AI native platform for multi-omics research

LangGraph-based orchestration with RAG-powered assistants using Hugging Face and Ollama — bridging deterministic bio-computation with explainable AI interpretation.

Native support for WDL, Nextflow, Snakemake, and CWL. Cloud-agnostic Tool Execution Service ensures 1:1 parity between local and massive-scale genomic execution.

Optimized on NVIDIA PyTorch with CUDA on DGX Spark. High-performance GNNs for drug discovery and deep learning for single-cell transcriptomics.

Production-grade Model Registry and LIMS-X metadata system for total traceability — from raw FASTQ to drug-target intelligence and pathway enrichment.

Lab Integrations:
· Benchling (samples, sequences, entities, notebooks)
· REDCap (patient cohorts, clinical research data)
· eLabFTW (experiment export, file upload, tags)
· AWS S3 (import datasets, export results, MinIO compatible)
· Zenodo (publish with DOI, open science, reproducible research)
· LIMS built-in
· ELN-ready export: PDF reports (WeasyPrint) · ELN JSON (machine-readable) · Benchling notebook export · eLabFTW experiment export · CSV/TSV universal format

Sign in with Google, GitHub, or Microsoft, alongside secure email/password authentication via JWT + zero-trust auth service. Existing accounts can link a provider identity with a one-time confirmation step.

90% average test coverage across 28 microservices. Sub-3ms latency for critical service handshakes. Built for clinical-grade stability from day one.

Extensible plugin architecture with OnboardAI documentation browser. 30,000+ lines of living documentation updated after every commit.

Each bioinformatics workflow is modular, allowing researchers to customize, extend, or replace analysis steps without breaking reproducibility.

Runs consistently across local machines, Slurm-based HPC clusters, and cloud platforms like AWS, Azure, and GCP with identical execution logic.

320 Docker images + 1,000 ARM64 SIF images
1,320+ total container images
Available: ghcr.io/omnibioai & huggingface.co/omnibioai
ARM64 + x86_64 support

LangGraph-based agentic workflows dynamically plan, execute, and adapt bioinformatics pipelines, enabling multi-step reasoning across tools, datasets, and analysis stages.

Full-stack error tracking and performance monitoring via Sentry.io across all 28 microservices — with release tracking, error alerting, and performance tracing built in from day one.

WebSocket-based shared workspaces with live chat, object-level comments, annotation history, and full audit trails for reproducible science.

Publish analysis results to Zenodo with automatic DOI generation for citable, reproducible research. Search and import public datasets directly from Zenodo.

Export analysis results directly to eLabFTW electronic lab notebook. Creates experiment entries, uploads output files, and adds tags automatically. Open-source ELN used by thousands of academic labs worldwide.

Search, view, create, update, and comment on ServiceNow incidents via OAuth 2.0 client credentials — manage IT service tickets directly from OmniBioAI.

Access Entra ID users and groups, SharePoint sites and document libraries, Outlook mailbox data, and Teams and channel listings through admin-approved Microsoft Graph access.

Search issues with JQL, create issues, add comments, and transition workflow status directly from OmniBioAI.

Search and retrieve Confluence knowledge, create and update pages, and manage scientific and technical documentation from OmniBioAI.

Connect genomic analysis workflows with AWS HealthOmics, bringing managed bioinformatics workflows and sequencing data processing into OmniBioAI's agentic execution layer.

Connect OmniBioAI agents with cloud infrastructure and services, enabling secure interaction with cloud-native compute, storage, and research environments.

Connect genomic and bioinformatics workflows with DNAnexus for cloud-based data management, analysis, and scalable research execution.

Connect AI agents to Databricks data and analytics environments, enabling research data workflows, SQL analytics, and scalable computational workloads.

Give OmniBioAI workflows controlled access to Snowflake data platforms for querying, analytics, and data-driven scientific workflows.

Connect agents with Git-based development environments to inspect repositories, work with source code, and integrate software development workflows into scientific automation.

Connect experiment tracking and machine-learning lifecycle workflows with MLflow, allowing agents to work with experiments, runs, metrics, parameters, and registered models.

Bring Kubernetes-native compute and orchestration into OmniBioAI, enabling agents to interact with scalable containerized workloads and research infrastructure.

Connect OmniBioAI with the Hugging Face ecosystem for models, datasets, repositories, and AI research workflows — bringing modern machine-learning assets into agentic bioinformatics.

Connect agentic workflows with Apache Airflow for pipeline orchestration, DAG management, monitoring, and automated scientific data workflows.

Bring analytics engineering workflows into OmniBioAI with dbt, enabling agents to work with data transformations, models, documentation, and lineage.

Connect software engineering and DevOps workflows with Azure DevOps, including work items, repositories, pipelines, and project automation.

Connect OmniBioAI agents with Galaxy for reproducible biomedical analysis, workflow execution, tool discovery, and research computing across Galaxy environments.

Connect OmniBioAI agents with Terra for cloud-based genomics and biomedical research, enabling interaction with workspaces, datasets, workflows, and scalable analysis environments.

Connect OmniBioAI agents with Seven Bridges for cloud-based genomic data management, workflow execution, analysis monitoring, and reproducible biomedical research.

Connect sequencing projects, runs, samples, files, and analysis workflows from Illumina BaseSpace with OmniBioAI's agentic research and bioinformatics environment.

Connect OmniBioAI with KNIME scientific and data workflows, enabling agents to discover, execute, monitor, and capture provenance from external analytical workflows.

Import samples, sequences, and research entities directly from Benchling. Export analysis results back as notebook entries and supported Benchling entities, connecting laboratory R&D with computational workflows.

Import authorized research cohorts and study data from REDCap and integrate analysis results back into REDCap projects. Designed for academic medical centers and clinical research workflows.

Import datasets from S3 and export analysis results back to object storage. Supports AWS S3 and compatible storage platforms such as self-hosted MinIO.