AI Agents for Agritech

An advanced Multi-Agent Assistant leveraging AI agents, APIs, and RAG for precise agricultural insights.

Modern agriculture demands intelligent systems capable of synthesizing vast amounts of data into actionable advice. Agrigraph is a sophisticated assistant, created for a European project, that orchestrates multiple AI agents to provide farmers and agronomists with real-time weather data, historical analysis, and precise irrigation recommendations based on project documentation and sensor simulations.

Objectives

  1. Orchestrate specialized AI agents for multi-domain agricultural support.
  2. Integrate real-time and historical weather services (Open-Meteo).
  3. Implement Retrieval-Augmented Generation (RAG) for querying technical agricultural documents.
  4. Provide data-driven irrigation recommendations using tensiometer predictions.

My Role

  • Architected the multi-agent system using LangGraph and LangChain.
  • Developed custom tools for weather data retrieval and coordinate conversion.
  • Integrated a FAISS-based RAG pipeline for document intelligence.
  • Implemented the irrigation logic and agent orchestration workflows.


Tech Stack
Language Python 3.11+
Frameworks LangGraph, LangChain
Models Google Gemini (Generative AI)
Vector DB FAISS (Facebook AI Similarity Search)
DevOps uv, YAML files
Repository Structure

The project follows a modular graph-based architecture:

  • agents Specialist agent definitions and utilities
  • tools Custom implementations for Weather, RAG, and Sensors
  • graph LangGraph workflow and state orchestration
  • config YAML-based agent prompts and model settings

The Challenge: Fragmented Agricultural Intelligence

Decision-making in the field is often hindered by the difficulty of accessing and combining diverse information sources. Technical manuals, real-time weather forecasts, and soil sensor data are frequently disconnected, leading to suboptimal crop management.

Agrigraph solves this by acting as a Unified Knowledge Hub. The system integrates:

  1. Weather Intelligence: Automated retrieval of coordinates and meteorological data.
  2. Document Wisdom: RAG-powered querying of the IRRITRE project documentation.
  3. Sensor Insights: Predictive tools for soil moisture and irrigation needs.
  4. General Agronomy: A fallback agent for broad agricultural queries.
Overview of the multi-agent orchestration, showing how the Supervisor routes queries to specialized agents.

Methodology & Orchestration

Agrigraph utilizes a state-of-the-art multi-agent architecture to handle complex requests:

  1. Supervisor Agent: Analyzes user intent and routes tasks to the most qualified specialists, allowing for parallel task execution and response synthesis.
  2. Specialized Tooling: Each agent has access to specific tools (Open-Meteo API, FAISS vector stores, tensiometer models) to ensure high-fidelity responses.
  3. Reviewer Node: A final quality-control layer that refines the aggregated response for clarity and tone consistency.
Example of interaction between the Weather Agent and the Irrigation Specialist to provide a combined recommendation.

Results: Precision at Scale

By leveraging LLMs for both reasoning and data retrieval, Agrigraph provides a robust platform for digital agriculture.

Key outcomes:

  • Context-Aware Support: Answers complex questions by combining technical docs with real-time conditions.
  • Simplified Access: Users interact with a natural language interface instead of raw data silos.
  • Extensibility: The graph-based design allows for easy integration of new tools and sensors as the project evolves.
The Agrigraph CLI assistant in action, demonstrating agent collaboration.
Note: This image is a placeholder and does not display real production data.

Note: To maintain confidentiality, all company names, locations, dates, and specific proprietary values have been anonymized or modified. The analysis focuses on the technical methodology and challenges encountered during the project.