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1.10.2a1
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lorenze/fe
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79a01fca31 |
@@ -4,6 +4,39 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Mar 11, 2026">
|
||||
## v1.10.2a1
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add support for tool search, saving tokens, and dynamically injecting appropriate tools during execution for Anthropics.
|
||||
- Introduce more Brave Search tools.
|
||||
- Create action for nightly releases.
|
||||
|
||||
### Bug Fixes
|
||||
- Fix LockException under concurrent multi-process execution.
|
||||
- Resolve issues with grouping parallel tool results in a single user message.
|
||||
- Address MCP tools resolutions and eliminate all shared mutable connections.
|
||||
- Update LLM parameter handling in the human_feedback function.
|
||||
- Add missing list/dict methods to LockedListProxy and LockedDictProxy.
|
||||
- Propagate contextvars context to parallel tool call threads.
|
||||
- Bump gitpython dependency to >=3.1.41 to resolve CVE path traversal vulnerability.
|
||||
|
||||
### Refactoring
|
||||
- Refactor memory classes to be serializable.
|
||||
|
||||
### Documentation
|
||||
- Update changelog and version for v1.10.1.
|
||||
|
||||
## Contributors
|
||||
|
||||
@akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Mar 04, 2026">
|
||||
## v1.10.1
|
||||
|
||||
|
||||
@@ -4,6 +4,39 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 3월 11일">
|
||||
## v1.10.2a1
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- Anthropics에 대한 도구 검색 지원 추가, 토큰 저장, 실행 중 적절한 도구를 동적으로 주입하는 기능 추가.
|
||||
- 더 많은 Brave Search 도구 도입.
|
||||
- 야간 릴리스를 위한 액션 생성.
|
||||
|
||||
### 버그 수정
|
||||
- 동시 다중 프로세스 실행 중 LockException 수정.
|
||||
- 단일 사용자 메시지에서 병렬 도구 결과 그룹화 문제 해결.
|
||||
- MCP 도구 해상도 문제 해결 및 모든 공유 가변 연결 제거.
|
||||
- human_feedback 함수에서 LLM 매개변수 처리 업데이트.
|
||||
- LockedListProxy 및 LockedDictProxy에 누락된 list/dict 메서드 추가.
|
||||
- 병렬 도구 호출 스레드에 contextvars 컨텍스트 전파.
|
||||
- CVE 경로 탐색 취약점을 해결하기 위해 gitpython 의존성을 >=3.1.41로 업데이트.
|
||||
|
||||
### 리팩토링
|
||||
- 메모리 클래스를 직렬화 가능하도록 리팩토링.
|
||||
|
||||
### 문서
|
||||
- v1.10.1에 대한 변경 로그 및 버전 업데이트.
|
||||
|
||||
## 기여자
|
||||
|
||||
@akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 3월 4일">
|
||||
## v1.10.1
|
||||
|
||||
|
||||
@@ -4,6 +4,39 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="11 mar 2026">
|
||||
## v1.10.2a1
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.10.2a1)
|
||||
|
||||
## O que mudou
|
||||
|
||||
### Recursos
|
||||
- Adicionar suporte para busca de ferramentas, salvamento de tokens e injeção dinâmica de ferramentas apropriadas durante a execução para Anthropics.
|
||||
- Introduzir mais ferramentas de Busca Brave.
|
||||
- Criar ação para lançamentos noturnos.
|
||||
|
||||
### Correções de Bugs
|
||||
- Corrigir LockException durante a execução concorrente de múltiplos processos.
|
||||
- Resolver problemas com a agrupação de resultados de ferramentas paralelas em uma única mensagem de usuário.
|
||||
- Abordar resoluções de ferramentas MCP e eliminar todas as conexões mutáveis compartilhadas.
|
||||
- Atualizar o manuseio de parâmetros LLM na função human_feedback.
|
||||
- Adicionar métodos de lista/dicionário ausentes a LockedListProxy e LockedDictProxy.
|
||||
- Propagar o contexto de contextvars para as threads de chamada de ferramentas paralelas.
|
||||
- Atualizar a dependência gitpython para >=3.1.41 para resolver a vulnerabilidade de travessia de diretórios CVE.
|
||||
|
||||
### Refatoração
|
||||
- Refatorar classes de memória para serem serializáveis.
|
||||
|
||||
### Documentação
|
||||
- Atualizar o changelog e a versão para v1.10.1.
|
||||
|
||||
## Contribuidores
|
||||
|
||||
@akaKuruma, @github-actions[bot], @giulio-leone, @greysonlalonde, @joaomdmoura, @jonathansampson, @lorenzejay, @lucasgomide, @mattatcha
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="04 mar 2026">
|
||||
## v1.10.1
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ import urllib.request
|
||||
import warnings
|
||||
|
||||
from crewai.agent.core import Agent
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
from crewai.crew import Crew
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.flow.flow import Flow
|
||||
@@ -100,6 +101,7 @@ __all__ = [
|
||||
"Knowledge",
|
||||
"LLMGuardrail",
|
||||
"Memory",
|
||||
"PlanningConfig",
|
||||
"Process",
|
||||
"Task",
|
||||
"TaskOutput",
|
||||
|
||||
@@ -22,6 +22,7 @@ from pydantic import (
|
||||
)
|
||||
from typing_extensions import Self
|
||||
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
from crewai.agent.utils import (
|
||||
ahandle_knowledge_retrieval,
|
||||
apply_training_data,
|
||||
@@ -191,13 +192,23 @@ class Agent(BaseAgent):
|
||||
default="safe",
|
||||
description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
|
||||
)
|
||||
reasoning: bool = Field(
|
||||
planning_config: PlanningConfig | None = Field(
|
||||
default=None,
|
||||
description="Configuration for agent planning before task execution.",
|
||||
)
|
||||
planning: bool = Field(
|
||||
default=False,
|
||||
description="Whether the agent should reflect and create a plan before executing a task.",
|
||||
)
|
||||
reasoning: bool = Field(
|
||||
default=False,
|
||||
description="[DEPRECATED: Use planning_config instead] Whether the agent should reflect and create a plan before executing a task.",
|
||||
deprecated=True,
|
||||
)
|
||||
max_reasoning_attempts: int | None = Field(
|
||||
default=None,
|
||||
description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
|
||||
description="[DEPRECATED: Use planning_config.max_attempts instead] Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
|
||||
deprecated=True,
|
||||
)
|
||||
embedder: EmbedderConfig | None = Field(
|
||||
default=None,
|
||||
@@ -264,8 +275,26 @@ class Agent(BaseAgent):
|
||||
if self.allow_code_execution:
|
||||
self._validate_docker_installation()
|
||||
|
||||
# Handle backward compatibility: convert reasoning=True to planning_config
|
||||
if self.reasoning and self.planning_config is None:
|
||||
import warnings
|
||||
|
||||
warnings.warn(
|
||||
"The 'reasoning' parameter is deprecated. Use 'planning_config=PlanningConfig()' instead.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
self.planning_config = PlanningConfig(
|
||||
max_attempts=self.max_reasoning_attempts,
|
||||
)
|
||||
|
||||
return self
|
||||
|
||||
@property
|
||||
def planning_enabled(self) -> bool:
|
||||
"""Check if planning is enabled for this agent."""
|
||||
return self.planning_config is not None or self.planning
|
||||
|
||||
def _setup_agent_executor(self) -> None:
|
||||
if not self.cache_handler:
|
||||
self.cache_handler = CacheHandler()
|
||||
@@ -334,7 +363,11 @@ class Agent(BaseAgent):
|
||||
ValueError: If the max execution time is not a positive integer.
|
||||
RuntimeError: If the agent execution fails for other reasons.
|
||||
"""
|
||||
handle_reasoning(self, task)
|
||||
# Only call handle_reasoning for legacy CrewAgentExecutor
|
||||
# For AgentExecutor, planning is handled in AgentExecutor.generate_plan()
|
||||
if self.executor_class is not AgentExecutor:
|
||||
handle_reasoning(self, task)
|
||||
|
||||
self._inject_date_to_task(task)
|
||||
|
||||
if self.tools_handler:
|
||||
@@ -572,7 +605,10 @@ class Agent(BaseAgent):
|
||||
ValueError: If the max execution time is not a positive integer.
|
||||
RuntimeError: If the agent execution fails for other reasons.
|
||||
"""
|
||||
handle_reasoning(self, task)
|
||||
if self.executor_class is not AgentExecutor:
|
||||
handle_reasoning(
|
||||
self, task
|
||||
) # we need this till CrewAgentExecutor migrates to AgentExecutor
|
||||
self._inject_date_to_task(task)
|
||||
|
||||
if self.tools_handler:
|
||||
@@ -1418,17 +1454,19 @@ class Agent(BaseAgent):
|
||||
except Exception as e:
|
||||
self._logger.log("error", f"Failed to save kickoff result to memory: {e}")
|
||||
|
||||
def _execute_and_build_output(
|
||||
def _build_output_from_result(
|
||||
self,
|
||||
result: dict[str, Any],
|
||||
executor: AgentExecutor,
|
||||
inputs: dict[str, str],
|
||||
response_format: type[Any] | None = None,
|
||||
) -> LiteAgentOutput:
|
||||
"""Execute the agent and build the output object.
|
||||
"""Build a LiteAgentOutput from an executor result dict.
|
||||
|
||||
Shared logic used by both sync and async execution paths.
|
||||
|
||||
Args:
|
||||
result: The result dictionary from executor.invoke / invoke_async.
|
||||
executor: The executor instance.
|
||||
inputs: Input dictionary for execution.
|
||||
response_format: Optional response format.
|
||||
|
||||
Returns:
|
||||
@@ -1436,8 +1474,6 @@ class Agent(BaseAgent):
|
||||
"""
|
||||
import json
|
||||
|
||||
# Execute the agent (this is called from sync path, so invoke returns dict)
|
||||
result = cast(dict[str, Any], executor.invoke(inputs))
|
||||
output = result.get("output", "")
|
||||
|
||||
# Handle response format conversion
|
||||
@@ -1485,91 +1521,39 @@ class Agent(BaseAgent):
|
||||
else str(raw_output)
|
||||
)
|
||||
|
||||
todo_results = LiteAgentOutput.from_todo_items(executor.state.todos.items)
|
||||
|
||||
return LiteAgentOutput(
|
||||
raw=raw_str,
|
||||
pydantic=formatted_result,
|
||||
agent_role=self.role,
|
||||
usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
|
||||
messages=executor.messages,
|
||||
messages=list(executor.state.messages),
|
||||
plan=executor.state.plan,
|
||||
todos=todo_results,
|
||||
replan_count=executor.state.replan_count,
|
||||
last_replan_reason=executor.state.last_replan_reason,
|
||||
)
|
||||
|
||||
def _execute_and_build_output(
|
||||
self,
|
||||
executor: AgentExecutor,
|
||||
inputs: dict[str, str],
|
||||
response_format: type[Any] | None = None,
|
||||
) -> LiteAgentOutput:
|
||||
"""Execute the agent synchronously and build the output object."""
|
||||
result = cast(dict[str, Any], executor.invoke(inputs))
|
||||
return self._build_output_from_result(result, executor, response_format)
|
||||
|
||||
async def _execute_and_build_output_async(
|
||||
self,
|
||||
executor: AgentExecutor,
|
||||
inputs: dict[str, str],
|
||||
response_format: type[Any] | None = None,
|
||||
) -> LiteAgentOutput:
|
||||
"""Execute the agent asynchronously and build the output object.
|
||||
|
||||
This is the async version of _execute_and_build_output that uses
|
||||
invoke_async() for native async execution within event loops.
|
||||
|
||||
Args:
|
||||
executor: The executor instance.
|
||||
inputs: Input dictionary for execution.
|
||||
response_format: Optional response format.
|
||||
|
||||
Returns:
|
||||
LiteAgentOutput with raw output, formatted result, and metrics.
|
||||
"""
|
||||
import json
|
||||
|
||||
# Execute the agent asynchronously
|
||||
"""Execute the agent asynchronously and build the output object."""
|
||||
result = await executor.invoke_async(inputs)
|
||||
output = result.get("output", "")
|
||||
|
||||
# Handle response format conversion
|
||||
formatted_result: BaseModel | None = None
|
||||
raw_output: str
|
||||
|
||||
if isinstance(output, BaseModel):
|
||||
formatted_result = output
|
||||
raw_output = output.model_dump_json()
|
||||
elif response_format:
|
||||
raw_output = str(output) if not isinstance(output, str) else output
|
||||
try:
|
||||
model_schema = generate_model_description(response_format)
|
||||
schema = json.dumps(model_schema, indent=2)
|
||||
instructions = self.i18n.slice("formatted_task_instructions").format(
|
||||
output_format=schema
|
||||
)
|
||||
|
||||
converter = Converter(
|
||||
llm=self.llm,
|
||||
text=raw_output,
|
||||
model=response_format,
|
||||
instructions=instructions,
|
||||
)
|
||||
|
||||
conversion_result = converter.to_pydantic()
|
||||
if isinstance(conversion_result, BaseModel):
|
||||
formatted_result = conversion_result
|
||||
except ConverterError:
|
||||
pass # Keep raw output if conversion fails
|
||||
else:
|
||||
raw_output = str(output) if not isinstance(output, str) else output
|
||||
|
||||
# Get token usage metrics
|
||||
if isinstance(self.llm, BaseLLM):
|
||||
usage_metrics = self.llm.get_token_usage_summary()
|
||||
else:
|
||||
usage_metrics = self._token_process.get_summary()
|
||||
|
||||
raw_str = (
|
||||
raw_output
|
||||
if isinstance(raw_output, str)
|
||||
else raw_output.model_dump_json()
|
||||
if isinstance(raw_output, BaseModel)
|
||||
else str(raw_output)
|
||||
)
|
||||
|
||||
return LiteAgentOutput(
|
||||
raw=raw_str,
|
||||
pydantic=formatted_result,
|
||||
agent_role=self.role,
|
||||
usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
|
||||
messages=executor.messages,
|
||||
)
|
||||
return self._build_output_from_result(result, executor, response_format)
|
||||
|
||||
def _process_kickoff_guardrail(
|
||||
self,
|
||||
|
||||
115
lib/crewai/src/crewai/agent/planning_config.py
Normal file
115
lib/crewai/src/crewai/agent/planning_config.py
Normal file
@@ -0,0 +1,115 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class PlanningConfig(BaseModel):
|
||||
"""Configuration for agent planning/reasoning before task execution.
|
||||
|
||||
This allows users to customize the planning behavior including prompts,
|
||||
iteration limits, the LLM used for planning, and the reasoning effort
|
||||
level that controls post-step observation and replanning behavior.
|
||||
|
||||
Note: To disable planning, don't pass a planning_config or set planning=False
|
||||
on the Agent. The presence of a PlanningConfig enables planning.
|
||||
|
||||
Attributes:
|
||||
reasoning_effort: Controls observation and replanning after each step.
|
||||
- "low": Observe each step (validates success), but skip the
|
||||
decide/replan/refine pipeline. Steps are marked complete and
|
||||
execution continues linearly. Fastest option.
|
||||
- "medium": Observe each step. On failure, trigger replanning.
|
||||
On success, skip refinement and continue. Balanced option.
|
||||
- "high": Full observation pipeline — observe every step, then
|
||||
route through decide_next_action which can trigger early goal
|
||||
achievement, full replanning, or lightweight refinement.
|
||||
Most adaptive but adds latency per step.
|
||||
max_attempts: Maximum number of planning refinement attempts.
|
||||
If None, will continue until the agent indicates readiness.
|
||||
max_steps: Maximum number of steps in the generated plan.
|
||||
system_prompt: Custom system prompt for planning. Uses default if None.
|
||||
plan_prompt: Custom prompt for creating the initial plan.
|
||||
refine_prompt: Custom prompt for refining the plan.
|
||||
llm: LLM to use for planning. Uses agent's LLM if None.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from crewai import Agent
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
|
||||
# Simple usage — fast, linear execution (default)
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
backstory="Expert researcher",
|
||||
planning_config=PlanningConfig(),
|
||||
)
|
||||
|
||||
# Balanced — replan only when steps fail
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
backstory="Expert researcher",
|
||||
planning_config=PlanningConfig(
|
||||
reasoning_effort="medium",
|
||||
),
|
||||
)
|
||||
|
||||
# Full adaptive planning with refinement and replanning
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
backstory="Expert researcher",
|
||||
planning_config=PlanningConfig(
|
||||
reasoning_effort="high",
|
||||
max_attempts=3,
|
||||
max_steps=10,
|
||||
plan_prompt="Create a focused plan for: {description}",
|
||||
llm="gpt-4o-mini", # Use cheaper model for planning
|
||||
),
|
||||
)
|
||||
```
|
||||
"""
|
||||
|
||||
reasoning_effort: Literal["low", "medium", "high"] = Field(
|
||||
default="medium",
|
||||
description=(
|
||||
"Controls post-step observation and replanning behavior. "
|
||||
"'low' observes steps but skips replanning/refinement (fastest). "
|
||||
"'medium' observes and replans only on step failure (balanced). "
|
||||
"'high' runs full observation pipeline with replanning, refinement, "
|
||||
"and early goal detection (most adaptive, highest latency)."
|
||||
),
|
||||
)
|
||||
max_attempts: int | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Maximum number of planning refinement attempts. "
|
||||
"If None, will continue until the agent indicates readiness."
|
||||
),
|
||||
)
|
||||
max_steps: int = Field(
|
||||
default=20,
|
||||
description="Maximum number of steps in the generated plan.",
|
||||
ge=1,
|
||||
)
|
||||
system_prompt: str | None = Field(
|
||||
default=None,
|
||||
description="Custom system prompt for planning. Uses default if None.",
|
||||
)
|
||||
plan_prompt: str | None = Field(
|
||||
default=None,
|
||||
description="Custom prompt for creating the initial plan.",
|
||||
)
|
||||
refine_prompt: str | None = Field(
|
||||
default=None,
|
||||
description="Custom prompt for refining the plan.",
|
||||
)
|
||||
llm: str | Any | None = Field(
|
||||
default=None,
|
||||
description="LLM to use for planning. Uses agent's LLM if None.",
|
||||
)
|
||||
|
||||
model_config = {"arbitrary_types_allowed": True}
|
||||
@@ -28,13 +28,20 @@ if TYPE_CHECKING:
|
||||
|
||||
|
||||
def handle_reasoning(agent: Agent, task: Task) -> None:
|
||||
"""Handle the reasoning process for an agent before task execution.
|
||||
"""Handle the reasoning/planning process for an agent before task execution.
|
||||
|
||||
This function checks if planning is enabled for the agent and, if so,
|
||||
creates a plan that gets appended to the task description.
|
||||
|
||||
Note: This function is used by CrewAgentExecutor (legacy path).
|
||||
For AgentExecutor, planning is handled in AgentExecutor.generate_plan().
|
||||
|
||||
Args:
|
||||
agent: The agent performing the task.
|
||||
task: The task to execute.
|
||||
"""
|
||||
if not agent.reasoning:
|
||||
# Check if planning is enabled using the planning_enabled property
|
||||
if not getattr(agent, "planning_enabled", False):
|
||||
return
|
||||
|
||||
try:
|
||||
@@ -43,13 +50,13 @@ def handle_reasoning(agent: Agent, task: Task) -> None:
|
||||
AgentReasoningOutput,
|
||||
)
|
||||
|
||||
reasoning_handler = AgentReasoning(task=task, agent=agent)
|
||||
reasoning_output: AgentReasoningOutput = (
|
||||
reasoning_handler.handle_agent_reasoning()
|
||||
planning_handler = AgentReasoning(agent=agent, task=task)
|
||||
planning_output: AgentReasoningOutput = (
|
||||
planning_handler.handle_agent_reasoning()
|
||||
)
|
||||
task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
|
||||
task.description += f"\n\nPlanning:\n{planning_output.plan.plan}"
|
||||
except Exception as e:
|
||||
agent._logger.log("error", f"Error during reasoning process: {e!s}")
|
||||
agent._logger.log("error", f"Error during planning: {e!s}")
|
||||
|
||||
|
||||
def build_task_prompt_with_schema(task: Task, task_prompt: str, i18n: I18N) -> str:
|
||||
|
||||
356
lib/crewai/src/crewai/agents/planner_observer.py
Normal file
356
lib/crewai/src/crewai/agents/planner_observer.py
Normal file
@@ -0,0 +1,356 @@
|
||||
"""PlannerObserver: Observation phase after each step execution.
|
||||
|
||||
Implements the "Observe" phase. After every step execution, the Planner
|
||||
analyzes what happened, what new information was learned, and whether the
|
||||
remaining plan is still valid.
|
||||
|
||||
This is NOT an error detector — it runs on every step, including successes,
|
||||
to incorporate runtime observations into the remaining plan.
|
||||
|
||||
Refinements are structured (StepRefinement objects) and applied directly
|
||||
from the observation result — no second LLM call required.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.observation_events import (
|
||||
StepObservationCompletedEvent,
|
||||
StepObservationFailedEvent,
|
||||
StepObservationStartedEvent,
|
||||
)
|
||||
from crewai.utilities.i18n import I18N, get_i18n
|
||||
from crewai.utilities.llm_utils import create_llm
|
||||
from crewai.utilities.planning_types import StepObservation, TodoItem
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agent import Agent
|
||||
from crewai.task import Task
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PlannerObserver:
|
||||
"""Observes step execution results and decides on plan continuation.
|
||||
|
||||
After EVERY step execution, this class:
|
||||
1. Analyzes what the step accomplished
|
||||
2. Identifies new information learned
|
||||
3. Decides if the remaining plan is still valid
|
||||
4. Suggests lightweight refinements or triggers full replanning
|
||||
|
||||
LLM resolution (magical fallback):
|
||||
- If ``agent.planning_config.llm`` is explicitly set → use that
|
||||
- Otherwise → fall back to ``agent.llm`` (same LLM for everything)
|
||||
|
||||
Args:
|
||||
agent: The agent instance (for LLM resolution and config).
|
||||
task: Optional task context (for description and expected output).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
agent: Agent,
|
||||
task: Task | None = None,
|
||||
kickoff_input: str = "",
|
||||
) -> None:
|
||||
self.agent = agent
|
||||
self.task = task
|
||||
self.kickoff_input = kickoff_input
|
||||
self.llm = self._resolve_llm()
|
||||
self._i18n: I18N = get_i18n()
|
||||
|
||||
def _resolve_llm(self) -> Any:
|
||||
"""Resolve which LLM to use for observation/planning.
|
||||
|
||||
Mirrors AgentReasoning._resolve_llm(): uses planning_config.llm
|
||||
if explicitly set, otherwise falls back to agent.llm.
|
||||
|
||||
Returns:
|
||||
The resolved LLM instance.
|
||||
"""
|
||||
from crewai.llm import LLM
|
||||
|
||||
config = getattr(self.agent, "planning_config", None)
|
||||
if config is not None and config.llm is not None:
|
||||
if isinstance(config.llm, LLM):
|
||||
return config.llm
|
||||
return create_llm(config.llm)
|
||||
return self.agent.llm
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def observe(
|
||||
self,
|
||||
completed_step: TodoItem,
|
||||
result: str,
|
||||
all_completed: list[TodoItem],
|
||||
remaining_todos: list[TodoItem],
|
||||
) -> StepObservation:
|
||||
"""Observe a step's result and decide on plan continuation.
|
||||
|
||||
This runs after EVERY step execution — not just failures.
|
||||
|
||||
Args:
|
||||
completed_step: The todo item that was just executed.
|
||||
result: The final result string from the step.
|
||||
all_completed: All previously completed todos (for context).
|
||||
remaining_todos: The pending todos still in the plan.
|
||||
|
||||
Returns:
|
||||
StepObservation with the Planner's analysis. Any suggested
|
||||
refinements are structured StepRefinement objects ready for
|
||||
direct application — no second LLM call needed.
|
||||
"""
|
||||
agent_role = self.agent.role
|
||||
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
event=StepObservationStartedEvent(
|
||||
agent_role=agent_role,
|
||||
step_number=completed_step.step_number,
|
||||
step_description=completed_step.description,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
),
|
||||
)
|
||||
|
||||
messages = self._build_observation_messages(
|
||||
completed_step, result, all_completed, remaining_todos
|
||||
)
|
||||
|
||||
try:
|
||||
response = self.llm.call(
|
||||
messages,
|
||||
response_model=StepObservation,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
)
|
||||
|
||||
observation = self._parse_observation_response(response)
|
||||
|
||||
refinement_summaries = (
|
||||
[
|
||||
f"Step {r.step_number}: {r.new_description}"
|
||||
for r in observation.suggested_refinements
|
||||
]
|
||||
if observation.suggested_refinements
|
||||
else None
|
||||
)
|
||||
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
event=StepObservationCompletedEvent(
|
||||
agent_role=agent_role,
|
||||
step_number=completed_step.step_number,
|
||||
step_description=completed_step.description,
|
||||
step_completed_successfully=observation.step_completed_successfully,
|
||||
key_information_learned=observation.key_information_learned,
|
||||
remaining_plan_still_valid=observation.remaining_plan_still_valid,
|
||||
needs_full_replan=observation.needs_full_replan,
|
||||
replan_reason=observation.replan_reason,
|
||||
goal_already_achieved=observation.goal_already_achieved,
|
||||
suggested_refinements=refinement_summaries,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
),
|
||||
)
|
||||
|
||||
return observation
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Observation LLM call failed: {e}. Defaulting to conservative replan."
|
||||
)
|
||||
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
event=StepObservationFailedEvent(
|
||||
agent_role=agent_role,
|
||||
step_number=completed_step.step_number,
|
||||
step_description=completed_step.description,
|
||||
error=str(e),
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
),
|
||||
)
|
||||
|
||||
# Don't force a full replan — the step may have succeeded even if the
|
||||
# observer LLM failed to parse the result. Defaulting to "continue" is
|
||||
# far less disruptive than wiping the entire plan on every observer error.
|
||||
return StepObservation(
|
||||
step_completed_successfully=True,
|
||||
key_information_learned="",
|
||||
remaining_plan_still_valid=True,
|
||||
needs_full_replan=False,
|
||||
)
|
||||
|
||||
def _extract_task_section(self, text: str) -> str:
|
||||
"""Extract the ## Task body from a structured enriched instruction.
|
||||
|
||||
Falls back to the full text (capped at 2000 chars) for plain inputs.
|
||||
"""
|
||||
for marker in ("\n## Task\n", "\n## Task:", "## Task\n"):
|
||||
idx = text.find(marker)
|
||||
if idx >= 0:
|
||||
start = idx + len(marker)
|
||||
for end_marker in ("\n---\n", "\n## "):
|
||||
end = text.find(end_marker, start)
|
||||
if end > 0:
|
||||
return text[start:end].strip()
|
||||
return text[start : start + 2000].strip()
|
||||
return text[:2000] if len(text) > 2000 else text
|
||||
|
||||
def apply_refinements(
|
||||
self,
|
||||
observation: StepObservation,
|
||||
remaining_todos: list[TodoItem],
|
||||
) -> list[TodoItem]:
|
||||
"""Apply structured refinements from the observation directly to todo descriptions.
|
||||
|
||||
No LLM call needed — refinements are already structured StepRefinement
|
||||
objects produced by the observation call. This is a pure in-memory update.
|
||||
|
||||
Args:
|
||||
observation: The observation containing structured refinements.
|
||||
remaining_todos: The pending todos to update in-place.
|
||||
|
||||
Returns:
|
||||
The same todo list with updated descriptions where refinements applied.
|
||||
"""
|
||||
if not observation.suggested_refinements:
|
||||
return remaining_todos
|
||||
|
||||
todo_by_step: dict[int, TodoItem] = {t.step_number: t for t in remaining_todos}
|
||||
for refinement in observation.suggested_refinements:
|
||||
if refinement.step_number in todo_by_step and refinement.new_description:
|
||||
todo_by_step[refinement.step_number].description = refinement.new_description
|
||||
|
||||
return remaining_todos
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Message building
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_observation_messages(
|
||||
self,
|
||||
completed_step: TodoItem,
|
||||
result: str,
|
||||
all_completed: list[TodoItem],
|
||||
remaining_todos: list[TodoItem],
|
||||
) -> list[LLMMessage]:
|
||||
"""Build messages for the observation LLM call."""
|
||||
task_desc = ""
|
||||
task_goal = ""
|
||||
if self.task:
|
||||
task_desc = self.task.description or ""
|
||||
task_goal = self.task.expected_output or ""
|
||||
elif self.kickoff_input:
|
||||
# Standalone kickoff path — no Task object, but we have the raw input.
|
||||
# Extract just the ## Task section so the observer sees the actual goal,
|
||||
# not the full enriched instruction with env/tools/verification noise.
|
||||
task_desc = self._extract_task_section(self.kickoff_input)
|
||||
task_goal = "Complete the task successfully"
|
||||
|
||||
system_prompt = self._i18n.retrieve("planning", "observation_system_prompt")
|
||||
|
||||
# Build context of what's been done
|
||||
completed_summary = ""
|
||||
if all_completed:
|
||||
completed_lines = []
|
||||
for todo in all_completed:
|
||||
result_preview = (todo.result or "")[:200]
|
||||
completed_lines.append(
|
||||
f" Step {todo.step_number}: {todo.description}\n"
|
||||
f" Result: {result_preview}"
|
||||
)
|
||||
completed_summary = "\n## Previously completed steps:\n" + "\n".join(
|
||||
completed_lines
|
||||
)
|
||||
|
||||
# Build remaining plan
|
||||
remaining_summary = ""
|
||||
if remaining_todos:
|
||||
remaining_lines = [
|
||||
f" Step {todo.step_number}: {todo.description}"
|
||||
for todo in remaining_todos
|
||||
]
|
||||
remaining_summary = "\n## Remaining plan steps:\n" + "\n".join(
|
||||
remaining_lines
|
||||
)
|
||||
|
||||
user_prompt = self._i18n.retrieve("planning", "observation_user_prompt").format(
|
||||
task_description=task_desc,
|
||||
task_goal=task_goal,
|
||||
completed_summary=completed_summary,
|
||||
step_number=completed_step.step_number,
|
||||
step_description=completed_step.description,
|
||||
step_result=result,
|
||||
remaining_summary=remaining_summary,
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_prompt},
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def _parse_observation_response(response: Any) -> StepObservation:
|
||||
"""Parse the LLM response into a StepObservation.
|
||||
|
||||
The LLM may return:
|
||||
- A StepObservation instance directly (streaming + litellm path)
|
||||
- A JSON string (non-streaming path serialises model_dump_json())
|
||||
- A dict (some provider paths)
|
||||
- Something else (unexpected)
|
||||
|
||||
We handle all cases to avoid silently falling back to a
|
||||
hardcoded success default.
|
||||
"""
|
||||
|
||||
if isinstance(response, StepObservation):
|
||||
return response
|
||||
|
||||
# JSON string path — most common miss before this fix
|
||||
if isinstance(response, str):
|
||||
text = response.strip()
|
||||
try:
|
||||
return StepObservation.model_validate_json(text)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
# Some LLMs wrap the JSON in markdown fences
|
||||
if text.startswith("```"):
|
||||
lines = text.split("\n")
|
||||
# Strip first and last lines (``` markers)
|
||||
inner = "\n".join(lines[1:-1] if lines[-1].strip() == "```" else lines[1:])
|
||||
try:
|
||||
return StepObservation.model_validate_json(inner.strip())
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
# Dict path
|
||||
if isinstance(response, dict):
|
||||
try:
|
||||
return StepObservation.model_validate(response)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
# Last resort — log what we got so it's diagnosable
|
||||
logger.warning(
|
||||
"Could not parse observation response (type=%s). "
|
||||
"Falling back to default success observation. Preview: %.200s",
|
||||
type(response).__name__,
|
||||
str(response),
|
||||
)
|
||||
return StepObservation(
|
||||
step_completed_successfully=True,
|
||||
key_information_learned=str(response) if response else "",
|
||||
remaining_plan_still_valid=True,
|
||||
)
|
||||
616
lib/crewai/src/crewai/agents/step_executor.py
Normal file
616
lib/crewai/src/crewai/agents/step_executor.py
Normal file
@@ -0,0 +1,616 @@
|
||||
"""StepExecutor: Isolated executor for a single plan step.
|
||||
|
||||
Implements the direct-action execution pattern from Plan-and-Act
|
||||
(arxiv 2503.09572): the Executor receives one step description,
|
||||
makes a single LLM call, executes any tool call returned, and
|
||||
returns the result immediately.
|
||||
|
||||
There is no inner loop. Recovery from failure (retry, replan) is
|
||||
the responsibility of PlannerObserver and AgentExecutor — keeping
|
||||
this class single-purpose and fast.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from datetime import datetime
|
||||
import json
|
||||
import time
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.agents.parser import AgentAction, AgentFinish
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.tool_usage_events import (
|
||||
ToolUsageErrorEvent,
|
||||
ToolUsageFinishedEvent,
|
||||
ToolUsageStartedEvent,
|
||||
)
|
||||
from crewai.utilities.agent_utils import (
|
||||
build_tool_calls_assistant_message,
|
||||
check_native_tool_support,
|
||||
enforce_rpm_limit,
|
||||
execute_single_native_tool_call,
|
||||
format_message_for_llm,
|
||||
is_tool_call_list,
|
||||
process_llm_response,
|
||||
setup_native_tools,
|
||||
)
|
||||
from crewai.utilities.i18n import I18N, get_i18n
|
||||
from crewai.utilities.planning_types import TodoItem
|
||||
from crewai.utilities.printer import Printer
|
||||
from crewai.utilities.step_execution_context import StepExecutionContext, StepResult
|
||||
from crewai.utilities.string_utils import sanitize_tool_name
|
||||
from crewai.utilities.tool_utils import execute_tool_and_check_finality
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agent import Agent
|
||||
from crewai.agents.tools_handler import ToolsHandler
|
||||
from crewai.crew import Crew
|
||||
from crewai.llms.base_llm import BaseLLM
|
||||
from crewai.task import Task
|
||||
from crewai.tools.base_tool import BaseTool
|
||||
from crewai.tools.structured_tool import CrewStructuredTool
|
||||
|
||||
|
||||
class StepExecutor:
|
||||
"""Executes a SINGLE todo item using direct-action execution.
|
||||
|
||||
The StepExecutor owns its own message list per invocation. It never reads
|
||||
or writes the AgentExecutor's state. Results flow back via StepResult.
|
||||
|
||||
Execution pattern (per Plan-and-Act, arxiv 2503.09572):
|
||||
1. Build messages from todo + context
|
||||
2. Call LLM once (with or without native tools)
|
||||
3. If tool call → execute it → return tool result
|
||||
4. If text answer → return it directly
|
||||
No inner loop — recovery is PlannerObserver's responsibility.
|
||||
|
||||
Args:
|
||||
llm: The language model to use for execution.
|
||||
tools: Structured tools available to the executor.
|
||||
agent: The agent instance (for role/goal/verbose/config).
|
||||
original_tools: Original BaseTool instances (needed for native tool schema).
|
||||
tools_handler: Optional tools handler for caching and delegation tracking.
|
||||
task: Optional task context.
|
||||
crew: Optional crew context.
|
||||
function_calling_llm: Optional separate LLM for function calling.
|
||||
request_within_rpm_limit: Optional RPM limit function.
|
||||
callbacks: Optional list of callbacks.
|
||||
i18n: Optional i18n instance.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
llm: BaseLLM,
|
||||
tools: list[CrewStructuredTool],
|
||||
agent: Agent,
|
||||
original_tools: list[BaseTool] | None = None,
|
||||
tools_handler: ToolsHandler | None = None,
|
||||
task: Task | None = None,
|
||||
crew: Crew | None = None,
|
||||
function_calling_llm: BaseLLM | Any | None = None,
|
||||
request_within_rpm_limit: Callable[[], bool] | None = None,
|
||||
callbacks: list[Any] | None = None,
|
||||
i18n: I18N | None = None,
|
||||
) -> None:
|
||||
self.llm = llm
|
||||
self.tools = tools
|
||||
self.agent = agent
|
||||
self.original_tools = original_tools or []
|
||||
self.tools_handler = tools_handler
|
||||
self.task = task
|
||||
self.crew = crew
|
||||
self.function_calling_llm = function_calling_llm
|
||||
self.request_within_rpm_limit = request_within_rpm_limit
|
||||
self.callbacks = callbacks or []
|
||||
self._i18n: I18N = i18n or get_i18n()
|
||||
self._printer: Printer = Printer()
|
||||
|
||||
# Native tool support — set up once
|
||||
self._use_native_tools = check_native_tool_support(
|
||||
self.llm, self.original_tools
|
||||
)
|
||||
self._openai_tools: list[dict[str, Any]] = []
|
||||
self._available_functions: dict[str, Callable[..., Any]] = {}
|
||||
if self._use_native_tools and self.original_tools:
|
||||
(
|
||||
self._openai_tools,
|
||||
self._available_functions,
|
||||
_,
|
||||
) = setup_native_tools(self.original_tools)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def execute(self, todo: TodoItem, context: StepExecutionContext) -> StepResult:
|
||||
"""Execute a single todo item using a multi-turn action loop.
|
||||
|
||||
Enforces the RPM limit, builds a fresh message list, then iterates
|
||||
LLM call → tool execution → observation until the LLM signals it is
|
||||
done (text answer) or max_step_iterations is reached. Never touches
|
||||
external AgentExecutor state.
|
||||
|
||||
Args:
|
||||
todo: The todo item to execute.
|
||||
context: Immutable context with task info and dependency results.
|
||||
|
||||
Returns:
|
||||
StepResult with the outcome.
|
||||
"""
|
||||
start_time = time.monotonic()
|
||||
tool_calls_made: list[str] = []
|
||||
|
||||
try:
|
||||
enforce_rpm_limit(self.request_within_rpm_limit)
|
||||
messages = self._build_isolated_messages(todo, context)
|
||||
|
||||
if self._use_native_tools:
|
||||
result_text = self._execute_native(messages, tool_calls_made)
|
||||
else:
|
||||
result_text = self._execute_text_parsed(messages, tool_calls_made)
|
||||
self._validate_expected_tool_usage(todo, tool_calls_made)
|
||||
|
||||
elapsed = time.monotonic() - start_time
|
||||
return StepResult(
|
||||
success=True,
|
||||
result=result_text,
|
||||
tool_calls_made=tool_calls_made,
|
||||
execution_time=elapsed,
|
||||
)
|
||||
except Exception as e:
|
||||
elapsed = time.monotonic() - start_time
|
||||
return StepResult(
|
||||
success=False,
|
||||
result="",
|
||||
error=str(e),
|
||||
tool_calls_made=tool_calls_made,
|
||||
execution_time=elapsed,
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Message building
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_isolated_messages(
|
||||
self, todo: TodoItem, context: StepExecutionContext
|
||||
) -> list[LLMMessage]:
|
||||
"""Build a fresh message list for this step's execution.
|
||||
|
||||
System prompt tells the LLM it is an Executor focused on one step.
|
||||
User prompt provides the step description, dependencies, and tools.
|
||||
"""
|
||||
system_prompt = self._build_system_prompt()
|
||||
user_prompt = self._build_user_prompt(todo, context)
|
||||
|
||||
return [
|
||||
format_message_for_llm(system_prompt, role="system"),
|
||||
format_message_for_llm(user_prompt, role="user"),
|
||||
]
|
||||
|
||||
def _build_system_prompt(self) -> str:
|
||||
"""Build the Executor's system prompt."""
|
||||
role = self.agent.role if self.agent else "Assistant"
|
||||
goal = self.agent.goal if self.agent else "Complete tasks efficiently"
|
||||
backstory = getattr(self.agent, "backstory", "") or ""
|
||||
|
||||
tools_section = ""
|
||||
if self.tools and not self._use_native_tools:
|
||||
tool_names = ", ".join(sanitize_tool_name(t.name) for t in self.tools)
|
||||
tools_section = self._i18n.retrieve(
|
||||
"planning", "step_executor_tools_section"
|
||||
).format(tool_names=tool_names)
|
||||
|
||||
return self._i18n.retrieve("planning", "step_executor_system_prompt").format(
|
||||
role=role,
|
||||
backstory=backstory,
|
||||
goal=goal,
|
||||
tools_section=tools_section,
|
||||
)
|
||||
|
||||
def _extract_task_section(self, task_description: str) -> str:
|
||||
"""Extract the most relevant portion of the task description.
|
||||
|
||||
For structured descriptions (e.g. harbor_agent-style with ## Task
|
||||
and ## Instructions sections), extracts just the task body so the
|
||||
executor sees the requirements without duplicating tool/verification
|
||||
instructions that are already in the system prompt.
|
||||
|
||||
For plain descriptions, returns the full text (up to 2000 chars).
|
||||
"""
|
||||
# Try to extract between "## Task" and the next "---" separator
|
||||
# or next "##" heading — this isolates the task spec from env/tool noise.
|
||||
for marker in ("\n## Task\n", "\n## Task:", "## Task\n"):
|
||||
idx = task_description.find(marker)
|
||||
if idx >= 0:
|
||||
start = idx + len(marker)
|
||||
# End at the first horizontal rule or next top-level ## section
|
||||
for end_marker in ("\n---\n", "\n## "):
|
||||
end = task_description.find(end_marker, start)
|
||||
if end > 0:
|
||||
return task_description[start:end].strip()
|
||||
# No end marker — take up to 2000 chars
|
||||
return task_description[start : start + 2000].strip()
|
||||
|
||||
# No structured format — use the full description, reasonably truncated
|
||||
if len(task_description) > 2000:
|
||||
return task_description[:2000] + "\n... [truncated]"
|
||||
return task_description
|
||||
|
||||
def _build_user_prompt(self, todo: TodoItem, context: StepExecutionContext) -> str:
|
||||
"""Build the user prompt for this specific step."""
|
||||
parts: list[str] = []
|
||||
|
||||
# Include overall task context so the executor knows the full goal and
|
||||
# required output format/location — critical for knowing WHAT to produce.
|
||||
# We extract only the task body (not tool instructions or verification
|
||||
# sections) to avoid duplicating directives already in the system prompt.
|
||||
if context.task_description:
|
||||
task_section = self._extract_task_section(context.task_description)
|
||||
if task_section:
|
||||
parts.append(
|
||||
self._i18n.retrieve(
|
||||
"planning", "step_executor_task_context"
|
||||
).format(
|
||||
task_context=task_section,
|
||||
)
|
||||
)
|
||||
|
||||
parts.append(
|
||||
self._i18n.retrieve("planning", "step_executor_user_prompt").format(
|
||||
step_description=todo.description,
|
||||
)
|
||||
)
|
||||
|
||||
if todo.tool_to_use:
|
||||
parts.append(
|
||||
self._i18n.retrieve("planning", "step_executor_suggested_tool").format(
|
||||
tool_to_use=todo.tool_to_use,
|
||||
)
|
||||
)
|
||||
|
||||
# Include dependency results (final results only, no traces)
|
||||
if context.dependency_results:
|
||||
parts.append(
|
||||
self._i18n.retrieve("planning", "step_executor_context_header")
|
||||
)
|
||||
for step_num, result in sorted(context.dependency_results.items()):
|
||||
parts.append(
|
||||
self._i18n.retrieve(
|
||||
"planning", "step_executor_context_entry"
|
||||
).format(step_number=step_num, result=result)
|
||||
)
|
||||
|
||||
parts.append(self._i18n.retrieve("planning", "step_executor_complete_step"))
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Multi-turn execution loop
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _execute_text_parsed(
|
||||
self,
|
||||
messages: list[LLMMessage],
|
||||
tool_calls_made: list[str],
|
||||
max_step_iterations: int = 15,
|
||||
) -> str:
|
||||
"""Execute step using text-parsed tool calling with a multi-turn loop.
|
||||
|
||||
Iterates LLM call → tool execution → observation until the LLM
|
||||
produces a Final Answer or max_step_iterations is reached.
|
||||
This allows the agent to: run a command, see the output, adjust its
|
||||
approach, and run another command — all within a single plan step.
|
||||
"""
|
||||
use_stop_words = self.llm.supports_stop_words() if self.llm else False
|
||||
last_tool_result = ""
|
||||
|
||||
for _ in range(max_step_iterations):
|
||||
answer = self.llm.call(
|
||||
messages,
|
||||
callbacks=self.callbacks,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
)
|
||||
|
||||
if not answer:
|
||||
raise ValueError("Empty response from LLM")
|
||||
|
||||
answer_str = str(answer)
|
||||
formatted = process_llm_response(answer_str, use_stop_words)
|
||||
|
||||
if isinstance(formatted, AgentFinish):
|
||||
return str(formatted.output)
|
||||
|
||||
if isinstance(formatted, AgentAction):
|
||||
tool_calls_made.append(formatted.tool)
|
||||
tool_result = self._execute_text_tool_with_events(formatted)
|
||||
last_tool_result = tool_result
|
||||
# Append the assistant's reasoning + action, then the observation.
|
||||
# _build_observation_message handles vision sentinels so the LLM
|
||||
# receives an image content block instead of raw base64 text.
|
||||
messages.append({"role": "assistant", "content": answer_str})
|
||||
messages.append(self._build_observation_message(tool_result))
|
||||
continue
|
||||
|
||||
# Raw text response with no Final Answer marker — treat as done
|
||||
return answer_str
|
||||
|
||||
# Max iterations reached — return the last tool result we accumulated
|
||||
return last_tool_result
|
||||
|
||||
def _execute_text_tool_with_events(self, formatted: AgentAction) -> str:
|
||||
"""Execute text-parsed tool calls with tool usage events."""
|
||||
args_dict = self._parse_tool_args(formatted.tool_input)
|
||||
agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
|
||||
started_at = datetime.now()
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=ToolUsageStartedEvent(
|
||||
tool_name=formatted.tool,
|
||||
tool_args=args_dict,
|
||||
from_agent=self.agent,
|
||||
from_task=self.task,
|
||||
agent_key=agent_key,
|
||||
),
|
||||
)
|
||||
|
||||
try:
|
||||
fingerprint_context = {}
|
||||
if (
|
||||
self.agent
|
||||
and hasattr(self.agent, "security_config")
|
||||
and hasattr(self.agent.security_config, "fingerprint")
|
||||
):
|
||||
fingerprint_context = {
|
||||
"agent_fingerprint": str(self.agent.security_config.fingerprint)
|
||||
}
|
||||
|
||||
tool_result = execute_tool_and_check_finality(
|
||||
agent_action=formatted,
|
||||
fingerprint_context=fingerprint_context,
|
||||
tools=self.tools,
|
||||
i18n=self._i18n,
|
||||
agent_key=self.agent.key if self.agent else None,
|
||||
agent_role=self.agent.role if self.agent else None,
|
||||
tools_handler=self.tools_handler,
|
||||
task=self.task,
|
||||
agent=self.agent,
|
||||
function_calling_llm=self.function_calling_llm,
|
||||
crew=self.crew,
|
||||
)
|
||||
except Exception as e:
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=ToolUsageErrorEvent(
|
||||
tool_name=formatted.tool,
|
||||
tool_args=args_dict,
|
||||
from_agent=self.agent,
|
||||
from_task=self.task,
|
||||
agent_key=agent_key,
|
||||
error=e,
|
||||
),
|
||||
)
|
||||
raise
|
||||
|
||||
crewai_event_bus.emit(
|
||||
self,
|
||||
event=ToolUsageFinishedEvent(
|
||||
output=str(tool_result.result),
|
||||
tool_name=formatted.tool,
|
||||
tool_args=args_dict,
|
||||
from_agent=self.agent,
|
||||
from_task=self.task,
|
||||
agent_key=agent_key,
|
||||
started_at=started_at,
|
||||
finished_at=datetime.now(),
|
||||
),
|
||||
)
|
||||
return str(tool_result.result)
|
||||
|
||||
def _parse_tool_args(self, tool_input: Any) -> dict[str, Any]:
|
||||
"""Parse tool args from the parser output into a dict payload for events."""
|
||||
if isinstance(tool_input, dict):
|
||||
return tool_input
|
||||
if isinstance(tool_input, str):
|
||||
stripped_input = tool_input.strip()
|
||||
if not stripped_input:
|
||||
return {}
|
||||
try:
|
||||
parsed = json.loads(stripped_input)
|
||||
if isinstance(parsed, dict):
|
||||
return parsed
|
||||
return {"input": parsed}
|
||||
except json.JSONDecodeError:
|
||||
return {"input": stripped_input}
|
||||
return {"input": str(tool_input)}
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internal: Vision support
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _parse_vision_sentinel(raw: str) -> tuple[str, str] | None:
|
||||
"""Parse a VISION_IMAGE sentinel into (media_type, base64_data), or None."""
|
||||
prefix = "VISION_IMAGE:"
|
||||
if not raw.startswith(prefix):
|
||||
return None
|
||||
rest = raw[len(prefix) :]
|
||||
sep = rest.find(":")
|
||||
if sep <= 0:
|
||||
return None
|
||||
return rest[:sep], rest[sep + 1 :]
|
||||
|
||||
@staticmethod
|
||||
def _build_observation_message(tool_result: str) -> LLMMessage:
|
||||
"""Build an observation message, converting vision sentinels to image blocks.
|
||||
|
||||
When a tool returns a VISION_IMAGE sentinel (e.g. from read_image),
|
||||
we build a multimodal content block so the LLM can actually *see*
|
||||
the image rather than receiving a wall of base64 text.
|
||||
|
||||
Uses the standard image_url / data-URI format so each LLM provider's
|
||||
SDK (OpenAI, LiteLLM, etc.) handles the provider-specific conversion.
|
||||
|
||||
Format: ``VISION_IMAGE:<media_type>:<base64_data>``
|
||||
"""
|
||||
parsed = StepExecutor._parse_vision_sentinel(tool_result)
|
||||
if parsed:
|
||||
media_type, b64_data = parsed
|
||||
return {
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Observation: Here is the image:"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": f"data:{media_type};base64,{b64_data}",
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
return {"role": "user", "content": f"Observation: {tool_result}"}
|
||||
|
||||
def _validate_expected_tool_usage(
|
||||
self,
|
||||
todo: TodoItem,
|
||||
tool_calls_made: list[str],
|
||||
) -> None:
|
||||
"""Fail step execution when a required tool is configured but not called."""
|
||||
expected_tool = getattr(todo, "tool_to_use", None)
|
||||
if not expected_tool:
|
||||
return
|
||||
expected_tool_name = sanitize_tool_name(expected_tool)
|
||||
available_tool_names = {
|
||||
sanitize_tool_name(tool.name)
|
||||
for tool in self.tools
|
||||
if getattr(tool, "name", "")
|
||||
} | set(self._available_functions.keys())
|
||||
if expected_tool_name not in available_tool_names:
|
||||
return
|
||||
called_names = {sanitize_tool_name(name) for name in tool_calls_made}
|
||||
if expected_tool_name not in called_names:
|
||||
raise ValueError(
|
||||
f"Expected tool '{expected_tool_name}' was not called "
|
||||
f"for step {todo.step_number}."
|
||||
)
|
||||
|
||||
def _execute_native(
|
||||
self,
|
||||
messages: list[LLMMessage],
|
||||
tool_calls_made: list[str],
|
||||
max_step_iterations: int = 15,
|
||||
) -> str:
|
||||
"""Execute step using native function calling with a multi-turn loop.
|
||||
|
||||
Iterates LLM call → tool execution → appended results until the LLM
|
||||
returns a text answer (no more tool calls) or max_step_iterations is
|
||||
reached. This lets the agent run a shell command, observe the output,
|
||||
correct mistakes, and issue follow-up commands — all within one step.
|
||||
"""
|
||||
accumulated_results: list[str] = []
|
||||
|
||||
for _ in range(max_step_iterations):
|
||||
answer = self.llm.call(
|
||||
messages,
|
||||
tools=self._openai_tools,
|
||||
callbacks=self.callbacks,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
)
|
||||
|
||||
if not answer:
|
||||
raise ValueError("Empty response from LLM")
|
||||
|
||||
if isinstance(answer, BaseModel):
|
||||
return answer.model_dump_json()
|
||||
|
||||
if isinstance(answer, list) and answer and is_tool_call_list(answer):
|
||||
# _execute_native_tool_calls appends assistant + tool messages
|
||||
# to `messages` as a side-effect, so the next LLM call will
|
||||
# see the full conversation history including tool outputs.
|
||||
result = self._execute_native_tool_calls(
|
||||
answer, messages, tool_calls_made
|
||||
)
|
||||
accumulated_results.append(result)
|
||||
continue
|
||||
|
||||
# Text answer → LLM decided the step is done
|
||||
return str(answer)
|
||||
|
||||
# Max iterations reached — return everything we accumulated
|
||||
return "\n".join(filter(None, accumulated_results))
|
||||
|
||||
def _execute_native_tool_calls(
|
||||
self,
|
||||
tool_calls: list[Any],
|
||||
messages: list[LLMMessage],
|
||||
tool_calls_made: list[str],
|
||||
) -> str:
|
||||
"""Execute a batch of native tool calls and return their results.
|
||||
|
||||
Returns the result of the first tool marked result_as_answer if any,
|
||||
otherwise returns all tool results concatenated.
|
||||
"""
|
||||
assistant_message, _reports = build_tool_calls_assistant_message(tool_calls)
|
||||
if assistant_message:
|
||||
messages.append(assistant_message)
|
||||
|
||||
tool_results: list[str] = []
|
||||
for tool_call in tool_calls:
|
||||
call_result = execute_single_native_tool_call(
|
||||
tool_call,
|
||||
available_functions=self._available_functions,
|
||||
original_tools=self.original_tools,
|
||||
structured_tools=self.tools,
|
||||
tools_handler=self.tools_handler,
|
||||
agent=self.agent,
|
||||
task=self.task,
|
||||
crew=self.crew,
|
||||
event_source=self,
|
||||
printer=self._printer,
|
||||
verbose=bool(self.agent and self.agent.verbose),
|
||||
)
|
||||
|
||||
if call_result.func_name:
|
||||
tool_calls_made.append(call_result.func_name)
|
||||
|
||||
if call_result.result_as_answer:
|
||||
return str(call_result.result)
|
||||
|
||||
if call_result.tool_message:
|
||||
raw_content = call_result.tool_message.get("content", "")
|
||||
if isinstance(raw_content, str):
|
||||
parsed = self._parse_vision_sentinel(raw_content)
|
||||
if parsed:
|
||||
media_type, b64_data = parsed
|
||||
# Replace the sentinel with a standard image_url content block.
|
||||
# Each provider's _format_messages handles conversion to
|
||||
# its native format (e.g. Anthropic image blocks).
|
||||
modified: LLMMessage = cast(
|
||||
LLMMessage, dict(call_result.tool_message)
|
||||
)
|
||||
modified["content"] = [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": f"data:{media_type};base64,{b64_data}",
|
||||
},
|
||||
}
|
||||
]
|
||||
messages.append(modified)
|
||||
tool_results.append("[image]")
|
||||
else:
|
||||
messages.append(call_result.tool_message)
|
||||
if raw_content:
|
||||
tool_results.append(raw_content)
|
||||
else:
|
||||
messages.append(call_result.tool_message)
|
||||
if raw_content:
|
||||
tool_results.append(str(raw_content))
|
||||
|
||||
return "\n".join(tool_results) if tool_results else ""
|
||||
@@ -75,6 +75,14 @@ from crewai.events.types.mcp_events import (
|
||||
MCPToolExecutionFailedEvent,
|
||||
MCPToolExecutionStartedEvent,
|
||||
)
|
||||
from crewai.events.types.observation_events import (
|
||||
GoalAchievedEarlyEvent,
|
||||
PlanRefinementEvent,
|
||||
PlanReplanTriggeredEvent,
|
||||
StepObservationCompletedEvent,
|
||||
StepObservationFailedEvent,
|
||||
StepObservationStartedEvent,
|
||||
)
|
||||
from crewai.events.types.reasoning_events import (
|
||||
AgentReasoningCompletedEvent,
|
||||
AgentReasoningFailedEvent,
|
||||
@@ -535,6 +543,64 @@ class EventListener(BaseEventListener):
|
||||
event.error,
|
||||
)
|
||||
|
||||
# ----------- OBSERVATION EVENTS (Plan-and-Execute) -----------
|
||||
|
||||
@crewai_event_bus.on(StepObservationStartedEvent)
|
||||
def on_step_observation_started(
|
||||
_: Any, event: StepObservationStartedEvent
|
||||
) -> None:
|
||||
self.formatter.handle_observation_started(
|
||||
event.agent_role,
|
||||
event.step_number,
|
||||
event.step_description,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(StepObservationCompletedEvent)
|
||||
def on_step_observation_completed(
|
||||
_: Any, event: StepObservationCompletedEvent
|
||||
) -> None:
|
||||
self.formatter.handle_observation_completed(
|
||||
event.agent_role,
|
||||
event.step_number,
|
||||
event.step_completed_successfully,
|
||||
event.remaining_plan_still_valid,
|
||||
event.key_information_learned,
|
||||
event.needs_full_replan,
|
||||
event.goal_already_achieved,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(StepObservationFailedEvent)
|
||||
def on_step_observation_failed(
|
||||
_: Any, event: StepObservationFailedEvent
|
||||
) -> None:
|
||||
self.formatter.handle_observation_failed(
|
||||
event.step_number,
|
||||
event.error,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(PlanRefinementEvent)
|
||||
def on_plan_refinement(_: Any, event: PlanRefinementEvent) -> None:
|
||||
self.formatter.handle_plan_refinement(
|
||||
event.step_number,
|
||||
event.refined_step_count,
|
||||
event.refinements,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(PlanReplanTriggeredEvent)
|
||||
def on_plan_replan_triggered(_: Any, event: PlanReplanTriggeredEvent) -> None:
|
||||
self.formatter.handle_plan_replan(
|
||||
event.replan_reason,
|
||||
event.replan_count,
|
||||
event.completed_steps_preserved,
|
||||
)
|
||||
|
||||
@crewai_event_bus.on(GoalAchievedEarlyEvent)
|
||||
def on_goal_achieved_early(_: Any, event: GoalAchievedEarlyEvent) -> None:
|
||||
self.formatter.handle_goal_achieved_early(
|
||||
event.steps_completed,
|
||||
event.steps_remaining,
|
||||
)
|
||||
|
||||
# ----------- AGENT LOGGING EVENTS -----------
|
||||
|
||||
@crewai_event_bus.on(AgentLogsStartedEvent)
|
||||
|
||||
@@ -93,6 +93,14 @@ from crewai.events.types.memory_events import (
|
||||
MemorySaveFailedEvent,
|
||||
MemorySaveStartedEvent,
|
||||
)
|
||||
from crewai.events.types.observation_events import (
|
||||
GoalAchievedEarlyEvent,
|
||||
PlanRefinementEvent,
|
||||
PlanReplanTriggeredEvent,
|
||||
StepObservationCompletedEvent,
|
||||
StepObservationFailedEvent,
|
||||
StepObservationStartedEvent,
|
||||
)
|
||||
from crewai.events.types.reasoning_events import (
|
||||
AgentReasoningCompletedEvent,
|
||||
AgentReasoningFailedEvent,
|
||||
@@ -437,6 +445,39 @@ class TraceCollectionListener(BaseEventListener):
|
||||
) -> None:
|
||||
self._handle_action_event("agent_reasoning_failed", source, event)
|
||||
|
||||
# Observation events (Plan-and-Execute)
|
||||
@event_bus.on(StepObservationStartedEvent)
|
||||
def on_step_observation_started(
|
||||
source: Any, event: StepObservationStartedEvent
|
||||
) -> None:
|
||||
self._handle_action_event("step_observation_started", source, event)
|
||||
|
||||
@event_bus.on(StepObservationCompletedEvent)
|
||||
def on_step_observation_completed(
|
||||
source: Any, event: StepObservationCompletedEvent
|
||||
) -> None:
|
||||
self._handle_action_event("step_observation_completed", source, event)
|
||||
|
||||
@event_bus.on(StepObservationFailedEvent)
|
||||
def on_step_observation_failed(
|
||||
source: Any, event: StepObservationFailedEvent
|
||||
) -> None:
|
||||
self._handle_action_event("step_observation_failed", source, event)
|
||||
|
||||
@event_bus.on(PlanRefinementEvent)
|
||||
def on_plan_refinement(source: Any, event: PlanRefinementEvent) -> None:
|
||||
self._handle_action_event("plan_refinement", source, event)
|
||||
|
||||
@event_bus.on(PlanReplanTriggeredEvent)
|
||||
def on_plan_replan_triggered(
|
||||
source: Any, event: PlanReplanTriggeredEvent
|
||||
) -> None:
|
||||
self._handle_action_event("plan_replan_triggered", source, event)
|
||||
|
||||
@event_bus.on(GoalAchievedEarlyEvent)
|
||||
def on_goal_achieved_early(source: Any, event: GoalAchievedEarlyEvent) -> None:
|
||||
self._handle_action_event("goal_achieved_early", source, event)
|
||||
|
||||
@event_bus.on(KnowledgeRetrievalStartedEvent)
|
||||
def on_knowledge_retrieval_started(
|
||||
source: Any, event: KnowledgeRetrievalStartedEvent
|
||||
|
||||
99
lib/crewai/src/crewai/events/types/observation_events.py
Normal file
99
lib/crewai/src/crewai/events/types/observation_events.py
Normal file
@@ -0,0 +1,99 @@
|
||||
"""Observation events for the Plan-and-Execute architecture.
|
||||
|
||||
Emitted during the Observation phase (PLAN-AND-ACT Section 3.3) when the
|
||||
PlannerObserver analyzes step execution results and decides on plan
|
||||
continuation, refinement, or replanning.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from crewai.events.base_events import BaseEvent
|
||||
|
||||
|
||||
class ObservationEvent(BaseEvent):
|
||||
"""Base event for observation phase events."""
|
||||
|
||||
type: str
|
||||
agent_role: str
|
||||
step_number: int
|
||||
step_description: str = ""
|
||||
from_task: Any | None = None
|
||||
from_agent: Any | None = None
|
||||
|
||||
def __init__(self, **data: Any) -> None:
|
||||
super().__init__(**data)
|
||||
self._set_task_params(data)
|
||||
self._set_agent_params(data)
|
||||
|
||||
|
||||
class StepObservationStartedEvent(ObservationEvent):
|
||||
"""Emitted when the Planner begins observing a step's result.
|
||||
|
||||
Fires after every step execution, before the observation LLM call.
|
||||
"""
|
||||
|
||||
type: str = "step_observation_started"
|
||||
|
||||
|
||||
class StepObservationCompletedEvent(ObservationEvent):
|
||||
"""Emitted when the Planner finishes observing a step's result.
|
||||
|
||||
Contains the full observation analysis: what was learned, whether
|
||||
the plan is still valid, and what action to take next.
|
||||
"""
|
||||
|
||||
type: str = "step_observation_completed"
|
||||
step_completed_successfully: bool = True
|
||||
key_information_learned: str = ""
|
||||
remaining_plan_still_valid: bool = True
|
||||
needs_full_replan: bool = False
|
||||
replan_reason: str | None = None
|
||||
goal_already_achieved: bool = False
|
||||
suggested_refinements: list[str] | None = None
|
||||
|
||||
|
||||
class StepObservationFailedEvent(ObservationEvent):
|
||||
"""Emitted when the observation LLM call itself fails.
|
||||
|
||||
The system defaults to continuing the plan when this happens,
|
||||
but the event allows monitoring/alerting on observation failures.
|
||||
"""
|
||||
|
||||
type: str = "step_observation_failed"
|
||||
error: str = ""
|
||||
|
||||
|
||||
class PlanRefinementEvent(ObservationEvent):
|
||||
"""Emitted when the Planner refines upcoming step descriptions.
|
||||
|
||||
This is the lightweight refinement path — no full replan, just
|
||||
sharpening pending todo descriptions based on new information.
|
||||
"""
|
||||
|
||||
type: str = "plan_refinement"
|
||||
refined_step_count: int = 0
|
||||
refinements: list[str] | None = None
|
||||
|
||||
|
||||
class PlanReplanTriggeredEvent(ObservationEvent):
|
||||
"""Emitted when the Planner triggers a full replan.
|
||||
|
||||
The remaining plan was deemed fundamentally wrong and will be
|
||||
regenerated from scratch, preserving completed step results.
|
||||
"""
|
||||
|
||||
type: str = "plan_replan_triggered"
|
||||
replan_reason: str = ""
|
||||
replan_count: int = 0
|
||||
completed_steps_preserved: int = 0
|
||||
|
||||
|
||||
class GoalAchievedEarlyEvent(ObservationEvent):
|
||||
"""Emitted when the Planner detects the goal was achieved early.
|
||||
|
||||
Remaining steps will be skipped and execution will finalize.
|
||||
"""
|
||||
|
||||
type: str = "goal_achieved_early"
|
||||
steps_remaining: int = 0
|
||||
steps_completed: int = 0
|
||||
@@ -9,7 +9,7 @@ class ReasoningEvent(BaseEvent):
|
||||
type: str
|
||||
attempt: int = 1
|
||||
agent_role: str
|
||||
task_id: str
|
||||
task_id: str | None = None
|
||||
task_name: str | None = None
|
||||
from_task: Any | None = None
|
||||
agent_id: str | None = None
|
||||
|
||||
@@ -936,6 +936,152 @@ To enable tracing, do any one of these:
|
||||
)
|
||||
self.print_panel(error_content, "❌ Reasoning Error", "red")
|
||||
|
||||
# ----------- OBSERVATION EVENTS (Plan-and-Execute) -----------
|
||||
|
||||
def handle_observation_started(
|
||||
self,
|
||||
agent_role: str,
|
||||
step_number: int,
|
||||
step_description: str,
|
||||
) -> None:
|
||||
"""Handle step observation started event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
content = Text()
|
||||
content.append("Observation Started\n", style="cyan bold")
|
||||
content.append("Agent: ", style="white")
|
||||
content.append(f"{agent_role}\n", style="cyan")
|
||||
content.append("Step: ", style="white")
|
||||
content.append(f"{step_number}\n", style="cyan")
|
||||
if step_description:
|
||||
desc_preview = step_description[:80] + (
|
||||
"..." if len(step_description) > 80 else ""
|
||||
)
|
||||
content.append("Description: ", style="white")
|
||||
content.append(f"{desc_preview}\n", style="cyan")
|
||||
|
||||
self.print_panel(content, "🔍 Observing Step Result", "cyan")
|
||||
|
||||
def handle_observation_completed(
|
||||
self,
|
||||
agent_role: str,
|
||||
step_number: int,
|
||||
step_completed: bool,
|
||||
plan_valid: bool,
|
||||
key_info: str,
|
||||
needs_replan: bool,
|
||||
goal_achieved: bool,
|
||||
) -> None:
|
||||
"""Handle step observation completed event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
if goal_achieved:
|
||||
style = "green"
|
||||
status = "Goal Achieved Early"
|
||||
elif needs_replan:
|
||||
style = "yellow"
|
||||
status = "Replan Needed"
|
||||
elif plan_valid:
|
||||
style = "green"
|
||||
status = "Plan Valid — Continue"
|
||||
else:
|
||||
style = "red"
|
||||
status = "Step Failed"
|
||||
|
||||
content = Text()
|
||||
content.append("Observation Complete\n", style=f"{style} bold")
|
||||
content.append("Step: ", style="white")
|
||||
content.append(f"{step_number}\n", style=style)
|
||||
content.append("Status: ", style="white")
|
||||
content.append(f"{status}\n", style=style)
|
||||
if key_info:
|
||||
info_preview = key_info[:120] + ("..." if len(key_info) > 120 else "")
|
||||
content.append("Learned: ", style="white")
|
||||
content.append(f"{info_preview}\n", style=style)
|
||||
|
||||
self.print_panel(content, "🔍 Observation Result", style)
|
||||
|
||||
def handle_observation_failed(
|
||||
self,
|
||||
step_number: int,
|
||||
error: str,
|
||||
) -> None:
|
||||
"""Handle step observation failure event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
error_content = self.create_status_content(
|
||||
"Observation Failed",
|
||||
"Error",
|
||||
"red",
|
||||
Step=str(step_number),
|
||||
Error=error,
|
||||
)
|
||||
self.print_panel(error_content, "❌ Observation Error", "red")
|
||||
|
||||
def handle_plan_refinement(
|
||||
self,
|
||||
step_number: int,
|
||||
refined_count: int,
|
||||
refinements: list[str] | None,
|
||||
) -> None:
|
||||
"""Handle plan refinement event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
content = Text()
|
||||
content.append("Plan Refined\n", style="cyan bold")
|
||||
content.append("After Step: ", style="white")
|
||||
content.append(f"{step_number}\n", style="cyan")
|
||||
content.append("Steps Updated: ", style="white")
|
||||
content.append(f"{refined_count}\n", style="cyan")
|
||||
if refinements:
|
||||
for r in refinements[:3]:
|
||||
content.append(f" • {r[:80]}\n", style="white")
|
||||
|
||||
self.print_panel(content, "✏️ Plan Refinement", "cyan")
|
||||
|
||||
def handle_plan_replan(
|
||||
self,
|
||||
reason: str,
|
||||
replan_count: int,
|
||||
preserved_count: int,
|
||||
) -> None:
|
||||
"""Handle plan replan triggered event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
content = Text()
|
||||
content.append("Full Replan Triggered\n", style="yellow bold")
|
||||
content.append("Reason: ", style="white")
|
||||
content.append(f"{reason}\n", style="yellow")
|
||||
content.append("Replan #: ", style="white")
|
||||
content.append(f"{replan_count}\n", style="yellow")
|
||||
content.append("Preserved Steps: ", style="white")
|
||||
content.append(f"{preserved_count}\n", style="yellow")
|
||||
|
||||
self.print_panel(content, "🔄 Dynamic Replan", "yellow")
|
||||
|
||||
def handle_goal_achieved_early(
|
||||
self,
|
||||
steps_completed: int,
|
||||
steps_remaining: int,
|
||||
) -> None:
|
||||
"""Handle goal achieved early event."""
|
||||
if not self.verbose:
|
||||
return
|
||||
|
||||
content = Text()
|
||||
content.append("Goal Achieved Early!\n", style="green bold")
|
||||
content.append("Completed: ", style="white")
|
||||
content.append(f"{steps_completed} steps\n", style="green")
|
||||
content.append("Skipped: ", style="white")
|
||||
content.append(f"{steps_remaining} remaining steps\n", style="green")
|
||||
|
||||
self.print_panel(content, "🎯 Early Goal Achievement", "green")
|
||||
|
||||
# ----------- AGENT LOGGING EVENTS -----------
|
||||
|
||||
def handle_agent_logs_started(
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -6,9 +6,27 @@ from typing import Any
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.utilities.planning_types import TodoItem
|
||||
from crewai.utilities.types import LLMMessage
|
||||
|
||||
|
||||
class TodoExecutionResult(BaseModel):
|
||||
"""Summary of a single todo execution."""
|
||||
|
||||
step_number: int = Field(description="Step number in the plan")
|
||||
description: str = Field(description="What the todo was supposed to do")
|
||||
tool_used: str | None = Field(
|
||||
default=None, description="Tool that was used for this step"
|
||||
)
|
||||
status: str = Field(description="Final status: completed, failed, pending")
|
||||
result: str | None = Field(
|
||||
default=None, description="Result or error message from execution"
|
||||
)
|
||||
depends_on: list[int] = Field(
|
||||
default_factory=list, description="Step numbers this depended on"
|
||||
)
|
||||
|
||||
|
||||
class LiteAgentOutput(BaseModel):
|
||||
"""Class that represents the result of a LiteAgent execution."""
|
||||
|
||||
@@ -24,12 +42,75 @@ class LiteAgentOutput(BaseModel):
|
||||
)
|
||||
messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
|
||||
|
||||
plan: str | None = Field(
|
||||
default=None, description="The execution plan that was generated, if any"
|
||||
)
|
||||
todos: list[TodoExecutionResult] = Field(
|
||||
default_factory=list,
|
||||
description="List of todos that were executed with their results",
|
||||
)
|
||||
replan_count: int = Field(
|
||||
default=0, description="Number of times the plan was regenerated"
|
||||
)
|
||||
last_replan_reason: str | None = Field(
|
||||
default=None, description="Reason for the last replan, if any"
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_todo_items(cls, todo_items: list[TodoItem]) -> list[TodoExecutionResult]:
|
||||
"""Convert TodoItem objects to TodoExecutionResult summaries.
|
||||
|
||||
Args:
|
||||
todo_items: List of TodoItem objects from execution.
|
||||
|
||||
Returns:
|
||||
List of TodoExecutionResult summaries.
|
||||
"""
|
||||
return [
|
||||
TodoExecutionResult(
|
||||
step_number=item.step_number,
|
||||
description=item.description,
|
||||
tool_used=item.tool_to_use,
|
||||
status=item.status,
|
||||
result=item.result,
|
||||
depends_on=item.depends_on,
|
||||
)
|
||||
for item in todo_items
|
||||
]
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
"""Convert pydantic_output to a dictionary."""
|
||||
if self.pydantic:
|
||||
return self.pydantic.model_dump()
|
||||
return {}
|
||||
|
||||
@property
|
||||
def completed_todos(self) -> list[TodoExecutionResult]:
|
||||
"""Get only the completed todos."""
|
||||
return [t for t in self.todos if t.status == "completed"]
|
||||
|
||||
@property
|
||||
def failed_todos(self) -> list[TodoExecutionResult]:
|
||||
"""Get only the failed todos."""
|
||||
return [t for t in self.todos if t.status == "failed"]
|
||||
|
||||
@property
|
||||
def had_plan(self) -> bool:
|
||||
"""Check if the agent executed with a plan."""
|
||||
return self.plan is not None or len(self.todos) > 0
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""Return the raw output as a string."""
|
||||
return self.raw
|
||||
|
||||
def __repr__(self) -> str:
|
||||
"""Return a detailed representation including todo summary."""
|
||||
parts = [f"LiteAgentOutput(role={self.agent_role!r}"]
|
||||
if self.todos:
|
||||
completed = len(self.completed_todos)
|
||||
total = len(self.todos)
|
||||
parts.append(f", todos={completed}/{total} completed")
|
||||
if self.replan_count > 0:
|
||||
parts.append(f", replans={self.replan_count}")
|
||||
parts.append(")")
|
||||
return "".join(parts)
|
||||
|
||||
@@ -618,6 +618,50 @@ class AnthropicCompletion(BaseLLM):
|
||||
return redacted_block
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _convert_image_blocks(content: Any) -> Any:
|
||||
"""Convert OpenAI-style image_url blocks to Anthropic image blocks.
|
||||
|
||||
Upstream code (e.g. StepExecutor) uses the standard ``image_url``
|
||||
format with a ``data:`` URI. Anthropic rejects that — it requires
|
||||
``{"type": "image", "source": {"type": "base64", ...}}``.
|
||||
|
||||
Non-list content and blocks that are not ``image_url`` are passed
|
||||
through unchanged.
|
||||
"""
|
||||
if not isinstance(content, list):
|
||||
return content
|
||||
|
||||
converted: list[dict[str, Any]] = []
|
||||
for block in content:
|
||||
if not isinstance(block, dict) or block.get("type") != "image_url":
|
||||
converted.append(block)
|
||||
continue
|
||||
|
||||
image_info = block.get("image_url", {})
|
||||
url = image_info.get("url", "") if isinstance(image_info, dict) else ""
|
||||
if url.startswith("data:") and ";base64," in url:
|
||||
# Parse data:<media_type>;base64,<data>
|
||||
header, b64_data = url.split(";base64,", 1)
|
||||
media_type = (
|
||||
header.split("data:", 1)[1] if "data:" in header else "image/png"
|
||||
)
|
||||
converted.append(
|
||||
{
|
||||
"type": "image",
|
||||
"source": {
|
||||
"type": "base64",
|
||||
"media_type": media_type,
|
||||
"data": b64_data,
|
||||
},
|
||||
}
|
||||
)
|
||||
else:
|
||||
# Non-data URI — pass through as-is (Anthropic supports url source)
|
||||
converted.append(block)
|
||||
|
||||
return converted
|
||||
|
||||
def _format_messages_for_anthropic(
|
||||
self, messages: str | list[LLMMessage]
|
||||
) -> tuple[list[LLMMessage], str | None]:
|
||||
@@ -656,10 +700,11 @@ class AnthropicCompletion(BaseLLM):
|
||||
tool_call_id = message.get("tool_call_id", "")
|
||||
if not tool_call_id:
|
||||
raise ValueError("Tool message missing required tool_call_id")
|
||||
tool_content = self._convert_image_blocks(content) if content else ""
|
||||
tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": tool_call_id,
|
||||
"content": content if content else "",
|
||||
"content": tool_content,
|
||||
}
|
||||
pending_tool_results.append(tool_result)
|
||||
elif role == "assistant":
|
||||
@@ -718,7 +763,12 @@ class AnthropicCompletion(BaseLLM):
|
||||
|
||||
role_str = role if role is not None else "user"
|
||||
if isinstance(content, list):
|
||||
formatted_messages.append({"role": role_str, "content": content})
|
||||
formatted_messages.append(
|
||||
{
|
||||
"role": role_str,
|
||||
"content": self._convert_image_blocks(content),
|
||||
}
|
||||
)
|
||||
else:
|
||||
content_str = content if content is not None else ""
|
||||
formatted_messages.append(
|
||||
|
||||
@@ -1847,7 +1847,10 @@ class BedrockCompletion(BaseLLM):
|
||||
converse_messages.append({"role": "user", "content": pending_tool_results})
|
||||
|
||||
# CRITICAL: Handle model-specific conversation requirements
|
||||
# Cohere and some other models require conversation to end with user message
|
||||
# Cohere and some other models require conversation to end with user message.
|
||||
# Anthropic models on Bedrock also reject assistant messages in the final
|
||||
# position when tools are present ("pre-filling the assistant response is
|
||||
# not supported").
|
||||
if converse_messages:
|
||||
last_message = converse_messages[-1]
|
||||
if last_message["role"] == "assistant":
|
||||
@@ -1874,6 +1877,20 @@ class BedrockCompletion(BaseLLM):
|
||||
"content": [{"text": "Continue your response."}],
|
||||
}
|
||||
)
|
||||
# Anthropic (Claude) models reject assistant-last messages when
|
||||
# tools are in the request. Append a user message so the
|
||||
# Converse API accepts the payload.
|
||||
elif "anthropic" in self.model.lower() or "claude" in self.model.lower():
|
||||
converse_messages.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"text": "Please continue and provide your final answer."
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
# Ensure first message is from user (required by Converse API)
|
||||
if not converse_messages:
|
||||
|
||||
@@ -74,9 +74,28 @@
|
||||
"consolidation_user": "New content to consider storing:\n{new_content}\n\nExisting similar memories:\n{records_summary}\n\nReturn the consolidation plan as structured output."
|
||||
},
|
||||
"reasoning": {
|
||||
"initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
|
||||
"refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
|
||||
"create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience, exactly what tools to use and how to use them\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task or if you could do better.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
|
||||
"refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties. As you refine your plan, be specific about which available tools you will use, how you will use them, and why they are the best choices for each step. Clearly outline your tool usage strategy as part of your improved plan.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
|
||||
"initial_plan": "You are {role}. Create a focused execution plan using only the essential steps needed.",
|
||||
"refine_plan": "You are {role}. Refine your plan to address the specific gap while keeping it minimal.",
|
||||
"create_plan_prompt": "You are {role}.\n\nTask: {description}\n\nExpected output: {expected_output}\n\nAvailable tools: {tools}\n\nCreate a focused plan with ONLY the essential steps needed. Most tasks require just 2-5 steps. Do NOT pad with unnecessary steps like \"review\", \"verify\", \"document\", or \"finalize\" unless explicitly required.\n\nFor each step, specify the action and which tool to use (if any).\n\nConclude with:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
|
||||
"refine_plan_prompt": "Your plan:\n{current_plan}\n\nYou indicated you're not ready. Address the specific gap while keeping the plan minimal.\n\nConclude with READY or NOT READY."
|
||||
},
|
||||
"planning": {
|
||||
"system_prompt": "You are a strategic planning assistant. Create concrete, executable plans where every step produces a verifiable result.",
|
||||
"create_plan_prompt": "Create an execution plan for the following task:\n\n## Task\n{description}\n\n## Expected Output\n{expected_output}\n\n## Available Tools\n{tools}\n\n## Planning Principles\nFocus on CONCRETE, EXECUTABLE steps. Each step must clearly state WHAT ACTION to take and HOW to verify it succeeded. The number of steps should match the task complexity. Hard limit: {max_steps} steps.\n\n## Rules:\n- Each step must have a clear DONE criterion\n- Do NOT group unrelated actions: if steps can fail independently, keep them separate\n- NO standalone \"thinking\" or \"planning\" steps — act, don't just observe\n- The last step must produce the required output\n\nAfter your plan, state READY or NOT READY.",
|
||||
"refine_plan_prompt": "Your previous plan:\n{current_plan}\n\nYou indicated you weren't ready. Refine your plan to address the specific gap.\n\nKeep the plan minimal - only add steps that directly address the issue.\n\nConclude with READY or NOT READY as before.",
|
||||
"observation_system_prompt": "You are a Planning Agent observing execution progress. After each step completes, you analyze what happened and decide whether the remaining plan is still valid.\n\nReason step-by-step about:\n1. Did this step produce a concrete, verifiable result? (file created, command succeeded, service running, etc.) — or did it only explore without acting?\n2. What new information was learned from this step's result?\n3. Whether the remaining steps still make sense given this new information\n4. What refinements, if any, are needed for upcoming steps\n5. Whether the overall goal has already been achieved\n\nCritical: mark `step_completed_successfully=false` if:\n- The step result is only exploratory (ls, pwd, cat) without producing the required artifact or action\n- A command returned a non-zero exit code and the error was not recovered\n- The step description required creating/building/starting something and the result shows it was not done\n\nBe conservative about triggering full replans — only do so when the remaining plan is fundamentally wrong, not just suboptimal.\n\nIMPORTANT: Set step_completed_successfully=false if:\n- The step's stated goal was NOT achieved (even if other things were done)\n- The first meaningful action returned an error (file not found, command not found, etc.)\n- The result is exploration/discovery output rather than the concrete action the step required\n- The step ran out of attempts without producing the required output\nSet needs_full_replan=true if the current plan's remaining steps reference paths or state that don't exist yet and need to be created first.",
|
||||
"observation_user_prompt": "## Original task\n{task_description}\n\n## Expected output\n{task_goal}\n{completed_summary}\n\n## Just completed step {step_number}\nDescription: {step_description}\nResult: {step_result}\n{remaining_summary}\n\nAnalyze this step's result and provide your observation.",
|
||||
"step_executor_system_prompt": "You are {role}. {backstory}\n\nYour goal: {goal}\n\nYou are executing ONE specific step in a larger plan. Your ONLY job is to fully complete this step — not to plan ahead.\n\nKey rules:\n- **ACT FIRST.** Execute the primary action of this step immediately. Do NOT read or explore files before attempting the main action unless exploration IS the step's goal.\n- If the step says 'run X', run X NOW. If it says 'write file Y', write Y NOW.\n- If the step requires producing an output file (e.g. /app/move.txt, report.jsonl, summary.csv), you MUST write that file using a tool call — do NOT just state the answer in text.\n- You may use tools MULTIPLE TIMES. After each tool use, check the result. If it failed, try a different approach.\n- Only output your Final Answer AFTER the concrete outcome is verified (file written, build succeeded, command exited 0).\n- If a command is not found or a path does not exist, fix it (different PATH, install missing deps, use absolute paths).\n- Do NOT spend more than 3 tool calls on exploration/analysis before attempting the primary action.{tools_section}",
|
||||
"step_executor_tools_section": "\n\nAvailable tools: {tool_names}\n\nYou may call tools multiple times in sequence. Use this format for EACH tool call:\nThought: <what you observed and what you will try next>\nAction: <tool_name>\nAction Input: <input>\n\nAfter observing each result, decide: is the step complete? If yes:\nThought: The step is done because <evidence>\nFinal Answer: <concise summary of what was accomplished and the key result>",
|
||||
"step_executor_user_prompt": "## Current Step\n{step_description}",
|
||||
"step_executor_suggested_tool": "\nSuggested tool: {tool_to_use}",
|
||||
"step_executor_context_header": "\n## Context from previous steps:",
|
||||
"step_executor_context_entry": "Step {step_number} result: {result}",
|
||||
"step_executor_complete_step": "\n**Execute the primary action of this step NOW.** If the step requires writing a file, write it. If it requires running a command, run it. Verify the outcome with a follow-up tool call, then give your Final Answer. Your Final Answer must confirm what was DONE (file created at path X, command succeeded), not just what should be done.",
|
||||
"todo_system_prompt": "You are {role}. Your goal: {goal}\n\nYou are executing a specific step in a multi-step plan. Focus only on completing the current step. Use the suggested tool if one is provided. Be concise and provide clear results that can be used by subsequent steps.",
|
||||
"synthesis_system_prompt": "You are {role}. You have completed a multi-step task. Synthesize the results from all steps into a single, coherent final response that directly addresses the original task. Do NOT list step numbers or say 'Step 1 result'. Produce a clean, polished answer as if you did it all at once.",
|
||||
"synthesis_user_prompt": "## Original Task\n{task_description}\n\n## Results from each step\n{combined_steps}\n\nSynthesize these results into a single, coherent final answer.",
|
||||
"replan_enhancement_prompt": "\n\nIMPORTANT: Previous execution attempt did not fully succeed. Please create a revised plan that accounts for the following context from the previous attempt:\n\n{previous_context}\n\nConsider:\n1. What steps succeeded and can be built upon\n2. What steps failed and why they might have failed\n3. Alternative approaches that might work better\n4. Whether dependencies need to be restructured",
|
||||
"step_executor_task_context": "## Task Context\nThe following is the full task you are helping complete. Keep this in mind — especially any required output files, exact filenames, and expected formats.\n\n{task_context}\n\n---\n"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -3,6 +3,8 @@ from __future__ import annotations
|
||||
import asyncio
|
||||
from collections.abc import Callable, Sequence
|
||||
import concurrent.futures
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
import inspect
|
||||
import json
|
||||
import re
|
||||
@@ -39,6 +41,7 @@ from crewai.utilities.types import LLMMessage
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agent import Agent
|
||||
from crewai.agents.crew_agent_executor import CrewAgentExecutor
|
||||
from crewai.agents.tools_handler import ToolsHandler
|
||||
from crewai.experimental.agent_executor import AgentExecutor
|
||||
from crewai.lite_agent import LiteAgent
|
||||
from crewai.llm import LLM
|
||||
@@ -335,6 +338,66 @@ def enforce_rpm_limit(
|
||||
request_within_rpm_limit()
|
||||
|
||||
|
||||
def _prepare_llm_call(
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
|
||||
messages: list[LLMMessage],
|
||||
printer: Printer,
|
||||
verbose: bool = True,
|
||||
) -> list[LLMMessage]:
|
||||
"""Shared pre-call logic: run before hooks and resolve messages.
|
||||
|
||||
Args:
|
||||
executor_context: Optional executor context for hook invocation.
|
||||
messages: The messages to send to the LLM.
|
||||
printer: Printer instance for output.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
The resolved messages list (may come from executor_context).
|
||||
|
||||
Raises:
|
||||
ValueError: If a before hook blocks the call.
|
||||
"""
|
||||
if executor_context is not None:
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
|
||||
raise ValueError("LLM call blocked by before_llm_call hook")
|
||||
messages = executor_context.messages
|
||||
return messages
|
||||
|
||||
|
||||
def _validate_and_finalize_llm_response(
|
||||
answer: Any,
|
||||
executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
|
||||
printer: Printer,
|
||||
verbose: bool = True,
|
||||
) -> str | BaseModel | Any:
|
||||
"""Shared post-call logic: validate response and run after hooks.
|
||||
|
||||
Args:
|
||||
answer: The raw LLM response.
|
||||
executor_context: Optional executor context for hook invocation.
|
||||
printer: Printer instance for output.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
The potentially modified response.
|
||||
|
||||
Raises:
|
||||
ValueError: If the response is None or empty.
|
||||
"""
|
||||
if not answer:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
return _setup_after_llm_call_hooks(
|
||||
executor_context, answer, printer, verbose=verbose
|
||||
)
|
||||
|
||||
|
||||
def get_llm_response(
|
||||
llm: LLM | BaseLLM,
|
||||
messages: list[LLMMessage],
|
||||
@@ -371,11 +434,7 @@ def get_llm_response(
|
||||
Exception: If an error occurs.
|
||||
ValueError: If the response is None or empty.
|
||||
"""
|
||||
|
||||
if executor_context is not None:
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
|
||||
raise ValueError("LLM call blocked by before_llm_call hook")
|
||||
messages = executor_context.messages
|
||||
messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
|
||||
|
||||
try:
|
||||
answer = llm.call(
|
||||
@@ -389,16 +448,9 @@ def get_llm_response(
|
||||
)
|
||||
except Exception as e:
|
||||
raise e
|
||||
if not answer:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
return _setup_after_llm_call_hooks(
|
||||
executor_context, answer, printer, verbose=verbose
|
||||
return _validate_and_finalize_llm_response(
|
||||
answer, executor_context, printer, verbose=verbose
|
||||
)
|
||||
|
||||
|
||||
@@ -428,6 +480,7 @@ async def aget_llm_response(
|
||||
from_agent: Optional agent context for the LLM call.
|
||||
response_model: Optional Pydantic model for structured outputs.
|
||||
executor_context: Optional executor context for hook invocation.
|
||||
verbose: Whether to print output.
|
||||
|
||||
Returns:
|
||||
The response from the LLM as a string, Pydantic model (when response_model is provided),
|
||||
@@ -437,10 +490,7 @@ async def aget_llm_response(
|
||||
Exception: If an error occurs.
|
||||
ValueError: If the response is None or empty.
|
||||
"""
|
||||
if executor_context is not None:
|
||||
if not _setup_before_llm_call_hooks(executor_context, printer, verbose=verbose):
|
||||
raise ValueError("LLM call blocked by before_llm_call hook")
|
||||
messages = executor_context.messages
|
||||
messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
|
||||
|
||||
try:
|
||||
answer = await llm.acall(
|
||||
@@ -454,16 +504,9 @@ async def aget_llm_response(
|
||||
)
|
||||
except Exception as e:
|
||||
raise e
|
||||
if not answer:
|
||||
if verbose:
|
||||
printer.print(
|
||||
content="Received None or empty response from LLM call.",
|
||||
color="red",
|
||||
)
|
||||
raise ValueError("Invalid response from LLM call - None or empty.")
|
||||
|
||||
return _setup_after_llm_call_hooks(
|
||||
executor_context, answer, printer, verbose=verbose
|
||||
return _validate_and_finalize_llm_response(
|
||||
answer, executor_context, printer, verbose=verbose
|
||||
)
|
||||
|
||||
|
||||
@@ -1157,6 +1200,386 @@ def extract_tool_call_info(
|
||||
return None
|
||||
|
||||
|
||||
def is_tool_call_list(response: list[Any]) -> bool:
|
||||
"""Check if a response from the LLM is a list of tool calls.
|
||||
|
||||
Supports OpenAI, Anthropic, Bedrock, and Gemini formats.
|
||||
|
||||
Args:
|
||||
response: The response to check.
|
||||
|
||||
Returns:
|
||||
True if the response appears to be a list of tool calls.
|
||||
"""
|
||||
if not response:
|
||||
return False
|
||||
first_item = response[0]
|
||||
# OpenAI-style
|
||||
if hasattr(first_item, "function") or (
|
||||
isinstance(first_item, dict) and "function" in first_item
|
||||
):
|
||||
return True
|
||||
# Anthropic-style (ToolUseBlock)
|
||||
if hasattr(first_item, "type") and getattr(first_item, "type", None) == "tool_use":
|
||||
return True
|
||||
if hasattr(first_item, "name") and hasattr(first_item, "input"):
|
||||
return True
|
||||
# Bedrock-style
|
||||
if isinstance(first_item, dict) and "name" in first_item and "input" in first_item:
|
||||
return True
|
||||
# Gemini-style
|
||||
if hasattr(first_item, "function_call") and first_item.function_call:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def check_native_tool_support(llm: Any, original_tools: list[BaseTool] | None) -> bool:
|
||||
"""Check if the LLM supports native function calling and tools are available.
|
||||
|
||||
Args:
|
||||
llm: The LLM instance.
|
||||
original_tools: Original BaseTool instances.
|
||||
|
||||
Returns:
|
||||
True if native function calling is supported and tools exist.
|
||||
"""
|
||||
return (
|
||||
hasattr(llm, "supports_function_calling")
|
||||
and callable(getattr(llm, "supports_function_calling", None))
|
||||
and llm.supports_function_calling()
|
||||
and bool(original_tools)
|
||||
)
|
||||
|
||||
|
||||
def setup_native_tools(
|
||||
original_tools: list[BaseTool],
|
||||
) -> tuple[
|
||||
list[dict[str, Any]],
|
||||
dict[str, Callable[..., Any]],
|
||||
dict[str, BaseTool | CrewStructuredTool],
|
||||
]:
|
||||
"""Convert tools to OpenAI schema format for native function calling.
|
||||
|
||||
Args:
|
||||
original_tools: Original BaseTool instances.
|
||||
|
||||
Returns:
|
||||
Tuple of (openai_tools_schema, available_functions_dict, tool_name_mapping).
|
||||
"""
|
||||
return convert_tools_to_openai_schema(original_tools)
|
||||
|
||||
|
||||
def build_tool_calls_assistant_message(
|
||||
tool_calls: list[Any],
|
||||
) -> tuple[LLMMessage | None, list[dict[str, Any]]]:
|
||||
"""Build an assistant message containing tool call reports.
|
||||
|
||||
Extracts info from each tool call, builds the standard assistant message
|
||||
format, and preserves raw Gemini parts when applicable.
|
||||
|
||||
Args:
|
||||
tool_calls: Raw tool call objects from the LLM response.
|
||||
|
||||
Returns:
|
||||
Tuple of (assistant_message, tool_calls_to_report).
|
||||
assistant_message is None if no valid tool calls found.
|
||||
"""
|
||||
tool_calls_to_report: list[dict[str, Any]] = []
|
||||
for tool_call in tool_calls:
|
||||
info = extract_tool_call_info(tool_call)
|
||||
if not info:
|
||||
continue
|
||||
call_id, func_name, func_args = info
|
||||
tool_calls_to_report.append(
|
||||
{
|
||||
"id": call_id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": func_name,
|
||||
"arguments": func_args
|
||||
if isinstance(func_args, str)
|
||||
else json.dumps(func_args),
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
if not tool_calls_to_report:
|
||||
return None, []
|
||||
|
||||
assistant_message: LLMMessage = {
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": tool_calls_to_report,
|
||||
}
|
||||
# Preserve raw parts for Gemini compatibility
|
||||
if all(type(tc).__qualname__ == "Part" for tc in tool_calls):
|
||||
assistant_message["raw_tool_call_parts"] = list(tool_calls)
|
||||
|
||||
return assistant_message, tool_calls_to_report
|
||||
|
||||
|
||||
@dataclass
|
||||
class NativeToolCallResult:
|
||||
"""Result from executing a single native tool call."""
|
||||
|
||||
call_id: str
|
||||
func_name: str
|
||||
result: str
|
||||
from_cache: bool = False
|
||||
result_as_answer: bool = False
|
||||
tool_message: LLMMessage = field(default_factory=dict) # type: ignore[assignment]
|
||||
|
||||
|
||||
def execute_single_native_tool_call(
|
||||
tool_call: Any,
|
||||
*,
|
||||
available_functions: dict[str, Callable[..., Any]],
|
||||
original_tools: list[BaseTool],
|
||||
structured_tools: list[CrewStructuredTool] | None,
|
||||
tools_handler: ToolsHandler | None,
|
||||
agent: Agent | None,
|
||||
task: Task | None,
|
||||
crew: Any | None,
|
||||
event_source: Any,
|
||||
printer: Printer | None = None,
|
||||
verbose: bool = False,
|
||||
) -> NativeToolCallResult:
|
||||
"""Execute a single native tool call with full lifecycle management.
|
||||
|
||||
Handles: arg parsing, tool lookup, max-usage check, cache read/write,
|
||||
before/after hooks, event emission, and result_as_answer detection.
|
||||
|
||||
Args:
|
||||
tool_call: Raw tool call object from the LLM.
|
||||
available_functions: Map of sanitized tool name -> callable.
|
||||
original_tools: Original BaseTool list (for cache_function, result_as_answer).
|
||||
structured_tools: Structured tools list (for hook context).
|
||||
tools_handler: Optional handler with cache.
|
||||
agent: The agent instance.
|
||||
task: The current task.
|
||||
crew: The crew instance.
|
||||
event_source: The object to use as event emitter source.
|
||||
printer: Optional printer for verbose logging.
|
||||
verbose: Whether to print verbose output.
|
||||
|
||||
Returns:
|
||||
NativeToolCallResult with all execution details.
|
||||
"""
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.tool_usage_events import (
|
||||
ToolUsageErrorEvent,
|
||||
ToolUsageFinishedEvent,
|
||||
ToolUsageStartedEvent,
|
||||
)
|
||||
from crewai.hooks.tool_hooks import (
|
||||
ToolCallHookContext,
|
||||
get_after_tool_call_hooks,
|
||||
get_before_tool_call_hooks,
|
||||
)
|
||||
|
||||
info = extract_tool_call_info(tool_call)
|
||||
if not info:
|
||||
return NativeToolCallResult(
|
||||
call_id="", func_name="", result="Unrecognized tool call format"
|
||||
)
|
||||
|
||||
call_id, func_name, func_args = info
|
||||
|
||||
# Parse arguments
|
||||
if isinstance(func_args, str):
|
||||
try:
|
||||
args_dict = json.loads(func_args)
|
||||
except json.JSONDecodeError:
|
||||
args_dict = {}
|
||||
else:
|
||||
args_dict = func_args
|
||||
|
||||
agent_key = getattr(agent, "key", "unknown") if agent else "unknown"
|
||||
|
||||
# Find original tool for cache_function and result_as_answer
|
||||
original_tool: BaseTool | None = None
|
||||
for tool in original_tools:
|
||||
if sanitize_tool_name(tool.name) == func_name:
|
||||
original_tool = tool
|
||||
break
|
||||
|
||||
# Check max usage count
|
||||
max_usage_reached = False
|
||||
if (
|
||||
original_tool
|
||||
and original_tool.max_usage_count is not None
|
||||
and original_tool.current_usage_count >= original_tool.max_usage_count
|
||||
):
|
||||
max_usage_reached = True
|
||||
|
||||
# Check cache
|
||||
from_cache = False
|
||||
input_str = json.dumps(args_dict) if args_dict else ""
|
||||
result = "Tool not found"
|
||||
|
||||
if tools_handler and tools_handler.cache:
|
||||
cached_result = tools_handler.cache.read(tool=func_name, input=input_str)
|
||||
if cached_result is not None:
|
||||
result = (
|
||||
str(cached_result)
|
||||
if not isinstance(cached_result, str)
|
||||
else cached_result
|
||||
)
|
||||
from_cache = True
|
||||
|
||||
# Emit tool started event
|
||||
started_at = datetime.now()
|
||||
crewai_event_bus.emit(
|
||||
event_source,
|
||||
event=ToolUsageStartedEvent(
|
||||
tool_name=func_name,
|
||||
tool_args=args_dict,
|
||||
from_agent=agent,
|
||||
from_task=task,
|
||||
agent_key=agent_key,
|
||||
),
|
||||
)
|
||||
|
||||
track_delegation_if_needed(func_name, args_dict, task)
|
||||
|
||||
# Find structured tool for hooks
|
||||
structured_tool: CrewStructuredTool | None = None
|
||||
for structured in structured_tools or []:
|
||||
if sanitize_tool_name(structured.name) == func_name:
|
||||
structured_tool = structured
|
||||
break
|
||||
|
||||
# Before hooks
|
||||
hook_blocked = False
|
||||
before_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=agent,
|
||||
task=task,
|
||||
crew=crew,
|
||||
)
|
||||
try:
|
||||
for hook in get_before_tool_call_hooks():
|
||||
if hook(before_hook_context) is False:
|
||||
hook_blocked = True
|
||||
break
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
error_event_emitted = False
|
||||
if hook_blocked:
|
||||
result = f"Tool execution blocked by hook. Tool: {func_name}"
|
||||
elif not from_cache and not max_usage_reached:
|
||||
if func_name in available_functions:
|
||||
try:
|
||||
tool_func = available_functions[func_name]
|
||||
raw_result = tool_func(**args_dict)
|
||||
|
||||
# Cache result
|
||||
if tools_handler and tools_handler.cache:
|
||||
should_cache = True
|
||||
if original_tool:
|
||||
should_cache = original_tool.cache_function(
|
||||
args_dict, raw_result
|
||||
)
|
||||
if should_cache:
|
||||
tools_handler.cache.add(
|
||||
tool=func_name, input=input_str, output=raw_result
|
||||
)
|
||||
|
||||
result = (
|
||||
str(raw_result) if not isinstance(raw_result, str) else raw_result
|
||||
)
|
||||
except Exception as e:
|
||||
result = f"Error executing tool: {e}"
|
||||
if task:
|
||||
task.increment_tools_errors()
|
||||
crewai_event_bus.emit(
|
||||
event_source,
|
||||
event=ToolUsageErrorEvent(
|
||||
tool_name=func_name,
|
||||
tool_args=args_dict,
|
||||
from_agent=agent,
|
||||
from_task=task,
|
||||
agent_key=agent_key,
|
||||
error=e,
|
||||
),
|
||||
)
|
||||
error_event_emitted = True
|
||||
elif max_usage_reached and original_tool:
|
||||
result = (
|
||||
f"Tool '{func_name}' has reached its usage limit of "
|
||||
f"{original_tool.max_usage_count} times and cannot be used anymore."
|
||||
)
|
||||
|
||||
# After hooks
|
||||
after_hook_context = ToolCallHookContext(
|
||||
tool_name=func_name,
|
||||
tool_input=args_dict,
|
||||
tool=structured_tool, # type: ignore[arg-type]
|
||||
agent=agent,
|
||||
task=task,
|
||||
crew=crew,
|
||||
tool_result=result,
|
||||
)
|
||||
try:
|
||||
for after_hook in get_after_tool_call_hooks():
|
||||
hook_result = after_hook(after_hook_context)
|
||||
if hook_result is not None:
|
||||
result = hook_result
|
||||
after_hook_context.tool_result = result
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
# Emit tool finished event (only if error event wasn't already emitted)
|
||||
if not error_event_emitted:
|
||||
crewai_event_bus.emit(
|
||||
event_source,
|
||||
event=ToolUsageFinishedEvent(
|
||||
output=result,
|
||||
tool_name=func_name,
|
||||
tool_args=args_dict,
|
||||
from_agent=agent,
|
||||
from_task=task,
|
||||
agent_key=agent_key,
|
||||
started_at=started_at,
|
||||
finished_at=datetime.now(),
|
||||
),
|
||||
)
|
||||
|
||||
# Build tool result message
|
||||
tool_message: LLMMessage = {
|
||||
"role": "tool",
|
||||
"tool_call_id": call_id,
|
||||
"name": func_name,
|
||||
"content": result,
|
||||
}
|
||||
|
||||
if verbose and printer:
|
||||
cache_info = " (from cache)" if from_cache else ""
|
||||
printer.print(
|
||||
content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
|
||||
color="green",
|
||||
)
|
||||
|
||||
# Check result_as_answer
|
||||
is_result_as_answer = bool(
|
||||
original_tool
|
||||
and hasattr(original_tool, "result_as_answer")
|
||||
and original_tool.result_as_answer
|
||||
)
|
||||
|
||||
return NativeToolCallResult(
|
||||
call_id=call_id,
|
||||
func_name=func_name,
|
||||
result=result,
|
||||
from_cache=from_cache,
|
||||
result_as_answer=is_result_as_answer,
|
||||
tool_message=tool_message,
|
||||
)
|
||||
|
||||
|
||||
def parse_tool_call_args(
|
||||
func_args: dict[str, Any] | str,
|
||||
func_name: str,
|
||||
|
||||
@@ -100,7 +100,13 @@ class I18N(BaseModel):
|
||||
def retrieve(
|
||||
self,
|
||||
kind: Literal[
|
||||
"slices", "errors", "tools", "reasoning", "hierarchical_manager_agent", "memory"
|
||||
"slices",
|
||||
"errors",
|
||||
"tools",
|
||||
"reasoning",
|
||||
"planning",
|
||||
"hierarchical_manager_agent",
|
||||
"memory",
|
||||
],
|
||||
key: str,
|
||||
) -> str:
|
||||
|
||||
278
lib/crewai/src/crewai/utilities/planning_types.py
Normal file
278
lib/crewai/src/crewai/utilities/planning_types.py
Normal file
@@ -0,0 +1,278 @@
|
||||
"""Types for agent planning and todo tracking."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
from uuid import uuid4
|
||||
|
||||
from pydantic import BaseModel, Field, field_validator
|
||||
|
||||
|
||||
# Todo status type
|
||||
TodoStatus = Literal["pending", "running", "completed", "failed"]
|
||||
|
||||
|
||||
class PlanStep(BaseModel):
|
||||
"""A single step in the reasoning plan."""
|
||||
|
||||
step_number: int = Field(description="Step number (1-based)")
|
||||
description: str = Field(description="What to do in this step")
|
||||
tool_to_use: str | None = Field(
|
||||
default=None, description="Tool to use for this step, if any"
|
||||
)
|
||||
depends_on: list[int] = Field(
|
||||
default_factory=list, description="Step numbers this step depends on"
|
||||
)
|
||||
|
||||
|
||||
class TodoItem(BaseModel):
|
||||
"""A single todo item representing a step in the execution plan."""
|
||||
|
||||
id: str = Field(default_factory=lambda: str(uuid4()))
|
||||
step_number: int = Field(description="Order of this step in the plan (1-based)")
|
||||
description: str = Field(description="What needs to be done")
|
||||
tool_to_use: str | None = Field(
|
||||
default=None, description="Tool to use for this step, if any"
|
||||
)
|
||||
status: TodoStatus = Field(default="pending", description="Current status")
|
||||
depends_on: list[int] = Field(
|
||||
default_factory=list, description="Step numbers this depends on"
|
||||
)
|
||||
result: str | None = Field(
|
||||
default=None, description="Result after completion, if any"
|
||||
)
|
||||
|
||||
|
||||
class TodoList(BaseModel):
|
||||
"""Collection of todos for tracking plan execution."""
|
||||
|
||||
items: list[TodoItem] = Field(default_factory=list)
|
||||
|
||||
@property
|
||||
def current_todo(self) -> TodoItem | None:
|
||||
"""Get the currently running todo item."""
|
||||
for item in self.items:
|
||||
if item.status == "running":
|
||||
return item
|
||||
return None
|
||||
|
||||
@property
|
||||
def next_pending(self) -> TodoItem | None:
|
||||
"""Get the next pending todo item."""
|
||||
for item in self.items:
|
||||
if item.status == "pending":
|
||||
return item
|
||||
return None
|
||||
|
||||
@property
|
||||
def is_complete(self) -> bool:
|
||||
"""Check if all todos are in a terminal state (completed or failed)."""
|
||||
return len(self.items) > 0 and all(
|
||||
item.status in ("completed", "failed") for item in self.items
|
||||
)
|
||||
|
||||
@property
|
||||
def pending_count(self) -> int:
|
||||
"""Count of pending todos."""
|
||||
return sum(1 for item in self.items if item.status == "pending")
|
||||
|
||||
@property
|
||||
def completed_count(self) -> int:
|
||||
"""Count of completed todos."""
|
||||
return sum(1 for item in self.items if item.status == "completed")
|
||||
|
||||
def get_by_step_number(self, step_number: int) -> TodoItem | None:
|
||||
"""Get a todo by its step number."""
|
||||
for item in self.items:
|
||||
if item.step_number == step_number:
|
||||
return item
|
||||
return None
|
||||
|
||||
def mark_running(self, step_number: int) -> None:
|
||||
"""Mark a todo as running by step number."""
|
||||
item = self.get_by_step_number(step_number)
|
||||
if item:
|
||||
item.status = "running"
|
||||
|
||||
def mark_completed(self, step_number: int, result: str | None = None) -> None:
|
||||
"""Mark a todo as completed by step number."""
|
||||
item = self.get_by_step_number(step_number)
|
||||
if item:
|
||||
item.status = "completed"
|
||||
if result is not None:
|
||||
item.result = result
|
||||
|
||||
def mark_failed(self, step_number: int, result: str | None = None) -> None:
|
||||
"""Mark a todo as failed by step number."""
|
||||
item = self.get_by_step_number(step_number)
|
||||
if item:
|
||||
item.status = "failed"
|
||||
if result is not None:
|
||||
item.result = result
|
||||
|
||||
def _dependencies_satisfied(self, item: TodoItem) -> bool:
|
||||
"""Check if all dependencies for a todo item are in a terminal state.
|
||||
|
||||
A dependency is satisfied when it has finished executing — either
|
||||
successfully (completed) or not (failed). This prevents downstream
|
||||
todos from being permanently blocked when a dependency fails.
|
||||
The executor/observer is responsible for deciding whether to skip,
|
||||
replan, or continue when a dependency has failed.
|
||||
|
||||
Args:
|
||||
item: The todo item to check dependencies for.
|
||||
|
||||
Returns:
|
||||
True if all dependencies are in a terminal state, False otherwise.
|
||||
"""
|
||||
for dep_num in item.depends_on:
|
||||
dep = self.get_by_step_number(dep_num)
|
||||
if dep is None or dep.status not in ("completed", "failed"):
|
||||
return False
|
||||
return True
|
||||
|
||||
def get_ready_todos(self) -> list[TodoItem]:
|
||||
"""Get all todos that are ready to execute (pending with satisfied dependencies).
|
||||
|
||||
Returns:
|
||||
List of TodoItem objects that can be executed now.
|
||||
"""
|
||||
ready: list[TodoItem] = []
|
||||
for item in self.items:
|
||||
if item.status != "pending":
|
||||
continue
|
||||
if self._dependencies_satisfied(item):
|
||||
ready.append(item)
|
||||
return ready
|
||||
|
||||
@property
|
||||
def can_parallelize(self) -> bool:
|
||||
"""Check if multiple todos can run in parallel.
|
||||
|
||||
Returns:
|
||||
True if more than one todo is ready to execute.
|
||||
"""
|
||||
return len(self.get_ready_todos()) > 1
|
||||
|
||||
@property
|
||||
def running_count(self) -> int:
|
||||
"""Count of currently running todos."""
|
||||
return sum(1 for item in self.items if item.status == "running")
|
||||
|
||||
def get_completed_todos(self) -> list[TodoItem]:
|
||||
"""Get all completed todos.
|
||||
|
||||
Returns:
|
||||
List of completed TodoItem objects.
|
||||
"""
|
||||
return [item for item in self.items if item.status == "completed"]
|
||||
|
||||
def get_failed_todos(self) -> list[TodoItem]:
|
||||
"""Get all failed todos.
|
||||
|
||||
Returns:
|
||||
List of failed TodoItem objects.
|
||||
"""
|
||||
return [item for item in self.items if item.status == "failed"]
|
||||
|
||||
def get_pending_todos(self) -> list[TodoItem]:
|
||||
"""Get all pending todos.
|
||||
|
||||
Returns:
|
||||
List of pending TodoItem objects.
|
||||
"""
|
||||
return [item for item in self.items if item.status == "pending"]
|
||||
|
||||
def replace_pending_todos(self, new_items: list[TodoItem]) -> None:
|
||||
"""Replace all pending todos with new items.
|
||||
|
||||
Preserves completed, failed, and running todos, replaces only pending ones.
|
||||
Used during replanning to swap in a new plan for remaining work.
|
||||
|
||||
Args:
|
||||
new_items: The new todo items to replace pending ones.
|
||||
"""
|
||||
non_pending = [item for item in self.items if item.status != "pending"]
|
||||
self.items = non_pending + new_items
|
||||
|
||||
|
||||
class StepRefinement(BaseModel):
|
||||
"""A structured in-place update for a single pending step.
|
||||
|
||||
Returned as part of StepObservation when the Planner learns new
|
||||
information that makes a pending step description more specific.
|
||||
Applied directly — no second LLM call required.
|
||||
"""
|
||||
|
||||
step_number: int = Field(description="The step number to update (1-based)")
|
||||
new_description: str = Field(
|
||||
description="The updated, more specific description for this step"
|
||||
)
|
||||
|
||||
|
||||
class StepObservation(BaseModel):
|
||||
"""Planner's observation after a step execution completes.
|
||||
|
||||
Returned by the PlannerObserver after EVERY step — not just failures.
|
||||
The Planner uses this to decide whether to continue, refine, or replan.
|
||||
|
||||
Based on PLAN-AND-ACT (Section 3.3): the Planner observes what the Executor
|
||||
did and incorporates new information into the remaining plan.
|
||||
|
||||
Attributes:
|
||||
step_completed_successfully: Whether the step achieved its objective.
|
||||
key_information_learned: New information revealed by this step
|
||||
(e.g., "Found 3 products: A, B, C"). Used to refine upcoming steps.
|
||||
remaining_plan_still_valid: Whether pending todos still make sense
|
||||
given the new information. True does NOT mean no refinement needed.
|
||||
suggested_refinements: Structured in-place updates to pending step
|
||||
descriptions. Each entry targets a specific step by number. These
|
||||
are applied directly without a second LLM call.
|
||||
Example: [{"step_number": 3, "new_description": "Select product B (highest rated)"}]
|
||||
needs_full_replan: The remaining plan is fundamentally wrong and must
|
||||
be regenerated from scratch. Mutually exclusive with
|
||||
remaining_plan_still_valid (if this is True, that should be False).
|
||||
replan_reason: Explanation of why a full replan is needed (None if not).
|
||||
goal_already_achieved: The overall task goal has been satisfied early.
|
||||
No more steps needed — skip remaining todos and finalize.
|
||||
"""
|
||||
|
||||
step_completed_successfully: bool = Field(
|
||||
description="Whether the step achieved what it was asked to do"
|
||||
)
|
||||
key_information_learned: str = Field(
|
||||
default="",
|
||||
description="What new information this step revealed",
|
||||
)
|
||||
remaining_plan_still_valid: bool = Field(
|
||||
default=True,
|
||||
description="Whether the remaining pending todos still make sense given new information",
|
||||
)
|
||||
suggested_refinements: list[StepRefinement] | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Structured updates to pending step descriptions based on new information. "
|
||||
"Each entry specifies a step_number and new_description. "
|
||||
"Applied directly — no separate replan needed."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("suggested_refinements", mode="before")
|
||||
@classmethod
|
||||
def coerce_single_refinement_to_list(cls, v):
|
||||
"""Coerce a single dict refinement into a list to handle LLM returning a single object."""
|
||||
if isinstance(v, dict):
|
||||
return [v]
|
||||
return v
|
||||
needs_full_replan: bool = Field(
|
||||
default=False,
|
||||
description="The remaining plan is fundamentally wrong and must be regenerated",
|
||||
)
|
||||
replan_reason: str | None = Field(
|
||||
default=None,
|
||||
description="Explanation of why a full replan is needed",
|
||||
)
|
||||
goal_already_achieved: bool = Field(
|
||||
default=False,
|
||||
description="The overall task goal has been satisfied early; no more steps needed",
|
||||
)
|
||||
@@ -1,10 +1,13 @@
|
||||
"""Handles planning/reasoning for agents before task execution."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Final, Literal, cast
|
||||
from typing import TYPE_CHECKING, Any, Final, Literal, cast
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from crewai.agent import Agent
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.reasoning_events import (
|
||||
AgentReasoningCompletedEvent,
|
||||
@@ -12,14 +15,24 @@ from crewai.events.types.reasoning_events import (
|
||||
AgentReasoningStartedEvent,
|
||||
)
|
||||
from crewai.llm import LLM
|
||||
from crewai.task import Task
|
||||
from crewai.utilities.llm_utils import create_llm
|
||||
from crewai.utilities.planning_types import PlanStep
|
||||
from crewai.utilities.string_utils import sanitize_tool_name
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.agent import Agent
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
from crewai.task import Task
|
||||
|
||||
|
||||
class ReasoningPlan(BaseModel):
|
||||
"""Model representing a reasoning plan for a task."""
|
||||
|
||||
plan: str = Field(description="The detailed reasoning plan for the task.")
|
||||
steps: list[PlanStep] = Field(
|
||||
default_factory=list, description="Structured steps to execute"
|
||||
)
|
||||
ready: bool = Field(description="Whether the agent is ready to execute the task.")
|
||||
|
||||
|
||||
@@ -29,24 +42,63 @@ class AgentReasoningOutput(BaseModel):
|
||||
plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
|
||||
|
||||
|
||||
# Aliases for backward compatibility
|
||||
PlanningPlan = ReasoningPlan
|
||||
AgentPlanningOutput = AgentReasoningOutput
|
||||
|
||||
|
||||
FUNCTION_SCHEMA: Final[dict[str, Any]] = {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "create_reasoning_plan",
|
||||
"description": "Create or refine a reasoning plan for a task",
|
||||
"description": "Create or refine a reasoning plan for a task with structured steps",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"plan": {
|
||||
"type": "string",
|
||||
"description": "The detailed reasoning plan for the task.",
|
||||
"description": "A brief summary of the overall plan.",
|
||||
},
|
||||
"steps": {
|
||||
"type": "array",
|
||||
"description": "List of discrete steps to execute the plan",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"step_number": {
|
||||
"type": "integer",
|
||||
"description": "Step number (1-based)",
|
||||
},
|
||||
"description": {
|
||||
"type": "string",
|
||||
"description": "What to do in this step",
|
||||
},
|
||||
"tool_to_use": {
|
||||
"type": ["string", "null"],
|
||||
"description": "Tool to use for this step, or null if no tool needed",
|
||||
},
|
||||
"depends_on": {
|
||||
"type": "array",
|
||||
"items": {"type": "integer"},
|
||||
"description": "Step numbers this step depends on (empty array if none)",
|
||||
},
|
||||
},
|
||||
"required": [
|
||||
"step_number",
|
||||
"description",
|
||||
"tool_to_use",
|
||||
"depends_on",
|
||||
],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
},
|
||||
"ready": {
|
||||
"type": "boolean",
|
||||
"description": "Whether the agent is ready to execute the task.",
|
||||
},
|
||||
},
|
||||
"required": ["plan", "ready"],
|
||||
"required": ["plan", "steps", "ready"],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
},
|
||||
}
|
||||
@@ -54,41 +106,101 @@ FUNCTION_SCHEMA: Final[dict[str, Any]] = {
|
||||
|
||||
class AgentReasoning:
|
||||
"""
|
||||
Handles the agent reasoning process, enabling an agent to reflect and create a plan
|
||||
before executing a task.
|
||||
Handles the agent planning/reasoning process, enabling an agent to reflect
|
||||
and create a plan before executing a task.
|
||||
|
||||
Attributes:
|
||||
task: The task for which the agent is reasoning.
|
||||
agent: The agent performing the reasoning.
|
||||
llm: The language model used for reasoning.
|
||||
task: The task for which the agent is planning (optional).
|
||||
agent: The agent performing the planning.
|
||||
config: The planning configuration.
|
||||
llm: The language model used for planning.
|
||||
logger: Logger for logging events and errors.
|
||||
description: Task description or input text for planning.
|
||||
expected_output: Expected output description.
|
||||
"""
|
||||
|
||||
def __init__(self, task: Task, agent: Agent) -> None:
|
||||
"""Initialize the AgentReasoning with a task and an agent.
|
||||
def __init__(
|
||||
self,
|
||||
agent: Agent,
|
||||
task: Task | None = None,
|
||||
*,
|
||||
description: str | None = None,
|
||||
expected_output: str | None = None,
|
||||
) -> None:
|
||||
"""Initialize the AgentReasoning with an agent and optional task.
|
||||
|
||||
Args:
|
||||
task: The task for which the agent is reasoning.
|
||||
agent: The agent performing the reasoning.
|
||||
agent: The agent performing the planning.
|
||||
task: The task for which the agent is planning (optional).
|
||||
description: Task description or input text (used if task is None).
|
||||
expected_output: Expected output (used if task is None).
|
||||
"""
|
||||
self.task = task
|
||||
self.agent = agent
|
||||
self.llm = cast(LLM, agent.llm)
|
||||
self.task = task
|
||||
# Use task attributes if available, otherwise use provided values
|
||||
self._description = description or (
|
||||
task.description if task else "Complete the requested task"
|
||||
)
|
||||
self._expected_output = expected_output or (
|
||||
task.expected_output if task else "Complete the task successfully"
|
||||
)
|
||||
self.config = self._get_planning_config()
|
||||
self.llm = self._resolve_llm()
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
def handle_agent_reasoning(self) -> AgentReasoningOutput:
|
||||
"""Public method for the reasoning process that creates and refines a plan for the task until the agent is ready to execute it.
|
||||
@property
|
||||
def description(self) -> str:
|
||||
"""Get the task/input description."""
|
||||
return self._description
|
||||
|
||||
@property
|
||||
def expected_output(self) -> str:
|
||||
"""Get the expected output."""
|
||||
return self._expected_output
|
||||
|
||||
def _get_planning_config(self) -> PlanningConfig:
|
||||
"""Get the planning configuration from the agent.
|
||||
|
||||
Returns:
|
||||
AgentReasoningOutput: The output of the agent reasoning process.
|
||||
The planning configuration, using defaults if not set.
|
||||
"""
|
||||
# Emit a reasoning started event (attempt 1)
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
|
||||
if self.agent.planning_config is not None:
|
||||
return self.agent.planning_config
|
||||
# Fallback for backward compatibility
|
||||
return PlanningConfig(
|
||||
max_attempts=getattr(self.agent, "max_reasoning_attempts", None),
|
||||
)
|
||||
|
||||
def _resolve_llm(self) -> LLM:
|
||||
"""Resolve which LLM to use for planning.
|
||||
|
||||
Returns:
|
||||
The LLM to use - either from config or the agent's LLM.
|
||||
"""
|
||||
if self.config.llm is not None:
|
||||
if isinstance(self.config.llm, LLM):
|
||||
return self.config.llm
|
||||
return create_llm(self.config.llm)
|
||||
return cast(LLM, self.agent.llm)
|
||||
|
||||
def handle_agent_reasoning(self) -> AgentReasoningOutput:
|
||||
"""Public method for the planning process that creates and refines a plan
|
||||
for the task until the agent is ready to execute it.
|
||||
|
||||
Returns:
|
||||
AgentReasoningOutput: The output of the agent planning process.
|
||||
"""
|
||||
task_id = str(self.task.id) if self.task else "kickoff"
|
||||
|
||||
# Emit a planning started event (attempt 1)
|
||||
try:
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
AgentReasoningStartedEvent(
|
||||
agent_role=self.agent.role,
|
||||
task_id=str(self.task.id),
|
||||
task_id=task_id,
|
||||
attempt=1,
|
||||
from_task=self.task,
|
||||
),
|
||||
@@ -98,13 +210,13 @@ class AgentReasoning:
|
||||
pass
|
||||
|
||||
try:
|
||||
output = self.__handle_agent_reasoning()
|
||||
output = self._execute_planning()
|
||||
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
AgentReasoningCompletedEvent(
|
||||
agent_role=self.agent.role,
|
||||
task_id=str(self.task.id),
|
||||
task_id=task_id,
|
||||
plan=output.plan.plan,
|
||||
ready=output.plan.ready,
|
||||
attempt=1,
|
||||
@@ -115,71 +227,76 @@ class AgentReasoning:
|
||||
|
||||
return output
|
||||
except Exception as e:
|
||||
# Emit reasoning failed event
|
||||
# Emit planning failed event
|
||||
try:
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
AgentReasoningFailedEvent(
|
||||
agent_role=self.agent.role,
|
||||
task_id=str(self.task.id),
|
||||
task_id=task_id,
|
||||
error=str(e),
|
||||
attempt=1,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
),
|
||||
)
|
||||
except Exception as e:
|
||||
logging.error(f"Error emitting reasoning failed event: {e}")
|
||||
except Exception as event_error:
|
||||
logging.error(f"Error emitting planning failed event: {event_error}")
|
||||
|
||||
raise
|
||||
|
||||
def __handle_agent_reasoning(self) -> AgentReasoningOutput:
|
||||
"""Private method that handles the agent reasoning process.
|
||||
def _execute_planning(self) -> AgentReasoningOutput:
|
||||
"""Execute the planning process.
|
||||
|
||||
Returns:
|
||||
The output of the agent reasoning process.
|
||||
The output of the agent planning process.
|
||||
"""
|
||||
plan, ready = self.__create_initial_plan()
|
||||
plan, steps, ready = self._create_initial_plan()
|
||||
plan, steps, ready = self._refine_plan_if_needed(plan, steps, ready)
|
||||
|
||||
plan, ready = self.__refine_plan_if_needed(plan, ready)
|
||||
|
||||
reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
|
||||
reasoning_plan = ReasoningPlan(plan=plan, steps=steps, ready=ready)
|
||||
return AgentReasoningOutput(plan=reasoning_plan)
|
||||
|
||||
def __create_initial_plan(self) -> tuple[str, bool]:
|
||||
"""Creates the initial reasoning plan for the task.
|
||||
def _create_initial_plan(self) -> tuple[str, list[PlanStep], bool]:
|
||||
"""Creates the initial plan for the task.
|
||||
|
||||
Returns:
|
||||
The initial plan and whether the agent is ready to execute the task.
|
||||
A tuple of the plan summary, list of steps, and whether the agent is ready.
|
||||
"""
|
||||
reasoning_prompt = self.__create_reasoning_prompt()
|
||||
planning_prompt = self._create_planning_prompt()
|
||||
|
||||
if self.llm.supports_function_calling():
|
||||
plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
|
||||
return plan, ready
|
||||
response = _call_llm_with_reasoning_prompt(
|
||||
llm=self.llm,
|
||||
prompt=reasoning_prompt,
|
||||
task=self.task,
|
||||
reasoning_agent=self.agent,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
plan_type="initial_plan",
|
||||
plan, steps, ready = self._call_with_function(
|
||||
planning_prompt, "create_plan"
|
||||
)
|
||||
return plan, steps, ready
|
||||
|
||||
response = self._call_llm_with_prompt(
|
||||
prompt=planning_prompt,
|
||||
plan_type="create_plan",
|
||||
)
|
||||
|
||||
return self.__parse_reasoning_response(str(response))
|
||||
plan, ready = self._parse_planning_response(str(response))
|
||||
return plan, [], ready # No structured steps from text parsing
|
||||
|
||||
def __refine_plan_if_needed(self, plan: str, ready: bool) -> tuple[str, bool]:
|
||||
"""Refines the reasoning plan if the agent is not ready to execute the task.
|
||||
def _refine_plan_if_needed(
|
||||
self, plan: str, steps: list[PlanStep], ready: bool
|
||||
) -> tuple[str, list[PlanStep], bool]:
|
||||
"""Refines the plan if the agent is not ready to execute the task.
|
||||
|
||||
Args:
|
||||
plan: The current reasoning plan.
|
||||
plan: The current plan.
|
||||
steps: The current list of steps.
|
||||
ready: Whether the agent is ready to execute the task.
|
||||
|
||||
Returns:
|
||||
The refined plan and whether the agent is ready to execute the task.
|
||||
The refined plan, steps, and whether the agent is ready to execute.
|
||||
"""
|
||||
|
||||
attempt = 1
|
||||
max_attempts = self.agent.max_reasoning_attempts
|
||||
max_attempts = self.config.max_attempts
|
||||
task_id = str(self.task.id) if self.task else "kickoff"
|
||||
current_attempt = attempt + 1
|
||||
|
||||
while not ready and (max_attempts is None or attempt < max_attempts):
|
||||
# Emit event for each refinement attempt
|
||||
@@ -188,62 +305,81 @@ class AgentReasoning:
|
||||
self.agent,
|
||||
AgentReasoningStartedEvent(
|
||||
agent_role=self.agent.role,
|
||||
task_id=str(self.task.id),
|
||||
attempt=attempt + 1,
|
||||
task_id=task_id,
|
||||
attempt=current_attempt,
|
||||
from_task=self.task,
|
||||
),
|
||||
)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
refine_prompt = self.__create_refine_prompt(plan)
|
||||
refine_prompt = self._create_refine_prompt(plan)
|
||||
|
||||
if self.llm.supports_function_calling():
|
||||
plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
|
||||
plan, steps, ready = self._call_with_function(
|
||||
refine_prompt, "refine_plan"
|
||||
)
|
||||
else:
|
||||
response = _call_llm_with_reasoning_prompt(
|
||||
llm=self.llm,
|
||||
response = self._call_llm_with_prompt(
|
||||
prompt=refine_prompt,
|
||||
task=self.task,
|
||||
reasoning_agent=self.agent,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
plan_type="refine_plan",
|
||||
)
|
||||
plan, ready = self.__parse_reasoning_response(str(response))
|
||||
plan, ready = self._parse_planning_response(str(response))
|
||||
steps = [] # No structured steps from text parsing
|
||||
|
||||
# Emit completed event for this refinement attempt
|
||||
try:
|
||||
crewai_event_bus.emit(
|
||||
self.agent,
|
||||
AgentReasoningCompletedEvent(
|
||||
agent_role=self.agent.role,
|
||||
task_id=task_id,
|
||||
plan=plan,
|
||||
ready=ready,
|
||||
attempt=current_attempt,
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
),
|
||||
)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
attempt += 1
|
||||
|
||||
if max_attempts is not None and attempt >= max_attempts:
|
||||
self.logger.warning(
|
||||
f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
|
||||
f"Agent planning reached maximum attempts ({max_attempts}) "
|
||||
"without being ready. Proceeding with current plan."
|
||||
)
|
||||
break
|
||||
|
||||
return plan, ready
|
||||
return plan, steps, ready
|
||||
|
||||
def __call_with_function(self, prompt: str, prompt_type: str) -> tuple[str, bool]:
|
||||
"""Calls the LLM with function calling to get a reasoning plan.
|
||||
def _call_with_function(
|
||||
self, prompt: str, plan_type: Literal["create_plan", "refine_plan"]
|
||||
) -> tuple[str, list[PlanStep], bool]:
|
||||
"""Calls the LLM with function calling to get a plan.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to send to the LLM.
|
||||
prompt_type: The type of prompt (initial_plan or refine_plan).
|
||||
plan_type: The type of plan being created.
|
||||
|
||||
Returns:
|
||||
A tuple containing the plan and whether the agent is ready.
|
||||
A tuple containing the plan summary, list of steps, and whether the agent is ready.
|
||||
"""
|
||||
self.logger.debug(f"Using function calling for {prompt_type} reasoning")
|
||||
self.logger.debug(f"Using function calling for {plan_type} planning")
|
||||
|
||||
try:
|
||||
system_prompt = self.agent.i18n.retrieve("reasoning", prompt_type).format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
)
|
||||
system_prompt = self._get_system_prompt()
|
||||
|
||||
# Prepare a simple callable that just returns the tool arguments as JSON
|
||||
def _create_reasoning_plan(plan: str, ready: bool = True) -> str:
|
||||
"""Return the reasoning plan result in JSON string form."""
|
||||
return json.dumps({"plan": plan, "ready": ready})
|
||||
def _create_reasoning_plan(
|
||||
plan: str,
|
||||
steps: list[dict[str, Any]] | None = None,
|
||||
ready: bool = True,
|
||||
) -> str:
|
||||
"""Return the planning result in JSON string form."""
|
||||
return json.dumps({"plan": plan, "steps": steps or [], "ready": ready})
|
||||
|
||||
response = self.llm.call(
|
||||
[
|
||||
@@ -255,19 +391,33 @@ class AgentReasoning:
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
)
|
||||
|
||||
self.logger.debug(f"Function calling response: {response[:100]}...")
|
||||
|
||||
try:
|
||||
result = json.loads(response)
|
||||
if "plan" in result and "ready" in result:
|
||||
return result["plan"], result["ready"]
|
||||
# Parse steps from the response
|
||||
steps: list[PlanStep] = []
|
||||
raw_steps = result.get("steps", [])
|
||||
try:
|
||||
for step_data in raw_steps:
|
||||
step = PlanStep(
|
||||
step_number=step_data.get("step_number", 0),
|
||||
description=step_data.get("description", ""),
|
||||
tool_to_use=step_data.get("tool_to_use"),
|
||||
depends_on=step_data.get("depends_on", []),
|
||||
)
|
||||
steps.append(step)
|
||||
except Exception as step_error:
|
||||
self.logger.warning(
|
||||
f"Failed to parse step: {step_data}, error: {step_error}"
|
||||
)
|
||||
return result["plan"], steps, result["ready"]
|
||||
except (json.JSONDecodeError, KeyError):
|
||||
pass
|
||||
|
||||
response_str = str(response)
|
||||
return (
|
||||
response_str,
|
||||
[],
|
||||
"READY: I am ready to execute the task." in response_str,
|
||||
)
|
||||
|
||||
@@ -277,13 +427,7 @@ class AgentReasoning:
|
||||
)
|
||||
|
||||
try:
|
||||
system_prompt = self.agent.i18n.retrieve(
|
||||
"reasoning", prompt_type
|
||||
).format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
)
|
||||
system_prompt = self._get_system_prompt()
|
||||
|
||||
fallback_response = self.llm.call(
|
||||
[
|
||||
@@ -297,78 +441,165 @@ class AgentReasoning:
|
||||
fallback_str = str(fallback_response)
|
||||
return (
|
||||
fallback_str,
|
||||
[],
|
||||
"READY: I am ready to execute the task." in fallback_str,
|
||||
)
|
||||
except Exception as inner_e:
|
||||
self.logger.error(f"Error during fallback text parsing: {inner_e!s}")
|
||||
return (
|
||||
"Failed to generate a plan due to an error.",
|
||||
[],
|
||||
True,
|
||||
) # Default to ready to avoid getting stuck
|
||||
|
||||
def __get_agent_backstory(self) -> str:
|
||||
"""
|
||||
Safely gets the agent's backstory, providing a default if not available.
|
||||
def _call_llm_with_prompt(
|
||||
self,
|
||||
prompt: str,
|
||||
plan_type: Literal["create_plan", "refine_plan"],
|
||||
) -> str:
|
||||
"""Calls the LLM with the planning prompt.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to send to the LLM.
|
||||
plan_type: The type of plan being created.
|
||||
|
||||
Returns:
|
||||
str: The agent's backstory or a default value.
|
||||
The LLM response.
|
||||
"""
|
||||
system_prompt = self._get_system_prompt()
|
||||
|
||||
response = self.llm.call(
|
||||
[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
from_task=self.task,
|
||||
from_agent=self.agent,
|
||||
)
|
||||
return str(response)
|
||||
|
||||
def _get_system_prompt(self) -> str:
|
||||
"""Get the system prompt for planning.
|
||||
|
||||
Returns:
|
||||
The system prompt, either custom or from i18n.
|
||||
"""
|
||||
if self.config.system_prompt is not None:
|
||||
return self.config.system_prompt
|
||||
|
||||
# Try new "planning" section first, fall back to "reasoning" for compatibility
|
||||
try:
|
||||
return self.agent.i18n.retrieve("planning", "system_prompt")
|
||||
except (KeyError, AttributeError):
|
||||
# Fallback to reasoning section for backward compatibility
|
||||
return self.agent.i18n.retrieve("reasoning", "initial_plan").format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self._get_agent_backstory(),
|
||||
)
|
||||
|
||||
def _get_agent_backstory(self) -> str:
|
||||
"""Safely gets the agent's backstory, providing a default if not available.
|
||||
|
||||
Returns:
|
||||
The agent's backstory or a default value.
|
||||
"""
|
||||
return getattr(self.agent, "backstory", "No backstory provided")
|
||||
|
||||
def __create_reasoning_prompt(self) -> str:
|
||||
"""
|
||||
Creates a prompt for the agent to reason about the task.
|
||||
def _create_planning_prompt(self) -> str:
|
||||
"""Creates a prompt for the agent to plan the task.
|
||||
|
||||
Returns:
|
||||
str: The reasoning prompt.
|
||||
The planning prompt.
|
||||
"""
|
||||
available_tools = self.__format_available_tools()
|
||||
available_tools = self._format_available_tools()
|
||||
|
||||
return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
description=self.task.description,
|
||||
expected_output=self.task.expected_output,
|
||||
tools=available_tools,
|
||||
)
|
||||
# Use custom prompt if provided
|
||||
if self.config.plan_prompt is not None:
|
||||
return self.config.plan_prompt.format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self._get_agent_backstory(),
|
||||
description=self.description,
|
||||
expected_output=self.expected_output,
|
||||
tools=available_tools,
|
||||
max_steps=self.config.max_steps,
|
||||
)
|
||||
|
||||
def __format_available_tools(self) -> str:
|
||||
"""
|
||||
Formats the available tools for inclusion in the prompt.
|
||||
# Try new "planning" section first
|
||||
try:
|
||||
return self.agent.i18n.retrieve("planning", "create_plan_prompt").format(
|
||||
description=self.description,
|
||||
expected_output=self.expected_output,
|
||||
tools=available_tools,
|
||||
max_steps=self.config.max_steps,
|
||||
)
|
||||
except (KeyError, AttributeError):
|
||||
# Fallback to reasoning section for backward compatibility
|
||||
return self.agent.i18n.retrieve("reasoning", "create_plan_prompt").format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self._get_agent_backstory(),
|
||||
description=self.description,
|
||||
expected_output=self.expected_output,
|
||||
tools=available_tools,
|
||||
)
|
||||
|
||||
def _format_available_tools(self) -> str:
|
||||
"""Formats the available tools for inclusion in the prompt.
|
||||
|
||||
Returns:
|
||||
str: Comma-separated list of tool names.
|
||||
Comma-separated list of tool names.
|
||||
"""
|
||||
try:
|
||||
return ", ".join(
|
||||
[sanitize_tool_name(tool.name) for tool in (self.task.tools or [])]
|
||||
)
|
||||
# Try task tools first, then agent tools
|
||||
tools = []
|
||||
if self.task:
|
||||
tools = self.task.tools or []
|
||||
if not tools:
|
||||
tools = getattr(self.agent, "tools", []) or []
|
||||
if not tools:
|
||||
return "No tools available"
|
||||
return ", ".join([sanitize_tool_name(tool.name) for tool in tools])
|
||||
except (AttributeError, TypeError):
|
||||
return "No tools available"
|
||||
|
||||
def __create_refine_prompt(self, current_plan: str) -> str:
|
||||
"""
|
||||
Creates a prompt for the agent to refine its reasoning plan.
|
||||
def _create_refine_prompt(self, current_plan: str) -> str:
|
||||
"""Creates a prompt for the agent to refine its plan.
|
||||
|
||||
Args:
|
||||
current_plan: The current reasoning plan.
|
||||
current_plan: The current plan.
|
||||
|
||||
Returns:
|
||||
str: The refine prompt.
|
||||
The refine prompt.
|
||||
"""
|
||||
return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self.__get_agent_backstory(),
|
||||
current_plan=current_plan,
|
||||
)
|
||||
# Use custom prompt if provided
|
||||
if self.config.refine_prompt is not None:
|
||||
return self.config.refine_prompt.format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self._get_agent_backstory(),
|
||||
current_plan=current_plan,
|
||||
max_steps=self.config.max_steps,
|
||||
)
|
||||
|
||||
# Try new "planning" section first
|
||||
try:
|
||||
return self.agent.i18n.retrieve("planning", "refine_plan_prompt").format(
|
||||
current_plan=current_plan,
|
||||
)
|
||||
except (KeyError, AttributeError):
|
||||
# Fallback to reasoning section for backward compatibility
|
||||
return self.agent.i18n.retrieve("reasoning", "refine_plan_prompt").format(
|
||||
role=self.agent.role,
|
||||
goal=self.agent.goal,
|
||||
backstory=self._get_agent_backstory(),
|
||||
current_plan=current_plan,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def __parse_reasoning_response(response: str) -> tuple[str, bool]:
|
||||
"""
|
||||
Parses the reasoning response to extract the plan and whether
|
||||
the agent is ready to execute the task.
|
||||
def _parse_planning_response(response: str) -> tuple[str, bool]:
|
||||
"""Parses the planning response to extract the plan and readiness.
|
||||
|
||||
Args:
|
||||
response: The LLM response.
|
||||
@@ -380,25 +611,13 @@ class AgentReasoning:
|
||||
return "No plan was generated.", False
|
||||
|
||||
plan = response
|
||||
ready = False
|
||||
|
||||
if "READY: I am ready to execute the task." in response:
|
||||
ready = True
|
||||
ready = "READY: I am ready to execute the task." in response
|
||||
|
||||
return plan, ready
|
||||
|
||||
def _handle_agent_reasoning(self) -> AgentReasoningOutput:
|
||||
"""
|
||||
Deprecated method for backward compatibility.
|
||||
Use handle_agent_reasoning() instead.
|
||||
|
||||
Returns:
|
||||
AgentReasoningOutput: The output of the agent reasoning process.
|
||||
"""
|
||||
self.logger.warning(
|
||||
"The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
|
||||
)
|
||||
return self.handle_agent_reasoning()
|
||||
# Alias for backward compatibility
|
||||
AgentPlanning = AgentReasoning
|
||||
|
||||
|
||||
def _call_llm_with_reasoning_prompt(
|
||||
@@ -409,7 +628,9 @@ def _call_llm_with_reasoning_prompt(
|
||||
backstory: str,
|
||||
plan_type: Literal["initial_plan", "refine_plan"],
|
||||
) -> str:
|
||||
"""Calls the LLM with the reasoning prompt.
|
||||
"""Deprecated: Calls the LLM with the reasoning prompt.
|
||||
|
||||
This function is kept for backward compatibility.
|
||||
|
||||
Args:
|
||||
llm: The language model to use.
|
||||
@@ -417,7 +638,7 @@ def _call_llm_with_reasoning_prompt(
|
||||
task: The task for which the agent is reasoning.
|
||||
reasoning_agent: The agent performing the reasoning.
|
||||
backstory: The agent's backstory.
|
||||
plan_type: The type of plan being created ("initial_plan" or "refine_plan").
|
||||
plan_type: The type of plan being created.
|
||||
|
||||
Returns:
|
||||
The LLM response.
|
||||
|
||||
64
lib/crewai/src/crewai/utilities/step_execution_context.py
Normal file
64
lib/crewai/src/crewai/utilities/step_execution_context.py
Normal file
@@ -0,0 +1,64 @@
|
||||
"""Context and result types for isolated step execution in Plan-and-Execute architecture.
|
||||
|
||||
These types mediate between the AgentExecutor (orchestrator) and StepExecutor (per-step worker).
|
||||
StepExecutionContext carries only final results from dependencies — never LLM message histories.
|
||||
StepResult carries only the outcome of a step — never internal execution traces.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class StepExecutionContext:
|
||||
"""Immutable context passed to a StepExecutor for a single todo.
|
||||
|
||||
Contains only the information the Executor needs to complete one step:
|
||||
the task description, goal, and final results from dependency steps.
|
||||
No LLM message history, no execution traces, no shared mutable state.
|
||||
|
||||
Attributes:
|
||||
task_description: The original task description (from Task or kickoff input).
|
||||
task_goal: The expected output / goal of the overall task.
|
||||
dependency_results: Mapping of step_number → final result string
|
||||
for all completed dependencies of the current step.
|
||||
"""
|
||||
|
||||
task_description: str
|
||||
task_goal: str
|
||||
dependency_results: dict[int, str] = field(default_factory=dict)
|
||||
|
||||
def get_dependency_result(self, step_number: int) -> str | None:
|
||||
"""Get the final result of a dependency step.
|
||||
|
||||
Args:
|
||||
step_number: The step number to look up.
|
||||
|
||||
Returns:
|
||||
The result string if available, None otherwise.
|
||||
"""
|
||||
return self.dependency_results.get(step_number)
|
||||
|
||||
|
||||
@dataclass
|
||||
class StepResult:
|
||||
"""Result returned by a StepExecutor after executing a single todo.
|
||||
|
||||
Contains the final outcome and metadata for debugging/metrics.
|
||||
Tool call details are for audit logging only — they are NOT passed
|
||||
to subsequent steps or the Planner.
|
||||
|
||||
Attributes:
|
||||
success: Whether the step completed successfully.
|
||||
result: The final output string from the step.
|
||||
error: Error message if the step failed (None on success).
|
||||
tool_calls_made: List of tool names invoked (for debugging/logging only).
|
||||
execution_time: Wall-clock time in seconds for the step execution.
|
||||
"""
|
||||
|
||||
success: bool
|
||||
result: str
|
||||
error: str | None = None
|
||||
tool_calls_made: list[str] = field(default_factory=list)
|
||||
execution_time: float = 0.0
|
||||
@@ -1456,7 +1456,7 @@ def test_agent_execute_task_with_tool():
|
||||
)
|
||||
|
||||
result = agent.execute_task(task)
|
||||
assert "you should always think about what to do" in result
|
||||
assert "test query" in result
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@@ -1475,9 +1475,9 @@ def test_agent_execute_task_with_custom_llm():
|
||||
)
|
||||
|
||||
result = agent.execute_task(task)
|
||||
assert "In circuits they thrive" in result
|
||||
assert "Artificial minds awake" in result
|
||||
assert "Future's coded drive" in result
|
||||
assert "Artificial minds" in result
|
||||
assert "Code and circuits" in result
|
||||
assert "Future undefined" in result
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,240 +1,345 @@
|
||||
"""Tests for reasoning in agents."""
|
||||
"""Tests for planning/reasoning in agents."""
|
||||
|
||||
import json
|
||||
import warnings
|
||||
|
||||
import pytest
|
||||
|
||||
from crewai import Agent, Task
|
||||
from crewai import Agent, PlanningConfig, Task
|
||||
from crewai.llm import LLM
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_llm_responses():
|
||||
"""Fixture for mock LLM responses."""
|
||||
return {
|
||||
"ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
|
||||
"not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
|
||||
"ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
|
||||
"execution": "4",
|
||||
}
|
||||
# =============================================================================
|
||||
# Tests for PlanningConfig configuration (no LLM calls needed)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_agent_with_reasoning(mock_llm_responses):
|
||||
"""Test agent with reasoning."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
def test_planning_config_default_values():
|
||||
"""Test PlanningConfig default values."""
|
||||
config = PlanningConfig()
|
||||
|
||||
assert config.max_attempts is None
|
||||
assert config.max_steps == 20
|
||||
assert config.system_prompt is None
|
||||
assert config.plan_prompt is None
|
||||
assert config.refine_prompt is None
|
||||
assert config.llm is None
|
||||
|
||||
|
||||
def test_planning_config_custom_values():
|
||||
"""Test PlanningConfig with custom values."""
|
||||
config = PlanningConfig(
|
||||
max_attempts=5,
|
||||
max_steps=15,
|
||||
system_prompt="Custom system",
|
||||
plan_prompt="Custom plan: {description}",
|
||||
refine_prompt="Custom refine: {current_plan}",
|
||||
llm="gpt-4",
|
||||
)
|
||||
|
||||
assert config.max_attempts == 5
|
||||
assert config.max_steps == 15
|
||||
assert config.system_prompt == "Custom system"
|
||||
assert config.plan_prompt == "Custom plan: {description}"
|
||||
assert config.refine_prompt == "Custom refine: {current_plan}"
|
||||
assert config.llm == "gpt-4"
|
||||
|
||||
|
||||
def test_agent_with_planning_config_custom_prompts():
|
||||
"""Test agent with PlanningConfig using custom prompts."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
custom_system_prompt = "You are a specialized planner."
|
||||
custom_plan_prompt = "Plan this task: {description}"
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test custom prompts",
|
||||
backstory="I am a test agent.",
|
||||
llm=llm,
|
||||
planning_config=PlanningConfig(
|
||||
system_prompt=custom_system_prompt,
|
||||
plan_prompt=custom_plan_prompt,
|
||||
max_steps=10,
|
||||
),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Just test that the agent is created properly
|
||||
assert agent.planning_config is not None
|
||||
assert agent.planning_config.system_prompt == custom_system_prompt
|
||||
assert agent.planning_config.plan_prompt == custom_plan_prompt
|
||||
assert agent.planning_config.max_steps == 10
|
||||
|
||||
|
||||
def test_agent_with_planning_config_disabled():
|
||||
"""Test agent with PlanningConfig disabled."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test disabled planning",
|
||||
backstory="I am a test agent.",
|
||||
llm=llm,
|
||||
planning=False,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Planning should be disabled
|
||||
assert agent.planning_enabled is False
|
||||
|
||||
|
||||
def test_planning_enabled_property():
|
||||
"""Test the planning_enabled property on Agent."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
# With planning_config enabled
|
||||
agent_with_planning = Agent(
|
||||
role="Test Agent",
|
||||
goal="Test",
|
||||
backstory="Test",
|
||||
llm=llm,
|
||||
planning=True,
|
||||
)
|
||||
assert agent_with_planning.planning_enabled is True
|
||||
|
||||
# With planning_config disabled
|
||||
agent_disabled = Agent(
|
||||
role="Test Agent",
|
||||
goal="Test",
|
||||
backstory="Test",
|
||||
llm=llm,
|
||||
planning=False,
|
||||
)
|
||||
assert agent_disabled.planning_enabled is False
|
||||
|
||||
# Without planning_config
|
||||
agent_no_planning = Agent(
|
||||
role="Test Agent",
|
||||
goal="Test",
|
||||
backstory="Test",
|
||||
llm=llm,
|
||||
)
|
||||
assert agent_no_planning.planning_enabled is False
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Tests for backward compatibility with reasoning=True (no LLM calls)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_agent_with_reasoning_backward_compat():
|
||||
"""Test agent with reasoning=True (backward compatibility)."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
# This should emit a deprecation warning
|
||||
with warnings.catch_warnings(record=True):
|
||||
warnings.simplefilter("always")
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Should have created a PlanningConfig internally
|
||||
assert agent.planning_config is not None
|
||||
assert agent.planning_enabled is True
|
||||
|
||||
|
||||
def test_agent_with_reasoning_and_max_attempts_backward_compat():
|
||||
"""Test agent with reasoning=True and max_reasoning_attempts (backward compatibility)."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
backstory="I am a test agent.",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
verbose=True,
|
||||
max_reasoning_attempts=5,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Simple math task: What's 2+2?",
|
||||
expected_output="The answer should be a number.",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
agent.llm.call = lambda messages, *args, **kwargs: (
|
||||
mock_llm_responses["ready"]
|
||||
if any("create a detailed plan" in msg.get("content", "") for msg in messages)
|
||||
else mock_llm_responses["execution"]
|
||||
)
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == mock_llm_responses["execution"]
|
||||
assert "Reasoning Plan:" in task.description
|
||||
# Should have created a PlanningConfig with max_attempts
|
||||
assert agent.planning_config is not None
|
||||
assert agent.planning_config.max_attempts == 5
|
||||
|
||||
|
||||
def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
|
||||
"""Test agent with reasoning that requires refinement."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
# =============================================================================
|
||||
# Tests for Agent.kickoff() with planning (uses AgentExecutor)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_kickoff_with_planning():
|
||||
"""Test Agent.kickoff() with planning enabled generates a plan."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
role="Math Assistant",
|
||||
goal="Help solve math problems step by step",
|
||||
backstory="A helpful math tutor",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=2,
|
||||
verbose=True,
|
||||
planning_config=PlanningConfig(max_attempts=1),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Complex math task: What's the derivative of x²?",
|
||||
expected_output="The answer should be a mathematical expression.",
|
||||
agent=agent,
|
||||
)
|
||||
result = agent.kickoff("What is 15 + 27?")
|
||||
|
||||
call_count = [0]
|
||||
|
||||
def mock_llm_call(messages, *args, **kwargs):
|
||||
if any(
|
||||
"create a detailed plan" in msg.get("content", "") for msg in messages
|
||||
) or any("refine your plan" in msg.get("content", "") for msg in messages):
|
||||
call_count[0] += 1
|
||||
if call_count[0] == 1:
|
||||
return mock_llm_responses["not_ready"]
|
||||
return mock_llm_responses["ready_after_refine"]
|
||||
return "2x"
|
||||
|
||||
agent.llm.call = mock_llm_call
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == "2x"
|
||||
assert call_count[0] == 2 # Should have made 2 reasoning calls
|
||||
assert "Reasoning Plan:" in task.description
|
||||
assert result is not None
|
||||
assert "42" in str(result)
|
||||
|
||||
|
||||
def test_agent_with_reasoning_max_attempts_reached():
|
||||
"""Test agent with reasoning that reaches max attempts without being ready."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_kickoff_without_planning():
|
||||
"""Test Agent.kickoff() without planning skips plan generation."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
role="Math Assistant",
|
||||
goal="Help solve math problems",
|
||||
backstory="A helpful assistant",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=2,
|
||||
verbose=True,
|
||||
# No planning_config = no planning
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Complex math task: Solve the Riemann hypothesis.",
|
||||
expected_output="A proof or disproof of the hypothesis.",
|
||||
agent=agent,
|
||||
)
|
||||
result = agent.kickoff("What is 8 * 7?")
|
||||
|
||||
call_count = [0]
|
||||
|
||||
def mock_llm_call(messages, *args, **kwargs):
|
||||
if any(
|
||||
"create a detailed plan" in msg.get("content", "") for msg in messages
|
||||
) or any("refine your plan" in msg.get("content", "") for msg in messages):
|
||||
call_count[0] += 1
|
||||
return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
|
||||
return "This is an unsolved problem in mathematics."
|
||||
|
||||
agent.llm.call = mock_llm_call
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == "This is an unsolved problem in mathematics."
|
||||
assert (
|
||||
call_count[0] == 2
|
||||
) # Should have made exactly 2 reasoning calls (max_attempts)
|
||||
assert "Reasoning Plan:" in task.description
|
||||
assert result is not None
|
||||
assert "56" in str(result)
|
||||
|
||||
|
||||
def test_agent_reasoning_error_handling():
|
||||
"""Test error handling during the reasoning process."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_kickoff_with_planning_disabled():
|
||||
"""Test Agent.kickoff() with planning explicitly disabled via planning=False."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
role="Math Assistant",
|
||||
goal="Help solve math problems",
|
||||
backstory="A helpful assistant",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
planning=False, # Explicitly disable planning
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Task that will cause an error",
|
||||
expected_output="Output that will never be generated",
|
||||
agent=agent,
|
||||
)
|
||||
result = agent.kickoff("What is 100 / 4?")
|
||||
|
||||
call_count = [0]
|
||||
|
||||
def mock_llm_call_error(*args, **kwargs):
|
||||
call_count[0] += 1
|
||||
if call_count[0] <= 2: # First calls are for reasoning
|
||||
raise Exception("LLM error during reasoning")
|
||||
return "Fallback execution result" # Return a value for task execution
|
||||
|
||||
agent.llm.call = mock_llm_call_error
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == "Fallback execution result"
|
||||
assert call_count[0] > 2 # Ensure we called the mock multiple times
|
||||
assert result is not None
|
||||
assert "25" in str(result)
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
|
||||
def test_agent_with_function_calling():
|
||||
"""Test agent with reasoning using function calling."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_kickoff_multi_step_task_with_planning():
|
||||
"""Test Agent.kickoff() with a multi-step task that benefits from planning."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
role="Math Tutor",
|
||||
goal="Solve multi-step math problems",
|
||||
backstory="An expert tutor who explains step by step",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
verbose=True,
|
||||
planning_config=PlanningConfig(max_attempts=1, max_steps=5),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Simple math task: What's 2+2?",
|
||||
expected_output="The answer should be a number.",
|
||||
agent=agent,
|
||||
# Task requires: find primes, sum them, then double
|
||||
result = agent.kickoff(
|
||||
"Find the first 3 prime numbers, add them together, then multiply by 2."
|
||||
)
|
||||
|
||||
agent.llm.supports_function_calling = lambda: True
|
||||
|
||||
def mock_function_call(messages, *args, **kwargs):
|
||||
if "tools" in kwargs:
|
||||
return json.dumps(
|
||||
{"plan": "I'll solve this simple math problem: 2+2=4.", "ready": True}
|
||||
)
|
||||
return "4"
|
||||
|
||||
agent.llm.call = mock_function_call
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == "4"
|
||||
assert "Reasoning Plan:" in task.description
|
||||
assert "I'll solve this simple math problem: 2+2=4." in task.description
|
||||
assert result is not None
|
||||
# First 3 primes: 2, 3, 5 -> sum = 10 -> doubled = 20
|
||||
assert "20" in str(result)
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Test requires updates for native tool calling changes")
|
||||
def test_agent_with_function_calling_fallback():
|
||||
"""Test agent with reasoning using function calling that falls back to text parsing."""
|
||||
llm = LLM("gpt-3.5-turbo")
|
||||
# =============================================================================
|
||||
# Tests for Agent.execute_task() with planning (uses CrewAgentExecutor)
|
||||
# These test the legacy path via handle_reasoning()
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_execute_task_with_planning():
|
||||
"""Test Agent.execute_task() with planning via CrewAgentExecutor."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Test Agent",
|
||||
goal="To test the reasoning feature",
|
||||
backstory="I am a test agent created to verify the reasoning feature works correctly.",
|
||||
role="Math Assistant",
|
||||
goal="Help solve math problems",
|
||||
backstory="A helpful math tutor",
|
||||
llm=llm,
|
||||
reasoning=True,
|
||||
verbose=True,
|
||||
planning_config=PlanningConfig(max_attempts=1),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Simple math task: What's 2+2?",
|
||||
expected_output="The answer should be a number.",
|
||||
description="What is 9 + 11?",
|
||||
expected_output="A number",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
agent.llm.supports_function_calling = lambda: True
|
||||
result = agent.execute_task(task)
|
||||
|
||||
def mock_function_call(messages, *args, **kwargs):
|
||||
if "tools" in kwargs:
|
||||
return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
|
||||
return "4"
|
||||
assert result is not None
|
||||
assert "20" in str(result)
|
||||
# Planning should be appended to task description
|
||||
assert "Planning:" in task.description
|
||||
|
||||
agent.llm.call = mock_function_call
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_execute_task_without_planning():
|
||||
"""Test Agent.execute_task() without planning."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Math Assistant",
|
||||
goal="Help solve math problems",
|
||||
backstory="A helpful assistant",
|
||||
llm=llm,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="What is 12 * 3?",
|
||||
expected_output="A number",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result == "4"
|
||||
assert "Reasoning Plan:" in task.description
|
||||
assert "Invalid JSON that will trigger fallback" in task.description
|
||||
assert result is not None
|
||||
assert "36" in str(result)
|
||||
# No planning should be added
|
||||
assert "Planning:" not in task.description
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_agent_execute_task_with_planning_refine():
|
||||
"""Test Agent.execute_task() with planning that requires refinement."""
|
||||
llm = LLM("gpt-4o-mini")
|
||||
|
||||
agent = Agent(
|
||||
role="Math Tutor",
|
||||
goal="Solve complex math problems step by step",
|
||||
backstory="An expert tutor",
|
||||
llm=llm,
|
||||
planning_config=PlanningConfig(max_attempts=2),
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
task = Task(
|
||||
description="Calculate the area of a circle with radius 5 (use pi = 3.14)",
|
||||
expected_output="The area as a number",
|
||||
agent=agent,
|
||||
)
|
||||
|
||||
result = agent.execute_task(task)
|
||||
|
||||
assert result is not None
|
||||
# Area = pi * r^2 = 3.14 * 25 = 78.5
|
||||
assert "78" in str(result) or "79" in str(result)
|
||||
assert "Planning:" in task.description
|
||||
|
||||
@@ -359,17 +359,34 @@ def test_sets_flow_context_when_inside_flow():
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_guardrail_is_called_using_string():
|
||||
"""Test that a string guardrail triggers events and retries correctly.
|
||||
|
||||
Uses a callable guardrail that deterministically fails on the first
|
||||
attempt and passes on the second. This tests the guardrail event
|
||||
machinery (started/completed events, retry loop) without depending
|
||||
on the LLM to comply with contradictory constraints.
|
||||
"""
|
||||
guardrail_events: dict[str, list] = defaultdict(list)
|
||||
from crewai.events.event_types import (
|
||||
LLMGuardrailCompletedEvent,
|
||||
LLMGuardrailStartedEvent,
|
||||
)
|
||||
|
||||
# Deterministic guardrail: fail first call, pass second
|
||||
call_count = {"n": 0}
|
||||
|
||||
def fail_then_pass_guardrail(output):
|
||||
call_count["n"] += 1
|
||||
if call_count["n"] == 1:
|
||||
return (False, "Missing required format — please use a numbered list")
|
||||
return (True, output)
|
||||
|
||||
agent = Agent(
|
||||
role="Sports Analyst",
|
||||
goal="Gather information about the best soccer players",
|
||||
backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
|
||||
guardrail="""Only include Brazilian players, both women and men""",
|
||||
goal="List the best soccer players",
|
||||
backstory="You are an expert at gathering and organizing information.",
|
||||
guardrail=fail_then_pass_guardrail,
|
||||
guardrail_max_retries=3,
|
||||
)
|
||||
|
||||
condition = threading.Condition()
|
||||
@@ -388,7 +405,7 @@ def test_guardrail_is_called_using_string():
|
||||
guardrail_events["completed"].append(event)
|
||||
condition.notify()
|
||||
|
||||
result = agent.kickoff(messages="Top 10 best players in the world?")
|
||||
result = agent.kickoff(messages="Top 5 best soccer players in the world?")
|
||||
|
||||
with condition:
|
||||
success = condition.wait_for(
|
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