KNIME Analytics Platform REST API
KNIME Analytics Platform REST API for data scientists and engineers to deploy, execute, and manage KNIME workflows as REST services — enabling automated data pipeline execution, model serving, workflow scheduling, and data transformation through KNIME's open-source visual data science platform. Enables AI agents to manage workflow execution for KNIME workflow trigger and result retrieval automation, handle data input for parameterized workflow data injection and result extraction automation, access model serving for deployed ML model inference endpoint automation, retrieve job management for workflow job status monitoring and result polling automation, manage workflow repository for workflow version management and deployment automation, handle schedule management for automated workflow scheduling and trigger automation, access data access for workflow input/output data retrieval automation, retrieve execution context for workflow container service and REST API automation, manage component services for KNIME node and component REST exposure automation, and integrate KNIME with data lakes, databases, and ML platforms for open-source analytics automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Open-source data science. Self-hosted. Basic auth. Data residency controlled by operator. Workflow and analytical model data.
⚡ Reliability
Best When
A data science or analytics team wanting AI agents to automate KNIME workflow execution, model serving, and data pipeline management through KNIME Server's REST API on self-hosted or KNIME Cloud infrastructure.
Avoid When
KNIME SERVER IS REQUIRED FOR REST API: KNIME Analytics Platform REST API requires KNIME Server or KNIME Business Hub; automated desktop-community assumption creates no_rest_api for teams using only the free desktop application; automated must deploy KNIME Server. WORKFLOW MUST BE DEPLOYED: REST execution requires workflows deployed to KNIME Server repository; automated local-workflow assumption creates workflow_not_found for executing workflows not deployed to server; automated must deploy workflows to server before REST execution. EXECUTION IS ASYNCHRONOUS: KNIME workflow jobs execute asynchronously; automated sync-result assumption creates incomplete_result for result retrieval before job completes; automated must poll job status until terminal state. PARAMETERIZATION REQUIRES CONTAINER NODES: Passing inputs to KNIME workflows via REST requires Container Input/Output nodes in the workflow; automated generic-parameter assumption creates input_rejected for workflows without properly configured container nodes; automated must design workflows with container nodes for REST parameterization.
Use Cases
- • Executing parameterized KNIME data transformation workflows via REST for data pipeline automation agents
- • Serving deployed ML models from KNIME workflows as inference endpoints for model serving automation agents
- • Scheduling and monitoring batch data processing jobs for analytics workflow automation agents
- • Integrating KNIME workflow outputs into downstream systems for enterprise data pipeline agents
Not For
- • Real-time streaming analytics (KNIME is batch-oriented workflow execution, not stream processing infrastructure)
- • Organizations without KNIME Server or KNIME Business Hub (REST API requires KNIME Server; Community Edition is desktop-only)
- • High-frequency low-latency ML inference (KNIME workflow execution adds overhead; dedicated model servers are faster)
Interface
Authentication
KNIME uses Basic Auth or API key for Server REST API. REST API with JSON. Zurich, Switzerland HQ. Founded 2008 by Michael Berthold (spinoff from University of Konstanz). Products: KNIME Analytics Platform (open-source desktop), KNIME Server (self-hosted), KNIME Business Hub (enterprise self-hosted), KNIME Cloud. 2M+ users. Open-source with commercial server. Industries: pharma, finance, manufacturing, retail. GitHub: knime/knime-core. Competes with Dataiku, Alteryx, and RapidMiner for no-code/low-code data science.
Pricing
Zurich CH. Open-source desktop free. Server license required for REST API. 2M+ community users.
Agent Metadata
Known Gotchas
- ⚠ CONTAINER NODES ARE REQUIRED FOR PARAMETERIZATION: KNIME workflows must contain Container Input and Container Output nodes to accept REST parameters; automated direct-parameter assumption creates parameter_ignored for workflows without container nodes; automated must verify workflow has container nodes before parameterized execution
- ⚠ JOB EXECUTION IS ASYNCHRONOUS: KNIME workflow jobs are submitted and execute asynchronously; automated sync-result assumption creates empty_result for immediate result retrieval after job submission; automated must poll job status endpoint until Executed state before retrieving results
- ⚠ JOB RESULTS HAVE LIMITED RETENTION: KNIME Server job results are retained for configurable period then deleted; automated permanent-result assumption creates result_not_found for results retrieved after retention period; automated must retrieve results promptly after job completion
- ⚠ WORKFLOW VERSIONS MATTER: KNIME Server supports workflow versioning; automated latest-version assumption creates unexpected_result for executions that run older deployed versions; automated must specify workflow version or ensure latest is deployed
- ⚠ CONCURRENT EXECUTION IS LICENSE-LIMITED: KNIME Server concurrent execution is limited by licensed executor slots; automated unlimited-parallel assumption creates job_queued for workflows exceeding concurrent execution capacity; automated must handle queued job state and implement backoff
Alternatives
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Scores are editorial opinions as of 2026-03-07.