Plan, edit, verify
Reads your codebase, plans steps, picks the best model, edits files, runs tests, and self-reviews before declaring anything done.
Alphacode reads your repo, picks the right model, edits files, runs tests, and ships — with your permission at every risky step.
Reads your codebase, plans steps, picks the best model, edits files, runs tests, and self-reviews before declaring anything done.
Claude, GPT, Gemini, Copilot, Cursor, Bedrock, OpenRouter, or any OpenAI-compatible endpoint. Switch mid-conversation with Ctrl+T.
Big jobs split into independent pieces, run by multiple agents in parallel, then merged and reviewed automatically.
Blocks catastrophic commands outright. Confirms before anything risky. Never sends your code anywhere by default.
File editing, regex/AST search, shell, browser control, web fetch, memory, scheduling, diagrams, PDFs. No plugin surgery.
Sessions saved to disk. alphacode --resume reopens exactly where you left off — even after a crash.
A planner agent decomposes your goal into independent DAG nodes. Multiple agents execute them concurrently — each with the right model — then results merge and self-review.
First-class for the big ones, OpenAI-compatible for the long tail. Anything that speaks HTTP + JSON works.
Don't see yours? Point api_base at any OpenAI-compatible endpoint and you're done.
$ curl -fsSL https://raw.githubusercontent.com/dragonked2/alphacode/main/scripts/install.sh | bash
PS> iwr -useb https://raw.githubusercontent.com/dragonked2/alphacode/main/scripts/install.ps1 | iex
git clone https://github.com/dragonked2/alphacode.git
cd alphacode
cargo build --release
1which alphacode
2alphacode --version
3alphacode doctor
4alphacode
A coding agent can look fine with one session and balloon when you actually use it. RAM snapshots for one and ten concurrent sessions, measured against Codex CLI, Claude Code, Cursor Agent, GitHub Copilot CLI, Antigravity CLI, OpenCode, and pi.
Legacy benchmark snapshots, kept for historical comparison — Alphacode has improved further since these were measured. See full methodology and current numbers in the docs.
Yes. MIT-licensed. You pay for the model API you point it at (or run a local one). No telemetry, no accounts, no upsells.
Only to the model provider you configure. The CLI is local. Nothing leaves your machine unless a model call needs context.
Any OpenAI-compatible HTTP endpoint. Tested with Claude (Anthropic), GPT-4o/5 (OpenAI), Gemini, Copilot, Cursor, Bedrock, and OpenRouter.
A planner turns your goal into a task DAG. Workers — each with their own model — execute independent nodes in parallel. A reviewer node merges and verifies the diff.
Yes. Pure terminal. Pipe-friendly. Plays well with tmux, screen, and SSH.
It's MIT, model-agnostic across 54 providers, swarm-capable, and Rust-fast — using roughly 3–14x less memory than Claude Code, Codex CLI, Cursor Agent, and other terminal agents in our own measurements. See benchmarks above, or the full methodology in the docs.
Open any project, run alphacode, and describe what you want changed.