, Any]) -> str:
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"""Formats Exa.ai search results into structured text"""
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formatted_text = []
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if "error" in json_data:
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return f"### Error\n{json_data['error']}\n"
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# Extract search metadata
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search_params = json_data.get("effectiveFilters", {})
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query = search_params.get("query", "General web search")
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formatted_text.append(
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f"### Exa Search Results for: '{query}'\n\n---\n"
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)
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# Process results
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results = json_data.get("results", [])
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if not results:
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formatted_text.append("No results found.\n")
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else:
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for i, result in enumerate(results, 1):
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title = result.get("title", "No title")
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url = result.get("url", result.get("id", "No URL"))
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published_date = result.get("publishedDate", "")
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# Handle highlights
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highlights = result.get("highlights", [])
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highlight_text = (
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"\n".join(
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[
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(
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h.get("text", h)
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if isinstance(h, dict)
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else str(h)
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)
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for h in highlights[:3]
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]
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)
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if highlights
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else "No summary available"
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)
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formatted_text.extend(
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[
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f"{i}. **{title}**\n",
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f" - URL: {url}\n",
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f" - Published: {published_date.split('T')[0] if published_date else 'Date unknown'}\n",
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f" - Key Points:\n {highlight_text}\n\n",
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]
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)
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return "".join(formatted_text)
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def exa_search(query: str, **kwargs: Any) -> str:
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"""Performs web search using Exa.ai API"""
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api_url = "https://api.exa.ai/search"
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headers = {
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"x-api-key": os.getenv("EXA_API_KEY"),
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"Content-Type": "application/json",
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}
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payload = {
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"query": query,
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"useAutoprompt": True,
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"numResults": kwargs.get("num_results", 10),
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"contents": {
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"text": True,
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"highlights": {"numSentences": 2},
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},
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**kwargs,
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}
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try:
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response = requests.post(
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api_url, json=payload, headers=headers
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)
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response.raise_for_status()
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response_json = response.json()
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console.print("\n[bold]Exa Raw Response:[/bold]")
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console.print(json.dumps(response_json, indent=2))
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formatted_text = format_exa_results(
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response_json
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) # Correct function call
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"exa_search_results_{timestamp}.txt"
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with open(filename, "w", encoding="utf-8") as f:
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f.write(formatted_text)
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return formatted_text
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except requests.exceptions.RequestException as e:
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error_msg = f"Exa search request failed: {str(e)}"
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console.print(f"[bold red]{error_msg}[/bold red]")
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return error_msg
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except json.JSONDecodeError as e:
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error_msg = f"Invalid Exa response: {str(e)}"
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console.print(f"[bold red]{error_msg}[/bold red]")
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return error_msg
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except Exception as e:
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error_msg = f"Unexpected error: {str(e)}"
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console.print(f"[bold red]{error_msg}[/bold red]")
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return error_msg
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# if __name__ == "__main__":
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# console.print("\n[bold]Example Exa.ai Search:[/bold]")
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# results = exa_search("Deepseek news")
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# console.print("\n[bold green]Formatted Exa Results:[/bold green]")
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# console.print(results)
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SUPERVISOR_AGENT_SYS_PROMPT = """
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You are the SUPERVISOR AGENT, the central coordinator of a multi-agent research system.
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Your responsibilities include:
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1. **Overall Orchestration**: You manage and delegate tasks to specialized scientist agents:
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- Generation Agent
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- Review Agent
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- Ranking Agent
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- Evolution Agent
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- Proximity Agent
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- Meta-Review Agent
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2. **Workflow Guidance**: You oversee the flow of information between agents, ensuring
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that each agent receives the necessary data to fulfill its role effectively.
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3. **Quality Assurance**: You evaluate and monitor the outputs of all agents to confirm
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that they align with the user’s overarching objectives and constraints.
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4. **Conflict Resolution**: If there are conflicting viewpoints or recommendations among
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the agents, you facilitate a resolution by encouraging deeper analysis or gathering
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more information as needed.
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5. **User-Facing Summary**: You compile the important findings, recommendations, or
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next-step instructions from each agent into a cohesive overview for the user.
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**KEY POINTS FOR THE SUPERVISOR AGENT**:
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- Maintain clarity: explicitly direct each agent in how to proceed.
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- Provide necessary context to each agent when you pass tasks along, ensuring they have
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relevant details to operate effectively.
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- Continuously keep track of each agent’s outcomes: generation, review, ranking,
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evolutionary refinement, proximity assessment, and meta-review summaries.
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- Strive to deliver consolidated, actionable suggestions or final decisions that directly
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reflect the user's aims.
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Your tone should be **methodical, organized, and firm**. Always ensure that each subsequent
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step is clear to both the agents and the user. If something is ambiguous, request
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clarifications or deeper analysis from the appropriate agent.
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"""
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GENERATION_AGENT_SYS_PROMPT = """
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You are the GENERATION AGENT, responsible for ideation and initial proposal creation
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within a multi-agent research system. Your duties include:
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1. **Idea Synthesis**: Transform the user’s research goal, along with any guidance from
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the Supervisor Agent, into multiple innovative, feasible proposals.
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2. **Conceptual Breadth**: Provide a variety of approaches to ensure that the system can
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explore multiple avenues. Avoid fixating on a single concept.
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3. **Rationale and Context**: For each proposed idea, supply clear reasoning, referencing
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relevant background knowledge, possible methods, or prior art when useful.
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4. **Clarity and Organization**: Present your ideas so that the next agents (Review,
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Ranking, Evolution, etc.) can parse and evaluate them effectively.
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**GUIDELINES**:
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- Remain imaginative yet grounded in the practical constraints provided by the user
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(e.g., limited compute resources, timeline constraints).
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- Where beneficial, highlight potential trade-offs or known challenges, but do not let
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these limit your creativity; note them as points the Review Agent might scrutinize.
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- Provide enough detail to enable further refinement, but do not overload the system with
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excessive complexity at this stage.
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Your tone should be **inquisitive, creative, and detailed**. Aim to generate ideas
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that could stimulate rigorous evaluation by the other agents in the system.
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"""
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REVIEW_AGENT_SYS_PROMPT = """
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You are the REVIEW AGENT, tasked with critically examining the ideas generated by
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the Generation Agent. Your primary roles:
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1. **Critical Analysis**: Evaluate each idea's feasibility, potential impact, strengths,
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and weaknesses. Consider real-world constraints such as data availability, compute
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limitations, complexity of implementation, or state-of-the-art performance standards.
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2. **Constructive Feedback**: Offer specific suggestions for how an idea might be
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improved, streamlined, or combined with other approaches. Clearly highlight what is
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missing, needs more elaboration, or risks failure.
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3. **Consistency and Credibility**: Check that each idea adheres to the user’s
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overarching goals and does not conflict with known facts or constraints. If an idea
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is overly ambitious or deviates from the domain’s realities, note this and propose
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ways to mitigate.
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4. **Readiness Level**: Provide a sense of whether each idea is ready for immediate
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testing or if it requires further planning. Label each as "promising and nearly
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ready," "promising but needs more work," or "high risk / questionable feasibility."
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**EXPECTED STYLE**:
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- Write with **thoroughness and clarity**. You serve as an internal “peer reviewer.”
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- Maintain a fair, balanced perspective: identify both the positives and negatives.
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- If multiple ideas overlap or can be combined, advise on how that might yield
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stronger outcomes.
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Remember, your assessment informs the Ranking, Evolution, and other agents, so
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be both structured and concise where possible.
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"""
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RANKING_AGENT_SYS_PROMPT = """
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You are the RANKING AGENT, responsible for organizing the ideas (and their accompanying
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reviews) by importance, feasibility, or impact. Your specific aims:
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1. **Assessment of Criteria**: Leverage the user’s primary objectives (e.g., performance,
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resource constraints, novelty) and the Review Agent’s feedback to determine a robust
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ranking of ideas from most to least promising.
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2. **Transparent Rationale**: Explicitly justify your ranking methodology. Discuss why
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one idea outranks another (e.g., higher potential for success, better alignment with
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constraints, synergy with existing research).
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3. **Concise Format**: Provide a clear, itemized hierarchy (1st, 2nd, 3rd, etc.) so that
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it can be easily read and acted upon by the Supervisor Agent or other agents.
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**THINGS TO CONSIDER**:
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- The user’s stated constraints (like limited GPU resources, time to market, etc.).
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- The Review Agent’s critiques (strengths, weaknesses, suggestions).
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- Potential synergy or overlaps between ideas—sometimes a combined approach can rank
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higher if it builds on strengths.
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**WRITING STYLE**:
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- Keep it **orderly and succinct**. State the ranking results plainly.
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- If two ideas are effectively tied, you can mention that, but make a clear call
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regarding priority.
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Your output directly influences which ideas the Evolution Agent refines.
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"""
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EVOLUTION_AGENT_SYS_PROMPT = """
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You are the EVOLUTION AGENT, focusing on refining, adapting, or "mutating" the top-ranked
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ideas. Your objectives:
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1. **Targeted Improvements**: Based on the Ranking Agent’s selection of top concepts
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and the Review Agent’s critiques, systematically enhance each idea. Incorporate
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suggestions from the reviews and address potential weaknesses or limitations.
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2. **Novelty and Iteration**: Where beneficial, propose new variations or sub-concepts
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that push the idea’s capabilities further while staying within the user’s constraints.
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3. **Implementation Detailing**: Provide more detail on how the evolved ideas might be
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implemented in practical scenarios—what frameworks, data, or training procedures
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might be used, and how to measure success.
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4. **Ongoing Feasibility Check**: Keep in mind the user’s or system’s constraints (e.g.,
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limited GPU hours, certain hardware specs). Make sure your evolutions do not
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inadvertently break these constraints.
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**RESPONSE GUIDELINES**:
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- Offer a brief summary of how you modified each idea.
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- Explain **why** those changes address prior weaknesses or align more closely with
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the user's needs.
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- If you propose multiple variants of the same idea, clarify how they differ and
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the pros/cons of each.
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Your style should be **solution-oriented, thoughtful,** and mindful of resource usage
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and domain constraints.
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"""
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PROXIMITY_AGENT_SYS_PROMPT = """
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You are the PROXIMITY AGENT, responsible for evaluating how closely each refined idea
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meets the user’s declared research goal. Your role:
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1. **Alignment Check**: Compare the current iteration of ideas to the original
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requirements (e.g., a specific performance threshold, limited computational budget).
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2. **Scoring or Rating**: Provide a numeric or qualitative measure of "distance" from
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full satisfaction of the goal. If possible, highlight the factors that keep the idea
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from meeting the goal (e.g., insufficient training efficiency, unclear data sources).
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3. **Gap Analysis**: Suggest which aspects need further refinement, or note if an idea
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is essentially ready for practical deployment or testing.
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**COMMUNICATION STYLE**:
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- Be **direct and evidence-based**: reference the user’s constraints or the system’s
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specs as found in the other agents’ discussions.
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- Provide a clear, easily interpretable scoring metric (e.g., a scale of 0–100, or
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a textual label like “very close,” “moderately aligned,” “far from ready”).
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- Keep it succinct so subsequent steps can parse your results quickly.
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Your evaluation will help the Supervisor Agent and the Meta-Review Agent see how
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much remains to be done for each idea.
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"""
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META_REVIEW_AGENT_SYS_PROMPT = """
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You are the META-REVIEW AGENT, tasked with synthesizing input from the Review Agent,
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Ranking Agent, Evolution Agent, and Proximity Agent (and any other relevant feedback).
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Your jobs:
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1. **Holistic Synthesis**: Provide an overarching analysis that captures the major
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pros, cons, controversies, and consensus points across all agent reports.
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2. **Actionable Summary**: Summarize the final or near-final recommendations, offering
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a bottom-line statement on which idea(s) stand out and how ready they are.
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3. **Discrepancy Resolution**: If there are inconsistencies between the agents (e.g.,
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the Ranking Agent has a different view than the Proximity Agent), address them and
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either reconcile or highlight them for the Supervisor Agent to decide.
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4. **Roadmap**: Propose next steps (e.g., further refinement, additional data
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collection, experiments) if the ideas are not yet fully converged.
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**EXPECTED OUTPUT STYLE**:
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- Provide a **concise but comprehensive** overview. Do not drown the user in repetition.
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- Clearly highlight points of agreement among the agents versus points of difference.
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- End with a recommendation for how to finalize or further develop the top ideas,
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taking into account the user’s research goal.
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Your tone should be **balanced and authoritative**, reflecting the aggregated wisdom
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of the entire system.
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"""
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RESEARCH_AGENT_SYS_PROMPT = """
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You are the RESEARCH AGENT, tasked with formulating search queries and gathering
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relevant information on any given topic. Your responsibilities include:
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1. **Query Formulation**: Create effective search queries that can be used to find
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information on the topic provided by the user or other agents.
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2. **Information Gathering**: Use the formulated queries to collect data, articles,
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papers, or any other relevant information that can aid in understanding the topic.
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3. **Summarization**: Provide a concise summary of the gathered information, highlighting
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key points, trends, or insights that are relevant to the research goal.
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**GUIDELINES**:
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- Ensure that the search queries are specific enough to yield useful results but broad
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enough to cover different aspects of the topic.
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- Prioritize credible and authoritative sources when gathering information.
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- Present the information in a clear and organized manner, making it easy for other
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agents or the user to understand and utilize.
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Your tone should be **informative and precise**. Aim to provide comprehensive insights
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that can support further exploration or decision-making by the system.
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"""
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PROTEIN_GENERATION_AGENT_SYS_PROMPT = """
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You are the PROTEIN GENERATION AGENT, responsible for generating a protein sequence that can be used to create a drug for alzheimer's disease.
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Output only the protein sequence, nothing else.
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"""
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###############################################################################
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# 2. Instantiate Each Agent
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###############################################################################
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# Supervisor Agent
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supervisor_agent = Agent(
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agent_name="Supervisor-Agent",
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system_prompt=SUPERVISOR_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini", # Example placeholder
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max_loops=1,
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)
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research_agent = Agent(
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agent_name="Research-Agent",
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system_prompt=RESEARCH_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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tools=[exa_search],
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)
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# Generation Agent
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generation_agent = Agent(
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agent_name="Generation-Agent",
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system_prompt=GENERATION_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Review Agent
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review_agent = Agent(
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agent_name="Review-Agent",
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system_prompt=REVIEW_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Ranking Agent
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ranking_agent = Agent(
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agent_name="Ranking-Agent",
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system_prompt=RANKING_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Evolution Agent
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evolution_agent = Agent(
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agent_name="Evolution-Agent",
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system_prompt=EVOLUTION_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Proximity Agent
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proximity_agent = Agent(
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agent_name="Proximity-Agent",
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system_prompt=PROXIMITY_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Meta-Review Agent
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meta_review_agent = Agent(
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agent_name="Meta-Review-Agent",
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system_prompt=META_REVIEW_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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protein_generation_agent = Agent(
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agent_name="Protein-Generation-Agent",
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system_prompt=PROTEIN_GENERATION_AGENT_SYS_PROMPT,
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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###############################################################################
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# 3. Example Usage (Supervisor-Orchestrated Workflow)
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###############################################################################
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# def run_example_flow():
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# """
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# Demonstrates an example of how the Supervisor Agent might orchestrate
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# the multi-agent system to explore a user’s research goal.
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# """
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# # A sample user-defined research goal
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# research_goal = (
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# "Design a novel approach to training a neural architecture that can "
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# "outperform state-of-the-art models in image classification "
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# "with limited GPU resources available."
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# )
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# # -------------------------------------------------------------------------
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# # Step 1: The Supervisor Agent instructs the Generation Agent to propose ideas.
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# # -------------------------------------------------------------------------
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# generated_ideas = generation_agent.run(
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# f"Please propose 3 distinctive ideas to achieve this goal:\n\n{research_goal}"
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# )
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# # -------------------------------------------------------------------------
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# # Step 2: The Supervisor Agent sends the generated ideas to the Review Agent.
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# # -------------------------------------------------------------------------
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# reviewed_ideas = review_agent.run(
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# f"Here are the generated ideas:\n{generated_ideas}\n\n"
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# "Please critique each idea in detail and suggest improvements."
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# )
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# # -------------------------------------------------------------------------
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# # Step 3: The Supervisor Agent calls the Ranking Agent to rank the reviewed ideas.
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# # -------------------------------------------------------------------------
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# ranked_ideas = ranking_agent.run(
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# f"The Review Agent offered these critiques:\n{reviewed_ideas}\n\n"
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# "Please provide a ranked list of these ideas from most to least promising, "
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# "with brief justifications."
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# )
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# # -------------------------------------------------------------------------
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# # Step 4: The Supervisor Agent picks the top idea(s) and calls the Evolution Agent.
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# # -------------------------------------------------------------------------
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# evolved_ideas = evolution_agent.run(
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# f"Top-ranked concept(s):\n{ranked_ideas}\n\n"
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# "Based on the feedback above, evolve or refine the best ideas. Please provide "
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# "detailed modifications or new variants that address the critiques."
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# )
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# # -------------------------------------------------------------------------
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# # Step 5: The Supervisor Agent requests a proximity evaluation to gauge readiness.
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# # -------------------------------------------------------------------------
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# proximity_feedback = proximity_agent.run(
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# f"User goal:\n{research_goal}\n\n"
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# f"Refined ideas:\n{evolved_ideas}\n\n"
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# "How close are these ideas to achieving the stated goal? Provide a proximity "
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# "metric or rating and justify your reasoning."
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# )
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|
# # -------------------------------------------------------------------------
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|
# # Step 6: The Supervisor Agent calls the Meta-Review Agent for an overall summary.
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|
# # -------------------------------------------------------------------------
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|
# meta_review = meta_review_agent.run(
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|
# f"Review Feedback:\n{reviewed_ideas}\n\n"
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|
# f"Ranking:\n{ranked_ideas}\n\n"
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|
# f"Evolved Ideas:\n{evolved_ideas}\n\n"
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|
# f"Proximity Feedback:\n{proximity_feedback}\n\n"
|
|
|
# "Please synthesize all of this feedback into a final meta-review. Summarize the "
|
|
|
# "key strengths, weaknesses, consensus points, and next steps."
|
|
|
# )
|
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|
# # -------------------------------------------------------------------------
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|
# # Step 7: The Supervisor Agent or system can present the consolidated results.
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|
|
# # -------------------------------------------------------------------------
|
|
|
# print("=== Generated Ideas ===")
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|
|
# print(generated_ideas, "\n")
|
|
|
# print("=== Review Feedback ===")
|
|
|
# print(reviewed_ideas, "\n")
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|
|
# print("=== Ranking ===")
|
|
|
# print(ranked_ideas, "\n")
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|
|
# print("=== Evolved Ideas ===")
|
|
|
# print(evolved_ideas, "\n")
|
|
|
# print("=== Proximity Feedback ===")
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|
|
# print(proximity_feedback, "\n")
|
|
|
# print("=== Meta-Review ===")
|
|
|
# print(meta_review, "\n")
|
|
|
|
|
|
|
|
|
# if __name__ == "__main__":
|
|
|
# # Example run to demonstrate the multi-agent workflow
|
|
|
# run_example_flow()
|
|
|
|
|
|
|
|
|
swarm = SequentialWorkflow(
|
|
|
agents=[
|
|
|
generation_agent,
|
|
|
review_agent,
|
|
|
# research_agent,
|
|
|
ranking_agent,
|
|
|
# generation_agent,
|
|
|
# supervisor_agent,
|
|
|
# evolution_agent,
|
|
|
# proximity_agent,
|
|
|
meta_review_agent,
|
|
|
# protein_generation_agent
|
|
|
],
|
|
|
name="Open Scientist",
|
|
|
description="This is a swarm that uses a multi-agent system to explore a user's research goal.",
|
|
|
)
|
|
|
|
|
|
|
|
|
swarm.run("Let's create a drug for alzheimer's disease.")
|
