idZddlmZddlZddlZddlmZddlmZddl Z ddl m Z ddl m Z erddl mZd Zed d fddZddZedkr edSdS)zRCLI tool to generate Agent configuration files from natural language descriptions.) annotationsN)Path) TYPE_CHECKING) create_client) AgentConfig) LLMClientaYou are an expert at creating AI agent personas. Given a natural language description, generate a JSON object with exactly these fields: - "role": A concise job title for the agent (e.g., "Chief Product Officer") - "system_prompt": A detailed system prompt that captures the persona's expertise, communication style, and focus areas. The prompt should instruct the agent on how to behave in a meeting context. Output ONLY the JSON object, no other text. Example input: "Mimics Steve Jobs' demanding product perspective" Example output: { "role": "Chief Product Officer", "system_prompt": "You are an extremely demanding product manager who obsesses over simplicity and user experience. In meetings, you challenge every assumption, push for 10x better solutions, and reject anything that feels mediocre. You focus on: user delight, simplicity, integration of hardware and software thinking, and saying no to 1000 things." }agentsjsonnamestr descriptionclientr output_dirrfmtreturnrc|td|dg}|}|drUd|vr|dddn|}|dr |dd}|}t j|}t||d |d  }| d d |dkrdnd} ||d| z } | } |dkr-| tj | d ddn,| t j| ddd|S)aGenerate an AgentConfig from a natural language description. Args: name: Unique identifier for the agent (used as filename). description: Natural language description of the agent persona. client: LLM client implementing the LLMClient protocol. output_dir: Directory where the config file will be saved. fmt: Output format, either "json" or "yaml". Returns: The populated AgentConfig instance. user)rolecontent)systemmessagesz``` Nr system_prompt)r rrT)parentsexist_okyamlr .F) allow_unicodedefault_flow_stylezutf-8)encoding) ensure_asciiindent)chatAGENT_BUILDER_SYSTEM_PROMPTstrip startswithsplitendswithr loadsrmkdir model_dump write_textrdumpdumps) r r rrr raw_responsecleaneddataconfigext output_path config_dicts 4/root/projects/multi-agents-meeting/agent_builder.pybuild_agent_configr:"s&;;*!k::;L   ""G%  "/3w'--a((++G   E " " #crclG--// :g  D   &\?+F TD1116MM&&vC$.K##%%K f}} Ik% P P P      J{q A A A    MNonec tjd}|ddd|ddd|d d d |d ddgdd|}t }t |j|j|t|j |j }td|jd|j d|j d|jd|j dS)z"CLI entry point for agent_builder.z=Generate an Agent config from a natural language description.)r z --descriptionTz2Natural language description of the agent persona.)requiredhelpz--namezUnique name for the agent.z --output-dirr z5Directory to save the config file (default: agents/).)defaultr?z--formatr rzOutput format (default: json).)choicesr@r?)r r rrrzAgent 'z' (z ) saved to /rN) argparseArgumentParser add_argument parse_argsrr:r r rrformatprintr)parserargsrr5s r9mainrK\s.  $SF $=qrrr 46RSSS ?vwww  VV,rUsXX""""""  (((((("""""")(((((( &tH~~ 77777tkkkk. zDFFFFFr;