Set it up your way.
Choose your model, reasoning and permissions. Bring repository instructions and reusable prompts. The terminal’s model-aware palette keeps your current model in view.
model = "gpt-6-astra"
reasoning = "high" Make it your default Experimental
Fast. Accurate. Light on RAM.
Extensible in any language.
Read code, make changes and run tests with your choice of model. See how light the harness is
curl -fsSL https://octet.skaft.org/install | bash irm https://github.com/skaft-software/octet/releases/download/v0.9.5/install-octet.ps1 | iex cargo install --locked --git https://github.com/skaft-software/octet --tag v0.9.5 --bins octet-coding-agent git clone --branch v0.9.5 --depth 1 https://github.com/skaft-software/octet.git
cd octet
cargo install --locked --path crates/octet-coding-agent --bins Native Rust. Your choice of model. No Node or Python required by the core.
Measured by Buffon Provisional results
One prompt with tools · compared with Pi on Node
Octet 0.9.4 vs Pi 1.3.0 on Node 24.19.0. Linux x64; 12 measured rounds. Peak memory includes all processes.
Octet also scored higher than Codex on Terminal-Bench 2.1 with the same model.
The figures are reductions from Pi on Node’s median in Buffon’s “One prompt with tools” workload. CPU time measures processor work, not elapsed waiting time. Buffon’s all-process peak-memory metric is separate from single-process RSS.
| Measurement | Octet 0.9.4 | Pi 1.3.0 on Node |
|---|---|---|
| Peak memory, all processes | 4.57 MiB | 86.35 MiB |
| CPU time | 24.21 ms | 517.58 ms |
This is one workload, not an overall ranking. Octet is not ahead in every metric: the CSV reports about 9.8% more harness-added time per turn in the separate RPC test. Some modes were not measured. These results do not measure the current v0.9.5 release.
The export and published report were checked together on 2026-10-20. Buffon labels its combined results provisional; hardware and measurement conditions matter. Its authors also develop PiG, another tested harness.
Separate historical Terminal-Bench runs: Octet/Ygg 0.6.2 and Codex 0.144.0 used GPT-5.6 Sol at maximum reasoning, with 89 tasks and 5 trials per task. Raw verifier scores are compared on the same 445-trial basis, before either audit. Run dates and adjudication differ; this is not an official combined ranking or a measurement of v0.9.5. Codex submission.
Explore all Buffon results Inspect the source reportBuild tools in the language you already use.
Source SDKs. C/C++ use native wrappers.
Start buildingCloud or local. A familiar provider or your own endpoint. Switch models without switching tools, and keep your workflow yours.
Use native routes for Anthropic, OpenAI, Gemini, Bedrock and more. Choose a model with /model and keep working in the same session.
Setup guideoctet --model anthropic/claude-sonnet-5Set the provider’s credentials before starting.
Guided setup connects to LM Studio or a local OpenAI-compatible server. Choose the endpoint and model, review the settings, then save.
Setup guideoctet setup --preset lm-studio --manual-model local-modelStart your model server first. Setup previews changes before saving.
Connect a self-hosted model or compatible gateway. Keep multiple providers in one registry and select each model by its own ID.
Setup guideoctet setup --endpoint http://127.0.0.1:8000/v1/ --offline --manual-model Qwen3.8-27BExample endpoint and model. Replace them with your server’s values.
Start with guided setup. Keep your preferences in a simple configuration file. Go further with your own instructions, skills and tools.
Choose your model, reasoning and permissions. Bring repository instructions and reusable prompts. The terminal’s model-aware palette keeps your current model in view.
model = "gpt-6-astra"
reasoning = "high" Make it your default Add tools through local processes that speak JSON-RPC. Optional integrations bring subagents, codemode, computer use, web search and MCP into the workflow.
Ask a question about a codebase. Trace a bug. Make a change and run its tests. octet works with the files and tools already in your repository.
Sessions stay on disk. Resume where you left off, branch from an earlier message, or export the conversation for a handoff.
octet is a free, open-source, high-performance coding agent built in Rust. It works in your repository: read code, make changes and run tests, using your choice of cloud or local model. Run your first task ↗
octet draws on Pi’s agent architecture and terminal interaction patterns; its TUI crate is a Rust port of Pi’s TUI architecture. octet is a separate project, not a drop-in replacement for Pi extensions. Review the migration guide before moving a Pi setup. Moving from Pi ↗
Yes. Extensions are local processes that speak JSON-RPC over stdin and stdout. Use any language that can read and write JSON lines; Rust and Python are not required. Installing, enabling and trusting a package are separate steps. Write an extension ↗
Yes. Choose OpenRouter or another hosted provider, or connect a local OpenAI-compatible model server. Model capabilities depend on the selected model and route. The website does not make inference requests or transmit your documentation searches. Choose a provider ↗
Free and open source. MIT licensed.