Multi‐Agent - joehubert/ai-agent-design-patterns GitHub Wiki

Home::Overview of Patterns

Classification

Architectural Pattern

Intent

To create a collaborative system of multiple specialized LLM agents that work together to solve complex problems by leveraging different roles, expertise areas, or perspectives, resulting in more comprehensive, balanced, and higher-quality outcomes than would be possible with a single agent.

Also Known As

  • Agent Teams
  • Agent Collaboration Framework
  • Multi-Agent Systems (MAS)
  • Collaborative AI Ensembles

Motivation

Complex real-world problems often require diverse skill sets, multiple perspectives, and specialized domain knowledge that may be difficult to consolidate in a single agent. Traditional approaches with a single LLM agent might:

  • Struggle with tasks requiring deep expertise across multiple domains
  • Fail to provide balanced perspectives on complex issues
  • Be limited by the constraints of a single prompt or context window
  • Lack the ability to critique and refine their own outputs effectively

The Multi-Agent pattern addresses these limitations by creating a team of specialized agents that collaborate on problem-solving. For example, in a software development scenario, one agent might act as a product manager defining requirements, another as a software architect designing the system, a third as a developer writing code, and a fourth as a tester focused on quality assurance. Each agent brings specialized expertise to the task, and their collaboration leads to more robust outcomes.

Applicability

When to use this pattern:

  • When tasks require multiple areas of specialized knowledge or perspectives
  • For complex problems that benefit from a division of labor
  • When critical thinking and mutual critique would improve outcomes
  • For scenarios where different personality traits or approaches would be beneficial
  • When the problem complexity exceeds what can be effectively handled in a single prompt
  • In situations requiring checks and balances to ensure balanced, objective outputs

Structure

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Components

The key elements participating in the pattern:

  • Orchestrator: Manages the overall workflow, delegates subtasks to specialized agents, integrates their outputs, and ensures cohesive collaboration.

  • Specialized Agents: Individual LLM instances configured for specific roles, such as:

    • Researcher: Gathers and synthesizes information
    • Critic: Evaluates proposals and identifies potential issues
    • Creative: Generates novel solutions and approaches
    • Implementer: Turns concepts into concrete outputs (code, plans, etc.)
    • Domain Expert: Provides specialized knowledge in particular fields
    • Summarizer: Condenses and integrates information from other agents

  • Communication Channels: Mechanisms for agents to share information, request assistance, and collaborate on tasks.

  • Memory System: Shared or individual knowledge bases that maintain context across agent interactions.

  • Evaluation Framework: Systems to assess the quality and coherence of collaborative outputs.

Interactions

How the components work together:

  1. The Orchestrator receives a task and analyzes its requirements
  2. Based on the task analysis, the Orchestrator assigns appropriate specialized agents
  3. Agents perform their designated functions, which may include:
    • Sequential processing (output from one agent becomes input for another)
    • Parallel processing (multiple agents work simultaneously on different aspects)
    • Iterative refinement (agents review and improve each other's work)
    • Debate (agents argue different perspectives to reach well-reasoned conclusions)
  4. The Orchestrator manages communication flows between agents, ensuring relevant information is shared
  5. The Orchestrator integrates outputs from multiple agents into a cohesive final product
  6. Evaluation processes assess the quality of the collaborative output and may trigger additional iteration if needed

Consequences

The results and trade-offs of using the pattern:

  • Benefits:

    • Enhanced problem-solving through diverse perspectives and specializations
    • More balanced and well-reasoned outputs through cross-validation
    • Ability to tackle larger, more complex problems through division of labor
    • Improved self-correction through mutual critique
    • Increased creativity through the combination of different approaches

  • Limitations:

    • Higher computational costs and resource requirements than single-agent systems
    • Increased system complexity and potential points of failure
    • Potential for conflicting priorities or approaches between agents
    • Challenge of maintaining coherence across multiple agents' outputs
    • Risk of information loss during hand-offs between agents

  • Performance implications:

    • Increased latency due to multiple processing steps and inter-agent communication
    • Higher token consumption across multiple LLM calls
    • Additional overhead for orchestration and integration

Implementation

Guidelines for implementing the pattern:

  1. Define clear roles and responsibilities for each agent based on the problem domain
  2. Design efficient communication protocols to minimize redundancy and maximize information transfer
  3. Implement effective orchestration logic to manage workflow and integration
  4. Consider the appropriate level of autonomy for each agent
  5. Establish clear criteria for when to use sequential vs. parallel processing
  6. Develop mechanisms to resolve conflicts or contradictions between agents
  7. Create appropriate shared memory or context to maintain coherence
  8. Implement evaluation frameworks to assess the quality of collaborative outputs

Common pitfalls:

  • Over-engineering with too many specialized agents for simple tasks
  • Unclear role boundaries leading to redundant work
  • Poor integration of outputs resulting in incoherent final results
  • Excessive back-and-forth communication creating unnecessary latency
  • Failing to provide sufficient context to each agent about the overall goal

Code Examples

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Variations

Common modifications or adaptations of the basic pattern:

  • Debate-Driven Multi-Agent: Agents with opposing viewpoints engage in structured debate to reach well-reasoned conclusions on complex issues.

  • Hierarchical Multi-Agent: Implements a management hierarchy where higher-level agents delegate to and integrate the work of lower-level specialized agents.

  • Dynamic Role Assignment: Allows agents to flexibly shift roles based on the evolving needs of the task rather than maintaining fixed specializations.

  • Human-in-the-Loop Multi-Agent: Incorporates human feedback and guidance at strategic points in the multi-agent workflow.

  • Self-Organizing Multi-Agent: Agents autonomously determine their collaboration structure based on the task requirements.

Real-World Examples

Systems or applications where this pattern has been successfully applied:

  • AutoGPT: An autonomous agent system that uses multiple LLM instances to accomplish complex, open-ended tasks through decomposition and specialization.

  • BabyAGI: A task management system that uses specialized agents for task creation, prioritization, and execution.

  • LangChain's Agent Executor with Tools: Implements a system where agents can use specialized tools to accomplish tasks, effectively creating a multi-agent system through tool specialization.

  • AI Research Assistants: Systems that combine research agents, critical analysis agents, and summarization agents to assist with academic literature review.

  • AI Software Development Environments: Platforms that use multi-agent approaches for different aspects of software development, from requirements gathering to coding and testing.

Related Patterns

Other patterns that:

  • Hierarchical Task Decomposition: Often used in conjunction with Multi-Agent pattern to break down complex tasks before distributing them to specialized agents.

  • Router: Can direct incoming requests to the appropriate specialized agent based on content analysis.

  • Planner: May work alongside a Multi-Agent system to coordinate the sequence of operations across multiple specialized agents.

  • Reflection: Individual agents within a Multi-Agent system often use reflection to improve their outputs before sharing with other agents.

  • Episodic Memory: Provides critical context maintenance across multiple agent interactions within a collaborative workflow.