initialize.py

import models
from agent import AgentConfig, AgentAPIConfig
from python.helpers import files

def initialize():
    
    # main chat model used by agents (smarter, more accurate)
    chat_llm = models.get_openai_chat(model_name="gpt-4-turbo-preview", temperature=0)
    # chat_llm = models.get_ollama_chat(model_name="llama3.2:3b-instruct-fp16", temperature=0)
    # chat_llm = models.get_lmstudio_chat(model_name="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF", temperature=0)
    # chat_llm = models.get_openrouter_chat(model_name="openai/o1-mini-2024-09-12")
    # chat_llm = models.get_azure_openai_chat(deployment_name="gpt-4o-mini", temperature=0)
    # chat_llm = models.get_anthropic_chat(model_name="claude-3-5-sonnet-20240620", temperature=0)
    # chat_llm = models.get_google_chat(model_name="gemini-1.5-flash", temperature=0)
    # chat_llm = models.get_mistral_chat(model_name="mistral-small-latest", temperature=0)
    # chat_llm = models.get_groq_chat(model_name="llama-3.2-90b-text-preview", temperature=0)
    # chat_llm = models.get_sambanova_chat(model_name="Meta-Llama-3.1-70B-Instruct-8k", temperature=0)

    # utility model used for helper functions (cheaper, faster)
    utility_llm = models.get_openai_chat(model_name="gpt-3.5-turbo", temperature=0)

    # embedding model used for memory - shared across all agents for consistency
    embedding_llm = models.get_openai_embedding(model_name="text-embedding-3-large")
    # embedding_llm = models.get_ollama_embedding(model_name="nomic-embed-text")
    # embedding_llm = models.get_huggingface_embedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
    # embedding_llm = models.get_lmstudio_embedding(model_name="nomic-ai/nomic-embed-text-v1.5-GGUF")

    # Define specialized configurations for different subordinate roles
    coder_config = AgentAPIConfig(
        chat_model=models.get_anthropic_chat(model_name="claude-3-opus-20240229", temperature=0),
        utility_model=models.get_openai_chat(model_name="gpt-3.5-turbo", temperature=0)
    )
    
    researcher_config = AgentAPIConfig(
        chat_model=models.get_openai_chat(model_name="gpt-4-turbo-preview", temperature=0),
        utility_model=models.get_openai_chat(model_name="gpt-3.5-turbo", temperature=0)
    )
    
    writer_config = AgentAPIConfig(
        chat_model=models.get_anthropic_chat(model_name="claude-3-sonnet-20240229", temperature=0.7),
        utility_model=models.get_openai_chat(model_name="gpt-3.5-turbo", temperature=0)
    )

    # agent configuration
    config = AgentConfig(
        chat_model=chat_llm,
        utility_model=utility_llm,
        embeddings_model=embedding_llm,
        # prompts_subdir = "dianoia-xl",
        # memory_subdir = "",
        knowledge_subdirs=["default", "custom"],
        auto_memory_count=0,
        # auto_memory_skip = 2,
        # rate_limit_seconds = 60,
        rate_limit_requests=30,
        # rate_limit_input_tokens = 0,
        # rate_limit_output_tokens = 0,
        # msgs_keep_max = 25,
        # msgs_keep_start = 5,
        # msgs_keep_end = 10,
        max_tool_response_length=3000,
        # response_timeout_seconds = 60,
        code_exec_docker_enabled=True,
        # code_exec_docker_name = "agent-zero-exe",
        # code_exec_docker_image = "frdel/agent-zero-exe:latest",
        # code_exec_docker_ports = { "22/tcp": 50022 }
        # code_exec_docker_volumes = { 
        #     files.get_abs_path("work_dir"): {"bind": "/root", "mode": "rw"},
        #     files.get_abs_path("instruments"): {"bind": "/instruments", "mode": "rw"},
        # },
        code_exec_ssh_enabled=True,
        # code_exec_ssh_addr = "localhost",
        # code_exec_ssh_port = 50022,
        # code_exec_ssh_user = "root",
        # code_exec_ssh_pass = "toor",
        # additional = {},
        
        # Role-specific API configurations for subordinate agents
        subordinate_configs={
            "coder": coder_config,
            "researcher": researcher_config,
            "writer": writer_config
        }
    )

    # return config object
    return config