Kubiya is the first LLM-native automation platform that runs entirely on your infrastructure. Unlike traditional automation platforms, Kubiya is designed from the ground up for AI agents with Serverless Container Tools, LLM-Friendly DAG Workflows, and Zero-Trust Security - all executing in your own environment.
What makes them special: Unlike traditional serverless functions with cold starts and language limitations, Kubiyaβs serverless tools are container-native and LLM-optimized.
π― LLM-Optimized Features:
π Natural Language Descriptions: Each tool has LLM-friendly documentation
π Semantic Discovery: LLMs can find tools by describing what they need
β‘ Instant Scaling: From 0 to 1000+ containers in seconds
π Your Infrastructure: Run on your Kubernetes, Docker, or VMs
π Live Streaming: Real-time output for LLM feedback
Example Tool Definition:
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{ "name": "analyze-logs", "description": "Analyze application logs for errors and patterns", "llm_prompt": "Use this tool when you need to investigate application issues, find error patterns, or analyze log data. It supports JSON, text, and structured logs.", "image": "python:3.11-slim", "packages": ["pandas", "numpy", "matplotlib"], "integrations": ["aws/s3", "elasticsearch"], "scaling": { "min_instances": 0, "max_instances": 100, "scale_to_zero_timeout": "5m" }}
What makes them special: Traditional workflows are code-heavy and hard for LLMs to understand. Kubiya workflows are declarative, visual, and LLM-optimized.
π§ LLM-Optimized Features:
π Natural Language Steps: Each step has human-readable descriptions
π― Intent-Based: Focus on βwhatβ not βhowβ
π Self-Documenting: Workflows explain themselves to LLMs
π Dynamic: LLMs can modify workflows on-the-fly
π Visual: Mermaid diagrams auto-generated for LLM understanding
Example: LLM Creating a Workflow
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# LLM Request: "Create a data pipeline that processes user events"# Kubiya generates:workflow = { "name": "user-events-pipeline", "description": "Process and analyze user events from multiple sources", "llm_summary": "This workflow extracts user events from Kafka, transforms them with pandas, validates data quality, and loads into data warehouse", "steps": [ { "name": "extract-events", "description": "Extract user events from Kafka topics", "tool": "kafka-consumer", "args": {"topics": ["user-clicks", "user-views"]} }, { "name": "transform-data", "description": "Clean and transform event data using pandas", "tool": "python-pandas", "depends_on": ["extract-events"] }, { "name": "load-warehouse", "description": "Load processed data into Snowflake", "tool": "snowflake-loader", "depends_on": ["transform-data"] } ]}
Why this matters: Most AI platforms send your data to their cloud. Kubiya keeps everything in your infrastructure for security, compliance, and performance.
π Security & Compliance Benefits:
π Data Locality: Your data never leaves your infrastructure
π‘οΈ Zero Trust: Every action validated by your policies
π Compliance: GDPR, SOC2 - your rules, your infrastructure
π Air-Gapped: Works completely offline if needed
ποΈ Full Visibility: Complete audit trails in your systems
β‘ Performance Benefits:
π Low Latency: Direct access to your resources
π No API Limits: Scale based on your infrastructure
πΎ Data Efficiency: No data transfer overhead
π― Resource Optimization: Use your existing capacity
Ready to build enterprise-grade AI applications? Kubiya provides the production infrastructure, security, and scale you need to deploy AI automation that actually works in the real world.
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Responses are generated using AI and may contain mistakes.