All source listed below is under MIT license if no LICENSE file stating different is available.

R

R is a command-line AI assistant written in C that provides an autonomous agent with full system access through function calling. It interfaces with OpenAI-compatible APIs and operates in an agentic loop, reasoning about tasks, executing tools, and iterating until goals are achieved.

Features

Autonomous Agent Architecture

ReAct (Reasoning + Acting) pattern implementation
Automatic tool execution loop with up to 300 iterations
Intelligent response parsing to detect incomplete work
Automatic continuation when tasks require multiple steps
Built-in retry logic for API failures

AI Provider Support

OpenAI API (default)
Anthropic Claude
Ollama (self-hosted)
Grok
Any OpenAI-compatible API endpoint

Tool System

The agent has access to 15+ tools exposed via JSON schema descriptions:

Tool	Description
`linux_terminal_execute`	Execute shell commands with output capture
`linux_terminal_execute_interactive`	Execute interactive commands (vim, htop, etc.)
`read_file`	Read file contents
`write_file`	Write content to files with automatic parent directory creation
`directory_glob`	List directory contents with file metadata
`mkdir`	Create directories recursively
`chdir`	Change working directory
`getpwd`	Get current working directory
`http_fetch`	Fetch URL contents via HTTP GET
`web_search`	Search the web for information
`web_search_news`	Search for news articles
`db_set`	Store key-value pairs in local SQLite database
`db_get`	Retrieve values from database
`db_query`	Execute arbitrary SQL queries
`index_source_directory`	Index source files with contents
`python_execute`	Execute Python code and capture output

Database

Local SQLite database at ~/.r.db
Persistent key-value storage
Full SQL query support
File version history tracking

REPL Interface

Readline integration with tab completion
Persistent command history at ~/.r_history
Markdown syntax highlighting in terminal output
Session management for separate conversation contexts

REPL Commands

Command	Description
`!dump`	Show raw message JSON
`!clear`	Clear conversation history
`!session`	Display current session info
`!verbose`	Toggle verbose mode
`!tools`	List available tools
`!models`	Show available models
`!model <name>`	Switch AI model
`exit`	Exit the REPL

Installation

Dependencies

libcurl json-c readline ncurses sqlite3 gnutls gmp openssl

On Debian/Ubuntu:

apt install libcurl4-openssl-dev libjson-c-dev libreadline-dev libncurses-dev libsqlite3-dev libgnutls28-dev libgmp-dev libssl-dev

Build

make build

The binary is output to bin/r and copied to ./r.

Cross-Compilation

make build_mingw    # Windows executable

AppImage

make appimage       # Creates portable r-x86_64.AppImage

Configuration

Environment Variables

Variable	Description	Example
`R_KEY`	API key (takes precedence over OPENAI_API_KEY)	`sk-...`
`OPENAI_API_KEY`	OpenAI API key (fallback)	`sk-...`
`OPENROUTER_API_KEY`	OpenRouter API key	`sk-or-...`
`R_BASE_URL`	Custom API base URL (without /v1/...)	`https://openrouter.ai/api`
`R_MODEL`	AI model name	`gpt-4o`, `x-ai/grok-code-fast-1`
`R_USE_TOOLS`	Enable/disable function calling	`true`, `false`, `1`, `0`
`R_USE_STRICT`	Enable strict mode for tool schemas	`true`, `false`, `1`, `0`
`R_SYSTEM_MESSAGE`	Custom system prompt injected at startup	Any text
`R_SESSION`	Default session name for conversation persistence	`myproject`, `$(date)`

Context Files

.rcontext.txt - Project-specific context (current directory)
~/.rcontext.txt - Global user context

Bash Integration

Integrate R as a fallback command handler in bash. When a command is not found, R will attempt to interpret it as a natural language query with automatic date-based sessions.

Add to ~/.bashrc:

export R_BASE_URL="https://openrouter.ai/api"
export R_KEY="$OPENROUTER_API_KEY"
export R_MODEL="x-ai/grok-code-fast-1"
export R_SESSION=$(date +%Y-%m-%d)

command_not_found_handle() {
    script_path="/path/to/r"
    first_argument="$1"
    if "$script_path" "$@"; then
        echo -e ""
    else
        echo "bash: $first_argument: command not found"
    fi
}

This enables:

Automatic session per day via R_SESSION=$(date +%Y-%m-%d)
Natural language commands directly in terminal
Fallback to standard "command not found" on failure

Why Terminal AI

Having an AI agent directly in your terminal eliminates context switching. Instead of copying error messages to a browser, describing your environment, or explaining file contents, the agent can see and act on everything directly.

Practical Examples

Debug errors instantly

./r "The build failed, check the error and fix it"
# Agent reads error output, identifies the issue, edits the file, rebuilds

Explore unfamiliar codebases

./r "How does authentication work in this project?"
# Agent searches files, reads relevant code, explains the flow

Automate repetitive tasks

./r "Rename all .jpeg files to .jpg and create a backup folder first"
# Agent creates directory, moves files with proper naming

System administration

./r "Check disk usage and find the largest files in /var/log"
# Agent runs du, finds large files, presents a summary

Database operations

./r "Show me all users who signed up last week"
# Agent queries the database, formats results

Web research while coding

./r "What's the correct syntax for a PostgreSQL upsert?"
# Agent searches the web, returns the answer with examples

Code generation

./r "Write a Python script that monitors CPU usage and alerts if above 90%"
# Agent writes the script, saves it, makes it executable

Git workflows

./r "Show what changed since yesterday and summarize the commits"
# Agent runs git log, git diff, provides summary

File operations

./r "Find all TODO comments in this project and list them by file"
# Agent greps through codebase, organizes findings

Data processing

cat data.csv | ./r --stdin "Calculate the average of the third column"
# Agent processes piped data, returns calculation

Multi-step tasks

./r "Set up a new Python virtualenv, install requests and pytest, create a basic test file"
# Agent executes multiple commands in sequence, creates files

Environment inspection

./r "What ports are in use and what processes are using them?"
# Agent runs netstat/lsof, explains the output

The agent maintains conversation context within sessions, so follow-up queries understand previous actions. Combined with the bash integration, any unrecognized command becomes a natural language query to the AI.

Usage

Interactive Mode

./r

Single Query

./r "What files are in the current directory?"

With Piped Input

cat error.log | ./r --stdin "Analyze these errors"

With Context File

./r --context project_docs.txt "Explain this project"

Session Management

./r --session=myproject       # Named session
./r -s myproject              # Short form

Options

Flag	Description
`--verbose`	Enable verbose output
`--stdin`	Read prompt from stdin
`--context <file>`	Include file as context
`--py <file>`	Include Python file
`--session <name>`	Use named session
`--nh`	Disable syntax highlighting
`--free`	Use free tier API
`--api`	API mode

Architecture

Header-Only Design

The codebase uses a header-only architecture where .h files contain both declarations and implementations. All code compiles through includes from main.c.

Core Components

File	Purpose
`main.c`	Entry point, REPL loop, argument parsing
`agent.h`	Autonomous agent loop with ReAct pattern
`r.h`	Global configuration and environment handling
`auth.h`	API key resolution
`openai.h`	API communication
`chat.h`	JSON prompt construction
`messages.h`	Conversation history management
`tools.h`	Tool definitions and execution dispatcher
`http_curl.h`	libcurl wrapper for HTTP requests
`line.h`	Readline integration
`db_utils.h`	SQLite operations
`browse.h`	Web search functionality
`markdown.h`	Terminal markdown rendering
`utils.h`	Path utilities
`indexer.h`	Source directory indexing

License

MIT

include
src
.gitignore
agent_benchmark.py
cmd.sh
Dockerfile
Makefile
README.md