langchain - 💡(How to fix) Fix Support agent teams [1 comments, 2 participants]

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langchain-ai/langchain#35982Fetched 2026-04-08 00:47:42
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Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

Like https://code.claude.com/docs/en/agent-teams

Use Case

Multi sub agent

Proposed Solution

No response

Alternatives Considered

No response

Additional Context

No response

extent analysis

Fix: Implementing Multi-Sub Agent Functionality

To implement a feature similar to agent teams as described in the use case, we can create a hierarchical structure of agents where a primary agent can manage and coordinate multiple sub-agents.

Fix Plan

  1. Define Agent Class: Create a base Agent class that has methods for receiving and processing tasks.
  2. Implement Sub-Agent Management: Extend the Agent class to manage sub-agents, allowing the primary agent to assign tasks and receive results from sub-agents.
  3. Task Distribution Logic: Implement logic in the primary agent to distribute tasks among sub-agents based on their capabilities or workload.

Example Code

from abc import ABC, abstractmethod
from typing import List

class Agent(ABC):
    def __init__(self, name):
        self.name = name

    @abstractmethod
    def process_task(self, task):
        pass

class SubAgent(Agent):
    def process_task(self, task):
        # Process the task and return the result
        return f"{self.name} processed: {task}"

class PrimaryAgent(Agent):
    def __init__(self, name):
        super().__init__(name)
        self.sub_agents = []

    def add_sub_agent(self, agent):
        self.sub_agents.append(agent)

    def process_task(self, task):
        # Distribute the task to a sub-agent
        sub_agent = self.sub_agents[0]  # Simple distribution logic
        return sub_agent.process_task(task)

# Usage
primary_agent = PrimaryAgent("Primary")
sub_agent1 = SubAgent("SubAgent1")
sub_agent2 = SubAgent("SubAgent2")

primary_agent.add_sub_agent(sub_agent1)
primary_agent.add_sub_agent(sub_agent2)

task_result = primary_agent.process_task("Example Task")
print(task_result)

Verification

  • Test the PrimaryAgent class by adding multiple SubAgent instances and verifying that tasks are distributed and processed correctly.
  • Ensure the process_task method of PrimaryAgent can handle different types of tasks and sub-agents.

Extra Tips

  • Consider using a more sophisticated task distribution strategy based on sub-agent capabilities or current workload.
  • Implement error handling for cases where a sub-agent fails to process a task.

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