oh-my-pi

The most capable agent surface that ships. Continuously tuned by real-world use — complete out of the box, open all the way down.

40+ providers · 32 built-in tools · 14 lsp ops · 28 dap ops · ~55k lines of Rust core.

Install

macOS · Linux

curl -fsSL https://omp.sh/install | sh

Homebrew

brew install can1357/tap/omp

Bun (recommended)

bun install -g @oh-my-pi/pi-coding-agent

Windows (PowerShell)

irm https://omp.sh/install.ps1 | iex

Pinned versions (mise)

mise use -g github:can1357/oh-my-pi

macOS · Linux · Windows · bun ≥ 1.3.14

Shell completions

omp generates its own completion scripts for bash, zsh, and fish from the live command/flag metadata, so they never drift from the actual CLI. Subcommands, flags, and enum values complete statically; model names (--model, --smol, --slow, --plan) resolve against the bundled model catalog and --resume against your on-disk sessions.

# zsh — add to ~/.zshrc (or write the output into a file on your $fpath)
eval "$(omp completions zsh)"

# bash — add to ~/.bashrc
eval "$(omp completions bash)"

# fish
omp completions fish > ~/.config/fish/completions/omp.fish

Every tool, benchmaxxed.

Edits that land on the first attempt. Reads that summarize files instead of dumping their content. Searches that return instantly. Pick any model — omp will get it right.

model	metric	what
Grok Code Fast 1	6.7% → 68.3%	Tenfold lift the moment the edit format stops eating the model alive.
Gemini 3 Flash	+5 pp	Over str_replace — beats Google's own best attempt at the format.
Grok 4 Fast	−61% tokens	Output collapses once the retry loop on bad diffs disappears.
MiniMax	2.1×	Pass rate more than doubles. Same weights, same prompt.

• read : summarized snippets · ideal defaults · selector hit rate
• search : fastest in the west
• lsp : everything your IDE knows, the agent knows
• prompts : adjusted relentlessly for each model

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The Pi you love, with batteries included.

Originally built on Mario Zechner's wonderful Pi, omp adds everything you're missing.

01 · Code execution w/ tool-calling

Most harnesses give the agent a Python sandbox and call it done. Ours runs persistent Python and a Bun worker, and either kernel can call back into the agent's own tools — read, search, task — over a loopback bridge. The agent loads a CSV with tool.read from inside Python, charts it from JavaScript, and never leaves the cell.

02 · LSP wired into every write

Ask for a rename and you get a rename. The call goes through workspace/willRenameFiles, so re-exports, barrel files, and aliased imports update before the file moves. Everything your IDE knows, the agent knows.

03 · Drives a real debugger

A C binary segfaults: the agent attaches lldb, steps to the bad pointer, reads the frame. A Go service hangs: it attaches dlv and walks the goroutines. A Python process is wedged: debugpy, pause, inspect, evaluate. Most agents are still sprinkling print statements.

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04 · Time-traveling stream rules

Your rules sit dormant until the model goes off-script. A regex match aborts the stream mid-token, injects the rule as a system reminder, and retries from the same point. You get course-correction without paying context tax on every turn. Injections survive compaction, so the fix sticks.

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05 · First-class subagents

Split a job across workers and get typed results back. task fans out into isolated worktrees, each worker runs its own tool surface, and the final yield is a schema-validated object the parent reads directly. No prose to parse, no merge conflicts between siblings, no orphaned edits.

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06 · A second model, watching every turn.

Pair a reviewer model to the 'advisor' role and it reads every turn the main agent takes, injecting notes inline — a quiet aside, a concern, or a hard blocker. It runs on its own context and its own model, so it catches what the doer rushed past. The main agent sees the note and course-corrects, or tells you why it won't.

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07 · Hand someone the link, they're in.

/collab puts your live session on a relay and hands back a link — and a QR. A teammate joins from another terminal with omp join, or just opens it in a browser. Share read-write to pair on the same agent, or /collab view for a read-only link anyone can watch but no one can steer. Frames are sealed client-side; the relay never sees your keys.

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08 · Read a pdf on arxiv, why not?

web_search chains eighteen ranked providers and hands whatever URLs it finds straight to read. Arxiv PDFs, GitHub pages, Stack Overflow threads come back as structured markdown with anchors intact — the same tool surface you use on local files. Cite, follow, quote, never lose where you came from.

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09 · Unapologetically native. Even on Windows.

Other agents shell out to rg, grep, find, and bash. On many machines those binaries don't exist, and on the ones where they do, every call costs a fork-exec round-trip. omp links the real implementations into the process. ripgrep, glob, find: in-process. brush is the bash, with sessions that survive across calls. The same omp binary runs on macOS, Linux, and Windows — no WSL bridge.

10 · Code review with priorities and a verdict

Get a clear verdict on whether the change ships, with every issue ranked P0 through P3 and scored for confidence. /review spawns dedicated reviewer subagents that sweep branches, single commits, or uncommitted work in parallel. You tackle what blocks release first; nothing important hides in a wall of prose.

11 · Hashline: edit by content hash

Perfect edits, fewer tokens. The model points at anchors instead of retyping the lines it wants to change, so whitespace battles and string-not-found loops just stop happening. Edit a stale file and the anchors diverge — we reject the patch before it corrupts anything. Grok 4 Fast spends 61% fewer output tokens on the same work.

12 · GitHub is just another filesystem

Other harnesses bolt on gh_issue_view, gh_pr_view, gh_search — each with its own parameters the agent has to learn and you have to debug. We skipped that. read already handles paths; PRs are paths. One interface to teach the model, one surface to keep correct.

13 · Hindsight: memory the agent curates

The agent remembers your codebase between sessions. It writes facts mid-run with retain, pulls them back with recall, and compresses each session into a mental model that loads on the first turn of the next one. Project-scoped by default, so what it learns about this repo stays with this repo.

14 · ACP: editor-drivable agent

Run omp inside Zed and you get the same agent you drive from the terminal — reading the buffer you're actually looking at, writing thr