NiFiMCP

NiFi has entered the chat.

Your next
NiFi engineer
is AI.

We’re building the open-source MCP bridge between your AI assistant and Apache NiFi. Ask in plain English. Investigate real flows. Get answers with receipts.

See the AI workflow
An abstract flowing N, sculpted in dark bronze

Your flows. Your AI. Your rules.

In development. Independent of Apache.

Your flow has some
explaining to do.

Give your AI the evidence to explain a route, investigate a queue, and trace a missing feed. Every answer should show its work.

Explore an illustrative AI investigation

Why did this feed go to unmatched?

Synthetic example

FindingEvidenceReference
Observed
Relationship: unmatchedROUTE eventReference: event 218
event 218
Observed
source.type is absentAllowed attributesReference: sample 04
sample 04
Inferred
The required attribute may be missingCurrent configurationReference: rule 07
rule 07

The sampled FlowFile took the unmatched relationship at RouteOnAttribute.

The current routing rule expects source.type. That attribute is absent in this sample. Historical rule values are unavailable, so the cause remains an inference.

Illustrative AI workflow using synthetic evidence. The working MCP server is in development; no production system is connected.

Bring your AI
to NiFi.

Your assistant can only reason about what it can see. We’re giving it a way into the flows, queues, and provenance behind your data.

Read the project brief

Context is the upgrade.

A local MCP connection is designed to let your assistant inspect stock Apache NiFi. Offline evidence bundles extend the workflow to disconnected environments.

Answers with receipts.

Findings should point to provenance, queue state, and configuration. Facts, hypotheses, and unknowns stay distinct. You stay in control of changes.

Open means yours.

Planned Apache 2.0 release. No required hosted model. No subscription required to investigate your flows.

Implementation in progress. Initial target: Apache NiFi 2.12.0. Compatibility will be published with tested releases.

AI for the flow.
Engineers for the hard parts.

Build with the open-source project. Bring in dedicated NiFi engineers to take the work through integration, deployment, and daily operations.

Crack the hard problem.

A focused diagnostic engagement, with a clear account of the findings and practical next steps.

Diagnostic

Make it operational.

Deployment, integration, and operator handover, shaped around your network and operating constraints.

Implementation

Bring in the builders.

Forward-deployed engineering, remote or on site, with a defined scope and agreed access arrangements.

Embedded engineering

Put AI to work
on your flows.

Bring a use case, a difficult pipeline, or an idea worth building. Help shape what AI can do for NiFi.

Please share only a high-level description. Keep credentials, production logs, personal data, and restricted information out of this form.