[BUG] Error executing tool. coworker mentioned not found, it must be one of the following options:

[wzdacyl]

Description

I'm trying to use Process.hierarchical to create a crew. But the manager agent is not able to find other coworker.

effort for the issue

I see some issue closed for the similar problem on #620 #602 and other issues around #300+. But it seems all of the issues are closed or fixed. But I still meet this issue. The issue happened on the crewai version of 0.67.1. I also tried 0.65.2. got the same issue.

I also tried to change my local llm from qwen2.5 to llama3.1 and gemma2. the same issue raise.

Is there anything wrong on my code? Please help

Steps to Reproduce

crewai run

Expected behavior

a coworker can be found by manager agent

Screenshots/Code snippets

my code

from crewai import Agent, Crew, Process, Task, LLM
from crewai.project import CrewBase, agent, crew, task
from pydantic import BaseModel, Field
from typing import List, Optional

@CrewBase
class DailyAgentCrew():
    """DailyAgent crew"""
    model_type = "qwen2.5:32b-instruct"
    llm_provider_uri = "http://127.0.0.1:11434"
        ...

    @agent
    def directory_summarizer(self) -> Agent:
        return Agent(
            config=self.agents_config['directory_summarizer'],
            llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            allow_delegation=True,
            verbose=True
        )
        ...

    @crew
    def crew(self) -> Crew:
        """Creates the DailyAgent crew"""
        return Crew(
            agents=[self.directory_info_extractor(), self.document_summarizer()], # Automatically created by the @agent decorator
            tasks=[self.directory_basic_info_extract_task(), self.document_summarize_task()], # Automatically created by the @task decorator
            process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
            manager_agent=self.directory_summarizer(),
            manager_llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            verbose=True,
        )

Operating System

macOS Sonoma

Python Version

3.11

crewAI Version

0.65.2

crewAI Tools Version

0.12.1

Virtual Environment

Venv

Evidence

# Agent: Directory Document Summarizer
## Using tool: Delegate work to coworker
## Tool Input: 
"{\"task\": \"Extract information from documents including title and path\", \"context\": \"Please provide a comprehensive list with titles and paths of all documents within the given directory /Users/mac/Documents/sourceCode/GPT/crewai_learn/daily_agent/src/summary_notes.\", \"coworker\": \"Directory Info Extractor\"}"
## Tool Output: 

Error executing tool. coworker mentioned not found, it must be one of the following options:
- directory document summarizer

# Agent: Directory Document Summarizer
## Using tool: Delegate work to coworker
## Tool Input: 
"{\"task\": \"Extract information from documents including title and path\", \"context\": \"Please provide a comprehensive list with titles and paths of all documents within the given directory /Users/mac/Documents/sourceCode/GPT/crewai_learn/daily_agent/src/summary_notes.\", \"coworker\": \"Directory Info Extractor\"}"
## Tool Output: 

Error executing tool. coworker mentioned not found, it must be one of the following options:
- directory document summarizer

Possible Solution

None

Additional context

Nothing

[bhancockio]

Hey @wzdacyl!

The underlying issue is that your LLM () is trying to delegate work to Directory Info Extractor as shown in the Tool Input `"{"task": "...", "context": "...", "coworker": "Directory Info Extractor"}"

The issue is that this Directory Info Extractor coworker does not exist. The only coworker that exists is the directory document summarizer

A lot of the smaller models have trouble with tool delegation. For better results while these smaller models are improving, we definitely recommend working with the more powerful LLMs.

I hope that clears things up!

[wzdacyl]

hello @bhancockio ,

Thanks for reply. But, i actually have the Agent in DailyAgentCrew like below.

from crewai import Agent, Crew, Process, Task, LLM
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import DirectorySearchTool, DirectoryReadTool, FileReadTool, MDXSearchTool, BaseTool, tool
from pydantic import BaseModel, Field
from typing import List, Optional

class DocumentBasicInfo(BaseModel):
    title: str = Field(..., description="The name of document")
    path: str = Field(..., description="The path of document")

class DirectoryBasicInfo(BaseModel):
    directory_files: List[DocumentBasicInfo] = Field(..., description="List of document basic info in directory")

class DocumentSummary(BaseModel):
    summary: str = Field(..., description="The summary of document")
    characters: str = Field(..., description="The characters of the document")
    document_ouline: str = Field(..., description="the outline of the document")

class DirectorySummary(BaseModel):
    directory_files: List[DocumentSummary] = Field(..., description="List of document summary in directory")

@CrewBase
class DailyAgentCrew():
    model_type = "qwen2.5:32b-instruct"
    llm_provider_uri = "http://127.0.0.1:11434"

    @agent
    def directory_summarizer(self) -> Agent:
        return Agent(
            config=self.agents_config['directory_summarizer'],
            llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            allow_delegation=True,
            verbose=True
        )

    @agent
    def directory_info_extractor(self) -> Agent:
        return Agent(
            config=self.agents_config['directory_info_extractor'],
            tools=[DirectoryReadTool(directory="/Users/mac/Documents/sourceCode/GPT/crewai_learn/daily_agent/src/summary_notes"),
            ],
            llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            allow_delegation=False,
            verbose=True
        )

    @agent
    def document_summarizer(self) -> Agent:
        return Agent(
            config=self.agents_config['document_summarizer'],
            tools=[FileReadTool()], # Example of custom tool, loaded on the beginning of file
            llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            allow_delegation=False,
            verbose=True
        )

    @task
    def directory_doc_summarize_task(self) -> Task:
        return Task(
            config=self.tasks_config['directory_doc_summarize_task'],
            output_json=DirectorySummary,
            agent=self.directory_summarizer(),
            output_file='DirectorySummary.json',
        )

    @task
    def directory_basic_info_extract_task(self) -> Task:
        return Task(
            config=self.tasks_config['directory_basic_info_extract_task'],
            output_json=DirectoryBasicInfo,
            agent=self.directory_summarizer(),
            output_file='DirectoryBasicInfo.json',
        )

    @task
    def document_summarize_task(self) -> Task:
        return Task(
            config=self.tasks_config['document_summarize_task'],
            output_json=DocumentSummary,
            agent=self.document_summarizer(),
        )

    @crew
    def crew(self) -> Crew:
        """Creates the DailyAgent crew"""
        return Crew(
            agents=[self.directory_info_extractor(), self.document_summarizer()],
            tasks=[self.directory_basic_info_extract_task(), self.document_summarize_task()], 
            process=Process.hierarchical, 
            manager_agent=self.directory_summarizer(),
            manager_llm=LLM(model="ollama/" + self.model_type, base_url=self.llm_provider_uri),
            verbose=True,
        )
...

and its agent.yaml

directory_info_extractor:
directory_summarizer:
  role: >
    Directory Document Summarizer
  goal: >
    Drive team member to summarize all the documents in the directory:{folder}
  backstory: >
    You are the leader of team. 
    Your team goals to ask team members to summarize all documentation in the directory:{folder}
    You are a very good leader who can assign appropriate job to members.
    You ask Directory Info Extractor to get document basic info which includes all title and path of each document.
    Then you distrbute each of the document to Documentation Summarizer, asking document summarizer to do summary work.
    Finally you gether all the summary and compose the final output.
    You make sure all the files in the folder are summarized.
    You make sure the quality of the summary for each of the folder.

directory_info_extractor:
  role: >
    Directory Info Extractor
  goal: >
    Extract the basic info of all the document in the directory:{folder}
  backstory: >
    Your goal is to extract the basic info of all the document in the directory:{folder}.
    You should extract the name and the path of each document in directory.

document_summarizer:
  role: >
    Documentation Summarizer
  goal: >
    Generate the summary of a document
  backstory: >
    You works at a summarization team.
    You are expert at summarizing a document.
    You can make a summary to include the outline of a document no matter what kind of context it is.