fabro/lib/crates/fabro-agent

agent

A programmable agentic loop for building coding agents. This crate provides the core session management, tool execution, and LLM interaction loop used to power interactive coding assistants.

Architecture

The crate is organized around a central Session that drives an agentic loop:

1. User input is appended to a conversation History
2. The session builds a Request with system prompt, history, and tools
3. An LLM generates a response (text and/or tool calls) via unified-llm
4. Tool calls are executed through a ToolRegistry against a Sandbox
5. Results are recorded and the loop continues until the LLM responds with text only (natural completion), a turn limit is reached, or the session is interrupted

User Input
    |
    v
[Session::process_input]
    |
    v
+-------------------+
| Build Request     |  <-- system prompt + history + tools
+-------------------+
    |
    v
+-------------------+
| LLM Call          |  <-- via unified-llm Client
+-------------------+
    |
    v
+-------------------+     +-------------------+
| Tool Calls?  -----+-yes-| Execute Tools     |
+-------------------+     | (parallel or seq)  |
    | no                  +-------------------+
    v                         |
  [Done]                      +---> loop back to Build Request

Key Components

• Session -- Manages the full agentic loop: LLM calls, tool execution, steering, follow-ups, interrupt handling, and event emission.
• AgentProfile (trait) -- Defines how to build system prompts, which tools to register, and what capabilities a provider supports. Ships with AnthropicProfile, OpenAiProfile, and GeminiProfile.
• Sandbox (trait) -- Abstracts filesystem, shell, grep, and glob operations. LocalSandbox provides a real implementation; the trait enables sandboxing and testing.
• ToolRegistry -- Maps tool names to definitions and async executor functions. Tools are registered per-profile.
• History -- Ordered list of Turn variants (User, Assistant, ToolResults, System, Steering) that converts to LLM messages.
• Emitter -- Broadcasts SessionEvents (tool calls, text, errors, warnings) over a tokio::sync::broadcast channel for UI or logging.
• SubAgentManager -- Spawns child Sessions on background tasks for delegated work, with depth limits.
• SessionConfig -- Tunable parameters: max turns, tool round limits, command timeouts, loop detection, output truncation limits, and user instructions.

Key Types and Traits

Session

The main entry point. Created with an LLM client, a provider profile, a sandbox, and a config.

AgentProfile

pub trait AgentProfile: Send + Sync {
    fn id(&self) -> String;
    fn model(&self) -> String;
    fn tool_registry(&self) -> &ToolRegistry;
    fn build_system_prompt(
        &self,
        env: &dyn Sandbox,
        env_context: &EnvContext,
        project_docs: &[String],
        user_instructions: Option<&str>,
    ) -> String;
    // ... default methods for tools(), knowledge_cutoff(), context_window_size()
}

Built-in profiles:

• AnthropicProfile -- 200K context, extended thinking beta headers, tools: read_file, write_file, edit_file, shell, grep, glob
• OpenAiProfile -- 128K context, reasoning effort support, tools: read_file, write_file, shell, grep, glob, apply_patch (Codex apply_patch format)
• GeminiProfile -- 1M context, safety settings, tools: all Anthropic tools plus read_many_files, list_dir, web_search, web_fetch

Sandbox

pub trait Sandbox: Send + Sync {
    async fn read_file_bytes(&self, path: &str) -> Result<Vec<u8>, String>;
    async fn read_file_text(&self, path: &str) -> Result<String, String>;
    async fn read_file(&self, path: &str, offset: Option<usize>, limit: Option<usize>) -> Result<String, String>; // line-numbered display
    async fn write_file(&self, path: &str, content: &str) -> Result<(), String>;
    async fn exec_command(&self, command: &str, timeout_ms: u64, ...) -> Result<ExecResult, String>;
    async fn grep(&self, pattern: &str, path: &str, options: &GrepOptions) -> Result<Vec<String>, String>;
    async fn glob(&self, pattern: &str, path: Option<&str>) -> Result<Vec<String>, String>;
    // ... plus delete_file, file_exists, list_directory, initialize, cleanup, platform info
}

LocalSandbox is the real implementation with env-var filtering (strips secrets), process group management, and ripgrep/grep fallback.

SessionConfig

pub struct SessionConfig {
    pub max_turns: usize,                    // 0 = unlimited
    pub max_tool_rounds_per_input: usize,    // default: 200
    pub default_command_timeout_ms: u64,     // default: 10s
    pub max_command_timeout_ms: u64,         // default: 600s
    pub enable_loop_detection: bool,         // default: true
    pub loop_detection_window: usize,        // default: 10
    pub max_subagent_depth: usize,           // default: 1
    pub user_instructions: Option<String>,
    pub reasoning_effort: Option<String>,
    // ... plus tool_output_limits, tool_line_limits, git_root
}

Usage

use agent::{
    AnthropicProfile, LocalSandbox, Session, SessionConfig,
};
use std::path::PathBuf;
use std::sync::Arc;
use unified_llm::client::Client;

// 1. Create an LLM client (via unified-llm)
let client: Client = /* configure unified-llm client */;

// 2. Choose a provider profile
let profile = Arc::new(AnthropicProfile::new("claude-sonnet-4-20250514"));

// 3. Create a sandbox
let env = Arc::new(LocalSandbox::new(
    PathBuf::from("/path/to/project"),
));

// 4. Configure the session
let config = SessionConfig {
    max_tool_rounds_per_input: 50,
    enable_loop_detection: true,
    user_instructions: Some("Always write tests first".into()),
    ..SessionConfig::default()
};

// 5. Create and initialize the session
let mut session = Session::new(client, profile, env, config, None);
session.initialize().await?;

// 6. Subscribe to events (for UI rendering)
let mut rx = session.subscribe();
tokio::spawn(async move {
    while let Ok(event) = rx.recv().await {
        // Handle SessionEvent: tool calls, text, errors, etc.
    }
});

// 7. Process user input
session.process_input("Fix the failing test in src/lib.rs").await?;

Steering and Follow-ups

Inject guidance mid-conversation or queue follow-up messages:

// Inject a steering message before the next LLM call
session.steer("Focus on the root cause, not symptoms".into());

// Queue a follow-up that runs after the current input completes
session.follow_up("Now run the test suite to verify".into());

Interrupt

Cancel a running session from another thread:

let cancel_token = session.cancel_token();
// From another task:
cancel_token.cancel();

Custom Tools

Register additional tools via the profile's ToolRegistry:

use agent::tool_registry::{RegisteredTool, ToolExecutor};
use unified_llm::types::ToolDefinition;
use std::sync::Arc;

let custom_tool = RegisteredTool {
    definition: ToolDefinition {
        name: "my_tool".into(),
        description: "Does something useful".into(),
        parameters: serde_json::json!({
            "type": "object",
            "properties": {
                "input": {"type": "string"}
            },
            "required": ["input"]
        }),
    },
    executor: Arc::new(|args, env| {
        Box::pin(async move {
            let input = args["input"].as_str().unwrap_or("");
            Ok(format!("Processed: {input}"))
        })
    }),
};

// Register on a mutable profile before creating the session
profile.tool_registry_mut().register(custom_tool);

Subagents

Spawn child sessions for delegated tasks:

use agent::subagent::SubAgentManager;

let mut profile = AnthropicProfile::new("claude-sonnet-4-20250514");
let manager = Arc::new(tokio::sync::Mutex::new(SubAgentManager::new(3)));
let factory = Arc::new(|| { /* create a new Session */ });

// Registers spawn_agent, send_input, wait, close_agent tools
profile.register_subagent_tools(manager, factory, 0);

Safety Features

• Loop detection -- Detects repeating tool call patterns (period 1, 2, or 3) and injects a steering warning
• Context window monitoring -- Emits Warning events (kind "context_window") when estimated usage exceeds 80%
• Tool argument validation -- Validates arguments against JSON Schema before execution
• Tool output truncation -- Per-tool character and line limits with head/tail or tail-only truncation modes
• Environment variable filtering -- LocalSandbox strips secrets (*_API_KEY, *_SECRET, *_TOKEN, *_PASSWORD, *_CREDENTIAL) from subprocess environments
• Command timeouts -- Configurable per-command with process group cleanup (SIGTERM then SIGKILL)
• Project doc discovery -- Automatically discovers AGENTS.md, CLAUDE.md, GEMINI.md, or .codex/instructions.md based on provider, with a 32KB budget