Lorenzejay/byoa (#776)

* better spacing

* works with llama index

* works on langchain custom just need delegation to work

* cleanup for custom_agent class

* works with different argument expectations for agent_executor

* cleanup for hierarchial process, better agent_executor args handler and added to the crew agent doc page

* removed code examples for langchain + llama index, added to docs instead

* added key output if return is not a str for and added some tests

* added hinting for CustomAgent class

* removed pass as it was not needed

* closer just need to figuire ou agentTools

* running agents - llamaindex and langchain with base agent

* some cleanup on baseAgent

* minimum for agent to run for base class and ensure it works with hierarchical process

* cleanup for original agent to take on BaseAgent class

* Agent takes on langchainagent and cleanup across

* token handling working for usage_metrics to continue working

* installed llama-index, updated docs and added better name

* fixed some type errors

* base agent holds token_process

* heirarchail process uses proper tools and no longer relies on hasattr for token_processes

* removal of test_custom_agent_executions

* this fixes copying agents

* leveraging an executor class for trigger llamaindex agent

* llama index now has ask_human

* executor mixins added

* added output converter base class

* type listed

* cleanup for output conversions and tokenprocess eliminated redundancy

* properly handling tokens

* simplified token calc handling

* original agent with base agent builder structure setup

* better docs

* no more llama-index dep

* cleaner docs

* test fixes

* poetry reverts and better docs

* base_agent_tools set for third party agents

* updated task and test fix

.gitignore

@@ -11,4 +11,5 @@ chroma.sqlite3
 old_en.json
 db/
 test.py
-rc-tests/*
+rc-tests/*
+*.pkl

docs/core-concepts/Agents.md

@@ -97,5 +97,53 @@ agent = Agent(
     )
 ```
 
+## Bring your Third Party Agents
+!!! note "Extend your Third Party Agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the crewai's BaseAgent class."
+
+    BaseAgent includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
+
+    CrewAI is a universal multi agent framework that allows for all agents to work together to automate tasks and solve problems.
+
+
+```py
+from crewai import Agent, Task, Crew
+from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
+
+from langchain.agents import load_tools
+
+langchain_tools = load_tools(["google-serper"], llm=llm)
+
+agent1 = CustomAgent(
+    role="backstory agent",
+    goal="who is {input}?",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task1 = Task(
+    expected_output="a short biography of {input}",
+    description="a short biography of {input}",
+    agent=agent1,
+)
+
+agent2 = Agent(
+    role="bio agent",
+    goal="summarize the short bio for {input} and if needed do more research",
+    backstory="agent backstory",
+    verbose=True,
+)
+
+task2 = Task(
+    description="a tldr summary of the short biography",
+    expected_output="5 bullet point summary of the biography",
+    agent=agent2,
+    context=[task1],
+)
+
+my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
+crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
+```
+
+
 ## Conclusion
 Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.

pyproject.toml

@@ -60,4 +60,4 @@ exclude = ["cli/templates/main.py", "cli/templates/crew.py"]
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"

src/crewai/__init__.py

@@ -2,3 +2,5 @@
 from crewai.crew import Crew
 from crewai.process import Process
 from crewai.task import Task
+
+__all__ = ["Agent", "Crew", "Process", "Task"]

src/crewai/agent.py

@@ -1,35 +1,26 @@
 import os
-import uuid
-from copy import deepcopy
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
 from langchain.agents.agent import RunnableAgent
 from langchain.agents.tools import tool as LangChainTool
 from langchain.tools.render import render_text_description
 from langchain_core.agents import AgentAction
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_openai import ChatOpenAI
-from pydantic import (
-    UUID4,
-    BaseModel,
-    ConfigDict,
-    Field,
-    InstanceOf,
-    PrivateAttr,
-    field_validator,
-    model_validator,
-)
-from pydantic_core import PydanticCustomError
-
-from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
+
+from pydantic import Field, InstanceOf, model_validator
+
+from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.utilities import I18N, Logger, Prompts, RPMController
+from crewai.tools.agent_tools import AgentTools
+from crewai.utilities import Prompts, Converter
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
-from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess
+from crewai.utilities.token_counter_callback import TokenCalcHandler
+from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 
-class Agent(BaseModel):
+class Agent(BaseAgent):
     """Represents an agent in a system.
 
     Each agent has a role, a goal, a backstory, and an optional language model (llm).
@@ -53,65 +44,17 @@ class Agent(BaseModel):
             callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
     """
 
-    __hash__ = object.__hash__  # type: ignore
-    _logger: Logger = PrivateAttr()
-    _rpm_controller: RPMController = PrivateAttr(default=None)
-    _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    _token_process: TokenProcess = TokenProcess()
-
-    formatting_errors: int = 0
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    id: UUID4 = Field(
-        default_factory=uuid.uuid4,
-        frozen=True,
-        description="Unique identifier for the object, not set by user.",
-    )
-    role: str = Field(description="Role of the agent")
-    goal: str = Field(description="Objective of the agent")
-    backstory: str = Field(description="Backstory of the agent")
-    cache: bool = Field(
-        default=True,
-        description="Whether the agent should use a cache for tool usage.",
-    )
-    config: Optional[Dict[str, Any]] = Field(
-        description="Configuration for the agent",
-        default=None,
-    )
-    max_rpm: Optional[int] = Field(
-        default=None,
-        description="Maximum number of requests per minute for the agent execution to be respected.",
-    )
-    verbose: bool = Field(
-        default=False, description="Verbose mode for the Agent Execution"
-    )
-    allow_delegation: bool = Field(
-        default=True, description="Allow delegation of tasks to agents"
-    )
-    tools: Optional[List[Any]] = Field(
-        default_factory=list, description="Tools at agents disposal"
-    )
-    max_iter: Optional[int] = Field(
-        default=25, description="Maximum iterations for an agent to execute a task"
-    )
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
     )
-    agent_executor: InstanceOf[CrewAgentExecutor] = Field(
-        default=None, description="An instance of the CrewAgentExecutor class."
-    )
-    crew: Any = Field(default=None, description="Crew to which the agent belongs.")
-    tools_handler: InstanceOf[ToolsHandler] = Field(
-        default=None, description="An instance of the ToolsHandler class."
-    )
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
     )
-    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
     llm: Any = Field(
         default_factory=lambda: ChatOpenAI(
             model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4o")
@@ -133,47 +76,18 @@ class Agent(BaseModel):
     response_template: Optional[str] = Field(
         default=None, description="Response format for the agent."
     )
+
     allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
 
-    _original_role: str | None = None
-    _original_goal: str | None = None
-    _original_backstory: str | None = None
-
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
         super().__init__(**config, **data)
 
-    @field_validator("id", mode="before")
-    @classmethod
-    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
-        if v:
-            raise PydanticCustomError(
-                "may_not_set_field", "This field is not to be set by the user.", {}
-            )
-
-    @model_validator(mode="after")
-    def set_attributes_based_on_config(self) -> "Agent":
-        """Set attributes based on the agent configuration."""
-        if self.config:
-            for key, value in self.config.items():
-                setattr(self, key, value)
-        return self
-
-    @model_validator(mode="after")
-    def set_private_attrs(self):
-        """Set private attributes."""
-        self._logger = Logger(self.verbose)
-        if self.max_rpm and not self._rpm_controller:
-            self._rpm_controller = RPMController(
-                max_rpm=self.max_rpm, logger=self._logger
-            )
-        return self
-
     @model_validator(mode="after")
     def set_agent_executor(self) -> "Agent":
-        """set agent executor is set."""
+        """Ensure agent executor and token process is set."""
         if hasattr(self.llm, "model_name"):
             token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
 
@@ -232,8 +146,7 @@ def execute_task(
 
         tools = tools or self.tools
         # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
-        parsed_tools = self._parse_tools(tools)
-
+        parsed_tools = self._parse_tools(tools or [])
         self.create_agent_executor(tools=tools)
         self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
@@ -253,33 +166,22 @@ def execute_task(
                 "tools": self.agent_executor.tools_description,
             }
         )["output"]
-
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-
         return result
 
-    def set_cache_handler(self, cache_handler: CacheHandler) -> None:
-        """Set the cache handler for the agent.
-
-        Args:
-            cache_handler: An instance of the CacheHandler class.
-        """
-        self.tools_handler = ToolsHandler()
-        if self.cache:
-            self.cache_handler = cache_handler
-            self.tools_handler.cache = cache_handler
-        self.create_agent_executor()
-
-    def set_rpm_controller(self, rpm_controller: RPMController) -> None:
-        """Set the rpm controller for the agent.
-
-        Args:
-            rpm_controller: An instance of the RPMController class.
-        """
-        if not self._rpm_controller:
-            self._rpm_controller = rpm_controller
-            self.create_agent_executor()
+    def format_log_to_str(
+        self,
+        intermediate_steps: List[Tuple[AgentAction, str]],
+        observation_prefix: str = "Observation: ",
+        llm_prefix: str = "",
+    ) -> str:
+        """Construct the scratchpad that lets the agent continue its thought process."""
+        thoughts = ""
+        for action, observation in intermediate_steps:
+            thoughts += action.log
+            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
+        return thoughts
 
     def create_agent_executor(self, tools=None) -> None:
         """Create an agent executor for the agent.
@@ -335,76 +237,32 @@ def create_agent_executor(self, tools=None) -> None:
         )
 
         stop_words = [self.i18n.slice("observation")]
+
         if self.response_template:
             stop_words.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
         bind = self.llm.bind(stop=stop_words)
+
         inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self)
         self.agent_executor = CrewAgentExecutor(
             agent=RunnableAgent(runnable=inner_agent), **executor_args
         )
 
-    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
-        """Interpolate inputs into the agent description and backstory."""
-        if self._original_role is None:
-            self._original_role = self.role
-        if self._original_goal is None:
-            self._original_goal = self.goal
-        if self._original_backstory is None:
-            self._original_backstory = self.backstory
-
-        if inputs:
-            self.role = self._original_role.format(**inputs)
-            self.goal = self._original_goal.format(**inputs)
-            self.backstory = self._original_backstory.format(**inputs)
-
-    def increment_formatting_errors(self) -> None:
-        """Count the formatting errors of the agent."""
-        self.formatting_errors += 1
-
-    def format_log_to_str(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        observation_prefix: str = "Observation: ",
-        llm_prefix: str = "",
-    ) -> str:
-        """Construct the scratchpad that lets the agent continue its thought process."""
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
-        return thoughts
-
-    def copy(self):
-        """Create a deep copy of the Agent."""
-        exclude = {
-            "id",
-            "_logger",
-            "_rpm_controller",
-            "_request_within_rpm_limit",
-            "_token_process",
-            "agent_executor",
-            "tools",
-            "tools_handler",
-            "cache_handler",
-        }
-
-        copied_data = self.model_dump(exclude=exclude)
-        copied_data = {k: v for k, v in copied_data.items() if v is not None}
-
-        copied_agent = Agent(**copied_data)
-        copied_agent.tools = deepcopy(self.tools)
+    def get_delegation_tools(self, agents: List[BaseAgent]):
+        agent_tools = AgentTools(agents=agents)
+        tools = agent_tools.tools()
+        return tools
 
-        return copied_agent
+    def get_output_converter(self, llm, text, model, instructions):
+        return Converter(llm=llm, text=text, model=model, instructions=instructions)
 
-    # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
     def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
         """Parse tools to be used for the task."""
-        # tentatively try to import from crewai_tools import BaseTool as CrewAITool
         tools_list = []
         try:
+            # tentatively try to import from crewai_tools import BaseTool as CrewAITool
             from crewai_tools import BaseTool as CrewAITool
 
             for tool in tools: