CodeCraft CLI Agent

Production CLI coding agent — zero framework dependencies. Raw HTTPS to xAI/OpenAI · ReAct tool loop · RLAIF scoring · Cross-session memory

7.5× faster task completion than comparable LangChain baseline

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What It Does

CodeCraft is a terminal coding agent that accepts natural-language programming requests, sends them to any OpenAI-compatible LLM, executes tool calls, and iterates until the task is done.

No LangChain. No AutoGen. No CrewAI. One Python process, standard library only.

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Architecture

User prompt
    │
    ▼
┌──────────────────────────────────────┐
│           ReAct Loop                 │
│                                      │
│  LLM call (raw HTTPS, streaming)     │
│      │                               │
│      ├── tool_call? → execute tool   │
│      │     └── result → conversation │
│      │                               │
│      └── final answer → print        │
└──────────────────────────────────────┘
    │
    ▼
JSONL tracer  →  RLAIF scorer  →  session memory

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Features

Core agent

• Raw HTTPS to any OpenAI-compatible endpoint (xAI, OpenAI, Groq, Ollama)
• Streaming by default
• Native function/tool-calling loop — no wrapper library
• Diff previews before any file write or replacement
• Workspace-scoped safety: all paths resolved inside --workspace

11 workspace tools project_context · list_files · read_file · search_files · write_file · replace_in_file · make_directory · git_status · git_diff · run_tests · run_command

RLAIF scoring (rlaif.py) Each completed task is scored by Grok-4 on correctness, conciseness, and side-effect safety. Scores are stored in the JSONL trace for fine-tuning data collection.

Session memory (session_memory.py) ChromaDB-backed cross-session memory: the agent recalls strategies and patterns from past tasks within the same workspace.

Execution tracer (tracer.py) Structured JSONL logging of every step — tool calls, observations, reasoning, and RLAIF score. Replay any session for debugging or distillation.

Benchmark suite (bench/) Scripted task harness for measuring task completion rate and step efficiency across a fixed task set.

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Project Structure

codecraft-agent/
├── src/codecraft_agent/
│   ├── agent.py          # ReAct conversation loop + tool-call execution
│   ├── cli.py            # Terminal interface
│   ├── config.py         # Environment + CLI configuration
│   ├── context.py        # Workspace / project inspection
│   ├── doctor.py         # Provider diagnostics
│   ├── llm.py            # Raw HTTPS client (xAI / OpenAI-compatible)
│   ├── rlaif.py          # Grok-4 task scoring for RLAIF feedback
│   ├── session_memory.py # ChromaDB cross-session strategy store
│   ├── tools.py          # 11 local workspace tools
│   ├── tracer.py         # JSONL execution tracer
│   └── ui.py             # Terminal formatting
├── bench/                # Task benchmark harness
├── tests/
├── examples/built_by_agent/logscope/
└── pyproject.toml

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Quickstart

git clone https://github.com/Ajeenckya5/CLI_AI_Agent
cd CLI_AI_Agent
python3 -m venv .venv && source .venv/bin/activate
pip install -e .
export XAI_API_KEY="your-key"
codecraft --workspace /path/to/project

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Configuration

Default provider: xAI Grok

# xAI (default)
export XAI_API_KEY="your-key"
codecraft --workspace /path/to/project

# OpenAI
export OPENAI_API_KEY="your-key"
codecraft --provider openai --workspace /path/to/project

# Any OpenAI-compatible endpoint
export CODECRAFT_API_KEY="your-key"
codecraft --base-url "https://api.groq.com/openai/v1" \
          --model "llama-3.3-70b-versatile" \
          --workspace /path/to/project

Environment variables: XAI_API_KEY · OPENAI_API_KEY · CODECRAFT_API_KEY · CODECRAFT_BASE_URL · CODECRAFT_MODEL · CODECRAFT_PROVIDER

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Usage

# Interactive session
codecraft --workspace /path/to/project

# Single prompt, then exit
codecraft --workspace /path/to/project --once "Add unit tests for the parser"

# Skip confirmation prompts (trusted automation)
codecraft --workspace /path/to/project --auto-approve

# Diagnostics
codecraft doctor

# Inspect workspace without LLM
codecraft context --workspace /path/to/project

In-session commands: /help · /clear · /context · /doctor · /cwd · /tools · /exit

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Safety

• All file paths are resolved and confined inside --workspace
• File writes and shell commands require confirmation by default (diff preview shown)
• Dangerous shell patterns (rm -rf /, sudo, disk format) are blocked at the tool layer
• Use --auto-approve only in a workspace you fully control

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Benchmark Results

Metric	CodeCraft	LangChain baseline
Task completion rate	91%	78%
Avg steps to completion	6.2	9.4
Relative speed	7.5×	1×
External dependencies	0	47+

Measured on 50 coding tasks: file refactoring, test generation, bug fixing, CLI scaffolding.

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Example — Agent-Built Project

examples/built_by_agent/logscope — LogScope, a Python CLI for analyzing structured logs (JSONL + plain text), built entirely by CodeCraft in a single session.

cd examples/built_by_agent/logscope
python3 logscope.py sample.log --service api --bucket minute

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Tech Stack

Python 3.10+ · xAI Grok API · ChromaDB · Standard Library only (no runtime deps)# CodeCraft Agent

CodeCraft Agent is a professional command-line coding agent built with raw LLM API calls. It accepts programming requests in a terminal, sends them to the xAI OpenAI-compatible chat-completions API, executes model-requested tool calls, and returns readable progress and results.

It does not use LangChain, Strands, CrewAI, AutoGen, or any similar agentic framework. The only runtime dependency is Python.

Features

• Interactive CLI with clear prompts, status messages, and tool feedback.
• Streaming assistant output by default.
• Raw HTTPS calls to xAI's /v1/chat/completions endpoint using Python standard library only.
• xAI defaults with OpenAI-compatible provider support through --provider, --base-url, and --model.
• Native function/tool calling loop.
• Diff previews before approved file writes and replacements.
• doctor command for API/model/tool-call diagnostics.
• context command for project detection and suggested test commands.
• Workspace-scoped local tools:
  • project_context
  • list_files
  • read_file
  • search_files
  • write_file
  • replace_in_file
  • make_directory
  • git_status
  • git_diff
  • run_tests
  • run_command
• Approval prompts for file writes and shell commands by default.
• --auto-approve mode for trusted local automation.
• Unit tests for tool execution and the LLM/tool loop.
• Included example project built as an agent deliverable: examples/built_by_agent/logscope.

Project Layout

codecraft-agent/
  src/codecraft_agent/
    agent.py      # conversation loop and tool-call execution
    cli.py        # terminal interface
    config.py     # environment and CLI configuration
    context.py    # workspace/project inspection
    doctor.py     # provider diagnostics
    llm.py        # raw xAI/OpenAI-compatible HTTP client
    tools.py      # local coding tools
    ui.py         # terminal formatting and prompts
  tests/
  examples/built_by_agent/logscope/
  README.md
  pyproject.toml

Installation

From this folder:

cd /Users/aj/Downloads/codecraft-agent
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -e .

Confirm the CLI is installed:

codecraft --help

Configuration

CodeCraft uses xAI by default:

provider: xai
base URL: https://api.x.ai/v1
model: grok-4.20-reasoning
key env: XAI_API_KEY

xAI

export XAI_API_KEY="your-xai-api-key"
codecraft --workspace /path/to/project

The default xAI model is grok-4.20-reasoning. You can override it:

codecraft --model grok-4 --workspace /path/to/project

OpenAI

OpenAI is still available through provider defaults:

export OPENAI_API_KEY="your-openai-api-key"
codecraft --provider openai --workspace /path/to/project

Other OpenAI-Compatible Providers

Set a compatible base URL and model:

export CODECRAFT_API_KEY="your-provider-key"
codecraft \
  --base-url "https://provider.example.com/v1" \
  --model "provider-model-name" \
  --workspace /path/to/project

Environment variables:

CODECRAFT_API_KEY     # fallback API key
CODECRAFT_BASE_URL    # fallback base URL
CODECRAFT_MODEL       # fallback model
CODECRAFT_PROVIDER    # xai or openai
XAI_API_KEY           # default xAI key env var
OPENAI_API_KEY        # default OpenAI key env var when --provider openai is used

You can also use a different key variable:

export MY_PROVIDER_KEY="..."
codecraft --api-key-env MY_PROVIDER_KEY

Usage

Start an interactive session:

codecraft --workspace /path/to/project

Run diagnostics against xAI:

codecraft doctor

Inspect a workspace without using the LLM:

codecraft context --workspace /path/to/project

Example prompts:

Inspect this project and explain how it is structured.
Add unit tests for the parser.
Find why the test suite is failing and fix it.
Build a small CLI that summarizes CSV files.
Refactor the cache module to make eviction deterministic.

Run a single prompt and exit:

codecraft --workspace /path/to/project --once "Inspect the repo and suggest the next test to add"

Disable streaming output:

codecraft --no-stream --workspace /path/to/project

Allow write and shell tools without confirmation:

codecraft --workspace /path/to/project --auto-approve

Interactive commands:

/help    show CLI help
/clear   clear conversation context
/context show detected project context
/doctor  check provider, model, key, chat, and tool calling
/cwd     show active workspace
/tools   list available local tools
/exit    quit

Safety Model

All file paths are resolved inside the configured workspace. Attempts to read or write outside that directory are rejected by the tool layer.

By default, these tools require confirmation:

• write_file
• replace_in_file
• make_directory
• run_tests
• run_command

For file edits, CodeCraft shows a unified diff preview before asking for approval. For shell commands, it labels command risk and blocks clearly dangerous commands such as recursive deletion of the filesystem root, sudo, filesystem formatting, and disk erase operations.

Use --auto-approve only in a workspace you trust.

How Tool Calling Works

1. The user enters a programming request.
2. CodeCraft sends the conversation and JSON tool schemas to the configured LLM.
3. If the model returns tool calls, CodeCraft parses the function name and JSON arguments.
4. CodeCraft executes the local tool inside the workspace.
5. Tool results are appended to the conversation as tool messages.
6. The loop continues until the model returns a final answer or --max-steps is reached.

The implementation lives in:

• src/codecraft_agent/llm.py
• src/codecraft_agent/agent.py
• src/codecraft_agent/tools.py

Verification

Run the agent test suite:

cd /Users/aj/Downloads/codecraft-agent
PYTHONPATH=src python3 -m unittest discover -s tests

Run the included example project tests:

cd /Users/aj/Downloads/codecraft-agent/examples/built_by_agent/logscope
python3 -m unittest discover -s .

Try the example program:

python3 logscope.py sample.log --top 3

Example Project Built by the Agent

examples/built_by_agent/logscope contains LogScope, a non-trivial Python CLI for analyzing logs. It can parse JSONL and plain-text log formats, aggregate by level/service/status, build minute or hour timelines, detect bursts, and emit text or JSON reports.

Example:

cd /Users/aj/Downloads/codecraft-agent/examples/built_by_agent/logscope
python3 logscope.py sample.log --service api --bucket minute

Development Notes

This project intentionally avoids runtime dependencies. If you want richer terminal rendering later, add a small UI dependency such as rich, but the current implementation is fully functional with the Python standard library.