angr is a platform-agnostic binary analysis framework developed by the Shellphish research group at UC Santa Barbara. It provides a comprehensive suite of program analysis capabilities including disassembly and IR lifting (via VEX), control-flow graph construction (CFG fast/emulated), symbolic execution, data-dependency analysis, value-set analysis, and a full decompiler pipeline that produces C-like pseudocode. The framework supports multiple architectures (x86, ARM, MIPS, PowerPC, etc.) and operating systems (Linux, Windows, CGC, FreeBSD, Java VM, UEFI) through a modular SimOS layer and extensive library procedure models (libc, POSIX, Linux kernel, Windows API, C++ stdlib, Java/JNI). The architecture is built around a mixin-based engine composition pattern, where execution engines (VEX, AIL, Soot, Pcode, unicorn) are combined into an UberEngine. Analyses are registered via an AnalysesHub and instantiated as project.analyses.Name. A knowledge-base plugin system stores functions, variables, types, cross-references, and other analysis results. The decompiler is particularly sophisticated, with dozens of optimization passes, peephole optimizations, region simplifiers, and multiple structuring algorithms (SAILR, Phoenix, Dream). Native Rust components (clarirs-core, clarirs-vsa, clarirs-z3) provide high-performance symbolic reasoning and solver integration. Key entry points include angr.Project for loading binaries, project.factory for block/engine creation, project.analyses for running analyses, and simulation_manager for symbolic execution. The framework also includes an MCP server for LLM integration, a distributed analysis framework, and database persistence via AngrDB. External dependencies include claripy (solver abstraction), cle (binary loader), pyvex (VEX IR), capstone/keystone (disassembly/assembly), unicorn (concrete execution), and Z3 (SMT solver).