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pandō IDE for agents

Your agent edits text. Let it refactor code.

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add tracing to async handlers
The Limitation

Today's tools are fast, flexible, and great at finding text.

But code isn't text.

It has structure, syntax, relationships, and meaning.
Text tools see none of it — so agents loop, re-check, and hope it compiles.

Code is Data.

The Category

The transactional layer for code. Agents are probabilistic; Pando is the deterministic system underneath.

Imagine a 1,000-page novel. Your protagonist is George. Halfway through, you decide he's now Camellia.
You need to update every reference — and change nothing else.

George → Camellia
Direct Reference

Trivial find & replace. Both tools get it right.

Pronoun Reference

Which one is George and which Alpha?

Aliased Reference

Did we catch all relations, like friend or coworker?

The Guarantee

Structure, not Strings.

pandō is

Exact

This gives you

Correct Syntax

Code generated by the agent is syntactically valid (compiler-checked before commit)

Deterministic AST

Transformations validate against the Abstract Syntax Tree before commit.

# Rename UserService → AuthService
 
rename UserService → AuthService
├ indexing symbol graph...
├ 47 references across 12 files
├ running compiler preflight...
└ preflight passed
 
done 0 syntax errors · 0 collateral changes
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pandō is

Safer

This gives you

Semantic Undo

Every edit gets a snapshot. Rollback from anything.

Atomic Transactions

Edits apply atomically. Either the whole graph updates, or nothing does.

# Atomic rename with rollback
 
rename db → database
├ snapshot S_20260223_091541 created
├ updated 23 true references
├ skipped 4 string literals (intentional)
└ skipped 2 comments (intentional)
 
applied atomically rollback available
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pandō is

Faster

This gives you

10–100×

Text tools require LLM verification for every match. pandō edits the reference graph directly. No uploads, no inference.

Local Execution

pandō talks to your local disk, not the LLM. Most searches take milliseconds — just a database query.

# Rename across large monorepo
 
rename processPayment → handlePayment
├ symbol lookup 0.3ms (local index)
├ references found 785 across 171 files
├ network calls 0
└ applying changes...
 
done in 14.2s all local
text-tool equivalent: ~25 min (785 LLM round-trips)
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pandō is

Leaner

This gives you

Token Savings

Achieves >100× token compression for some operations (e.g. rename costs the same for one or a thousand references)

O(n) → O(1) Cost

Token cost is O(n) (n = references) using text tools (for some ops). pandō is O(1): just the operation params + symbol name.

# Token cost: rename across 64 files
 
text tools (search & replace via LLM):
files sent to model 64
avg tokens / file ~800
total ~51,200 tokens O(n)
 
pandō:
tokens used ~40 O(1) — same for 1 or 1,000 refs
 
1,280× fewer tokens cost scales with intent, not codebase
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pandō is

Tighter

This gives you

Reduced Exposure

Some operations send no code at all to the LLM — just the intent of the transform.

Scalable Codemods

Operations like FMR require O(1) input, generate O(1) output, and modify arbitrary code volume.

# FMR: add structured logging
 
fmr Logger.info(*) → Logger.info(*, {structured: true})
├ scanning AST nodes...
├ 312 matches across 89 files
├ bytes sent to model 0
└ applying changes...
 
done in 2.1s 312 replacements · 0 bytes exposed
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Supported: Clojure · TypeScript · JavaScript · Python · Java · Dart · C# · C/C++ Expanding to: Rust, Go, Perl

LLMs treat code as text.
Pandō lets them grok it.

You wouldn’t code in Notepad. Why is your agent?

Free for personal use · Team? Let’s talk