Manage Appian Tasks with Ollama Qwen LLM and Postgres Memory

Description

This workflow is a simple example of using n8n as an AI chat interface into Appian. It connects a local LLM, persistent memory, and API tools to demonstrate how an agent can interact with Appian tasks.

What this workflow does
Chat interface: Accepts user input through a webhook or chat trigger
Local LLM (Ollama): Runs on qwen2.5:7b with an 8k context window
Conversation memory: Stores chat history in Postgres, keyed by sessionId
AI Agent node: Handles reasoning, follows system rules (helpful assistant persona, date formatting, iteration limits), and decides when to call tools
Appian integration tools:
List Tasks: Fetches a user’s tasks from Appian
Create Task: Submits data for a new task in Appian (title, description, hours, cost)

How it works
A user sends a chat message
The workflow normalizes fields such as text, username, and sessionId
The AI Agent processes the message using Ollama and Postgres memory
If the user asks about tasks, the agent calls the Appian APIs
The result, either a task list or confirmation of a new task, is returned through the webhook

Why this is useful
Demonstrates how to build a basic Appian connector in n8n with an AI chat front end
Shows how an LLM can decide when to call Appian APIs to list or create tasks
Provides a pattern that can be extended with more Appian endpoints, different models, or custom system prompts