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This PR updates subagent ID generation to use short 8-character hex
strings instead of full UUID v4 strings. Previously, subagent IDs were
36-character UUIDs (e.g. `550e8400-e29b-41d4-a716-446655440000`), which
were verbose in CLI output and unwieldy when the LLM needed to reference
them in tools like `send_input`, `wait`, and `close_agent`. The new
format generates IDs like `a3f1b20c` — compact, human-readable, and with
~4 billion possible values, effectively collision-free within a session.
The change is made at the source in `subagent.rs`, where UUID generation
is replaced with `format!("{:08x}",
uuid::Uuid::new_v4().as_fields().0)`. Because IDs are now inherently 8
characters, the display-layer truncations in `cli.rs` (5 occurrences)
and `run_progress.rs` (2 occurrences) are redundant and have been
removed — `agent_id` is used directly in format strings instead of a
`short_id` slice.
### Plan Summary
- **Replace UUID generation** in `subagent.rs`: use the first field of a
UUID v4 formatted as 8-char lowercase hex, yielding IDs like `a3f1b20c`
instead of full 36-char UUIDs
- **Remove `short_id` truncation** in `cli.rs` (5 places) and
`run_progress.rs` (2 places): since IDs are now already 8 chars, the
`let short_id = &agent_id[..8.min(agent_id.len())]` pattern is
eliminated and `{agent_id}` is used directly in all format strings
- No test changes required — existing tests use hardcoded IDs like
`"sa-1"` and don't assert on ID length or format
<details>
<summary>Full plan</summary>
````md
The plan has been written to `/home/daytona/workspace/plan.md`.
It covers:
- **4 files to modify**: `fabro-agent/Cargo.toml` (add `rand` dep), `subagent.rs` (replace UUID with 8-char hex), `cli.rs` (remove 5 `short_id` truncations), `run_progress.rs` (remove 2 `short_id` truncations)
- **Step-by-step implementation** with exact line references and before/after code
- **Verification commands** to confirm correctness
- **Test case analysis** explaining why no test changes are needed
````
</details>
### Fabro Details
<details>
<summary>Ran 3 stages in 18m 46s for $0.57</summary>
| Stage | Duration | Cost | Retries |
|---|---|---|---|
| start | 0s | – | 0 |
| plan | 1m 28s | $0.57 | 0 |
| implement | 17m 6s | – | 0 |
| **Total** | **18m 46s** | **$0.57** | **0** |
</details>
<details>
<summary>Ran <code>GhImplement.fabro</code> (4 nodes and 3
edges)</summary>
```dot
digraph GhImplement {
graph [
goal="Implement a GitHub issue",
model_stylesheet="
* { model: claude-opus-4-6; }
"
]
rankdir=LR
start [shape=Mdiamond, label="Start"]
exit [shape=Msquare, label="Exit"]
plan [label="Plan", prompt="Fetch the GitHub issue from the goal using: gh issue view $goal --json title,body,labels,comments\n\nRead the issue title, description, and any comments carefully. Analyze what code changes are needed to resolve the issue.\n\nWrite a detailed implementation plan to plan.md that includes:\n- Summary of the issue\n- Files to create or modify\n- Step-by-step implementation approach\n- Test cases to add or update\n\nThe plan should be specific enough for another agent to implement without seeing the original issue.\n\nRespond with the location of the plan file (plan.md)."]
implement [label="Implement", shape=house, stack.child_workflow="fabro/workflows/implement/workflow.fabro", manager.max_cycles=100]
start -> plan
plan -> implement [fidelity="summary:high"]
implement -> exit
}
```
</details>
⚒️ Generated with [Fabro](https://fabro.sh)
---------
Co-authored-by: Fabro <noreply@fabro.sh>
|
||
|---|---|---|
| .. | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
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:
- User input is appended to a conversation
History - The session builds a
Requestwith system prompt, history, and tools - An LLM generates a response (text and/or tool calls) via
unified-llm - Tool calls are executed through a
ToolRegistryagainst aSandbox - 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 aborted
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, abort handling, and event emission.ProviderProfile(trait) -- Defines how to build system prompts, which tools to register, and what capabilities a provider supports. Ships withAnthropicProfile,OpenAiProfile, andGeminiProfile.Sandbox(trait) -- Abstracts filesystem, shell, grep, and glob operations.LocalSandboxprovides 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 ofTurnvariants (User,Assistant,ToolResults,System,Steering) that converts to LLM messages.EventEmitter-- BroadcastsSessionEvents (tool calls, text, errors, warnings) over atokio::sync::broadcastchannel for UI or logging.SubAgentManager-- Spawns childSessions 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.
ProviderProfile
pub trait ProviderProfile: 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;
fn capabilities(&self) -> ProfileCapabilities;
fn knowledge_cutoff(&self) -> &str;
// ... default methods for tools(), provider_options(), supports_*()
}
Built-in profiles:
AnthropicProfile-- 200K context, extended thinking beta headers, tools:read_file,write_file,edit_file,shell,grep,globOpenAiProfile-- 128K context, reasoning effort support, tools:read_file,write_file,shell,grep,glob,apply_patch(v4a format)GeminiProfile-- 1M context, safety settings, tools: all Anthropic tools plusread_many_files,list_dir,web_search,web_fetch
Sandbox
pub trait Sandbox: Send + Sync {
async fn read_file(&self, path: &str, offset: Option<usize>, limit: Option<usize>) -> Result<String, String>;
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);
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());
Abort
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
ContextWindowWarningevents 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 --
LocalSandboxstrips 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.mdbased on provider, with a 32KB budget