mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-10 00:28:31 +00:00
WIP
This commit is contained in:
Brandon Hancock
@@ -1,7 +1,7 @@
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import re
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import shutil
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import subprocess
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from typing import Any, Dict, List, Literal, Optional, Sequence, Union
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from typing import Any, Dict, List, Literal, Optional, Sequence, Union, cast
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from pydantic import Field, InstanceOf, PrivateAttr, model_validator
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@@ -184,19 +184,12 @@ class Agent(BaseAgent):
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# append specific instructions to the task prompt to ensure
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# that the final answer does not include any code block markers
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if task.output_json or task.output_pydantic:
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# Generate the schema based on the output format
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if task.output_json:
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# schema = json.dumps(task.output_json, indent=2)
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schema = generate_model_description(task.output_json)
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task_prompt += "\n" + self.i18n.slice(
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"formatted_task_instructions"
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).format(output_format=schema)
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elif task.output_pydantic:
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schema = generate_model_description(task.output_pydantic)
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task_prompt += "\n" + self.i18n.slice(
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"formatted_task_instructions"
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).format(output_format=schema)
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# Choose the output format, preferring output_json if available
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output_format = (
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task.output_json if task.output_json else task.output_pydantic
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)
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schema = generate_model_description(cast(type, output_format))
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task_prompt += f"\n{self.i18n.slice('formatted_task_instructions').format(output_format=schema)}"
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if context:
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task_prompt = self.i18n.slice("task_with_context").format(
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@@ -163,6 +163,7 @@ class BaseAgent(ABC, BaseModel):
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tool meets these criteria, it is processed and added to the list of
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tools. Otherwise, a ValueError is raised.
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"""
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print(f"Validating tools: {tools}")
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processed_tools = []
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for tool in tools:
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if isinstance(tool, BaseTool):
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@@ -180,6 +181,7 @@ class BaseAgent(ABC, BaseModel):
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"Tool must be an instance of BaseTool or "
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"an object with 'name', 'func', and 'description' attributes."
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)
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print(f"Processed tools: {processed_tools}")
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return processed_tools
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@model_validator(mode="after")
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@@ -338,7 +340,15 @@ class BaseAgent(ABC, BaseModel):
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if self.cache:
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self.cache_handler = cache_handler
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self.tools_handler.cache = cache_handler
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self.create_agent_executor()
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print(f"Setting cache handler for agent: {self.id}")
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# Only create the executor if it hasn't been created yet.
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if self.agent_executor is None:
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self.create_agent_executor()
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else:
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print(
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"Agent executor already exists, skipping creation in set_cache_handler."
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)
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print(f"Cache handler set for agent: {self.id}")
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def increment_formatting_errors(self) -> None:
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self.formatting_errors += 1
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@@ -351,4 +361,12 @@ class BaseAgent(ABC, BaseModel):
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"""
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if not self._rpm_controller:
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self._rpm_controller = rpm_controller
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self.create_agent_executor()
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print(f"Setting RPM controller for agent: {self.id}")
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# Only create the executor if it hasn't been created yet.
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if self.agent_executor is None:
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self.create_agent_executor()
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else:
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print(
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"Agent executor already exists, skipping creation in set_rpm_controller."
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)
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print(f"RPM controller set for agent: {self.id}")
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@@ -82,7 +82,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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tool.name: tool for tool in self.tools
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}
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self.stop = stop_words
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self.llm.stop = list(set(self.llm.stop + self.stop))
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self.llm.stop = list(set((self.llm.stop or []) + self.stop))
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def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
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if "system" in self.prompt:
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254
src/crewai/agents/langchain_agent_adapter.py
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254
src/crewai/agents/langchain_agent_adapter.py
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@@ -0,0 +1,254 @@
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from typing import Any, List, Optional, Type, cast
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from pydantic import Field
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.task import Task
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from crewai.tools import BaseTool
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from crewai.utilities.converter import Converter, generate_model_description
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class LangChainAgentAdapter(BaseAgent):
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"""
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Adapter class to wrap a LangChain agent and make it compatible with CrewAI's BaseAgent interface.
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Note:
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- This adapter does not require LangChain as a dependency.
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- It wraps an external LangChain agent (passed as any type) and delegates calls
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such as execute_task() to the LangChain agent's invoke() method.
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- Extended logic is added to build prompts, incorporate memory, knowledge, training hints,
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and now a human feedback loop similar to what is done in CrewAgentExecutor.
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"""
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langchain_agent: Any = Field(
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...,
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description="The wrapped LangChain runnable agent instance. It is expected to have an 'invoke' method.",
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)
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function_calling_llm: Optional[Any] = Field(
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default=None, description="Optional function calling LLM."
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)
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step_callback: Optional[Any] = Field(
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default=None,
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description="Callback executed after each step of agent execution.",
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)
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allow_code_execution: Optional[bool] = Field(
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default=False, description="Enable code execution for the agent."
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)
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multimodal: bool = Field(
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default=False, description="Whether the agent is multimodal."
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)
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i18n: Any = None
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crew: Any = None
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knowledge: Any = None
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tools: Optional[List[BaseTool]] = None
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class Config:
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arbitrary_types_allowed = True
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def execute_task(
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self,
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task: Task,
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context: Optional[str] = None,
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tools: Optional[List[BaseTool]] = None,
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) -> str:
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"""
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Execute a task by building the full task prompt (with memory, knowledge, tool instructions,
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and training hints) then delegating execution to the wrapped LangChain agent.
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If the task requires human input, a feedback loop is run that mimics the CrewAgentExecutor.
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"""
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task_prompt = task.prompt()
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if task.output_json or task.output_pydantic:
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# Choose the output format, preferring output_json if available
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output_format = (
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task.output_json if task.output_json else task.output_pydantic
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)
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schema = generate_model_description(cast(type, output_format))
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instruction = self.i18n.slice("formatted_task_instructions").format(
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output_format=schema
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)
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task_prompt += f"\n{instruction}"
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if context:
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task_prompt = self.i18n.slice("task_with_context").format(
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task=task_prompt, context=context
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)
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if self.crew and self.crew.memory:
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from crewai.memory.contextual.contextual_memory import ContextualMemory
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contextual_memory = ContextualMemory(
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self.crew.memory_config,
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self.crew._short_term_memory,
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self.crew._long_term_memory,
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self.crew._entity_memory,
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self.crew._user_memory,
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)
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memory = contextual_memory.build_context_for_task(task, context)
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if memory.strip():
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task_prompt += self.i18n.slice("memory").format(memory=memory)
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if self.knowledge:
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agent_knowledge_snippets = self.knowledge.query([task.prompt()])
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if agent_knowledge_snippets:
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from crewai.knowledge.utils.knowledge_utils import (
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extract_knowledge_context,
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)
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agent_knowledge_context = extract_knowledge_context(
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agent_knowledge_snippets
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)
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if agent_knowledge_context:
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task_prompt += agent_knowledge_context
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if self.crew:
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knowledge_snippets = self.crew.query_knowledge([task.prompt()])
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if knowledge_snippets:
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from crewai.knowledge.utils.knowledge_utils import (
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extract_knowledge_context,
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)
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crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
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if crew_knowledge_context:
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task_prompt += crew_knowledge_context
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tools = tools or self.tools or []
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self.create_agent_executor(tools=tools)
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if self.crew and getattr(self.crew, "_train", False):
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task_prompt = self._training_handler(task_prompt=task_prompt)
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else:
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task_prompt = self._use_trained_data(task_prompt=task_prompt)
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try:
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# Initial invocation of the LangChain agent
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result = self.agent_executor.invoke(
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{
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"input": task_prompt,
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"tool_names": getattr(self.agent_executor, "tools_names", ""),
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"tools": getattr(self.agent_executor, "tools_description", ""),
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"ask_for_human_input": task.human_input,
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}
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)["output"]
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# If human feedback is required, enter a feedback loop
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if task.human_input:
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result = self._handle_human_feedback(result)
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except Exception as e:
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# Example: you could add retry logic here if desired.
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raise e
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return result
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def _handle_human_feedback(self, current_output: str) -> str:
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"""
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Implements a feedback loop that prompts the user for feedback and then instructs
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the underlying LangChain agent to regenerate its answer with the requested changes.
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"""
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while True:
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print("\nAgent output:")
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print(current_output)
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# Prompt the user for feedback
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feedback = input("\nEnter your feedback (or press Enter to accept): ")
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if not feedback.strip():
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break # No feedback provided, exit the loop
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# Construct a new prompt with explicit instructions
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new_prompt = (
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f"Below is your previous answer:\n{current_output}\n\n"
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f"Based on the following feedback: '{feedback}', please regenerate your answer with the requested details. "
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f"Specifically, display 10 bullet points in each section. Provide the complete updated answer below.\n\nUpdated answer:"
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)
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try:
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invocation = self.agent_executor.invoke(
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{
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"input": new_prompt,
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"tool_names": getattr(self.agent_executor, "tools_names", ""),
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"tools": getattr(self.agent_executor, "tools_description", ""),
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"ask_for_human_input": True,
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}
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)
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current_output = invocation["output"]
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except Exception as e:
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print("Error during re-invocation with feedback:", e)
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break
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return current_output
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def _generate_model_description(self, model: Any) -> str:
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"""
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Generates a string description (schema) for the expected output.
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This is a placeholder that should call the actual implementation.
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"""
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from crewai.utilities.converter import generate_model_description
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return generate_model_description(model)
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def _training_handler(self, task_prompt: str) -> str:
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"""
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Append training instructions from Crew data to the task prompt.
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"""
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from crewai.utilities.constants import TRAINING_DATA_FILE
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from crewai.utilities.training_handler import CrewTrainingHandler
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data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
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if data:
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agent_id = str(self.id)
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if data.get(agent_id):
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human_feedbacks = [
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i["human_feedback"] for i in data.get(agent_id, {}).values()
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]
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task_prompt += (
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"\n\nYou MUST follow these instructions: \n "
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+ "\n - ".join(human_feedbacks)
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)
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return task_prompt
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def _use_trained_data(self, task_prompt: str) -> str:
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"""
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Append pre-trained instructions from Crew data to the task prompt.
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"""
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from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE
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from crewai.utilities.training_handler import CrewTrainingHandler
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data = CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load()
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if data and (trained_data_output := data.get(getattr(self, "role", "default"))):
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task_prompt += (
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"\n\nYou MUST follow these instructions: \n - "
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+ "\n - ".join(trained_data_output["suggestions"])
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)
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return task_prompt
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def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
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"""
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Creates an agent executor using LangChain's AgentExecutor.
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"""
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from importlib import import_module
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langchain_agents = import_module("langchain.agents")
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AgentExecutor = getattr(langchain_agents, "AgentExecutor")
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used_tools = tools or self.tools or []
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print(f"Creating agent executor for langchain agent: {self.langchain_agent}")
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print("Passing tools: ", used_tools)
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self.agent_executor = AgentExecutor.from_agent_and_tools(
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agent=self.langchain_agent,
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tools=used_tools,
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verbose=getattr(self, "verbose", True),
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)
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print("Created agent executor for langchain agent")
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def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
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return tools
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def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
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return []
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def get_output_converter(
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self,
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llm: Any,
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text: str,
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model: Optional[Type] = None,
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instructions: str = "",
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) -> Converter:
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return Converter(llm=llm, text=text, model=model, instructions=instructions)
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@@ -455,8 +455,6 @@ class Crew(BaseModel):
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)
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return self
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@property
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def key(self) -> str:
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source = [agent.key for agent in self.agents] + [
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@@ -928,13 +926,13 @@ class Crew(BaseModel):
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def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
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if not task_outputs:
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raise ValueError("No task outputs available to create crew output.")
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# Filter out empty outputs and get the last valid one as the main output
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valid_outputs = [t for t in task_outputs if t.raw]
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if not valid_outputs:
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raise ValueError("No valid task outputs available to create crew output.")
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final_task_output = valid_outputs[-1]
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final_string_output = final_task_output.raw
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self._finish_execution(final_string_output)
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token_usage = self.calculate_usage_metrics()
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1
title=src/crewai/agents/langchain_agent_adapter.py
Normal file
1
title=src/crewai/agents/langchain_agent_adapter.py
Normal file
@@ -0,0 +1 @@
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Reference in New Issue
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