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flatline

flatline is a Python wrapper around the Ghidra decompiler engine. It gives you programmatic access to decompilation — pass a raw memory image and a function address, get back decompiled C code and structured metadata — without requiring a Ghidra installation, a GUI, or a project database. The native decompiler core is embedded directly in the package and driven through a stable Python API.

Features

  • Single-function decompilation from memory images — provide bytes, a base address, and a function entry point; receive decompiled C source with no intermediate files or tooling.
  • Structured output — every result includes both the C code string and a FunctionInfo object covering the function prototype, local variables, call sites, jump tables, and diagnostic flags.
  • Multi-ISA support — fixture-backed confidence for x86 32/64, AArch64, RISC-V 64, and MIPS32; other Ghidra-supported architectures are available on a best-effort basis.
  • Deterministic resource limits — AnalysisBudget caps the p-code instruction count per request, giving you a hard bound on analysis work regardless of input complexity.
  • Auto-discovered runtime data — Sleigh processor definitions ship via the companion ghidra-sleigh package and are discovered automatically; no manual path configuration is needed.
  • Shipped X-Ray viewer — flatline-xray is an optional interactive layer for the same memory-image contract, adding graph inspection and assembly browsing without changing the core API.

Quick start

import binascii
import flatline

# lea eax, [rdi+rsi]; ret  (x86-64, little-endian, with NOP padding)
image = binascii.unhexlify("8d0437c390909090909090909090909090909090")

request = flatline.DecompileRequest(
    memory_image=image,
    base_address=0x1000,
    function_address=0x1000,
    language_id="x86:LE:64:default",
    compiler_spec="gcc",
)

result = flatline.decompile_function(request)
print(result.c_code)

Next steps

  • Installation — install flatline and verify the setup.
  • Getting Started — a step-by-step tutorial covering the core workflow.
  • X-Ray — learn the shipped viewer and how it fits the core request model.