Building a Multi-Agent Tax Audit System with LangGraph and Odoo

TL;DR: I built a multi-agent system that audits invoices, detects fiscal inconsistencies, and generates compliance reports — integrated with Odoo ERP and standalone databases.

The Problem

Tax auditing for small and medium businesses in Tunisia is manual, error-prone, and slow. Most accounting firms rely on Excel and manual checks. Regulations change frequently, and keeping up is a full-time job.

I wanted to build something that could ingest financial data, run audit rules automatically, and produce compliance reports — without needing a team of accountants.

The Architecture

The system uses a LangGraph-based multi-agent architecture:

Agent 1: Data Ingestion Agent

Connects to Odoo ERP or standalone SQL databases
Extracts invoices, ledgers, and financial statements
Normalizes data into a unified schema

Agent 2: Audit Agent

Applies tax rules against the data
Detects anomalies: missing invoices, misclassified expenses, VAT discrepancies
Flags items for human review

Agent 3: Reporting Agent

Generates compliance reports
Produces a summary of findings with risk levels
Suggests corrective actions

Orchestrator

LangGraph manages the flow between agents
Handles state, retries, and error recovery

Technical Challenges

Schema Mismatch: Every Odoo instance is customized differently. The ingestion agent had to handle dynamic schemas — detecting table structures at runtime and mapping them to a canonical audit model.

Multi-Agent Coordination: Getting three agents to work together without stepping on each other's state was the hardest part. LangGraph's checkpointing was essential here.

Regional Tax Rules: Tunisian tax law isn't well-documented in English. Building the rules engine meant working directly with Arabic and French regulatory texts.