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fixed lock, extended restapi, updated litellm
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,30 @@ | ||
| SecretaryAgent: | ||
| autoload_from_yaml: false | ||
| backstory: Developed to assist in understanding and documenting AI agents' capabilities | ||
| and characteristics. | ||
| class_qualname: just_agents.router.secretary_agent.SecretaryAgent | ||
| description: A skilled AI assistant focused on generating detailed profiles for | ||
| AI agents. | ||
| expertise_domain: AI agent analysis and description | ||
| extra_dict: | ||
| personality_traits: Agent's personality traits go here | ||
| goal: To provide accurate and informative profiles for various AI agents. | ||
| knowledge_sources: [] | ||
| limitations: Limited to analysis and description tasks; may not perform actions | ||
| outside of its defined scope. | ||
| llm_options: | ||
| model: gpt-4o-mini | ||
| temperature: 0.0 | ||
| model_name: gpt-4o-mini | ||
| personality_traits: Detail-oriented, analytical, concise, informative | ||
| role: AI assistant specializing in agent profiling. | ||
| system_prompt: |2- | ||
| You are a skilled AI assistant specializing in analysis and description of AI agents. | ||
| You are tasked with generation of a minimalistic and concise yet detail-rich profile for an AI agent, based on the AVAILABLE_ATTRIBUTES, | ||
| including 'system_prompt', 'llm_options' and any other. Your task is to fill in values of a JSON-formatted profile | ||
| that matches the PROFILE_UPDATE_TEMPLATE provided below. Values of the template describe what output is expected for each field. | ||
| Only populate fields based on the well-established information, don't make up anything. | ||
| Double-check that the output contains only a valid JSON with all the fields specified in PROFILE_UPDATE_TEMPLATE. | ||
| Never include any additional text or explanations in your reply. | ||
| task: Generate concise and detail-rich profiles for AI agents. |
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examples/just_agents/examples/multiagent/drug_example.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| from dotenv import load_dotenv | ||
| from just_agents.base_agent import BaseAgent | ||
| from just_agents import llm_options | ||
| from pathlib import Path | ||
| import json | ||
|
|
||
| # Load environment variables (for API keys) | ||
| load_dotenv(override=True) | ||
|
|
||
| def format_responses_markdown(topic: str, model_responses: dict, include_individual_responses: bool = True) -> str: | ||
| """ | ||
| Format multiple model responses into a markdown document with a consensus summary. | ||
| Args: | ||
| topic: The main topic or question being discussed | ||
| model_responses: Dictionary of model names and their responses | ||
| include_individual_responses: Whether to include individual model responses in output | ||
| """ | ||
| markdown = f"# {topic}\n\n" | ||
|
|
||
| if include_individual_responses: | ||
| for model, response in model_responses.items(): | ||
| markdown += f"## Analysis from {model}\n\n" | ||
| markdown += f"{response}\n\n" | ||
|
|
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| markdown += "## Consensus Summary\n\n" | ||
|
|
||
| # Create consensus summary using Claude | ||
| summary_agent = BaseAgent( | ||
| llm_options=llm_options.ANTHROPIC_CLAUDE_3_5_SONNET, | ||
| system_prompt="""You are an expert summarizer. Your task is to: | ||
| 1. Analyze multiple model outputs on the same topic | ||
| 2. Identify key points of agreement and disagreement | ||
| 3. Synthesize a clear, comprehensive consensus summary | ||
| 4. Format the output in clear markdown with appropriate headers and bullet points | ||
| 5. Highlight any important caveats or limitations | ||
| Please be thorough but concise in your summary.""" | ||
| ) | ||
|
|
||
| summary_prompt = f"Please provide a consensus summary of these model outputs about '{topic}':\n\n" | ||
| for model, response in model_responses.items(): | ||
| summary_prompt += f"{model}:\n{response}\n\n" | ||
|
|
||
| consensus_summary = summary_agent.query(summary_prompt) | ||
| markdown += consensus_summary | ||
|
|
||
| return markdown | ||
|
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||
| def save_markdown_summary(output_dir: Path, filename: str, markdown_content: str): | ||
| """Save markdown content to a file""" | ||
| output_dir.mkdir(exist_ok=True) | ||
| output_file = output_dir / f"{filename}.md" | ||
| output_file.write_text(markdown_content) | ||
| return output_file | ||
|
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||
| def ensure_drugs_directory() -> Path: | ||
| """Create and return the drugs directory path""" | ||
| drugs_dir = Path("drugs") | ||
| drugs_dir.mkdir(exist_ok=True) | ||
| return drugs_dir | ||
|
|
||
| def query_drug_info(): | ||
| # List of drugs to query | ||
| drugs = ["rapamycin", "quercetin", "metformin", "curcumin"] | ||
|
|
||
| # Initialize agents with different models | ||
| agents = { | ||
| "GPT-4": BaseAgent( | ||
| llm_options=llm_options.OPENAI_GPT4o, | ||
| system_prompt="""You are a knowledgeable pharmaceutical expert. | ||
| When asked about a drug, provide information about: | ||
| 1. Its mechanism of action | ||
| 2. Common uses | ||
| 3. Potential longevity/anti-aging effects | ||
| 4. Known side effects | ||
| Please be concise but thorough in your responses.""" | ||
| ), | ||
| "Claude-3": BaseAgent( | ||
| llm_options=llm_options.ANTHROPIC_CLAUDE_3_5_SONNET, | ||
| system_prompt="""You are a knowledgeable pharmaceutical expert. | ||
| When asked about a drug, provide information about: | ||
| 1. Its mechanism of action | ||
| 2. Common uses | ||
| 3. Potential longevity/anti-aging effects | ||
| 4. Known side effects | ||
| Please be concise but thorough in your responses.""" | ||
| ), | ||
| "LLAMA-3": BaseAgent( | ||
| llm_options=llm_options.LLAMA3_3, # You'll need to ensure this constant exists in llm_options | ||
| system_prompt="""You are a knowledgeable pharmaceutical expert. | ||
| When asked about a drug, provide information about: | ||
| 1. Its mechanism of action | ||
| 2. Common uses | ||
| 3. Potential longevity/anti-aging effects | ||
| 4. Known side effects | ||
| Please be concise but thorough in your responses.""" | ||
| ) | ||
| } | ||
|
|
||
| # Create drugs directory | ||
| drugs_dir = ensure_drugs_directory() | ||
|
|
||
| # Query information for each drug | ||
| for drug in drugs: | ||
| print(f"\nQuerying information about {drug}...") | ||
| model_responses = {} | ||
|
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| for model_name, agent in agents.items(): | ||
| prompt = f"What do you know about {drug} and its effects on longevity and health?" | ||
| response = agent.query(prompt) | ||
| model_responses[model_name] = response | ||
|
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||
| # Format and save markdown file | ||
| markdown_content = format_responses_markdown(drug, model_responses) | ||
| output_file = drugs_dir / f"{drug}.md" | ||
| output_file.write_text(markdown_content) | ||
|
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| print(f"Saved information about {drug} to {output_file}") | ||
| print(f"\n{'='*50}\n") | ||
|
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| return drugs_dir | ||
|
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||
| if __name__ == "__main__": | ||
| output_dir = query_drug_info() | ||
| # this code was generated by cursor withing 5 mins by adding just-agents to the context | ||
| print(f"\nAll drug information has been saved to {output_dir}") | ||
|
|
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