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What is oxo-flow?

oxo-flow is a high-performance bioinformatics pipeline engine built in Rust. It compiles workflows into Directed Acyclic Graphs and orchestrates execution with native concurrency, per-rule environment isolation, and AI-powered assistance — all from a single binary.

🧬 Community: browse curated, rated, ready-to-run workflows — ports of popular nf-core & Snakemake pipelines, original designs, and community submissions — at oxo-flow-community.

• 🤖 AI Companion & Scientist Team — Grounded on deterministic engine APIs: natural language pipeline generation, intelligent refinement, 30+ domain diagnostic patterns, experimental repair proposals, and an opt-in Scientist Team multi-agent review profile. Powered by Claude, OpenAI, DeepSeek, or local Ollama.
• 🔀 DAG-first engine — Full static planning before compute: automatic dependency resolution, topological ordering, cycle detection, and target-driven (-t) minimal rule scheduling.
• 🛡️ Physical resource & disk guards — Multi-stage runtime disk-pressure guard (min_free_disk / reclaim_free_disk with cascade tombstone reclamation), global scratch directory isolation (tmp_dir / OXO_FLOW_TMPDIR), and concurrency-aware resource scheduling (CPU, memory, GPU, disk).
• 📦 8 isolated environment backends — conda, mamba, pixi, docker, singularity, venv, system, and HPC modules — with per-rule isolation and an opt-in content-addressable store (OXO_FLOW_ENV_STORE) with automatic GC.
• 🔒 Provenance and integrity controls — Run evidence records SHA-256 output checksums, configuration fingerprints, environment identities, and protected data tree audits (protected_data_trees); recorded checksums take precedence, with file modification timestamps (mtime) used only as a bounded fallback.
• ⚡ Rust performance & safety — Compiled native binary with no embedded Python or JVM runtime, fast startup, Tokio concurrency, and #![forbid(unsafe_code)] in every workspace crate; bioinformatics tools, containers, and environment managers remain explicit external dependencies.
• 🌐 Modern Web Studio & Desktop shell — Embedded React 19 SPA with interactive DAG canvas (React Flow + d3-dag), CodeMirror 6 TOML editor, and native desktop app (oxo-flow-desktop, wry + tao).
• 🧩 First-party VS Code IDE — traitome.oxo-flow extension published on both Open VSX and the VS Code Marketplace: schema-driven completion, background diagnostics, canonical formatting, and one-click run/dry-run/graph/resume actions.
• 📊 Reporting designed for clinical review — Deterministic HTML, JSON, Markdown, and PDF reports generated from checkpoint data; real tool QC metric parsing (fastp, STAR, bcftools, etc.), R-friendly TSV export (--r-data), and automated run snapshots. Clinical qualification is a separate evidence and governance process.
• 🗄️ Checkpoint resume — JSON-persisted execution state; resume interrupted workflows from the last checkpointed rule, with reuse governed by recorded freshness and provenance checks.
• 🚀 Three deployment modes — Personal workstation, team server with OAuth2 (ORCID/GitHub), or HPC cluster submit panel — all from the exact same binary.

Built for people and agents

The same engine serves both audiences — an agent calls the same gates a person does and gets the same evidence back. There is no shadow API and no "AI-only" execution path.

For people

• One binary, four first-party surfaces: CLI, React 19 Web Studio (interactive DAG canvas + TOML editor), VS Code extension, and a native desktop shell.
• Guided static gates — validate → dry-run → lint — with stable E/W diagnostic codes and remediation suggestions, before any compute.
• Readable execution: checkpoint resume, provenance receipts (--provenance, provenance verify), and HTML/JSON/Markdown/PDF reports generated from checkpoint data.
• Physically grounded runtime: 8 environment backends, disk-pressure guard (min_free_disk / reclaim_free_disk), scratch isolation (tmp_dir), and content-addressed invalidation.

For agents

• oxo-flow agent-docs scaffolds AGENTS.md, CLAUDE.md, and the full SKILL.md operating manual into a project — the manual ships inside the binary, so it works offline from the same single file you run.
• Deterministic contract: every gate supports --json with stable diagnostic codes and exit codes; errors use the structured {code, message, detail, suggestion} shape.
• AI is compiler-validated, not trust-me: template --ai and the web copilot run the same PipelineGenAgent through an orchestrator whose output must pass the engine's own schema/validate gates.
• Safety by construction: the skill's data-safety contract (inputs read-only, scratch confined to the project, append-only operation journal), [engine] protected_data_trees drift detection, and an explicit OXO_FLOW_AI_PROVIDER=disabled pin that no persisted tier can override.
• Self-contained knowledge: Bioconda, bio.tools, nf-core module, and pipeline-graph indexes are embedded in the binary; no network round-trip is required to validate a workflow.

See AGENTS.md for the contributor/agent context and docs/guide/src/SKILL.md for the canonical operating manual.

Why oxo-flow?

Feature	oxo-flow	Snakemake	Nextflow
Language	Rust — compiled, type-safe, #![forbid(unsafe_code)] in the core, AI, CLI, and web crates	Python	Groovy/JVM
Performance	Native binary, instant startup	Python + JIT overhead	JVM startup overhead
Workflow format	TOML (.oxoflow) — declarative, composable	Snakefile (Python DSL)	Nextflow DSL (Groovy)
Execution planning	Pure DAG compiled & verified before compute; target-driven (-t) minimal subgraphs	Dynamic DAG evaluated by Python runtime	Dataflow channels dynamically wired at runtime
Reproducibility model	SHA-256 output checksums, config fingerprints, protected data tree audits; recorded checksums take precedence over file timestamps	File modification timestamp (mtime), sensitive to clock/NFS drift	Task hash cache (.nextflow/cache) built primarily from input content hashes
Disk & scratch safety	Built-in runtime disk-pressure guard (Hold/Reclaim ladder) + global tmp_dir scratch isolation	None — unhandled out-of-disk crashes; tools spill into system /tmp	Disk monitoring via Wave/Tower; no engine-level cascade reclamation
Environment support	8 backends — conda, mamba, pixi, docker, singularity, venv, system, modules — per-rule	conda, singularity, docker	conda, docker, singularity, modules
Web interface	Built-in React 19 SPA + 100+ OpenAPI REST endpoints + SSE live events	External Snakemake-UI / panoptes	Nextflow Tower / Seqera Platform (commercial)
Editor & IDE	First-party VS Code IDE (Open VSX + VS Code Marketplace) & native desktop shell	Community editor plugins	Community editor plugins
Reporting	Built-in HTML/JSON/Markdown/PDF reports from checkpoint data + domain QC parser + --r-data TSV	Via MultiQC	Via Nextflow Tower / MultiQC
Cluster backends	SLURM, PBS, SGE, LSF, k8s, AWS Batch, GCP Batch	SLURM, PBS, SGE, LSF	SLURM, PBS, SGE, LSF, k8s, AWS Batch
Security	Shell sanitization, path traversal prevention, rate limiting; no OS-level tenant boundary yet	Limited	Limited
AI Companion	Architectural citizen — unified agent, 30+ domain diagnostic patterns, Scientist Team	Not built-in	Not built-in
Testing	Rust unit/integration suites, schema-drift checks, and AI evaluations; exact results are recorded per commit	pytest-based	Spock / varied

Design Principles

oxo-flow rests on six architectural pillars:

1. DAG as Pure Declarative Data — A workflow is mathematically a directed acyclic graph of pure data transformations, not an imperative script. All dependencies, wildcards, environments, and resource requirements are declared statically in .oxoflow TOML and validated before any job starts.
2. Content-Addressed Evidence over File Timestamps — Reproduction requires evidence, not a timestamp convention. oxo-flow anchors checkpoint state on SHA-256 output checksums, configuration fingerprints, content-addressable environment hashes, and protected data tree audits (protected_data_trees); recorded checksums take precedence, with mtime demoted to a bounded fallback. This detects declared state changes but does not by itself prove biological correctness or bitwise reproducibility across all tools.
3. Hard Physical Boundaries & Resource Guards — Real-world bioinformatics computation is constrained by physical limits: CPUs, GPUs, RAM, and finite disk volumes. oxo-flow enforces resource limits as first-class scheduling constraints, isolates scratch spaces via tmp_dir, and guards runs against out-of-space aborts through a multi-stage runtime disk-pressure ladder (Normal → Reclaim → Hold → Abort).
4. Environment Isolation — Every rule names its execution environment across 8 supported backends (conda, mamba, pixi, docker, singularity, venv, system, modules), with optional content-addressable deduplication. Environment isolation reduces tool conflicts; it is not a complete host or tenant security boundary.
5. Rust-Native Performance & Safety — Zero-cost abstractions, fearless concurrency with Tokio, and #![forbid(unsafe_code)] in every workspace crate ensure instant startup, minimal memory footprint, and reliable orchestration of tens of thousands of concurrent tasks without JVM or Python interpreter overhead.
6. AI as an Architectural Citizen — AI assistance is integrated into the engine's feedback loop through machine-readable errors, canonical TOML synthesis, domain diagnostics, and the multi-agent Scientist Team. Generation and diagnosis are engine-validated. Repair is experimental: a proposed patch requires independent semantic verification and a successful rerun before it can be called fixed.

Workflow Gallery

Learn oxo-flow incrementally with curated, validated example workflows — from a one-rule hello-world to reference-scale pipelines. Every workflow passes oxo-flow validate and is tested in CI. See the Workflow Gallery for the full catalog with detailed explanations, DAG visualizations, and CLI output.

Quick Start

Install from pre-built binaries

Download the latest release for your platform from GitHub Releases:

# Linux (x86_64)
curl -LO https://github.com/Traitome/oxo-flow/releases/latest/download/oxo-flow-latest-x86_64-unknown-linux-gnu.tar.gz
tar xzf oxo-flow-latest-x86_64-unknown-linux-gnu.tar.gz
sudo mv oxo-flow /usr/local/bin/

# macOS (Apple Silicon)
curl -LO https://github.com/Traitome/oxo-flow/releases/latest/download/oxo-flow-latest-aarch64-apple-darwin.tar.gz
tar xzf oxo-flow-latest-aarch64-apple-darwin.tar.gz
sudo mv oxo-flow /usr/local/bin/

Install with cargo

cargo install oxo-flow-cli

Install with Conda

conda install -c bioconda oxo-flow-cli

Version caveat — the bioconda recipe can lag a freshly cut release while the bot recipe update lands. Check conda search -c bioconda oxo-flow-cli for the currently delivered version; if it is behind the release notes you need, install from the pre-built binaries above or cargo install oxo-flow-cli (crates.io).

Run with Docker

Pre-built images are published to GitHub Container Registry on every release (and every push to main). Release images are multi-arch (linux/amd64 + linux/arm64, so Apple silicon and ARM servers pull a native image). :latest moves only after the release image passes a health smoke test; a rolling :<major.minor> tag (e.g. :0.19) tracks the newest patch of each minor line, and :main is a multi-arch dev build from source:

# Web UI at http://localhost:3000 (CLI is inside the same image)
docker run -d --name oxo-flow -p 3000:3000 ghcr.io/traitome/oxo-flow:latest

# Pin a release (or track a minor line)
docker run -d -p 3000:3000 ghcr.io/traitome/oxo-flow:0.30.0
docker run -d -p 3000:3000 ghcr.io/traitome/oxo-flow:0.30

# CLI one-shot usage (workflow files mounted from the host)
docker run --rm -v "$PWD:/work" -w /work ghcr.io/traitome/oxo-flow:latest \
  oxo-flow run my-pipeline.oxoflow

Health endpoint: GET /api/health returns "status":"ok" when the database is reachable.

Container configuration is env-only: the image sets OXO_FLOW_MODE=team, OXO_FLOW_HOST=0.0.0.0, and OXO_FLOW_PORT=3000 as defaults, and the CMD carries no CLI flags — so docker run -e OXO_FLOW_PORT=9090 … changes the actual bind port (the built-in healthcheck follows it). Note the published image is built without the postgres compile feature: pointing DATABASE_URL at a postgres:// URL makes the container exit at startup (see the docker-compose.yml comment for how to build a feature-enabled image).

Build from source

git clone https://github.com/Traitome/oxo-flow.git
cd oxo-flow
cargo build --release --workspace

# Build the web SPA so `oxo-flow serve` has assets to serve
# (the committed index.html references gitignored build output):
make frontend-build   # or: cd frontend && npm install && npm run build

# Install the CLI to your local cargo bin directory:
cargo install --path crates/oxo-flow-cli

# Binaries are in target/release/
# - oxo-flow        (CLI, includes `oxo-flow serve` for web UI)
# - oxo-flow-web    (Standalone web server)

Skipping the frontend build is safe: oxo-flow serve detects the missing SPA assets and shows an actionable "SPA not built" page instead of a blank screen.

First workflow

# Create a new pipeline project (creates my-pipeline/ directory and my-pipeline.oxoflow)
oxo-flow init my-pipeline
cd my-pipeline

# Validate the workflow
oxo-flow validate my-pipeline.oxoflow

# Preview execution plan
oxo-flow dry-run my-pipeline.oxoflow

# Execute with 8 parallel jobs
oxo-flow run my-pipeline.oxoflow -j 8

# Visualize the DAG (use -f dot for Graphviz DOT output)
oxo-flow graph my-pipeline.oxoflow -f dot > dag.dot
dot -Tpng dag.dot -o dag.png

# Export a transit-map-style diagram via nf-metro
oxo-flow graph my-pipeline.oxoflow -f metro -o pipeline.mmd
# Render locally (requires nf-metro) or paste the .mmd content into the
# online playground: https://seqeralabs.github.io/nf-metro/latest/playground/
nf-metro render pipeline.mmd -o pipeline.svg

# Generate an HTML report
oxo-flow report my-pipeline.oxoflow -f html -o report.html

Cluster / HPC

oxo-flow supports job submission to HPC and cloud batch schedulers including SLURM, PBS/PBS Pro, SGE/UGE, LSF, Kubernetes, AWS Batch, and GCP Batch. The oxo-flow cluster subcommand manages the full submission lifecycle.

# Submit a workflow to a SLURM cluster
oxo-flow cluster submit workflow.oxoflow --backend slurm --queue short -o jobs/

# Check submission status (no job ids: lists the user's jobs; --backend optional)
oxo-flow cluster status --backend slurm <job-id>
oxo-flow cluster status

# Cancel a submitted job
oxo-flow cluster cancel --backend slurm <job-id>

Supported backends: slurm, pbs, sge, lsf, kubernetes/k8s, aws-batch, gcp-batch. Cluster submissions are configured inline via oxo-flow cluster submit flags.

Workflows can also carry reusable config supplements (e.g. per-cluster thread and memory defaults) in profiles/<NAME>.toml next to the workflow:

# Run a workflow with a config supplement profile
oxo-flow run workflow.oxoflow --profile slurm

Workflow Format (.oxoflow)

oxo-flow uses a TOML-based workflow format that is human-readable, composable, and declarative:

[workflow]
name = "variant-calling"
version = "1.0.0"

[config]
reference = "/data/ref/GRCh38.fa"

[[rules]]
name = "fastp"
input = ["raw/{sample}_R1.fastq.gz", "raw/{sample}_R2.fastq.gz"]
output = ["trimmed/{sample}_R1.fastq.gz", "trimmed/{sample}_R2.fastq.gz"]
threads = 8
shell = "fastp -i {input[0]} -I {input[1]} -o {output[0]} -O {output[1]}"

[rules.environment]
conda = "envs/fastp.yaml"

[[rules]]
name = "bwa_align"
input = ["trimmed/{sample}_R1.fastq.gz", "trimmed/{sample}_R2.fastq.gz"]
output = ["aligned/{sample}.bam"]
threads = 16
memory = "32G"
shell = "bwa-mem2 mem -t {threads} {config.reference} {input[0]} {input[1]} | samtools sort -o {output[0]}"

[rules.environment]
docker = "biocontainers/bwa-mem2:2.2.1"

Wildcards like {sample} expand automatically via input file discovery. Features include reference directory conventions, environment groups, optional rules, and directory inputs. See the full Workflow Format Specification for details.

Editor Support (VS Code)

The first-party oxo-flow Pipeline extension (traitome.oxo-flow) makes VS Code a full .oxoflow IDE — and it is released in lockstep with the engine (VSIX with checksum attached to every GitHub release):

Note — the extension is published to both marketplaces: Open VSX (searched by default by VSCodium, Cursor, Windsurf and most forks) and the VS Code Marketplace (stock VS Code, live since v0.30.0). Install from either, or grab the release VSIX offline:

codium --install-extension traitome.oxo-flow              # Open VSX (forks)
code --install-extension oxo-flow-vscode-v<version>.vsix  # offline VSIX (any VS Code)

It provides [[rules]]-aware syntax highlighting (wildcards and {input[0]} placeholders), completion and hover docs generated from the canonical workflow JSON Schema, background validate/lint diagnostics anchored to the failing rule, canonical formatting, and one-click run / dry-run / graph / resume / AI-generate commands. See the VS Code Extension reference, or Editor Setup for other editors (Zed, Helix, Neovim, JetBrains, …).

CLI Commands

The oxo-flow binary provides 34 subcommands covering the complete workflow lifecycle. See the full CLI Reference for details.

Category	Commands
Execution	run, resume, dry-run (alias plan), test, batch
Development	init, agent-docs, validate, format, lint, debug, template, import
Inspection	graph, info, report, status, config (alias check), diff, provenance, schema, verify-data
Knowledge	knowledge (embedded Bioconda / nf-core / skills query)
Environment	env, export, clean, touch
Deployment	serve, cluster, publish, pull
AI & System	ai, completions, license

See the full CLI Reference for detailed usage of each subcommand.

Web API

The oxo-flow serve command starts an axum-powered REST server with 100+ endpoints across 9 domains (workflow, execution, dag, AI, auth, collaboration, observability, chat, clusters). Full API reference at the Web API reference (GET /api/openapi.json serves the live OpenAPI 3.1 spec).

Architecture & Workspace Layout

oxo-flow is organized as a Cargo workspace with four crates, plus a fifth (oxo-flow-desktop) that lives in crates/ but is excluded from the workspace so headless builds never need GUI toolchains:

oxo-flow/
├── crates/
│   ├── oxo-flow-core/     # Core library: DAG engine, executor, environment mgmt,
│   │                      # config parsing, scheduler, wildcard expansion, reporting
│   ├── oxo-flow-ai/       # AI companion: provider abstraction, skill system, agents
│   ├── oxo-flow-cli/      # CLI binary ("oxo-flow") — Clap-based, 34 subcommands
│   ├── oxo-flow-web/      # Web server ("oxo-flow-web") — axum REST API + frontend
│   └── oxo-flow-desktop/  # Native desktop shell ("oxo-flow-desktop", wry + tao) —
│                          # excluded from the workspace (own Cargo.toml)
├── editors/
│   └── vscode/            # First-party VS Code IDE extension (traitome.oxo-flow)
├── frontend/              # Modern React 19 + TypeScript + Vite web workspace
├── examples/              # Example .oxoflow workflows
├── tests/                 # Integration tests
└── docs/                  # Documentation (MkDocs)

Crate	Type	Binary	License
oxo-flow-core	Library	—	Apache-2.0
oxo-flow-ai	Library	—	Apache-2.0
oxo-flow-cli	Binary	oxo-flow	Apache-2.0
oxo-flow-web	Binary	oxo-flow-web	Dual Academic / Commercial
oxo-flow-desktop	Binary	oxo-flow-desktop	Apache-2.0

Key modules

Module	Crate	Responsibility
dag.rs	core	DAG construction, validation, topological sort
executor/	core	Task execution (local, cluster, cloud), checkpointing, staging
environment.rs	core	Environment management (conda, mamba, pixi, docker, singularity, venv, system, modules)
config/	core	Workflow configuration and .oxoflow file parsing
rule.rs	core	Rule/step definitions with inputs, outputs, shell, resources
scheduler.rs	core	Job scheduling with resource constraints
wildcard.rs	core	Wildcard pattern expansion ({sample}, {chr}, etc.)
report.rs	core	Modular report generation (HTML, JSON, PDF via wkhtmltopdf)
format.rs	core	Workflow formatting, linting, and 30+ diagnostic patterns
plugin.rs	core	Plugin system: signature-verified plugins from TOML manifests (inert [plugins] section parsed for forward compatibility)
ai_provider.rs	web	Multi-provider AI abstraction (Claude, OpenAI, DeepSeek, Ollama)
domains/ai/	web	AI translation, chat SSE streaming, agent orchestration
domains/execution/	web	Run lifecycle, diagnostics, pause/resume, retry
domains/workflow/	web	Pipeline CRUD, validation, DAG building, templating
provider.rs	ai	Multi-provider AI client abstraction with auto-detection
skill.rs	ai	Skill registry and discovery system
agent/	ai	Agent orchestration and tool-calling framework
mcp.rs	ai	MCP (Model Context Protocol) server integration

Three-Mode Deployment

oxo-flow serve starts the web interface with an embedded REST API and React SPA. Run it without arguments for local experimentation:

# Mode 1: Personal workstation (default) — SQLite, localhost:8080, no auth
oxo-flow serve
# → Open http://127.0.0.1:8080 in your browser

# Mode 2: Team server — multi-user (env-var passwords + optional ORCID/GitHub OAuth2)
oxo-flow serve --mode team --host 0.0.0.0

# Mode 3: HPC submit panel — the scheduler (SLURM/PBS/SGE/LSF) is probed at
#   startup when the server itself runs on a cluster login node
oxo-flow serve --mode hpc

Serve flags: --mode (personal | team | hpc), --host (default 127.0.0.1), -p/--port (default 8080), --base-path, and --open (open the browser on startup). Each flag also has an OXO_FLOW_* environment-variable form, plus a platform config file layer — see Deployment Modes.

PostgreSQL: both oxo-flow serve and the standalone oxo-flow-web binary read the DATABASE_URL environment variable — a postgres:// URL (on a build with the postgres feature) enables the PostgreSQL backend for storage-backed web domains; the default (sqlite://oxo-flow.db in the working directory) stays SQLite. Note that the published Docker image is built WITHOUT the postgres feature — rebuild with cargo build --features postgres to enable it. Run execution and lifecycle bookkeeping route through the unified StorageBackend trait. PostgreSQL code paths exist, but live parity, concurrency, crash recovery, and multi-instance behavior are unproven; do not treat the presence of a trait implementation as production-parity evidence.

Documentation

Full documentation is available at traitome.github.io/oxo-flow/latest/.

📖 Documentation Quick Links

If you are...	Recommended Start
New to oxo-flow	Quick Start · First Workflow
A Bioinformatician	Workflow Gallery
A Pipeline Engineer	Workflow Format Specification · CLI Reference
A DevOps/Cloud Admin	Environment Management · Running on Cluster
A Bioinformatics Core	Workflow Gallery · Environment Management

MkDocs source lives under docs/guide/src/.

Development

# Build all workspace crates
cargo build --workspace

# Run all tests (unit + integration)
cargo test --workspace

# Run the full CI suite (fmt + clippy + build + test + schema-drift + skill-drift + version-check + audit + deny + aws-legacy-tripwire + issue-id-tripwire + secrets + docs + frontend-lint + eval-tests + eval-lint + extension-test)
make ci

# Individual CI steps
cargo fmt -- --check                             # Check formatting
cargo clippy --workspace --all-targets -- -D warnings  # Lint (zero warnings)
cargo build --workspace                          # Compile
cargo test --workspace                           # Test

# Format code
cargo fmt

A bare cargo build at the repo root builds only the root oxo-flow library package (the integration-test crate) — not the CLI or web binaries. Pass --workspace whenever you want target/*/oxo-flow and target/*/oxo-flow-web.

Tech stack

Component	Technology
Language	Rust (2024 edition)
Async runtime	tokio
CLI framework	clap (derive)
Web framework	axum
Serialization	serde + TOML
Graph library	petgraph
Templating	tera
Error handling	thiserror (lib) / anyhow (bin)
Logging	tracing

License

This project uses a split licensing model:

Crate	License	Details
oxo-flow-core	Apache-2.0	Free and open-source
oxo-flow-ai	Apache-2.0	Free and open-source
oxo-flow-cli	Apache-2.0	Free and open-source
oxo-flow-web	Academic / Commercial	Free for academic and non-commercial use; commercial use requires a separate license

The core library, AI companion, and CLI are licensed under the Apache License 2.0 — you are free to use, modify, and distribute them without restriction.

The web interface (oxo-flow-web) is available under a dual license: free for academic and non-commercial use under the Academic License, and requiring a commercial license for commercial deployments. See LICENSE-ACADEMIC and LICENSE-COMMERCIAL for details.

Contributing

Contributions are welcome! Please see:

• CONTRIBUTING.md — Contribution guidelines
• ROADMAP.md — Project roadmap and areas where help is needed
• CODE_OF_CONDUCT.md — Community standards
• GOVERNANCE.md — Project governance and decision-making
• SECURITY.md — Security vulnerability reporting

Before submitting a PR, ensure all checks pass:

make ci

Citing

If you use oxo-flow in academic research, please cite:

Shixiang Wang, oxo-flow: compiled, memory-safe bioinformatics workflow orchestration, bioRxiv, 2026, https://doi.org/10.64898/2026.06.11.731578

Jia Ding, Yun Peng, Ruochen Wei, Boquan Wang, Jian-Guo Zhou, Shixiang Wang, BLIT: an R package for seamless integration of command-line bioinformatics tool universe, Bioinformatics Advances, Volume 6, Issue 1, 2026, vbag088, https://doi.org/10.1093/bioadv/vbag088

Community

• Bug reports — GitHub Issues (use bug report template)
• Feature requests — GitHub Issues (use feature request template)
• Documentation — traitome.github.io/oxo-flow/latest/
• Questions — Ask DeepWiki

🧪 Real-World Feedback

oxo-flow aims to work reliably across diverse computing environments — laptops, HPC clusters, GPU nodes. We cannot replicate every deployment scenario in CI. If you use oxo-flow, please share your experience (success or failure) as a GitHub Issue with the prefix [Real-World Testing]. Your feedback directly shapes our priorities.

Additional Resources

• LIMITATIONS.md — Known limitations and constraints
• REPRODUCIBILITY.md — Reproducibility controls, provenance, and evidence boundaries
• RELEASING.md — Release process and versioning policy
• TRADEMARK.md — Trademark usage guidelines
• docs/CHANGE_CONTROL.md — Change control for regulated environments
• docs/VALIDATION_PROTOCOL.md — IQ/OQ/PQ validation protocol

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Contributors

Thanks goes to these wonderful people:

Run make contributors to refresh the contributor list from git history.