gemini cli to refactor
This is an ambitious and exciting project! Refactoring an existing Rust program into an AI agent system using the Gemini CLI is a multi-faceted task. This manual will guide you through the process, leveraging Gemini CLI's capabilities for code analysis, generation, and even some light orchestration.
Important Note: The Gemini CLI is a powerful assistant, not an autonomous developer. While it can generate code and provide insights, you will still need to understand the generated code, test it thoroughly, and make critical architectural decisions.
Manual: Refactoring Rust Programs into an AI Agent System with Gemini CLI
This manual outlines a comprehensive approach to using the Gemini CLI to transform a set of existing Rust program files into a structured AI agent system. This system will likely involve components for data processing, decision-making, and interaction, all powered or assisted by AI models accessed via the Gemini CLI.
Prerequisites
* Rust Toolchain: Ensure you have Rust and Cargo installed and configured correctly.
* Gemini CLI: Install the Gemini CLI as per its official documentation:
npm install -g @google/gemini-cli
# OR
npx @google/gemini-cli # For single use without global install
# OR (macOS/Linux)
brew install google/gemini/gemini-cli
* Google Cloud Project & Gemini API Key: You'll need a Google Cloud Project with the Gemini API enabled and an API key. Configure the Gemini CLI with your key:
gemini configure
* Version Control: Strongly recommend using Git for version control. Commit frequently!
* Basic AI Concepts: Familiarity with AI agent architectures (e.g., perception-action loops, tool use) will be beneficial.
Phase 1: Preparation & Understanding the Existing Rust Codebase
Before you begin refactoring, you need a deep understanding of your current Rust programs.
1.1. Initial Codebase Scan with grep and Gemini
Use Gemini CLI's terminal tool (or your own grep) to get a quick overview of the codebase.
gemini "Analyze the following Rust project structure. Identify main entry points, common data structures, and significant modules. Start by listing all `.rs` files and their approximate line counts. Then, look for `fn main`, `struct`, `enum`, `impl`, and `mod` keywords." --tool terminal --prompt-file src/project_summary_prompt.md
src/project_summary_prompt.md example:
You are an expert Rust programmer and code architect.
My goal is to refactor this Rust project into an AI agent system.
Please help me understand the current codebase.
My current working directory is a Rust project.
First, list all Rust files and their line counts:
`find . -name "*.rs" -print0 | xargs -0 wc -l`
Then, provide a high-level summary of the project by looking for common patterns related to:
- Main functions (`fn main`)
- Struct definitions (`struct`)
- Enum definitions (`enum`)
- Trait implementations (`impl`)
- Module declarations (`mod`)
Based on this, suggest potential areas for:
1. Data input/output points.
2. Core logic that could become an AI 'thought' process.
3. Actions or side effects that an AI agent might perform.
1.2. Deep Dive into Key Files with file read and Gemini
Identify the core logic, data flow, and potential bottlenecks in your existing programs.
gemini "Explain the purpose and data flow of this Rust file. Focus on how data is processed, what external interactions occur, and what its main outputs are. Suggest how this module could fit into an AI agent's 'perception' or 'action' cycle." --tool "file read" --file src/lib.rs
Repeat this for other critical files (e.g., src/main.rs, critical modules).
1.3. Identify Agent Components
Based on your understanding, begin to map your existing Rust code to AI agent components:
* Perception: Which parts of your code read data, sense the environment, or receive inputs?
* Thought/Decision-Making: Where is the core logic that processes data and makes decisions? This is where the AI model (via Gemini CLI calls) will likely augment or replace existing logic.
* Action: Which parts of your code perform operations, write outputs, or interact with external systems?
* Memory: Do you need to store state or past interactions? How is this currently handled, and how could it be improved with an AI's context?
* Tools: What external systems or internal functions does your Rust code interact with (e.g., databases, APIs, file system)? These will become the "tools" your AI agent uses.
Phase 2: Architectural Design for the AI Agent System
This phase involves designing the new structure and defining interfaces.
2.1. Sketch the Agent Architecture with Gemini
Describe your vision for the AI agent system to Gemini and ask for architectural suggestions.
gemini "I want to refactor my Rust programs into an AI agent system. Here's a high-level overview of my existing code's functionality: [Provide a brief summary from Phase 1.1]. I envision a system with a central agent loop that perceives, thinks, and acts. Suggest a high-level Rust module structure (e.g., `src/agent/mod.rs`, `src/perceptors/mod.rs`, `src/actions/mod.rs`, `src/tools/mod.rs`) and key traits/interfaces for each component. Consider how asynchronous operations might fit."
2.2. Define Core Traits and Interfaces
Based on Gemini's suggestions and your own design, start defining the fundamental Rust traits for your agent components.
// src/agent/mod.rs (or similar)
pub trait Perceptor {
async fn perceive(&self) -> anyhow::Result<PerceptionData>;
}
pub trait DecisionMaker {
async fn decide(&self, perception: &PerceptionData, context: &AgentContext) -> anyhow::Result<AgentAction>;
}
pub trait ActionExecutor {
async fn execute(&self, action: &AgentAction) -> anyhow::Result<ActionOutcome>;
}
// ... and so on for Tools, Memory, etc.
Gemini CLI Help:
Ask Gemini to generate boilerplate for these traits, including associated types and common methods.
gemini "Generate Rust code for a trait `Perceptor` that has an async method `perceive` returning a `PerceptionData` struct. Also, define the `PerceptionData` struct with fields for raw sensor data (e.g., `String`), parsed insights (e.g., `Vec<String>`), and a timestamp."
2.3. Data Flow and Context Management
Crucially, design how data flows between your perception, decision, and action components, and how the AI agent maintains its internal state or "memory."
* AgentContext Struct: A central struct to hold the agent's state, memory, and references to tools.
* Shared State: Consider Arc<Mutex<AgentContext>> for shared, mutable state in an asynchronous environment.
Phase 3: Incremental Refactoring with Gemini CLI
This is where the bulk of the work happens. Refactor piece by piece, leveraging Gemini CLI for assistance.
3.1. Isolate and Convert Perception Modules
Take parts of your existing code that handle input or sensing and refactor them into Perceptor implementations.
* Identify a Target: Pick one input source (e.g., reading a file, listening on a network socket).
* Extract Functionality: Move the relevant code into a new module (e.g., src/perceptors/file_watcher.rs).
* Implement Perceptor Trait:
gemini "Given this existing Rust function that reads lines from a file: [Paste function code]. Refactor it into a struct `FilePerceptor` that implements the `Perceptor` trait (defined with `async fn perceive() -> anyhow::Result<PerceptionData>`). Make sure `PerceptionData` captures the file content and a timestamp. Handle file I/O errors gracefully." --tool "file read" --file src/old_put_reader.rs # Or paste code directly
* Test: Write unit tests for your new Perceptor implementation.
3.2. Define and Implement AI-Driven Decision Making
This is the core of your AI agent. The DecisionMaker will interact heavily with the Gemini API.
* GeminiDecisionMaker Struct: Create a struct that wraps the logic for interacting with the Gemini API. It will hold an API client.
* Implement DecisionMaker Trait:
// src/decision_makers/gemini_dm.rs
pub struct GeminiDecisionMaker {
// ... API client
}
impl DecisionMaker for GeminiDecisionMaker {
async fn decide(&self, perception: &PerceptionData, context: &AgentContext) -> anyhow::Result<AgentAction> {
// Construct the prompt for Gemini
let prompt = format!(
"You are an AI agent. Based on the following perception: \"{}\"\nAnd current context: \"{}\"\nWhat is the next best action to take? Provide a JSON object with 'action_type' (string) and 'payload' (string). Available tools: [list tools].",
perception.parsed_insights,
context.get_summary() // Assume context can provide a summary
);
// Use Gemini CLI's 'generate' functionality indirectly, or call its underlying API
// For direct CLI interaction:
let response_json = gemini_cli_call_function(&prompt).await?; // This is pseudocode for calling Gemini via CLI
// ... Parse response_json into AgentAction
Ok(action)
}
}
* Option A (Recommended for gemini-cli focus): Spawn gemini process: You can use tokio::process::Command to execute gemini as a subprocess and capture its output. This is a bit more complex but directly uses the CLI.
use tokio::process::Command;
async fn gemini_cli_call_function(prompt: &str) -> anyhow::Result<String> {
let output = Command::new("gemini")
.arg(prompt)
.arg("--json") // Request JSON output if possible
// Add any other arguments like --tool, --file, etc.
.output()
.await?;
if output.status.success() {
Ok(String::from_utf8(output.stdout)?)
} else {
Err(anyhow::anyhow!("Gemini CLI error: {}", String::from_utf8_lossy(&output.stderr)))
}
}
* Option B (More robust for production): Use a Rust Gemini client library: For a more direct and type-safe integration, consider using an official (or community-maintained) Rust client library for the Google Gemini API. This bypasses the CLI but gives you direct API control.
* Prompt Engineering: This is crucial. Iterate on your prompts to Gemini to get the desired AgentAction output.
* Example Prompt Structure:
You are an intelligent Rust program agent.
Your goal is to [state agent's ultimate objective].
Current Perception:
- Files changed: src/my_module.rs, Cargo.toml
- Error message: "undeclared lifetime `&'a`"
Agent History/Context:
- Last action: Tried to fix lifetime issue in `src/my_module.rs`.
- Known issues: Need to manage memory carefully.
Available Tools:
1. `read_file(path: String)`: Reads content of a file.
2. `write_file(path: String, content: String)`: Writes content to a file.
3. `run_command(command: String)`: Executes a shell command (e.g., `cargo check`).
4. `search_web(query: String)`: Performs a web search.
Based on the perception and context, determine the single best next action.
Respond with a JSON object in the format:
```json
{
"thought": "Brief reasoning for the action.",
"action": {
"name": "tool_name",
"parameters": {
"param1": "value1",
"param2": "value2"
}
}
}
If no action is needed, use {"thought": "No further action required.", "action": {"name": "none"}}.
3.3. Refactor Existing Logic into Tools and Actions
Convert the "Action" components and "Tools" identified in Phase 1 into standalone Rust functions or structs that can be called by your ActionExecutor.
* Create Tool Modules: For each external interaction (database, API, file I/O, shell commands), create a dedicated module (e.g., src/tools/file_io.rs, src/tools/db_access.rs).
*Implement ActionExecutor: This trait implementation will receive an AgentAction (parsed from Gemini's output) and call the appropriate tool function.
// src/action_executors/mod.rs
pub struct BasicActionExecutor { /* ... */ }
impl ActionExecutor for BasicActionExecutor {
async fn execute(&self, action: &AgentAction) -> anyhow::Result<ActionOutcome> {
match action.action_type.as_str() {
"read_file" => { /* Call your file_io::read_file function */ },
"write_file" => { /* Call your file_io::write_file function */ },
"run_command" => { /* Use tokio::process::Command */ },
// ... other actions
_ => return Err(anyhow::anyhow!("Unknown action type: {}", action.action_type)),
}
Ok(ActionOutcome::Success) // Or more detailed outcome
}
}
* Leverage Gemini for Tool Stub Generation:
gemini "Generate a Rust async function `read_file(path: &str) -> anyhow::Result<String>` that reads the content of a file. Handle `io::Error` gracefully and return an `anyhow::Result`."
3.4. Build the Agent Loop (Orchestration)
This is the central main.rs that ties everything together.
// src/main.rs
use crate::agent::{Perceptor, DecisionMaker, ActionExecutor, AgentContext};
use crate::perceptors::FilePerceptor; // Example
use crate::decision_makers::GeminiDecisionMaker; // Example
use crate::action_executors::BasicActionExecutor; // Example
#[tokio::main]
async fn main() -> anyhow::Result<()> {
let perceptor = FilePerceptor::new("data/input.txt"); // Or whatever your perceptor needs
let decision_maker = GeminiDecisionMaker::new(/* API client details */)?;
let action_executor = BasicActionExecutor::new();
let mut context = AgentContext::new(); // Initial context
loop {
println!("Agent: Perceiving...");
let perception = perceptor.perceive().await?;
context.update_perception(&perception); // Update context with latest perception
println!("Agent: Deciding...");
let action = decision_maker.decide(&perception, &context).await?;
context.update_last_action(&action); // Update context with last action taken
println!("Agent: Executing action: {:?}", action);
let outcome = action_executor.execute(&action).await?;
context.update_last_outcome(&outcome); // Update context with outcome
println!("Agent: Action outcome: {:?}", outcome);
// Add termination conditions, sleep, or loop control
if /* some condition to stop */ {
break;
}
tokio::time::sleep(std::time::Duration::from_secs(5)).await;
}
Ok(())
}
Gemini CLI Assistance:
Ask Gemini to help with the boilerplate for the main loop, including error handling and asynchronous patterns.
gemini "Generate the basic structure for an async Rust `main` function that implements a perceive-decide-act loop. It should use `tokio`, call `perceptor.perceive()`, `decision_maker.decide()`, and `action_executor.execute()`. Include basic error handling with `anyhow::Result` and a simple loop with a delay."
3.5. Memory and Context Management
Integrate how the agent remembers past interactions and maintains a coherent "understanding" of its environment.
* AgentContext Refinement: Add fields to AgentContext for storing:
* Past perceptions
* Past actions and outcomes
* Summary of key events
* Long-term memory (e.g., a vector of important facts)
* Context Summarization: You might even use Gemini CLI to help summarize the AgentContext before sending it as part of the prompt to the DecisionMaker.
gemini "Summarize the following agent context into a concise string that can be used in a prompt for a decision-making AI. Focus on the most recent events, critical observations, and pending goals. [Paste serialized AgentContext or relevant parts]."
Phase 4: Testing, Iteration, and Refinement
This is an iterative process. Expect to go back and forth between design, implementation, and testing.
4.1. Unit and Integration Testing
* Unit Tests: Test each Perceptor, DecisionMaker, ActionExecutor, and individual tool in isolation. Mock external dependencies for DecisionMaker (to avoid hitting the Gemini API for every test).
* Integration Tests: Test the entire agent loop with mocked or controlled external environments.
* End-to-End Tests: Run the agent against a realistic scenario.
4.2. Prompt Engineering Refinement
The quality of your agent directly depends on your prompts.
* Iterate: Experiment with different phrasing, examples, and constraints in your prompts.
* Few-Shot Examples: Provide examples of desired input/output pairs in your prompts to guide Gemini.
* Tool Descriptions: Ensure your tool descriptions in the prompt are clear and unambiguous.
* Gemini CLI prompt-file: Use this feature to manage complex prompts: gemini --prompt-file my_agent_prompt.md.
4.3. Performance and Cost Optimization
* Token Usage: Be mindful of token usage with the Gemini API. Summarize context effectively.
* Caching: Cache responses from Gemini for identical requests if applicable.
* Asynchronous Operations: Ensure all I/O-bound operations are async and await appropriately to maximize concurrency.
* Error Handling: Implement robust error handling, including retries for API calls.
Phase 5: Deployment and Monitoring (Beyond CLI, but good to keep in mind)
While the Gemini CLI is a development tool, consider how your AI agent system would operate in a production environment.
* Standalone Binary: Package your Rust agent into a single binary.
* Containerization: Use Docker to containerize your application for consistent deployment.
* Logging: Implement comprehensive logging for agent perceptions, decisions, actions, and errors.
* Monitoring: Set up monitoring for agent health, performance, and API usage.
Tips for Effective Gemini CLI Usage
* Be Specific: The more specific your request to Gemini, the better the output. Provide context, constraints, and desired formats.
* Use --tool: Leverage the built-in tools like file read, terminal, web search to provide Gemini with necessary context.
* Use --prompt-file: For longer or frequently used prompts, store them in files.
* Iterate and Refine: Don't expect perfect code on the first try. Use Gemini's output as a starting point and refine it yourself.
* Understand Limitations: Gemini is a language model. It can make mistakes, generate hallucinated code, or misunderstand complex architectural patterns. Always review its output.
* Break Down Tasks: Don't ask Gemini to refactor an entire codebase at once. Break it down into smaller, manageable refactoring units.
* Focus on Interfaces: Ask Gemini to help define traits and structs first, then implement them.
* Sandbox Mode (--sandbox): If you are asking Gemini to run commands or interact with the file system on your behalf, consider running it in --sandbox mode (requires Docker/Podman) for security.
This manual provides a structured approach to a complex refactoring task. By following these steps and creatively leveraging the Gemini CLI, you can significantly accelerate the development of your Rust-based AI agent system. Good luck!