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Memory and Orchestration

This section covers AgentMap's advanced features for building sophisticated, stateful workflows that can maintain context and intelligently route between different agents and capabilities.

Overview​

Memory and orchestration are key features that transform simple AgentMap workflows into intelligent, adaptive systems:

  • Memory Management: Enable agents to maintain conversation history and context
  • LangChain Integration: Leverage advanced memory strategies for optimal performance
  • Dynamic Orchestration: Route requests intelligently based on content and context
  • Prompt Management: Organize and reuse prompts across complex workflows

Quick Navigation​

Core Concepts​

Memory Systems​

AgentMap provides multiple memory approaches to suit different use cases:

  • Simple Memory: Basic conversation history tracking
  • LangChain Memory: Advanced strategies like buffer windows, token limits, and summarization
  • Shared Memory: Context sharing between multiple agents in a workflow

Orchestration Patterns​

Build intelligent routing systems that adapt to user input:

  • Dynamic Routing: Select the best agent based on request content
  • Multi-Strategy Selection: Combine algorithmic and AI-based routing
  • Context-Aware Decisions: Use conversation history to inform routing choices

Prompt Organization​

Maintain clean, reusable prompt libraries:

  • Registry-Based Prompts: Centralized prompt management
  • File-Based Prompts: Organize complex prompts in dedicated files
  • YAML Structures: Hierarchical prompt organization for complex scenarios

Integration Examples​

Memory + Orchestration​

Combine memory and orchestration for intelligent conversational flows:

GraphName,Node,Edge,AgentType,Input_Fields,Output_Field,Prompt,Context
ChatBot,Router,,orchestrator,available_nodes|user_input|conversation_memory,next_node,"Route based on input and context","memory:{""type"":""buffer_window"",""k"":5}"
ChatBot,GeneralChat,,claude,user_input|conversation_memory,response,"I handle general conversation","memory:{""type"":""buffer_window"",""k"":5}"
ChatBot,TaskHelper,,claude,user_input|conversation_memory,response,"I help with specific tasks","memory:{""type"":""buffer_window"",""k"":5}"

Orchestration + Prompts​

Use managed prompts with intelligent routing:

GraphName,Node,Edge,AgentType,Input_Fields,Output_Field,Prompt,Context
Support,Router,,orchestrator,available_nodes|user_input,next_node,prompt:router_instructions,
Support,TechSupport,,claude,user_input,response,prompt:technical_support,
Support,CustomerService,,claude,user_input,response,prompt:customer_service,

Complete Integration​

Combine all three features for maximum flexibility:

GraphName,Node,Edge,AgentType,Input_Fields,Output_Field,Prompt,Context
Advanced,Router,,orchestrator,available_nodes|user_input|session_memory,next_node,yaml:workflows.yaml#routing.intelligent,"memory:{""type"":""summary"",""memory_key"":""session_memory""}"
Advanced,Specialist,,claude,user_input|session_memory,response,yaml:workflows.yaml#responses.specialist,"memory:{""type"":""summary"",""memory_key"":""session_memory""}"
Advanced,Generalist,,claude,user_input|session_memory,response,yaml:workflows.yaml#responses.generalist,"memory:{""type"":""summary"",""memory_key"":""session_memory""}"

Best Practices​

  1. Start Simple: Begin with basic memory and add complexity as needed
  2. Choose Appropriate Memory Types: Match memory strategy to conversation length and complexity
  3. Design Clear Node Descriptions: Help orchestrators make better routing decisions
  4. Organize Prompts Logically: Create maintainable prompt libraries
  5. Test Integration Points: Verify memory, routing, and prompts work together smoothly

Getting Started​

  1. Memory Management: Start with basic conversation memory
  2. LangChain Memory Integration: Explore advanced memory strategies
  3. Orchestration Patterns: Add intelligent routing to your workflows
  4. Prompt Management: Organize your prompts for reusability

Advanced Patterns​

Once you're comfortable with the basics, explore these advanced patterns:

  • Multi-Agent Conversations: Orchestrate between specialized agents while maintaining shared context
  • Hierarchical Routing: Use nested orchestrators for complex decision trees
  • Context-Aware Prompting: Adapt prompts based on conversation history and user context
  • Dynamic Memory Management: Adjust memory strategies based on conversation flow

These features work together to create sophisticated AgentMap workflows that can handle complex, stateful interactions while remaining maintainable and easy to understand.