Executive Summary

This application is a multi-agent system designed to generate comprehensive travel plans by orchestrating several specialized agents. Each agent is responsible for a specific task in the overall workflow, and they interact via a directed graph that guides inputs, enriches them, collects human feedback, and ultimately provides curated travel proposals.

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Workflow Overview

1. User Input and Initial Setup

• User Input:
  The workflow begins when the user provides their travel preferences (e.g., “I would like to visit Barcelona”). This raw input is captured and passed to the system via the Manager class.

• Graph Initialization:
  The Manager (located in src/manager.py) sets up the primary state graph which defines the sequence and connections between various agent nodes. For certain scenarios, an alternative graph (graph_only_researchers) is available.

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2. Context Enrichment

• Context Enricher Agent:
  • Location: src/nodes/agents/context_enricher.py
  • Role: Clarifies and gathers essential travel details by prompting the user for additional information when necessary (using the human_in_the_loop tool).
• Outcome:
  Ensures that all subsequent agents have a rich context from which to operate.

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3. User Input Summarization

• User Input Summary Agent:
  • Location: src/nodes/llm/user_input_summary.py
  • Role: Reformulates the enriched details into a concise, first-person narrative.
• Outcome:
  Produces an “enriched user input” that consolidates all essential travel details for the planning stage.

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4. Trip Planning

• Planner Agent:
  • Location: src/nodes/llm/planner.py
  • Role: Generates two initial travel plan proposals (Plan A and Plan B) based on the enriched input.
• Process:
  The agent can adjust proposals based on any previous human feedback.

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5. Human Feedback Loop

• Human Loop Node:
  • Location: src/nodes/hil/human_loop.py
  • Role: Brings a human-in-the-loop phase into the process by presenting the user with the proposed plans.
• Process:
  The user reviews the proposals and provides feedback (e.g., choosing a preferred plan or suggesting modifications).

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6. Routing Based on Feedback

• Router Agent:
  • Location: src/nodes/llm/router.py
  • Role: Evaluates both the human feedback and the enriched input to determine the subsequent workflow path.
• Outcome:
  Routes the process back to the planner for revisions or progresses it to specialized research.

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7. Specialized Research

• Researchers Manager Agent:

  • Location: src/nodes/llm/research_manager.py
  • Role: Dispatches the workflow to specialized research agents based on the current state.

• Specialized Agents:

  • Flight Researcher Agent:
    • Location: src/nodes/agents/flight_researcher.py
    • Task: Searches for flight options via the Amadeus API.
  • Hotel Researcher Agent:
    • Location: src/nodes/agents/hotel_researcher.py
    • Task: Retrieves hotel accommodation offers via the Amadeus API.

• Persistent Caching for Research:
  Both the flight and hotel search tools use a Redis-based persistent caching mechanism (implemented in src/cache/redis_cache.py).

  • How It Works:
    When an API call for flight or hotel search is executed, its result is stored in Redis with a TTL of one day. Subsequent identical requests (same search parameters and within the same day) return the cached result, thereby reducing redundant API calls and speeding up the response.

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8. Finalization and Summary

• Trip Finalizer Agent:
  • Location: src/nodes/llm/finalizer.py
  • Role: Consolidates all gathered information—including flight proposals, hotel proposals, enriched input, and the preferred plan—into a comprehensive travel summary.
• Outcome:
  The user receives a final, detailed travel itinerary that clearly presents all available options.

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9. State Management and Execution Flow

• State Propagation:
  A mutable State object (defined in src/states/state.py) is passed along the workflow. Each agent enriches or modifies this state.

• Graph-Based Workflow:
  The interconnected nodes in the workflow (outlined in src/graphs/graph.py) allow for dynamic routing based on human feedback and intermediate results.

• Debug and Iteration:
  The Manager class supports a debug mode, which enables iteration and continuous refinement of the plan based on user input.

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Performance Optimization Through Persistent Caching

To improve efficiency and reduce the number of expensive API calls, the application incorporates a Redis-based caching mechanism:

• Caching Implementation:
  The persistent cache is implemented in the file src/cache/redis_cache.py using Redis.
  • Usage in Agents:
    Both search_flights (located in src/nodes/agents/tools/amadeus/search_flights.py) and search_hotels (located in src/nodes/agents/tools/amadeus/search_hotels.py) instantiate a local RedisCache instance during each tool invocation.
  • Key Strategy:
    Each cache entry is keyed using the API call parameters (e.g., origin, destination, dates, and number of travelers) concatenated with the current date.
  • Benefits:
    • Reduced Latency: Repeated identical API requests within the same day are served from cache.
    • Cost Efficiency: Avoids redundant API calls by reusing previously fetched data.
    • Scalability: Supports multiple agents or processes accessing the cache concurrently in distributed deployments.