1 files changed, 0 insertions, 110 deletions
diff --git a/autogpt/core/runner/cli_app/main.py b/autogpt/core/runner/cli_app/main.py
deleted file mode 100644
index 60af24bec..000000000
--- a/autogpt/core/runner/cli_app/main.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import click
-
-from autogpt.core.agent import AgentSettings, SimpleAgent
-from autogpt.core.runner.client_lib.logging import get_client_logger
-
-
-async def run_auto_gpt(user_configuration: dict):
-    """Run the Auto-GPT CLI client."""
-
-    client_logger = get_client_logger()
-    client_logger.debug("Getting agent settings")
-
-    agent_workspace = (
-        user_configuration.get("workspace", {}).get("configuration", {}).get("root", "")
-    )
-
-    if not agent_workspace:  # We don't have an agent yet.
-        #################
-        # Bootstrapping #
-        #################
-        # Step 1. Collate the user's settings with the default system settings.
-        agent_settings: AgentSettings = SimpleAgent.compile_settings(
-            client_logger,
-            user_configuration,
-        )
-
-        # Step 2. Get a name and goals for the agent.
-        # First we need to figure out what the user wants to do with the agent.
-        # We'll do this by asking the user for a prompt.
-        user_objective = click.prompt("What do you want Auto-GPT to do?")
-        # Ask a language model to determine a name and goals for a suitable agent.
-        name_and_goals = await SimpleAgent.determine_agent_name_and_goals(
-            user_objective,
-            agent_settings,
-            client_logger,
-        )
-        print(parse_agent_name_and_goals(name_and_goals))
-        # Finally, update the agent settings with the name and goals.
-        agent_settings.update_agent_name_and_goals(name_and_goals)
-
-        # Step 3. Provision the agent.
-        agent_workspace = SimpleAgent.provision_agent(agent_settings, client_logger)
-        print("agent is provisioned")
-
-    # launch agent interaction loop
-    agent = SimpleAgent.from_workspace(
-        agent_workspace,
-        client_logger,
-    )
-    print("agent is loaded")
-
-    plan = await agent.build_initial_plan()
-    print(parse_agent_plan(plan))
-
-    while True:
-        current_task, next_ability = await agent.determine_next_ability(plan)
-        print(parse_next_ability(current_task, next_ability))
-        user_input = click.prompt(
-            "Should the agent proceed with this ability?",
-            default="y",
-        )
-        ability_result = await agent.execute_next_ability(user_input)
-        print(parse_ability_result(ability_result))
-
-
-def parse_agent_name_and_goals(name_and_goals: dict) -> str:
-    parsed_response = f"Agent Name: {name_and_goals['agent_name']}\n"
-    parsed_response += f"Agent Role: {name_and_goals['agent_role']}\n"
-    parsed_response += "Agent Goals:\n"
-    for i, goal in enumerate(name_and_goals["agent_goals"]):
-        parsed_response += f"{i+1}. {goal}\n"
-    return parsed_response
-
-
-def parse_agent_plan(plan: dict) -> str:
-    parsed_response = f"Agent Plan:\n"
-    for i, task in enumerate(plan["task_list"]):
-        parsed_response += f"{i+1}. {task['objective']}\n"
-        parsed_response += f"Task type: {task['type']}  "
-        parsed_response += f"Priority: {task['priority']}\n"
-        parsed_response += f"Ready Criteria:\n"
-        for j, criteria in enumerate(task["ready_criteria"]):
-            parsed_response += f"    {j+1}. {criteria}\n"
-        parsed_response += f"Acceptance Criteria:\n"
-        for j, criteria in enumerate(task["acceptance_criteria"]):
-            parsed_response += f"    {j+1}. {criteria}\n"
-        parsed_response += "\n"
-
-    return parsed_response
-
-
-def parse_next_ability(current_task, next_ability: dict) -> str:
-    parsed_response = f"Current Task: {current_task.objective}\n"
-    ability_args = ", ".join(
-        f"{k}={v}" for k, v in next_ability["ability_arguments"].items()
-    )
-    parsed_response += f"Next Ability: {next_ability['next_ability']}({ability_args})\n"
-    parsed_response += f"Motivation: {next_ability['motivation']}\n"
-    parsed_response += f"Self-criticism: {next_ability['self_criticism']}\n"
-    parsed_response += f"Reasoning: {next_ability['reasoning']}\n"
-    return parsed_response
-
-
-def parse_ability_result(ability_result) -> str:
-    parsed_response = f"Ability: {ability_result['ability_name']}\n"
-    parsed_response += f"Ability Arguments: {ability_result['ability_args']}\n"
-    parsed_response = f"Ability Result: {ability_result['success']}\n"
-    parsed_response += f"Message: {ability_result['message']}\n"
-    parsed_response += f"Data: {ability_result['new_knowledge']}\n"
-    return parsed_response