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Merge branch 'main' into feat/individual-react-agent

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Brandon Hancock
2025-03-28 12:52:40 -04:00
parent 4bfb71d749 625748e462
commit 594784473b
81 changed files with 5652 additions and 925 deletions
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14
src/crewai/agent.py
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@@ -11,7 +11,7 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.knowledge.knowledge import Knowledge
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
from crewai.llm import LLM
from crewai.llm import BaseLLM
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.security import Fingerprint
from crewai.task import Task
@@ -76,10 +76,10 @@ class Agent(BaseAgent):
default=True,
description="Use system prompt for the agent.",
)
llm: Union[str, InstanceOf[LLM], Any] = Field(
llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
description="Language model that will run the agent.", default=None
)
function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
description="Language model that will run the agent.", default=None
)
system_template: Optional[str] = Field(
@@ -120,7 +120,9 @@ class Agent(BaseAgent):
self.agent_ops_agent_name = self.role
self.llm = create_llm(self.llm)
if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
if self.function_calling_llm and not isinstance(
self.function_calling_llm, BaseLLM
):
self.function_calling_llm = create_llm(self.function_calling_llm)
if not self.agent_executor:
@@ -142,15 +144,13 @@ class Agent(BaseAgent):
self.embedder = crew_embedder
if self.knowledge_sources:
full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
if isinstance(self.knowledge_sources, list) and all(
isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
):
self.knowledge = Knowledge(
sources=self.knowledge_sources,
embedder=self.embedder,
collection_name=knowledge_agent_name,
collection_name=self.role,
storage=self.knowledge_storage or None,
)
except (TypeError, ValueError) as e: