Give your agent eyes,
not guesses.

One CLI. Every debugger. Your AI agent stops guessing at runtime state and starts observing it.

Agents guess. Debuggers observe.

The bottleneck for agentic engineering is not generation. It is diagnosis.

Without dbg

The agent reads source code, builds a theory, rewrites something, and hopes. Wrong guess? It guesses again. Five cycles of guess-and-rebuild per bug. Tokens, time, and trust — burned.

With dbg

The agent sets a breakpoint, steps to the crash, inspects the variable, and reads the actual value. Root cause on the first pass. One cycle. Done.

Observe. Reason. Fix.

The agent receives a crash report. Instead of guessing, it launches a debug session, steps to the error, and reads the state.

claude — dbg session

Two commands. That's it.

Install the binary, point it at your agent. No configuration, no wrappers, no adapters.

Install

cargo install --git https://github.com/redknightlois/dbg

Connect to your agent

dbg --init claude    # or: dbg --init codex

The agent now knows when and how to use dbg. It picks the right backend, sets breakpoints, and interprets debugger output on its own.

Every language, one interface.

The agent learns dbg once. It works everywhere.

Debuggers

Rust

LLDB

C / C++

LLDB

Zig

LLDB

Python

PDB

Delve

.NET / C#

NetCoreDbg

Java

JDB

Kotlin

JDB

Profilers

pprof

Linux

perf

C / C++

callgrind

Memory

massif / memcheck

Python

cProfile

.NET

dotnet-trace

How it works.

Daemon, not subprocess.The debugger stays alive across commands. No startup cost per interaction, no lost state between steps.

PTY, not pipes.The debugger thinks it's in a real terminal. No pipe-mode quirks, no missing output, no behavior differences from docs.

Output cleaning.Backends strip DWARF indexing, thread lifecycle, symbol loading noise. The agent gets clean, token-efficient output.

One interface, every language.A backend trait maps each debugger's commands to a common CLI. The agent learns break, step, print once.