Inkeep vs n8n: Key Differences To Know

Inkeep and n8n enable enterprises to build or deploy pre-built automations. The main differences are the complexity of these automations and how well technical and business teams can work together.

This comparative article aims to help you choose the right platform for your specific needs.

What is n8n?

n8n is a low-code workflow automation platform designed to help teams complete tasks efficiently, with a focus on technical teams building pre-defined workflows.

What is Inkeep?

Inkeep is a low-code and pro-code AI agent automation platform designed for both technical and business teams to create teams of AI Agents that collaborate to get work done, just like human teams.

Key Difference #1: Workflows vs Agents vs Multi-Agents

Workflows, Agents, and Multi-Agents are similar but distinct concepts. Knowing the differences is key to understanding how Inkeep and n8n differ.

According to Anthropic, workflows are systems where LLMs and tools are orchestrated through predefined code paths. Agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. While according to Google, multi-agent systems are a collection of multiple specialized agents that collaborate, delegate tasks, and work together to solve a problem.

What separates true multi-agent systems from simple workflows with agents in the loop is Agent2Agent (A2A) protocols that enable agents to directly communicate with each other.

With the above in mind, n8n specializes in creating workflows; Inkeep specializes in creating multi-agent systems; both platforms enable users to build Agents.

Agent2Agent (A2A) is Key for Multi-Agent Systems

In n8n, AI Agents operate as a pre-defined step within a workflow. Creators of n8n workflows still need to pre-define available agents. At runtime, agents autonomously decide which tools to use and how to populate parameters. You can technically have multiple Agents in a workflow, but data passing from Agent to Agent is through edges and tools (e.g., Agent Tool, Call n8n Workflow Tool). n8n lacks A2A capabilities that truly make a system 'multi-agent'.

While in Inkeep, A2A is central to the multi-agent framework. This means AI Agents are simply assembled together, and they then autonomously work together on their own to get work done.

This is a significant difference because Inkeep's multi-agent Graph architecture enables Agents to work like a real team of humans autonomously. Team members specialize in different tasks; some are generalists, but they work towards a specific goal (defined by user inputs and prompts) and can hand off tasks to each other or ask for help and get answers back. Within a Graph, an Agent can choose to Transfer control of the chat to another Agent or Delegate a subtask for another Agent to do and wait for its response. This is akin to real human teams.

Additionally, Inkeep's A2A protocol support extends this team dynamic by allowing external agents built with other frameworks (like LangGraph, CrewAI, or Salesforce Agentforce) to integrate seamlessly into your Graph. External agents communicate over the A2A protocol so your Inkeep agents can delegate tasks to them as if they were native team members.

For this reason, Inkeep's approach is better for complex problems that need flexible thinking, while n8n's approach is better for simple, predictable tasks.

Here's a high-level, simplified visual overview of Workflows vs Multi-Agents.

Key Difference #2: Native Data Retrieval vs Integration

Data retrieval is crucial to ensure customization when automations are being executed.

Inkeep agents use Inkeep's RAG system to ground Agents in company data by retrieving relevant content from knowledge bases. Doing this in Inkeep is as simple as a drag-and-drop in the Inkeep console or through integration with third-party tools in the Inkeep console. Users can also choose to connect their own vector database.

• This approach augments the AI's context with verified information, which enables the generation of accurate responses.
• This is crucial when agents hand off and delegate tasks to each other; agents can be grounded in company policies, knowledge, and procedures, just like real human employees.

Moreover, Inkeep has an 'Artifact System' designed to create a traceable record for every source and decision made by agents. So when an agent uses a tool or interacts with another agent, the resulting information is automatically parsed and stored as an artifact.

• Each artifact captures the source of the information (such as the tool or agent used), the relevant content from the response, and metadata about the interaction.
• This creates a clear chain of evidence—essentially a paper trail—that documents where each piece of information originated, which can be crucial for compliance and auditability.

Unlike Inkeep, n8n does not offer its own native RAG engine. So a vector database integration is needed, which n8n has no shortage of. This approach requires having a vector database account and manually connecting it.

• At query time, users must design a workflow that generates an embedding for the user's question.
• This retrieves the most relevant document chunks from the vector store, and provides these as context to the LLM for answer generation.
• Thereby ensuring that responses are grounded in your defined source of truth, as only indexed documents are used for context and retrieval.

Key Difference #3: Developer Experience

Both Inkeep & n8n offer visual builders that make it easy to connect steps in a workflow or agents together.

A key difference lies in how they're connected in code.

n8n workflows are represented and connected through JSON. Each workflow is defined as a JSON object, which specifies the nodes, their parameters, and how they are connected to each other. This structure allows n8n to string nodes together and manage the flow of data between them in a visual and programmatic way.

Inkeep, on the other hand, provides a declarative TypeScript SDK that allows developers to define, connect, and manage multiple agents programmatically, enabling complex agent workflows and customization. This SDK is designed to give developers a high degree of control and flexibility, which enhances the developer experience.

But what separates Inkeep even further is the interoperability between going from code to UI, and vice versa. This means that developers can build multi-agent systems in declarative TypeScript, and push to UI to show their work visually to business teams. Business teams, on the other hand, can build visually, and send their visually built automations to developers in code. Inkeep's framework is highly interoperable in both modalities.

Practical Implications: Where Each Platform Excels

When n8n Excels:

• Regulated processes requiring audit trails and predictable execution paths
• Business automation with clear triggers, conditions, and outcomes
• Integration-heavy workflows connecting multiple enterprise systems
• AI-enhanced data pipelines with intelligent processing at defined stages
• Teams needing visual workflow design without deep AI expertise

When Inkeep Excels:

• Complex problem-solving requiring adaptive strategies
• Research and analysis where the path to a solution isn't predetermined
• Creative tasks needing iterative refinement between specialists
• Dynamic environments where requirements change mid-execution
• Scenarios requiring true agent autonomy and emergent agent primitives like A2A

Business Considerations

• Use n8n when you need predictable resource usage and costs.
• Use Inkeep for complex jobs that might require multiple individuals at a company.

If you need a task done, use n8n. If you want a job done, use Inkeep.

Technical Architecture Comparison

Conclusion

The choice between Inkeep and n8n fundamentally comes down to the task you need to automate.

• Inkeep excels as a sophisticated multi-agent automation platform with unique features like handoff/delegation patterns, source attribution, and dynamic context management
• n8n excels as a mature workflow automation platform with visual programming, extensive integrations, cost-effectiveness, and proven scalability

Both platforms are enterprise-ready with strong security and developer experience.

Your decision should align with whether you need advanced multi-agent capabilities or streamlined workflow automation for your AI implementations.