feat(agent): add agent group feature

* Add `divider_and_conquer` strategy for using in agent members
* Add `AgentMember` class
* Add `AgentGroup` class
* Add `hire_agent` and `create_agent` and `create_task`
* Add `test_agent_group` as an intergration test with VCR
* Add `AgentTaskStatus` for flow of doing tasks
* Add `autogpt_multi_agent.sh` file to run autogpt in multi agent mode

autogpts/autogpt/autogpt/agents/agent.py

@@ -5,6 +5,8 @@
 import time
 from datetime import datetime
 from typing import TYPE_CHECKING, Optional
+from autogpt.core.configuration.schema import SystemConfiguration
+from autogpt.core.prompting.base import PromptStrategy
 
 if TYPE_CHECKING:
  from autogpt.config import Config
@@ -63,7 +65,7 @@ class AgentConfiguration(BaseAgentConfiguration):
 
 class AgentSettings(BaseAgentSettings):
  config: AgentConfiguration = Field(default_factory=AgentConfiguration)
- prompt_config: OneShotAgentPromptConfiguration = Field(
+ prompt_config: SystemConfiguration = Field(
  default_factory=(
  lambda: OneShotAgentPromptStrategy.default_configuration.copy(deep=True)
  )
@@ -84,7 +86,7 @@ class Agent(
  description=__doc__,
  )
 
- prompt_strategy: OneShotAgentPromptStrategy
+ prompt_strategy: PromptStrategy
 
  def __init__(
  self,

autogpts/autogpt/autogpt/agents/agent_group.py

@@ -0,0 +1,124 @@
+import sys
+import logging
+from typing import Optional
+from uuid import uuid4
+from autogpt.agent_factory.profile_generator import AgentProfileGenerator
+from autogpt.agent_manager.agent_manager import AgentManager
+from autogpt.agents.agent import Agent, AgentConfiguration
+from autogpt.agents.agent_member import AgentMember, AgentMemberSettings
+from autogpt.config.config import ConfigBuilder
+from autogpt.core.resource.model_providers.openai import OpenAIProvider
+from autogpt.core.resource.model_providers.schema import ChatModelProvider
+from forge.sdk.model import ( Task, TaskRequestBody )
+
+info = "-v" in sys.argv
+debug = "-vv" in sys.argv
+granular = "--granular" in sys.argv
+
+logging.basicConfig(
+ level=logging.DEBUG if debug else logging.INFO if info else logging.WARNING
+)
+logger = logging.getLogger(__name__)
+
+
+class AgentGroup:
+
+ leader: AgentMember
+ members: dict[str, AgentMember]
+
+ def assign_group_to_all_of_member(self):
+ self.leader.assign_group(self)
+
+ def create_list_of_members(self):
+ members = self.leader.get_list_of_all_your_team_members()
+ self.members = {}
+ for agent_member in members:
+ self.members[agent_member.id] = agent_member
+
+ def __init__(
+ self,
+ leader: AgentMember
+ ):
+ self.leader = leader
+ self.assign_group_to_all_of_member()
+ self.create_list_of_members()
+
+ async def create_task(self, task: TaskRequestBody):
+ await self.leader.create_task(task)
+
+async def create_agent_member(
+ role: str,
+ initial_prompt:str,
+ llm_provider: ChatModelProvider,
+ boss: Optional['AgentMember'] = None,
+ recruiter: Optional['AgentMember'] = None,
+ create_agent: bool = False,
+) -> Optional[AgentMember]:
+ config = ConfigBuilder.build_config_from_env()
+ config.logging.plain_console_output = True
+
+ config.continuous_mode = False
+ config.continuous_limit = 20
+ config.noninteractive_mode = True
+ config.memory_backend = "no_memory"
+ settings = await generate_agent_profile_for_task(
+ task=initial_prompt,
+ app_config=config,
+ llm_provider=llm_provider
+ )
+ settings.agent_id = str(uuid4())
+
+ agent_member = AgentMember(
+ role=role,
+ initial_prompt=initial_prompt,
+ settings=settings,
+ boss=boss,
+ recruiter=recruiter,
+ create_agent=create_agent,
+ llm_provider=llm_provider,
+ )
+
+ agent_manager = AgentManager(config.app_data_dir)
+ agent_member.attach_fs(agent_manager.get_agent_dir(agent_member.id))
+
+ if boss:
+ boss.members.append(agent_member)
+
+ return agent_member
+
+async def generate_agent_profile_for_task(
+ task: str,
+ llm_provider: ChatModelProvider,
+ app_config
+) -> AgentMemberSettings:
+ agent_profile_generator = AgentProfileGenerator(
+ **AgentProfileGenerator.default_configuration.dict() # HACK
+ )
+
+ prompt = agent_profile_generator.build_prompt(task)
+ output = (
+ await llm_provider.create_chat_completion(
+ prompt.messages,
+ model_name=app_config.smart_llm,
+ functions=prompt.functions,
+ )
+ ).response
+
+ ai_profile, ai_directives = agent_profile_generator.parse_response_content(output)
+
+
+ return AgentMemberSettings(
+ name=Agent.default_settings.name,
+ description=Agent.default_settings.description,
+ task=task,
+ ai_profile=ai_profile,
+ directives=ai_directives,
+ config=AgentConfiguration(
+ fast_llm=app_config.fast_llm,
+ smart_llm=app_config.smart_llm,
+ allow_fs_access=not app_config.restrict_to_workspace,
+ use_functions_api=app_config.openai_functions,
+ plugins=app_config.plugins,
+ ),
+ history=Agent.default_settings.history.copy(deep=True),
+ )