In the world of software development, commit history is often an afterthought—a necessary evil that interrupts flow and breaks concentration. CommitLoom transforms this paradigm by acting as an autonomous AI agent that ensures every commit is structured, meaningful, and optimized—without the developer even thinking about it. Through intelligent perception, reasoning, and action, it turns the mundane task of commit creation into an automated, optimized process.
Originally designed as a personal AI assistant for commit optimization, CommitLoom has evolved into a specialized autonomous agent that transforms git workflow optimization through intelligent perception, reasoning, and action. Unlike conventional tools or basic LLM wrappers, CommitLoom operates as a true AI agent with sophisticated decision-making capabilities, environmental awareness, and autonomous operation. Its architecture demonstrates how focused AI agents can provide significant value through domain expertise and intelligent automation, even in individual development workflows.
CommitLoom demonstrates that a well-designed single agent can effectively handle complex workflows without the overhead of multi-agent orchestration:
Lightweight Agent Design ┌──────────────────────────────────────────────────────────┐ │ Core Dependencies │ │ ┌─────────────┐ │ │ │ Python │ • Minimal External Dependencies │ │ │ Standard │ • Focused Core Functionality │ │ │ Library │ • Efficient Resource Usage │ │ └─────────────┘ │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Git │ │ OpenAI │ │ Rich │ │ │ │ Interface │ │ API │ │ Terminal │ | │ └─────────────┘ └─────────────┘ └─────────────┘ │ └──────────────────────────────────────────────────────────┘
CommitLoom proves that a single, well-designed agent can:
CommitLoom Agent Architecture ┌──────────────────────────────────────────────────────────┐ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Perception │ │ Reasoning │ │ Action │ │ │ │ System │───>│ Engine │───>│ Generation │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ ▲ ▲ ▲ │ │ │ │ │ │ └─────────┼──────────────────┼──────────────────┼─────────┘ │ │ │ Git Changes Complexity Structured Analysis Decisions Output
CommitLoom's perception layer acts as the agent's environmental interface:
# Example of CommitLoom's autonomous perception system def analyze_diff_complexity(diff: str, changed_files: list[GitFile]) -> CommitAnalysis: """ Autonomous analysis system that builds a comprehensive understanding of code changes through multi-dimensional complexity assessment. """ warnings: list[Warning] = [] is_complex = False estimated_tokens, estimated_cost = CommitAnalyzer.estimate_tokens_and_cost(diff) # Autonomous complexity detection if estimated_tokens >= config.token_limit: is_complex = True warnings.append( Warning( level=WarningLevel.HIGH, message=( f"The diff exceeds token limit ({estimated_tokens:,} tokens). " f"Recommended limit is {config.token_limit:,} tokens." ), ) )
The agent's cognitive system demonstrates sophisticated reasoning through:
# Example of autonomous reasoning and decision making @dataclass class CommitAnalysis: """Results of the agent's autonomous reasoning process""" estimated_tokens: int estimated_cost: float num_files: int warnings: list[Warning] is_complex: bool
CommitLoom's action system demonstrates true agent autonomy through:
# Example of autonomous action generation with structured output @dataclass class CommitSuggestion: """Agent's structured action output""" title: str body: dict[str, dict[str, str | list[str]]] summary: str def format_body(self) -> str: """Intelligent formatting of commit message""" lines = [self.title, ""] for category, data in self.body.items(): emoji = data["emoji"] changes = data["changes"] lines.append(f"{emoji} {category}:") for change in changes: lines.append(f" - {change}") lines.append("") lines.append(self.summary) return "\n".join(lines)
CommitLoom supports both interactive and batch processing modes, enabling efficient handling of both real-time and bulk commit operations:
Interactive Mode: Developer CommitLoom Git │ │ │ ├─── Stage Changes ──────>│ │ │ │ │ │ ┌─────────────────────────────────────┐ │ │ │ 1. Analyze Changes │ │ │ │ 2. Evaluate Complexity │ │ │ │ 3. Generate Structured Message │ │ │ └─────────────────────────────────────┘ │ │ │ │ │<── Suggest Message ─────│ │ │ │ │ ├─── Approve Message ────>│ │ │ ├──── Create Commit ────> │ │ │ │ Batch Mode: Repository CommitLoom Git │ │ │ ├── Multiple Changes ────>│ │ │ │ │ │ ┌─────────────────────────────────────┐ │ │ │ 1. Batch Analysis │ │ │ │ 2. Intelligent Grouping │ │ │ │ 3. Parallel Processing │ │ │ └─────────────────────────────────────┘ │ │ │ │ │ ├── Optimized Commits ──> │ │ │ │
CommitLoom demonstrates innovative autonomous resource control, born from the need to optimize personal development costs:
@dataclass class TokenUsage: """Agent's autonomous resource tracking and optimization""" prompt_tokens: int completion_tokens: int total_tokens: int input_cost: float output_cost: float total_cost: float @classmethod def from_api_usage(cls, usage: dict[str, int], model: str) -> "TokenUsage": """Autonomous cost analysis and optimization""" prompt_tokens = usage["prompt_tokens"] completion_tokens = usage["completion_tokens"] total_tokens = usage["total_tokens"] input_cost = (prompt_tokens / 1_000_000) * config.model_costs[model].input output_cost = (completion_tokens / 1_000_000) * config.model_costs[model].output total_cost = input_cost + output_cost return cls( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, input_cost=input_cost, output_cost=output_cost, total_cost=total_cost, )
CommitLoom's resource management architecture:
Resource Management Flow: ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Token Usage │ │ Cost-Aware │ │ Optimization │ │ Estimation │───>│ Processing │───>│ Decisions │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ v v v ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Complexity │ │ Autonomous │ │ Resource │ │ Thresholds │<───│ Adjustment │<───│ Tracking │ └─────────────────┘ └─────────────────┘ └─────────────────┘
The agent implements sophisticated context understanding, developed through real-world usage in personal development:
def generate_prompt(self, diff: str, changed_files: list[GitFile]) -> str: """ Autonomous context analysis and prompt generation demonstrating environmental awareness and adaptation """ files_summary = ", ".join(f.path for f in changed_files) # Autonomous detection of binary files if diff.startswith("Binary files changed:"): return self._generate_binary_file_prompt(files_summary, diff) return self._generate_code_change_prompt(files_summary, diff)
CommitLoom's evolution demonstrates the power of focused AI agents:
CommitLoom's autonomous features include:
CommitLoom's agent architecture, built on the principles of autonomous perception, reasoning, and action, lays the foundation for future expansion. Its modular design and clear separation of concerns could enable integration with other development workflow agents, creating a coordinated system for code quality management—from commit creation to code review and continuous integration.
CommitLoom's technical architecture enables sophisticated processing and decision-making:
Commit Processing Pipeline: ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ Git Diff │ │ Semantic │ │ Structure │ │ Optimized │ │ Analysis │──>│ Analysis │──>│ Generation│──>│ Commit │ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │ │ │ │ v v v v ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │ Complexity│ │ Context │ │ Message │ │ Quality │ │ Detection │ │ Processing│ │ Formation │ │ Assurance │ └───────────┘ └───────────┘ └───────────┘ └───────────┘
CommitLoom's architecture demonstrates the power of focused design:
Core Orchestration: ┌───────────────────┐ │ Single Agent │ │ Orchestration │ └─────────┬─────────┘ │ v ┌───────────────────┐ │ Modular Systems │ ├───────────────────┤ │ • Git Interface │ │ • LLM Processing │ │ • Cost Control │ │ • Batch Handling │ └───────────────────┘
This efficient design enables:
CommitLoom represents a significant advancement in AI-powered developer tools, demonstrating how personal development needs can drive the creation of sophisticated autonomous agents. Born from the practical challenges of maintaining high-quality commit history, it has processed thousands of lines of code across multiple projects, proving that specialized AI agents can effectively augment developer workflows. Its evolution showcases:
As a focused AI agent that evolved from personal development needs, CommitLoom exemplifies how specialized autonomous systems can enhance software development, making it an ideal candidate for the Ready Tensor competition's emphasis on practical AI agents and development automation. Its success in optimizing individual development workflows demonstrates the potential for AI agents to transform software development practices, one commit at a time.
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