Describe your task in plain language — oxo-call fetches the tool's documentation, grounds the request with a built-in skill, and asks your LLM backend to generate the exact flags you need.
oxo-call wraps the full command-generation workflow — from fetching live documentation to grounding the LLM with expert skills — into a single two-word command.
Describe what you want in plain English. The model generates the right flags, grounded in the actual tool documentation.
Grabs --help output and optionally fetches remote docs or man pages. Works even if the tool isn't installed locally.
Preview the exact command that would be executed before committing. Never accidentally overwrite files again.
Expert knowledge for 100+ bioinformatics tools baked in as .toml skill files — concepts, pitfalls, and worked examples.
Run .oxo.toml pipelines natively — DAG parallelism, wildcard expansion, output caching. Export to Snakemake or Nextflow for HPC compatibility.
GitHub Copilot (default), OpenAI, Anthropic, or a local Ollama instance — switch with one config key.
Every run is logged as JSONL with exit code, timestamp, and generated command. Filter by tool for quick lookup.
Tool help is fetched and cached automatically on first use. Enrich with remote URLs, local files, or directories via oxo-call docs add.
Ships a wasm32-wasip1 binary for portable execution in WASI runtimes like Wasmtime without native installation.
Click an example to watch oxo-call ground the task in documentation, load the matching skill, and produce the exact command arguments.
Each built-in skill encodes domain-specific concepts, common pitfalls, and
worked examples so the LLM is guided beyond a raw --help dump.
oxo-call workflow runs bioinformatics pipelines directly with a
lightweight built-in engine. No Snakemake, Nextflow, or Conda needed.
Export to either format for HPC environments that require them.
Run .oxo.toml workflows directly — no Snakemake or Nextflow required. DAG-based parallelism, wildcard expansion, output caching.
fastp QC → STAR alignment → featureCounts quantification → MultiQC report. Run natively or export to Snakemake/Nextflow DSL2.
fastp → BWA-MEM2 → GATK MarkDuplicates → BQSR → HaplotypeCaller GVCF. GATK best-practices out of the box.
fastp → Bowtie2 → Picard deduplication → blacklist filtering → MACS3 peak calling. Ready for downstream footprinting.
fastp → host read removal (Bowtie2) → Kraken2 classification → Bracken species abundance estimation.
Describe any pipeline in plain language and get a complete, runnable .oxo.toml. One prompt → one deployable workflow.
Every template can be exported with workflow export --to snakemake or --to nextflow for HPC environments.
# List all built-in templates $ oxo-call workflow list # Preview the RNA-seq pipeline (no execution) $ oxo-call workflow dry-run rnaseq # Run a workflow (native engine — no Snakemake/Nextflow needed) $ oxo-call workflow run my_rnaseq.toml # Export to Snakemake for HPC submission $ oxo-call workflow export wgs.toml --to snakemake -o Snakefile # Generate a custom ChIP-seq workflow with LLM $ oxo-call workflow generate \ "ChIP-seq H3K27ac: QC → Bowtie2 → MACS3 peaks vs input control" \ -o chipseq.toml
# Install the latest release from crates.io
cargo install oxo-call
# Download pre-built binary (Linux x86_64 example) curl -LO https://github.com/Traitome/oxo-call/releases/latest/download/oxo-call-VERSION-x86_64-unknown-linux-gnu.tar.gz tar xzf oxo-call-*-x86_64-unknown-linux-gnu.tar.gz sudo mv oxo-call /usr/local/bin/ # Available for: Linux (x86_64/aarch64), macOS (Intel/Apple Silicon), Windows, WASM
# Clone and build from source (latest development version)
git clone https://github.com/Traitome/oxo-call
cd oxo-call
cargo install --path .
# GitHub Copilot (default — free for Copilot subscribers) oxo-call config set llm.api_token YOUR_GITHUB_TOKEN # OpenAI oxo-call config set llm.provider openai oxo-call config set llm.api_token sk-... # Local Ollama (no token needed) oxo-call config set llm.provider ollama oxo-call config set llm.model llama3.2
# Preview — no execution oxo-call dry-run samtools "sort input.bam by coordinate and output to sorted.bam" # Execute immediately oxo-call run bwa "align reads.fastq to reference.fa using 8 threads, output SAM" # Confirm before running oxo-call run --ask bcftools "call variants from my.bam against ref.fa"
# Download the .wasm binary from the GitHub Releases page, then: wasmtime oxo-call.wasm -- dry-run samtools "sort input.bam by coordinate"
# Public academic test license — publish for testing only { "schema": "oxo-call-license-v1", "license_id": "6548e181-e352-402a-ab72-4da51f49e7b5", "issued_to_org": "Public Academic Test License (any academic user)", "license_type": "academic", "scope": "org", "perpetual": true, "issued_at": "2026-03-12", "signature": "duKJcISYPdyZkw1PbyVil5zTjvLhAYsmbzRpH0n6eRYJET90p1b0rYiHO0cJ7IGR6NLEJWqkY1wBXUkfvUvECw==" } # Save it locally (Linux) mkdir -p ~/.config/oxo-call && cp docs/public-academic-test-license.oxo.json ~/.config/oxo-call/license.oxo.json # Save it locally (macOS) mkdir -p ~/Library/Application\ Support/io.traitome.oxo-call && cp docs/public-academic-test-license.oxo.json ~/Library/Application\ Support/io.traitome.oxo-call/license.oxo.json
Test use only. This public academic license is published for evaluation and testing. Academic users should still apply for a formal academic license for real use. Commercial users should contact the maintainer, pay the USD 200 authorization fee, and obtain a formal commercial license.
All results below are generated by oxo-bench, the standalone benchmarking
crate included in this repository. Every table is backed by a static CSV file in
docs/ — re-run cargo run -p oxo-bench -- export-csv to
refresh with your own hardware timings.
| Workflow | Tasks (expanded) | Parse (µs) | Expand (µs) | Cycle-free? |
|---|---|---|---|---|
| Loading bench_workflow.csv… | ||||
Parse and expand timings are averaged over 500 runs on a single-core.
Source: docs/bench_workflow.csv · generated by
cargo run -p oxo-bench -- export-csv
| Scenario ID | Assay | Samples | Read length (bp) | Reads / sample | Error rate | Total reads |
|---|---|---|---|---|---|---|
| Loading bench_scenarios.csv… | ||||||
All reads are generated deterministically with a fixed seed — re-running
oxo-bench simulate always produces byte-identical FASTQ files.
Source: docs/bench_scenarios.csv
| Category | Tool | Task description | Required patterns |
|---|---|---|---|
| Loading bench_eval_tasks.csv… | |||
These 12 tasks form the canonical evaluation suite used to measure model
accuracy, format-validity rate, and self-consistency. Run
oxo-bench eval-models --list to explore the full suite.
Source: docs/bench_eval_tasks.csv