Awesome

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Run a language model in local, without internet, to entertain you or help answering questions about radare2 or reverse engineering in general. Note that models used by r2ai are pulled from external sources which may behave different or respond unrealible information. That's why there's an ongoing effort into improving the post-finetuning using memgpt-like techniques which can't get better without your help!

Components

R2AI is structured into four independent components:

• r2ai (wip r2ai-native rewrite in C)
  • r2-like repl using r2pipe to comunicate with r2
  • supports auto solving mode
  • client and server openapi protocol
  • download and manage models from huggingface
• decai
  • lightweight r2js plugin
  • focus on decompilation
  • talks to r2ai, r2ai-server, openai, anthropic or ollama
• r2ai-plugin
  • requires r2lang-python
  • adds r2ai command inside r2
  • not recommended because of python versions pain
• r2ai-server
  • list and select models downloaded from r2ai
  • simple cli tool to start local openapi webservers
  • supports llamafile, llamacpp, r2ai-w and kobaldcpp

Features

• Support Auto mode (see below) to solve tasks using function calling
• Use local and remote language models (llama, ollama, openai, anthropic, ..)
• Support OpenAI, Anthropic, Bedrock
• Index large codebases or markdown books using a vector database
• Slurp file and perform actions on that
• Embed the output of an r2 command and resolve questions on the given data
• Define different system-level assistant role
• Set environment variables to provide context to the language model
• Live with repl and batch mode from cli or r2 prompt
• Accessible as an r2lang-python plugin, keeps session state inside radare2
• Scriptable from python, bash, r2pipe, and javascript (r2papi)
• Use different models, dynamically adjust query template
  • Load multiple models and make them talk between them

Installation

Running make will setup a python virtual environment in the current directory installing all the necessary dependencies and will get into a shell to run r2ai.

The installation is now splitted into two different targets:

• make install will place a symlink in $BINDIR/r2ai
• make install-plugin will install the native r2 plugin into your home
• make install-decai will install the decai r2js decompiler plugin
• make install-server will install the decai r2js decompiler plugin

Running

When installed via r2pm you can execute it like this:

r2pm -r r2ai

Additionally you can get the r2ai command inside r2 to run as an rlang plugin by installing the bindings:

r2pm -i rlang-python
r2pm -i r2ai-plugin

After this you should get the r2ai command inside the radare2 shell. Set the R2_DEBUG=1 environment to see the reasons why the plugin is not loaded if it's not there.

Windows

On Windows you may follow the same instructions, just ensure you have the right python environment ready and create the venv to use

git clone https://github.com/radareorg/r2ai
cd r2ai
set PATH=C:\Users\YOURUSERNAME\Local\Programs\Python\Python39\;%PATH%
python3 -m pip install .
python3 main.py

Usage

There are 4 different ways to run r2ai:

• Standalone and interactive: r2pm -r r2ai or python -m r2ai.cli
• Batch mode: r2ai -c '-r act as a calculator' -c '3+3=?'
• As an r2 plugin: r2 -i r2ai/plugin.py /bin/ls
• From radare2 (requires r2pm -ci rlang-python r2ai-plugin): r2 -c 'r2ai -h'
• Using r2pipe: #!pipe python -m r2ai.cli
• Define a macro command: '$r2ai=#!pipe python -m r2ai.cli

Auto mode

When using OpenAI, Claude or any of the Functionary local models you can use the auto mode which permits the language model to execute r2 commands, analyze the output in loop and in a loop until it is resolved. Here's a sample session to achieve that:

• Video https://infosec.exchange/@radareorg/111946255058894583

$ r2pm -i r2ai-plugin
(env)$ r2 /bin/ls
[0x00000000]> r2ai -m openai:gpt-4
[0x00000000]> r2ai ' list the imports for this program
[0x00000000]> r2ai ' draw me a donut
[0x00000000]> r2ai ' decompile current function and explain it

Examples

You can interact with r2ai from standalone python, from r2pipe via r2 keeping a global state or using the javascript interpreter embedded inside radare2.

• conversation.r2.js - load two models and make them talk to each other

Development/Testing

Just run make .. or well python3 -m r2ai.cli

TODO

• add "undo" command to drop the last message
• dump / restore conversational states (see -L command)
• Implement ~, | and > and other r2shell features