Build Custom LLM Chains and Agents with LangChain and n8n
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Unlock the power of custom Large Language Model (LLM) chains and AI agents with this n8n workflow. Seamlessly integrate your own code to define complex AI behaviors and leverage pre-built LangChain tools.
About This Workflow
This n8n workflow provides a powerful example of building custom LLM chains and agents using the LangChain integration. It showcases the flexibility of the code node for defining your own AI logic and tools, allowing you to create sophisticated AI-powered automations tailored to your specific needs. You can define custom LLM prompts, pipe them through language models, and even integrate external tools like Wikipedia search for richer AI interactions. This workflow empowers developers and automation enthusiasts to move beyond pre-configured nodes and craft truly unique AI solutions within the n8n visual programming environment.
Key Features
- Custom LLM Chain Definition: Utilize the
codenode to write and execute custom LangChain LLM chain logic, defining unique prompt templates and execution flows. - AI Agent Construction: Build intelligent AI agents capable of using tools by integrating custom code nodes and pre-built LangChain tools like
WikipediaQueryRun. - Node-Based Visual Automation: Combine the power of custom code with n8n's intuitive visual workflow builder for seamless integration and management.
- LangChain Integration: Leverage the robust capabilities of LangChain for building advanced LLM applications directly within your n8n workflows.
- Extensible Tooling: Easily define and integrate your own custom tools or use existing ones to enhance your AI agent's capabilities.
How To Use
- Trigger the Workflow: Initiate the workflow by clicking the "Execute Workflow" button.
- Define Your LLM Input: Use the first "Set" node to define the input prompt for your custom LLM chain (e.g., 'Tell me a joke').
- Create Custom LLM Chain: Configure the "Custom - LLM Chain Node" (
codenode) by writing your LangChain JavaScript code. This example shows how to create a prompt template and pipe it through an LLM. - Connect LLM to Chain: Ensure the "OpenAI" node (or your preferred LLM provider) is connected to the
ai_languageModelinput of the "Custom - LLM Chain Node." - Define Agent Input: Use the second "Set" node to define the input for your AI agent (e.g., 'What year was Einstein born?').
- Integrate Tools: Configure the "Custom - Wikipedia" node (
codenode) to define and load a LangChain tool, such asWikipediaQueryRun. - Build Your Agent: Set up the "Agent" node, connecting your LLM (
Chat OpenAI) and any custom tools (e.g., "Custom - Wikipedia") to its respective inputs. - Execute and Observe: Run the workflow to see your custom LLM chain and AI agent in action, interacting with the LLM and tools.
Apps Used
Workflow JSON
{
"id": "929415ce-c8b3-4ada-8df7-54541affcd56",
"name": "Build Custom LLM Chains and Agents with LangChain and n8n",
"nodes": 7,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 929415ce-c8b3...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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