🤖 Manage Appian tasks with Ollama Qwen LLM and Postgres memory
⚡ 172 views · 🤖 AI Chatbots & Agents
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
🔗 Nodes Used
Webhook, AI Agent, Ollama Chat Model, Chat Trigger, Postgres Chat Memory
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File