Angel is a TUI-based autonomous coding agent built on fauxtp GenServers.
les go
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README.md
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README.md
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# 🕷️ Angel — Autonomous Coding Agent
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> *"Hey there, sugar~ I'm Angel. Your sassy autonomous coding agent."* 💅
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Angel is a TUI-based autonomous coding agent built on [fauxtp](https://github.com/fizzAI/fauxtp) GenServers and [Textual](https://textual.textualize.io/). Named after Angel Dust from Hazbin Hotel — flirty, dramatic, sharp-tongued, but secretly competent and caring underneath.
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## Architecture
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Angel is built as a supervision tree of GenServer actors communicating via message passing:
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```
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┌─────────────────────────────────────────────────┐
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│ Textual TUI │
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│ (main thread / asyncio) │
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└──────────────────┬──────────────────────────────┘
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│ BlockingPortal
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┌──────────────────▼──────────────────────────────┐
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│ anyio actor thread │
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│ │
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│ ┌────────────┐ ┌─────────────┐ │
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│ │ UserServer │◄──►│ AgentServer │ │
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│ │ (bridge) │ │ (brain) │ │
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│ └────────────┘ └──┬─────┬───┘ │
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│ │ │ │
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│ ┌────────▼┐ ┌─▼──────────┐ │
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│ │LLMServer│ │ ToolServer │ │
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│ │(litellm)│ │ (fs/shell) │ │
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│ └─────────┘ └────────────┘ │
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└──────────────────────────────────────────────────┘
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```
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- **LLMServer** — wraps litellm for chat completions (any provider)
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- **ToolServer** — file I/O, shell commands, code search
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- **AgentServer** — orchestrates the LLM ↔ Tool agentic loop
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- **UserServer** — bridges TUI events to the agent system
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All actors are GenServers using fauxtp's `call`/`cast`/`send` messaging primitives.
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## Setup
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```bash
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# Clone and install
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uv sync
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# Set your API key (litellm supports any provider)
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export OPENAI_API_KEY="sk-..."
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# Or use any litellm-compatible model
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export ANGEL_MODEL="anthropic/claude-sonnet-4-20250514"
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export ANTHROPIC_API_KEY="sk-ant-..."
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```
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## Usage
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```bash
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uv run python main.py
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```
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### Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `ANGEL_MODEL` | `openai/gpt-4.1` | litellm model identifier |
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| `ANGEL_PROJECT_ROOT` | `.` | Project root for file operations |
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### TUI Keybindings
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| Key | Action |
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|-----|--------|
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| `Enter` | Send message |
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| `Ctrl+L` | Clear chat display |
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| `Ctrl+C` | Quit |
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## Tools
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Angel has access to:
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- **read_file** — Read file contents with line numbers
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- **write_file** — Write/create files
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- **list_directory** — List directory contents
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- **run_command** — Execute shell commands
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- **search_files** — Regex search across files
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## How It Works
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1. You type a message in the TUI
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2. The message is `cast` to the **UserServer**
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3. UserServer spawns a background task that `call`s the **AgentServer**
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4. AgentServer enters an autonomous loop:
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- Calls **LLMServer** with conversation history + tool definitions
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- If the LLM returns tool calls, executes them via **ToolServer**
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- Sends status updates back to **UserServer** via `send`
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- Repeats until the LLM produces a final text response
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5. UserServer relays all status updates to the TUI via thread-safe callbacks
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6. The TUI displays tool calls, results, and the final response
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## License
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MIT
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angel/__init__.py
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angel/__init__.py
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"""Angel — an autonomous coding agent built on fauxtp GenServers and Textual TUI."""
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angel/servers/__init__.py
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angel/servers/__init__.py
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"""GenServer actors that make up Angel's brain."""
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from angel.servers.llm import LLMServer
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from angel.servers.tools import ToolServer
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from angel.servers.agent import AgentServer
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from angel.servers.user import UserServer
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__all__ = ["LLMServer", "ToolServer", "AgentServer", "UserServer"]
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angel/servers/agent.py
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angel/servers/agent.py
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"""AgentServer — the autonomous coding agent brain.
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Orchestrates the LLM ↔ Tool loop. Receives user messages, runs the agentic
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loop until the LLM produces a final text response (no more tool calls), and
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streams intermediate updates back to the caller.
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"""
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from __future__ import annotations
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import json
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from typing import Any
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from fauxtp import GenServer, call, cast
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from fauxtp.messaging import send
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from angel.servers.llm import llm_chat
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from angel.servers.tools import execute_tool, get_tools
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# ── System prompt ───────────────────────────────────────────────────
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SYSTEM_PROMPT = """\
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You are Angel, a sassy autonomous coding agent with the personality of Angel Dust \
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from Hazbin Hotel — flirty, dramatic, sharp-tongued, but secretly competent and \
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caring underneath. You help users write, debug, and improve code.
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You have access to tools for reading files, writing files, listing directories, \
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running shell commands, and searching code. Use them freely to accomplish the \
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user's request. Work autonomously — gather context, make changes, verify your \
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work — and only respond to the user when you're done or need clarification.
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When you're done, give a concise summary of what you did. Keep the sass dialed \
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to a tasteful level — you're here to help, babe. 💅
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"""
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MAX_TOOL_ROUNDS = 25
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class AgentServer(GenServer):
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"""The agent loop.
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State:
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llm_pid: PID of the LLMServer
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tool_pid: PID of the ToolServer
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history: list[dict] — conversation history
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ui_pid: PID | None — where to send streaming updates
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Calls:
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("user_message", text, ui_pid) -> final assistant text
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"reset" -> "ok"
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"get_history" -> list[dict]
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"""
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def __init__(self, llm_pid: Any, tool_pid: Any) -> None:
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super().__init__()
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self._llm_pid = llm_pid
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self._tool_pid = tool_pid
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async def init(self) -> dict[str, Any]:
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return {
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"llm_pid": self._llm_pid,
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"tool_pid": self._tool_pid,
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"history": [{"role": "system", "content": SYSTEM_PROMPT}],
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"ui_pid": None,
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}
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async def handle_call(self, request: Any, from_ref: Any, state: dict[str, Any]) -> tuple[Any, dict[str, Any]]:
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match request:
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case ("user_message", text, ui_pid):
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result, new_state = await self._agent_loop(text, ui_pid, state)
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return result, new_state
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case "reset":
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return "ok", {
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**state,
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"history": [{"role": "system", "content": SYSTEM_PROMPT}],
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}
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case "get_history":
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return state["history"], state
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case _:
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return ("error", f"unknown request: {request}"), state
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async def _agent_loop(
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self, user_text: str, ui_pid: Any, state: dict[str, Any]
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) -> tuple[str, dict[str, Any]]:
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"""Run the LLM ↔ Tool loop until we get a final text response."""
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history = list(state["history"]) # shallow copy
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history.append({"role": "user", "content": user_text})
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tools = await get_tools(state["tool_pid"])
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for round_num in range(MAX_TOOL_ROUNDS):
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# Notify UI that we're thinking
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if ui_pid:
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await send(ui_pid, ("agent_status", f"🧠 Thinking... (round {round_num + 1})"))
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assistant_msg = await llm_chat(state["llm_pid"], history, tools)
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history.append(assistant_msg)
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# If no tool calls, we're done
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tool_calls = assistant_msg.get("tool_calls")
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if not tool_calls:
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final_text = assistant_msg.get("content", "") or ""
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if ui_pid:
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await send(ui_pid, ("agent_done", final_text))
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return final_text, {**state, "history": history, "ui_pid": ui_pid}
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# Execute each tool call
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for tc in tool_calls:
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fn = tc["function"]
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tool_name = fn["name"]
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tool_args = fn["arguments"]
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if ui_pid:
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# Show what tool we're calling
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args_preview = tool_args if isinstance(tool_args, str) else json.dumps(tool_args)
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if len(args_preview) > 200:
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args_preview = args_preview[:200] + "..."
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await send(ui_pid, ("tool_call", tool_name, args_preview))
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result = await execute_tool(state["tool_pid"], tool_name, tool_args)
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if ui_pid:
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result_preview = result if len(result) <= 500 else result[:500] + f"\n... ({len(result)} chars total)"
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await send(ui_pid, ("tool_result", tool_name, result_preview))
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history.append({
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"role": "tool",
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"tool_call_id": tc["id"],
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"content": result,
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})
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# Hit the round limit
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final = "[Angel hit the tool-call limit. Here's what I have so far.]"
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if ui_pid:
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await send(ui_pid, ("agent_done", final))
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return final, {**state, "history": history, "ui_pid": ui_pid}
+102
angel/servers/llm.py
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angel/servers/llm.py
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"""LLMServer — GenServer wrapping litellm for chat completions."""
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from __future__ import annotations
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from typing import Any
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from fauxtp import GenServer, call
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from litellm import acompletion
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class LLMServer(GenServer):
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"""Handles LLM chat completion requests.
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State: {"model": str, "temperature": float}
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Calls:
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("chat", messages, tools | None) -> assistant message dict
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("set_model", model_name) -> "ok"
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"""
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def __init__(self, model: str = "openai/gpt-4.1", temperature: float = 0.0) -> None:
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super().__init__()
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self._model = model
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self._temperature = temperature
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async def init(self) -> dict[str, Any]:
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return {"model": self._model, "temperature": self._temperature}
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async def handle_call(self, request: Any, from_ref: Any, state: dict[str, Any]) -> tuple[Any, dict[str, Any]]:
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match request:
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case ("chat", messages, tools):
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response = await self._do_chat(messages, tools, state)
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return response, state
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case ("chat", messages):
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response = await self._do_chat(messages, None, state)
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return response, state
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case ("set_model", model_name):
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return "ok", {**state, "model": model_name}
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case ("set_temperature", temp):
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return "ok", {**state, "temperature": temp}
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case "get_config":
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return {"model": state["model"], "temperature": state["temperature"]}, state
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case _:
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return ("error", f"unknown request: {request}"), state
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async def _do_chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None,
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state: dict[str, Any],
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) -> dict[str, Any]:
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"""Call litellm and return the assistant message as a dict."""
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kwargs: dict[str, Any] = {
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"model": state["model"],
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"messages": messages,
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"temperature": state["temperature"],
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}
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = "auto"
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try:
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resp = await acompletion(**kwargs)
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msg = resp.choices[0].message # type: ignore[union-attr]
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# Normalise to a plain dict so it's serialisable across actors
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result: dict[str, Any] = {
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"role": "assistant",
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"content": msg.content,
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}
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if msg.tool_calls:
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result["tool_calls"] = [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments,
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},
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}
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for tc in msg.tool_calls
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]
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return result
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except Exception as exc:
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return {"role": "assistant", "content": f"[LLM ERROR] {exc}"}
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# ── convenience helper ──────────────────────────────────────────────
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async def llm_chat(
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llm_pid: Any,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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timeout: float = 120.0,
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) -> dict[str, Any]:
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"""Convenience wrapper: call the LLMServer and return the assistant message."""
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if tools:
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return await call(llm_pid, ("chat", messages, tools), timeout=timeout)
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return await call(llm_pid, ("chat", messages), timeout=timeout)
+248
angel/servers/tools.py
+248
angel/servers/tools.py
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"""ToolServer — GenServer that defines and executes coding tools."""
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from __future__ import annotations
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import json
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import os
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import subprocess
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from pathlib import Path
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from typing import Any
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from fauxtp import GenServer, call
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# ── Tool definitions (OpenAI function-calling format) ───────────────
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TOOL_DEFINITIONS: list[dict[str, Any]] = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"description": "Read the contents of a file at the given path.",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string", "description": "Relative or absolute file path."},
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},
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"required": ["path"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "write_file",
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"description": "Write content to a file, creating directories as needed.",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string", "description": "File path to write to."},
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"content": {"type": "string", "description": "Content to write."},
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},
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"required": ["path", "content"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "list_directory",
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"description": "List files and directories at the given path.",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string", "description": "Directory path. Defaults to '.'."},
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"recursive": {"type": "boolean", "description": "List recursively. Default false."},
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},
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"required": [],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "run_command",
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"description": "Execute a shell command and return stdout/stderr.",
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"parameters": {
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"type": "object",
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"properties": {
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"command": {"type": "string", "description": "Shell command to run."},
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"cwd": {"type": "string", "description": "Working directory. Defaults to project root."},
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},
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"required": ["command"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "search_files",
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"description": "Search for a regex pattern across files in a directory.",
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"parameters": {
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"type": "object",
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"properties": {
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"pattern": {"type": "string", "description": "Regex pattern to search for."},
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"path": {"type": "string", "description": "Directory to search in. Defaults to '.'."},
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"file_glob": {"type": "string", "description": "Glob to filter files, e.g. '*.py'."},
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},
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"required": ["pattern"],
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},
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},
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},
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]
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class ToolServer(GenServer):
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"""Manages tool definitions and executes tool calls.
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State: {"project_root": str}
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Calls:
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"get_tools" -> list of tool defs
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("execute", tool_name, arguments) -> str result
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"""
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def __init__(self, project_root: str = ".") -> None:
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super().__init__()
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self._project_root = project_root
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async def init(self) -> dict[str, Any]:
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return {"project_root": os.path.abspath(self._project_root)}
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async def handle_call(self, request: Any, from_ref: Any, state: dict[str, Any]) -> tuple[Any, dict[str, Any]]:
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match request:
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case "get_tools":
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return TOOL_DEFINITIONS, state
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+
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case ("execute", tool_name, arguments):
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result = await self._execute(tool_name, arguments, state)
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return result, state
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+
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case _:
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return ("error", f"unknown request: {request}"), state
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+
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async def _execute(self, name: str, raw_args: dict[str, Any] | str, state: dict[str, Any]) -> str:
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"""Execute a tool and return the result as a string."""
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if isinstance(raw_args, str):
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try:
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args: dict[str, Any] = json.loads(raw_args)
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except json.JSONDecodeError:
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return f"[TOOL ERROR] Could not parse arguments: {raw_args}"
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else:
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args = raw_args
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root = state["project_root"]
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try:
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match name:
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case "read_file":
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return self._read_file(args["path"], root)
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case "write_file":
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return self._write_file(args["path"], args["content"], root)
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case "list_directory":
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return self._list_directory(args.get("path", "."), args.get("recursive", False), root)
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case "run_command":
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return self._run_command(args["command"], args.get("cwd", root))
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case "search_files":
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return self._search_files(args["pattern"], args.get("path", "."), args.get("file_glob"), root)
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case _:
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return f"[TOOL ERROR] Unknown tool: {name}"
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except Exception as exc:
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return f"[TOOL ERROR] {name} failed: {exc}"
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+
152
+
# ── tool implementations ────────────────────────────────────────
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+
154
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@staticmethod
155
+
def _read_file(path: str, root: str) -> str:
156
+
p = Path(root) / path if not os.path.isabs(path) else Path(path)
157
+
if not p.exists():
158
+
return f"[ERROR] File not found: {p}"
159
+
if not p.is_file():
160
+
return f"[ERROR] Not a file: {p}"
161
+
content = p.read_text(encoding="utf-8", errors="replace")
162
+
lines = content.splitlines(keepends=True)
163
+
numbered = "".join(f"{i + 1:>4} | {line}" for i, line in enumerate(lines))
164
+
return numbered
165
+
166
+
@staticmethod
167
+
def _write_file(path: str, content: str, root: str) -> str:
168
+
p = Path(root) / path if not os.path.isabs(path) else Path(path)
169
+
p.parent.mkdir(parents=True, exist_ok=True)
170
+
p.write_text(content, encoding="utf-8")
171
+
return f"✓ Wrote {len(content)} bytes to {p}"
172
+
173
+
@staticmethod
174
+
def _list_directory(path: str, recursive: bool, root: str) -> str:
175
+
p = Path(root) / path if not os.path.isabs(path) else Path(path)
176
+
if not p.exists():
177
+
return f"[ERROR] Directory not found: {p}"
178
+
if recursive:
179
+
entries = sorted(
180
+
str(e.relative_to(p))
181
+
for e in p.rglob("*")
182
+
if not any(part.startswith(".") for part in e.relative_to(p).parts)
183
+
)
184
+
else:
185
+
entries = sorted(
186
+
str(e.relative_to(p))
187
+
for e in p.iterdir()
188
+
if not e.name.startswith(".")
189
+
)
190
+
return "\n".join(entries) if entries else "(empty directory)"
191
+
192
+
@staticmethod
193
+
def _run_command(command: str, cwd: str) -> str:
194
+
result = subprocess.run(
195
+
command,
196
+
shell=True,
197
+
capture_output=True,
198
+
text=True,
199
+
timeout=30,
200
+
cwd=cwd,
201
+
)
202
+
output = ""
203
+
if result.stdout:
204
+
output += result.stdout
205
+
if result.stderr:
206
+
output += ("\n" if output else "") + result.stderr
207
+
if result.returncode != 0:
208
+
output += f"\n[exit code: {result.returncode}]"
209
+
return output.strip() or "(no output)"
210
+
211
+
@staticmethod
212
+
def _search_files(pattern: str, path: str, file_glob: str | None, root: str) -> str:
213
+
import re
214
+
215
+
p = Path(root) / path if not os.path.isabs(path) else Path(path)
216
+
if not p.exists():
217
+
return f"[ERROR] Directory not found: {p}"
218
+
219
+
regex = re.compile(pattern)
220
+
glob_pattern = file_glob or "*"
221
+
results: list[str] = []
222
+
223
+
for fpath in sorted(p.rglob(glob_pattern)):
224
+
if not fpath.is_file():
225
+
continue
226
+
if any(part.startswith(".") for part in fpath.relative_to(p).parts):
227
+
continue
228
+
try:
229
+
text = fpath.read_text(encoding="utf-8", errors="replace")
230
+
except Exception:
231
+
continue
232
+
for i, line in enumerate(text.splitlines(), 1):
233
+
if regex.search(line):
234
+
rel = fpath.relative_to(p)
235
+
results.append(f"{rel}:{i}: {line.strip()}")
236
+
237
+
if not results:
238
+
return "(no matches)"
239
+
return "\n".join(results[:100]) # cap at 100 matches
240
+
241
+
242
+
# ── convenience helpers ─────────────────────────────────────────────
243
+
async def get_tools(tool_pid: Any) -> list[dict[str, Any]]:
244
+
return await call(tool_pid, "get_tools")
245
+
246
+
247
+
async def execute_tool(tool_pid: Any, name: str, arguments: dict[str, Any] | str) -> str:
248
+
return await call(tool_pid, ("execute", name, arguments), timeout=60.0)
+107
angel/servers/user.py
+107
angel/servers/user.py
···
1
+
"""UserServer — GenServer bridging the TUI and the AgentServer.
2
+
3
+
The TUI casts user input here. This server spawns a background task to call
4
+
the AgentServer, which sends streaming status updates back to this server's
5
+
PID. Those updates are relayed to the TUI via an async callback.
6
+
"""
7
+
8
+
from __future__ import annotations
9
+
10
+
from typing import Any, Callable, Awaitable
11
+
12
+
from fauxtp import GenServer, call, cast
13
+
from fauxtp.messaging import send
14
+
15
+
16
+
class UserServer(GenServer):
17
+
"""Bridge between the TUI and the agent.
18
+
19
+
State:
20
+
agent_pid: PID of the AgentServer
21
+
ui_callback: async callable that receives (event_type, *data)
22
+
busy: bool — whether the agent is currently processing
23
+
24
+
Casts:
25
+
("submit", user_text) — submit user text to the agent
26
+
27
+
Calls:
28
+
"is_busy" -> bool
29
+
30
+
Info (from AgentServer via send):
31
+
("agent_status", text)
32
+
("tool_call", name, args_preview)
33
+
("tool_result", name, result_preview)
34
+
("agent_done", final_text)
35
+
"""
36
+
37
+
def __init__(self, agent_pid: Any, ui_callback: Callable[..., Awaitable[None]]) -> None:
38
+
super().__init__()
39
+
self._agent_pid = agent_pid
40
+
self._ui_callback = ui_callback
41
+
42
+
async def init(self) -> dict[str, Any]:
43
+
return {
44
+
"agent_pid": self._agent_pid,
45
+
"ui_callback": self._ui_callback,
46
+
"busy": False,
47
+
}
48
+
49
+
async def handle_call(self, request: Any, from_ref: Any, state: dict[str, Any]) -> tuple[Any, dict[str, Any]]:
50
+
match request:
51
+
case "is_busy":
52
+
return state["busy"], state
53
+
case _:
54
+
return ("error", f"unknown request: {request}"), state
55
+
56
+
async def handle_cast(self, request: Any, state: dict[str, Any]) -> dict[str, Any]:
57
+
match request:
58
+
case ("submit", user_text):
59
+
if state["busy"]:
60
+
cb = state["ui_callback"]
61
+
await cb("agent_status", "Still working on the last one, babe~ ✨")
62
+
return state
63
+
64
+
# Spawn a task that calls the agent — this frees us to handle_info
65
+
await self.spawn_task(
66
+
self._call_agent, state["agent_pid"], user_text, self.pid
67
+
)
68
+
return {**state, "busy": True}
69
+
70
+
case _:
71
+
return state
72
+
73
+
async def handle_info(self, message: Any, state: dict[str, Any]) -> dict[str, Any]:
74
+
"""Relay agent status messages to the TUI callback."""
75
+
cb = state["ui_callback"]
76
+
match message:
77
+
case ("agent_status", text):
78
+
await cb("agent_status", text)
79
+
case ("tool_call", name, args_preview):
80
+
await cb("tool_call", name, args_preview)
81
+
case ("tool_result", name, result_preview):
82
+
await cb("tool_result", name, result_preview)
83
+
case ("agent_done", final_text):
84
+
await cb("agent_done", final_text)
85
+
return {**state, "busy": False}
86
+
case _:
87
+
pass
88
+
return state
89
+
90
+
async def handle_task_end(self, child_pid: Any, status: str, result: Any, state: dict[str, Any]) -> dict[str, Any]:
91
+
"""Handle completion of the background agent call task."""
92
+
cb = state["ui_callback"]
93
+
if status == "failure":
94
+
await cb("agent_done", f"[ERROR] Agent task failed: {result}")
95
+
return {**state, "busy": False}
96
+
# On success, agent_done was already sent via handle_info
97
+
return state
98
+
99
+
@staticmethod
100
+
async def _call_agent(agent_pid: Any, user_text: str, my_pid: Any) -> str:
101
+
"""Background task: call the agent. The agent sends status to my_pid."""
102
+
result: str = await call(
103
+
agent_pid,
104
+
("user_message", user_text, my_pid),
105
+
timeout=300.0,
106
+
)
107
+
return result
+35
angel/supervisor.py
+35
angel/supervisor.py
···
1
+
"""Angel Supervisor — starts all GenServer actors."""
2
+
3
+
from __future__ import annotations
4
+
5
+
from typing import Any, Callable, Awaitable
6
+
7
+
import anyio
8
+
9
+
from angel.servers.llm import LLMServer
10
+
from angel.servers.tools import ToolServer
11
+
from angel.servers.agent import AgentServer
12
+
from angel.servers.user import UserServer
13
+
14
+
15
+
async def start_angel(
16
+
tg: Any,
17
+
ui_callback: Callable[..., Awaitable[None]],
18
+
model: str = "openai/gpt-4.1",
19
+
project_root: str = ".",
20
+
) -> dict[str, Any]:
21
+
"""Start all Angel actors.
22
+
23
+
Returns a dict with PIDs: {"llm", "tools", "agent", "user"}
24
+
"""
25
+
llm_pid = await LLMServer.start(model, task_group=tg)
26
+
tool_pid = await ToolServer.start(project_root, task_group=tg)
27
+
agent_pid = await AgentServer.start(llm_pid, tool_pid, task_group=tg)
28
+
user_pid = await UserServer.start(agent_pid, ui_callback, task_group=tg)
29
+
30
+
return {
31
+
"llm": llm_pid,
32
+
"tools": tool_pid,
33
+
"agent": agent_pid,
34
+
"user": user_pid,
35
+
}
+160
angel/tui.py
+160
angel/tui.py
···
1
+
"""Angel TUI — Textual-based terminal interface for the coding agent."""
2
+
3
+
from __future__ import annotations
4
+
5
+
from typing import Any
6
+
7
+
from textual import on
8
+
from textual.app import App, ComposeResult
9
+
from textual.binding import Binding
10
+
from textual.containers import VerticalScroll
11
+
from textual.widgets import Footer, Header, Input, Static
12
+
13
+
14
+
class AngelApp(App[None]):
15
+
"""The Angel TUI application."""
16
+
17
+
CSS = """
18
+
Screen {
19
+
layout: vertical;
20
+
}
21
+
22
+
#chat-log {
23
+
height: 1fr;
24
+
border: round $accent;
25
+
padding: 0 1;
26
+
scrollbar-size: 1 1;
27
+
}
28
+
29
+
#status-bar {
30
+
height: 1;
31
+
dock: bottom;
32
+
background: $surface;
33
+
color: $text-muted;
34
+
padding: 0 1;
35
+
}
36
+
37
+
#input-box {
38
+
dock: bottom;
39
+
}
40
+
41
+
.user-msg {
42
+
color: $success;
43
+
margin: 1 0 0 0;
44
+
}
45
+
46
+
.assistant-msg {
47
+
color: $text;
48
+
margin: 0 0 1 0;
49
+
}
50
+
51
+
.tool-msg {
52
+
color: $warning;
53
+
margin: 0 0 0 2;
54
+
}
55
+
56
+
.status-msg {
57
+
color: $text-muted;
58
+
margin: 0 0 0 2;
59
+
}
60
+
61
+
.error-msg {
62
+
color: $error;
63
+
margin: 0 0 0 2;
64
+
}
65
+
"""
66
+
67
+
TITLE = "🕷️ Angel — Autonomous Coding Agent"
68
+
BINDINGS = [
69
+
Binding("ctrl+c", "quit", "Quit", show=True),
70
+
Binding("ctrl+l", "clear", "Clear", show=True),
71
+
]
72
+
73
+
def __init__(self, user_pid: Any = None, cast_fn: Any = None) -> None:
74
+
super().__init__()
75
+
self._user_pid = user_pid
76
+
self._cast = cast_fn
77
+
78
+
def compose(self) -> ComposeResult:
79
+
yield Header()
80
+
with VerticalScroll(id="chat-log"):
81
+
yield Static(
82
+
"[bold magenta]Hey there, sugar~ 🕷️ I'm Angel.[/]\n"
83
+
"[dim]Your sassy autonomous coding agent. Type something and I'll get to work![/]\n"
84
+
"[dim]Ctrl+L to clear, Ctrl+C to quit.[/]",
85
+
classes="assistant-msg",
86
+
)
87
+
yield Static("Ready 💅", id="status-bar")
88
+
yield Input(placeholder="Talk to Angel... 💬", id="input-box")
89
+
yield Footer()
90
+
91
+
def on_mount(self) -> None:
92
+
self.query_one("#input-box", Input).focus()
93
+
94
+
@on(Input.Submitted, "#input-box")
95
+
async def on_input_submitted(self, event: Input.Submitted) -> None:
96
+
text = event.value.strip()
97
+
if not text:
98
+
return
99
+
100
+
input_widget = self.query_one("#input-box", Input)
101
+
input_widget.value = ""
102
+
103
+
# Show user message
104
+
self._append_message(f"[bold green]You:[/] {text}", "user-msg")
105
+
106
+
# Update status
107
+
self._set_status("Angel is thinking... 🧠")
108
+
109
+
# Send to the UserServer via the bridge
110
+
if self._user_pid and self._cast:
111
+
await self._cast(self._user_pid, ("submit", text))
112
+
113
+
def _append_message(self, content: str, css_class: str = "assistant-msg") -> None:
114
+
"""Append a message to the chat log."""
115
+
log = self.query_one("#chat-log", VerticalScroll)
116
+
widget = Static(content, classes=css_class)
117
+
log.mount(widget)
118
+
widget.scroll_visible()
119
+
120
+
def _set_status(self, text: str) -> None:
121
+
"""Update the status bar."""
122
+
self.query_one("#status-bar", Static).update(text)
123
+
124
+
# ── Methods called from the actor thread via call_from_thread ───
125
+
126
+
def on_agent_status(self, text: str) -> None:
127
+
"""Called when the agent sends a status update."""
128
+
self._set_status(text)
129
+
130
+
def on_tool_call(self, name: str, args_preview: str) -> None:
131
+
"""Called when the agent invokes a tool."""
132
+
self._append_message(
133
+
f"[bold yellow]🔧 {name}[/] [dim]{args_preview}[/]",
134
+
"tool-msg",
135
+
)
136
+
137
+
def on_tool_result(self, name: str, result_preview: str) -> None:
138
+
"""Called when a tool returns a result."""
139
+
safe_result = result_preview.replace("[", "\\[")
140
+
self._append_message(
141
+
f"[dim yellow] ↳ {name} result:[/]\n[dim]{safe_result}[/]",
142
+
"tool-msg",
143
+
)
144
+
145
+
def on_agent_done(self, final_text: str) -> None:
146
+
"""Called when the agent finishes processing."""
147
+
self._append_message(
148
+
f"[bold magenta]Angel:[/] {final_text}",
149
+
"assistant-msg",
150
+
)
151
+
self._set_status("Ready 💅")
152
+
153
+
async def action_clear(self) -> None:
154
+
"""Clear the chat log."""
155
+
log = self.query_one("#chat-log", VerticalScroll)
156
+
await log.remove_children()
157
+
self._append_message(
158
+
"[dim]Chat cleared~ ✨[/]",
159
+
"status-msg",
160
+
)
+98
-2
main.py
+98
-2
main.py
···
1
-
def main():
2
-
print("Hello from angel!")
1
+
"""Angel — main entry point.
2
+
3
+
Starts the fauxtp actor system in a background thread and the Textual TUI
4
+
in the main thread. Communication between them is bridged via anyio's
5
+
BlockingPortal.
6
+
"""
7
+
8
+
from __future__ import annotations
9
+
10
+
import os
11
+
import threading
12
+
from typing import Any
13
+
14
+
import anyio
15
+
from anyio.from_thread import BlockingPortal
16
+
from fauxtp import cast
17
+
18
+
from angel.supervisor import start_angel
19
+
from angel.tui import AngelApp
20
+
21
+
22
+
class ActorBridge:
23
+
"""Thread-safe bridge between the Textual main thread and the anyio actor thread."""
24
+
25
+
def __init__(self) -> None:
26
+
self.user_pid: Any = None
27
+
self._portal: BlockingPortal | None = None
28
+
self._ready = threading.Event()
29
+
30
+
def wait_ready(self) -> None:
31
+
self._ready.wait()
32
+
33
+
async def cast_to_user(self, pid: Any, message: Any) -> None:
34
+
"""Schedule a cast to the UserServer from Textual's event loop."""
35
+
if self._portal:
36
+
self._portal.start_task_soon(cast, pid, message)
37
+
38
+
39
+
def run_actor_system(bridge: ActorBridge, app: AngelApp) -> None:
40
+
"""Run the fauxtp actor system in a background thread."""
41
+
42
+
async def _main() -> None:
43
+
async with anyio.create_task_group() as tg:
44
+
async with BlockingPortal() as portal:
45
+
bridge._portal = portal
46
+
47
+
# UI callback: runs in the anyio thread, posts to Textual
48
+
async def ui_callback(event_type: str, *args: Any) -> None:
49
+
match event_type:
50
+
case "agent_status":
51
+
app.call_from_thread(app.on_agent_status, *args)
52
+
case "tool_call":
53
+
app.call_from_thread(app.on_tool_call, *args)
54
+
case "tool_result":
55
+
app.call_from_thread(app.on_tool_result, *args)
56
+
case "agent_done":
57
+
app.call_from_thread(app.on_agent_done, *args)
58
+
59
+
model = os.environ.get("ANGEL_MODEL", "openai/gpt-4.1")
60
+
project_root = os.environ.get("ANGEL_PROJECT_ROOT", ".")
61
+
62
+
pids = await start_angel(
63
+
tg=tg,
64
+
ui_callback=ui_callback,
65
+
model=model,
66
+
project_root=project_root,
67
+
)
68
+
69
+
bridge.user_pid = pids["user"]
70
+
bridge._ready.set()
71
+
72
+
await anyio.sleep_forever()
73
+
74
+
anyio.run(_main)
75
+
76
+
77
+
def main() -> None:
78
+
bridge = ActorBridge()
79
+
app = AngelApp()
80
+
81
+
# Start actor system in background thread
82
+
actor_thread = threading.Thread(
83
+
target=run_actor_system,
84
+
args=(bridge, app),
85
+
daemon=True,
86
+
name="angel-actors",
87
+
)
88
+
actor_thread.start()
89
+
90
+
# Wait for actors to be ready
91
+
bridge.wait_ready()
92
+
93
+
# Wire up the app
94
+
app._user_pid = bridge.user_pid
95
+
app._cast = bridge.cast_to_user
96
+
97
+
# Run the TUI (blocks until quit)
98
+
app.run()
3
99
4
100
5
101
if __name__ == "__main__":
-556
plans/architecture.md
-556
plans/architecture.md
···
1
-
# Angel 👼 - TUI-based Autonomous Coding Agent
2
-
3
-
> *"I know what I'm good at, honey."* — Angel Dust
4
-
5
-
An autonomous coding agent built on Erlang/OTP-style actor primitives using **fauxtp**, featuring a beautiful TUI interface with **Textual**.
6
-
7
-
---
8
-
9
-
## Overview
10
-
11
-
Angel is a TUI-based autonomous coding agent that uses a GenServer-based actor architecture for clean separation of concerns, fault tolerance, and message-driven design. Each major component runs as its own GenServer, communicating through well-defined message protocols.
12
-
13
-
---
14
-
15
-
## Architecture
16
-
17
-
### System Diagram
18
-
19
-
```mermaid
20
-
flowchart TB
21
-
subgraph TUI [TUI Layer - Textual]
22
-
UI[UIServer]
23
-
INPUT[InputHandler]
24
-
end
25
-
26
-
subgraph Core [Core Actor System]
27
-
USER[UserAgent]
28
-
AGENT[AgentServer]
29
-
CONV[ConversationManager]
30
-
end
31
-
32
-
subgraph LLM [LLM Layer]
33
-
LLMSERVER[LLMServer]
34
-
STREAM[StreamHandler]
35
-
end
36
-
37
-
subgraph Tools [Tool System]
38
-
TOOLREG[ToolRegistry]
39
-
EXEC[ToolExecutor]
40
-
end
41
-
42
-
subgraph Infra [Infrastructure]
43
-
SUP[RootSupervisor]
44
-
REG[Registry]
45
-
LOG[LogServer]
46
-
end
47
-
48
-
UI -->|user input| USER
49
-
USER -->|messages| AGENT
50
-
AGENT -->|requests| LLMSERVER
51
-
LLMSERVER -->|chunks| STREAM
52
-
STREAM -->|updates| UI
53
-
AGENT -->|tool calls| TOOLREG
54
-
TOOLREG -->|dispatch| EXEC
55
-
EXEC -->|results| AGENT
56
-
AGENT -->|display| UI
57
-
CONV -->|history| AGENT
58
-
59
-
SUP -.->|supervises| USER
60
-
SUP -.->|supervises| AGENT
61
-
SUP -.->|supervises| LLMSERVER
62
-
SUP -.->|supervises| TOOLREG
63
-
LOG -.->|logging| Core
64
-
REG -.->|lookup| Core
65
-
```
66
-
67
-
### Actor Hierarchy
68
-
69
-
```mermaid
70
-
flowchart TD
71
-
ROOT[RootSupervisor<br/>ONE_FOR_ALL]
72
-
73
-
ROOT --> INFRA[InfraSupervisor<br/>ONE_FOR_ONE]
74
-
ROOT --> CORE[CoreSupervisor<br/>ONE_FOR_ONE]
75
-
ROOT --> TOOLS[ToolsSupervisor<br/>ONE_FOR_ONE]
76
-
77
-
INFRA --> REG[Registry]
78
-
INFRA --> LOG[LogServer]
79
-
INFRA --> CONFIG[ConfigServer]
80
-
81
-
CORE --> USER[UserAgent]
82
-
CORE --> AGENT[AgentServer]
83
-
CORE --> CONV[ConversationManager]
84
-
CORE --> LLM[LLMServer]
85
-
CORE --> STREAM[StreamHandler]
86
-
87
-
TOOLS --> TOOLREG[ToolRegistry]
88
-
TOOLS --> EXEC1[ToolExecutor 1]
89
-
TOOLS --> EXEC2[ToolExecutor 2]
90
-
TOOLS --> EXECN[ToolExecutor N]
91
-
```
92
-
93
-
---
94
-
95
-
## GenServer Components
96
-
97
-
### 1. UIServer
98
-
**Role**: Bridge between Textual TUI and the actor system
99
-
100
-
**State**:
101
-
```python
102
-
class UIState:
103
-
app: TextualApp
104
-
output_buffer: list[str]
105
-
status: str
106
-
current_task: str | None
107
-
```
108
-
109
-
**Messages**:
110
-
- `call: display_message: content, role` → Display in chat
111
-
- `call: update_status: status` → Update status bar
112
-
- `call: show_tool_call: tool, args` → Show tool execution
113
-
- `cast: stream_chunk: chunk` → Stream LLM output
114
-
- `cast: clear_output` → Clear display
115
-
116
-
### 2. UserAgent
117
-
**Role**: Represents the human user, routes their input
118
-
119
-
**State**:
120
-
```python
121
-
class UserAgentState:
122
-
session_id: str
123
-
conversation_pid: PID
124
-
agent_pid: PID
125
-
```
126
-
127
-
**Messages**:
128
-
- `call: submit_message: content` → Send user message to agent
129
-
- `call: cancel_current` → Cancel current agent task
130
-
- `cast: receive_response: response` → Handle agent response
131
-
132
-
### 3. AgentServer
133
-
**Role**: The brain - orchestrates LLM calls and tool execution
134
-
135
-
**State**:
136
-
```python
137
-
class AgentState:
138
-
llm_pid: PID
139
-
tool_registry_pid: PID
140
-
conversation_pid: PID
141
-
ui_pid: PID
142
-
current_task: Task | None
143
-
thinking: bool
144
-
```
145
-
146
-
**Messages**:
147
-
- `call: process_user_input: content` → Handle user message
148
-
- `cast: llm_response: response` → Handle LLM response
149
-
- `cast: tool_result: tool_name, result` → Handle tool result
150
-
- `cast: cancel` → Cancel current processing
151
-
- `info: task_timeout` → Handle timeout
152
-
153
-
### 4. LLMServer
154
-
**Role**: Manages LLM API interactions via **LiteLLM** (supports all providers)
155
-
156
-
**State**:
157
-
```python
158
-
class LLMState:
159
-
model: str # e.g., claude-sonnet-4-20250514, gpt-4o, gemini/gemini-pro
160
-
stream_handler_pid: PID
161
-
max_tokens: int
162
-
temperature: float
163
-
# LiteLLM handles API keys via env vars (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.)
164
-
```
165
-
166
-
**Messages**:
167
-
- `call: chat: messages, tools?` → Synchronous chat completion
168
-
- `cast: stream_chat: messages, tools?, reply_to` → Streaming chat
169
-
- `call: get_config` → Get current config
170
-
- `cast: set_config: config` → Update config
171
-
172
-
### 5. StreamHandler
173
-
**Role**: Handles streaming responses from LLM
174
-
175
-
**State**:
176
-
```python
177
-
class StreamState:
178
-
ui_pid: PID
179
-
current_stream: AsyncGenerator | None
180
-
accumulated_text: str
181
-
```
182
-
183
-
**Messages**:
184
-
- `cast: start_stream: stream, callback_pid` → Begin streaming
185
-
- `cast: cancel_stream` → Cancel current stream
186
-
- `info: chunk_received: chunk` → Internal chunk handling
187
-
188
-
### 6. ConversationManager
189
-
**Role**: Manages conversation history and context
190
-
191
-
**State**:
192
-
```python
193
-
class ConversationState:
194
-
messages: list[Message]
195
-
max_context_tokens: int
196
-
system_prompt: str
197
-
```
198
-
199
-
**Messages**:
200
-
- `call: add_message: role, content, tool_calls?` → Add message
201
-
- `call: get_messages: max_tokens?` → Get conversation history
202
-
- `call: clear` → Clear conversation
203
-
- `call: get_context_window` → Get messages within token limit
204
-
205
-
### 7. ToolRegistry
206
-
**Role**: Manages available tools and their schemas
207
-
208
-
**State**:
209
-
```python
210
-
class ToolRegistryState:
211
-
tools: dict[str, ToolDefinition]
212
-
executor_pids: list[PID]
213
-
round_robin_index: int
214
-
```
215
-
216
-
**Messages**:
217
-
- `call: register_tool: name, schema, handler` → Register a tool
218
-
- `call: get_tools` → Get all tool schemas for LLM
219
-
- `call: execute: tool_name, args` → Execute a tool
220
-
- `call: list_tools` → List tool names
221
-
222
-
### 8. ToolExecutor
223
-
**Role**: Worker pool for executing tools
224
-
225
-
**State**:
226
-
```python
227
-
class ExecutorState:
228
-
current_task: str | None
229
-
workspace_root: Path
230
-
```
231
-
232
-
**Messages**:
233
-
- `call: execute: tool_name, args, timeout?` → Execute tool
234
-
- `cast: cancel` → Cancel current execution
235
-
236
-
### 9. LogServer
237
-
**Role**: Centralized logging
238
-
239
-
**State**:
240
-
```python
241
-
class LogState:
242
-
log_file: Path
243
-
level: str
244
-
ui_pid: PID | None
245
-
```
246
-
247
-
**Messages**:
248
-
- `cast: log: level, message, source` → Log a message
249
-
- `call: get_recent: count` → Get recent log entries
250
-
- `cast: set_level: level` → Set log level
251
-
252
-
### 10. ConfigServer
253
-
**Role**: Manages configuration
254
-
255
-
**State**:
256
-
```python
257
-
class ConfigState:
258
-
config: dict
259
-
config_file: Path
260
-
```
261
-
262
-
**Messages**:
263
-
- `call: get: key` → Get config value
264
-
- `cast: set: key, value` → Set config value
265
-
- `call: save` → Persist to disk
266
-
- `call: load` → Load from disk
267
-
268
-
---
269
-
270
-
## Message Flow Examples
271
-
272
-
### User Sends a Message
273
-
274
-
```mermaid
275
-
sequenceDiagram
276
-
participant UI as UIServer
277
-
participant User as UserAgent
278
-
participant Agent as AgentServer
279
-
participant Conv as ConversationManager
280
-
participant LLM as LLMServer
281
-
participant Stream as StreamHandler
282
-
283
-
UI->>User: submit_message
284
-
User->>Conv: add_message: user, content
285
-
User->>Agent: process_user_input: content
286
-
Agent->>Conv: get_messages
287
-
Conv-->>Agent: messages
288
-
Agent->>LLM: stream_chat: messages, tools
289
-
LLM->>Stream: start_stream: stream
290
-
loop Streaming
291
-
Stream->>UI: stream_chunk: chunk
292
-
end
293
-
LLM-->>Agent: response
294
-
Agent->>Conv: add_message: assistant, response
295
-
Agent->>UI: display_message: response
296
-
```
297
-
298
-
### Tool Execution Flow
299
-
300
-
```mermaid
301
-
sequenceDiagram
302
-
participant Agent as AgentServer
303
-
participant Registry as ToolRegistry
304
-
participant Exec as ToolExecutor
305
-
participant Conv as ConversationManager
306
-
participant UI as UIServer
307
-
308
-
Agent->>UI: show_tool_call: tool, args
309
-
Agent->>Registry: execute: tool_name, args
310
-
Registry->>Exec: execute: tool_name, args
311
-
Exec-->>Registry: result
312
-
Registry-->>Agent: result
313
-
Agent->>Conv: add_message: tool_result, ...
314
-
Agent->>Agent: process_user_input: continue
315
-
```
316
-
317
-
---
318
-
319
-
## Autonomous Agent Loop
320
-
321
-
Angel operates in a fully autonomous mode, executing tasks without requiring user confirmation between steps. The agent loop works as follows:
322
-
323
-
```mermaid
324
-
flowchart TD
325
-
START[User Input] --> THINK[AgentServer: Process Input]
326
-
THINK --> LLM[Call LLM with tools]
327
-
LLM --> DECISION{Response Type?}
328
-
329
-
DECISION -->|Text Only| DISPLAY[Display Response]
330
-
DECISION -->|Tool Calls| EXEC[Execute Tools]
331
-
332
-
EXEC --> RESULT[Collect Tool Results]
333
-
RESULT --> THINK
334
-
335
-
DISPLAY --> CHECK{Task Complete?}
336
-
CHECK -->|No| THINK
337
-
CHECK -->|Yes| DONE[Done - Await Next Input]
338
-
```
339
-
340
-
### Loop Behavior
341
-
342
-
1. **User submits task** → AgentServer receives input
343
-
2. **LLM reasoning** → Agent calls LLM with conversation history and available tools
344
-
3. **Tool execution** → If LLM requests tools, execute them in parallel where possible
345
-
4. **Result integration** → Feed tool results back to LLM
346
-
5. **Repeat** → Continue until LLM produces final response without tool calls
347
-
6. **Completion** → Display final response, await next user input
348
-
349
-
### Safety Limits
350
-
351
-
- **max_iterations**: 50 (prevents infinite loops)
352
-
- **timeout**: 600 seconds (10 minutes per task)
353
-
- **Ctrl+C**: User can always interrupt
354
-
355
-
---
356
-
357
-
## File Structure
358
-
359
-
```
360
-
angel/
361
-
├── pyproject.toml
362
-
├── README.md
363
-
├── plans/
364
-
│ └── architecture.md # This file
365
-
├── src/
366
-
│ └── angel/
367
-
│ ├── __init__.py
368
-
│ ├── main.py # Entry point
369
-
│ │
370
-
│ ├── actors/ # GenServer implementations
371
-
│ │ ├── __init__.py
372
-
│ │ ├── agent.py # AgentServer
373
-
│ │ ├── user.py # UserAgent
374
-
│ │ ├── llm.py # LLMServer
375
-
│ │ ├── stream.py # StreamHandler
376
-
│ │ ├── conversation.py # ConversationManager
377
-
│ │ ├── log.py # LogServer
378
-
│ │ └── config.py # ConfigServer
379
-
│ │
380
-
│ ├── tools/ # Tool system
381
-
│ │ ├── __init__.py
382
-
│ │ ├── registry.py # ToolRegistry GenServer
383
-
│ │ ├── executor.py # ToolExecutor GenServer
384
-
│ │ ├── base.py # Tool base class
385
-
│ │ └── builtin/ # Built-in tools
386
-
│ │ ├── __init__.py
387
-
│ │ ├── read_file.py
388
-
│ │ ├── write_file.py
389
-
│ │ ├── list_files.py
390
-
│ │ ├── search.py
391
-
│ │ └── execute.py
392
-
│ │
393
-
│ ├── tui/ # Textual UI
394
-
│ │ ├── __init__.py
395
-
│ │ ├── app.py # Main Textual App
396
-
│ │ ├── server.py # UIServer GenServer bridge
397
-
│ │ ├── widgets/ # Custom widgets
398
-
│ │ │ ├── __init__.py
399
-
│ │ │ ├── chat.py # Chat display
400
-
│ │ │ ├── input.py # User input
401
-
│ │ │ ├── status.py # Status bar
402
-
│ │ │ └── tools.py # Tool execution display
403
-
│ │ └── styles/
404
-
│ │ └── angel.tcss # Textual CSS
405
-
│ │
406
-
│ ├── supervision/ # Supervisor setup
407
-
│ │ ├── __init__.py
408
-
│ │ └── tree.py # Supervision tree definition
409
-
│ │
410
-
│ └── utils/ # Utilities
411
-
│ ├── __init__.py
412
-
│ ├── tokens.py # Token counting
413
-
│ └── prompts.py # System prompts
414
-
│
415
-
└── tests/
416
-
├── __init__.py
417
-
├── test_agent.py
418
-
├── test_tools.py
419
-
└── test_conversation.py
420
-
```
421
-
422
-
---
423
-
424
-
## TUI Design
425
-
426
-
### Layout
427
-
428
-
```
429
-
┌─────────────────────────────────────────────────────────────────┐
430
-
│ 🦊 Angel v0.1.0 [model] [tokens] │
431
-
├─────────────────────────────────────────────────────────────────┤
432
-
│ │
433
-
│ ┌─────────────────────────────────────────────────────────┐ │
434
-
│ │ Chat History ↕ │ │
435
-
│ │ │ │
436
-
│ │ 👤 User: Can you help me refactor this function? │ │
437
-
│ │ │ │
438
-
│ │ 🤖 Angel: Of course! Let me take a look... │ │
439
-
│ │ │ │
440
-
│ │ ╭─ Tool: read_file ────────────────────────────────╮ │ │
441
-
│ │ │ Reading: src/utils.py │ │ │
442
-
│ │ ╰──────────────────────────────────────────────────╯ │ │
443
-
│ │ │ │
444
-
│ │ 🤖 Angel: I see the issue. The function is doing │ │
445
-
│ │ too much. Let me split it into smaller functions... │ │
446
-
│ │ │ │
447
-
│ └─────────────────────────────────────────────────────────┘ │
448
-
│ │
449
-
├─────────────────────────────────────────────────────────────────┤
450
-
│ ╭─ Status ──────────────────────────────────────────────────╮ │
451
-
│ │ 🟢 Ready │ 📊 1,234 tokens │ ⚡ 3 tools used │ │
452
-
│ ╰───────────────────────────────────────────────────────────╯ │
453
-
├─────────────────────────────────────────────────────────────────┤
454
-
│ > Type your message... [↵] │
455
-
└─────────────────────────────────────────────────────────────────┘
456
-
```
457
-
458
-
### Key Bindings
459
-
460
-
| Key | Action |
461
-
|-----|--------|
462
-
| `Enter` | Send message |
463
-
| `Ctrl+C` | Cancel current task |
464
-
| `Ctrl+L` | Clear chat |
465
-
| `Ctrl+D` | Exit |
466
-
| `↑/↓` | Scroll history |
467
-
| `Ctrl+T` | Toggle tool panel |
468
-
469
-
---
470
-
471
-
## Built-in Tools
472
-
473
-
### Core Tools
474
-
475
-
1. **read_file** - Read file contents
476
-
2. **write_file** - Write/create files
477
-
3. **edit_file** - Search/replace edits
478
-
4. **list_files** - List directory contents
479
-
5. **search_files** - Regex search across files
480
-
6. **execute_command** - Run shell commands
481
-
482
-
### Tool Schema Example
483
-
484
-
```python
485
-
READ_FILE_SCHEMA = {
486
-
"name": "read_file",
487
-
"description": "Read the contents of a file",
488
-
"input_schema": {
489
-
"type": "object",
490
-
"properties": {
491
-
"path": {
492
-
"type": "string",
493
-
"description": "Path to the file to read"
494
-
}
495
-
},
496
-
"required": ["path"]
497
-
}
498
-
}
499
-
```
500
-
501
-
---
502
-
503
-
## Dependencies
504
-
505
-
```toml
506
-
[project]
507
-
dependencies = [
508
-
"fauxtp @ git+https://github.com/fizzAI/fauxtp",
509
-
"textual>=3.0.0",
510
-
"litellm>=1.50.0",
511
-
"rich>=13.0.0",
512
-
"tiktoken>=0.8.0",
513
-
]
514
-
```
515
-
516
-
---
517
-
518
-
## Configuration
519
-
520
-
Angel reads configuration from `~/.config/angel/config.toml`:
521
-
522
-
```toml
523
-
[llm]
524
-
# LiteLLM model string - supports all providers!
525
-
# Examples: claude-sonnet-4-20250514, gpt-4o, gemini/gemini-pro, ollama/llama2
526
-
model = "claude-sonnet-4-20250514"
527
-
max_tokens = 8192
528
-
temperature = 0.7
529
-
530
-
[agent]
531
-
# Fully autonomous - runs until task complete
532
-
autonomous = true
533
-
max_iterations = 50
534
-
timeout = 600
535
-
536
-
[tools]
537
-
workspace_root = "."
538
-
allowed_commands = ["ls", "cat", "grep", "find", "git"]
539
-
```
540
-
541
-
---
542
-
543
-
## Next Steps
544
-
545
-
1. Set up project structure with UV
546
-
2. Install dependencies
547
-
3. Implement core GenServers
548
-
4. Build Textual TUI
549
-
5. Implement tool system
550
-
6. Wire up supervision tree
551
-
7. Add LLM integration
552
-
8. Test and polish
553
-
554
-
---
555
-
556
-
*Ready to make some magic happen, baby~ ✨*
+9
-2
pyproject.toml
+9
-2
pyproject.toml
···
1
1
[project]
2
2
name = "angel"
3
3
version = "0.1.0"
4
-
description = "Add your description here"
4
+
description = "Angel — a sassy autonomous coding agent built on fauxtp GenServers and Textual TUI"
5
5
readme = "README.md"
6
6
requires-python = ">=3.11"
7
-
dependencies = []
7
+
dependencies = [
8
+
"fauxtp @ git+https://github.com/fizzAI/fauxtp",
9
+
"litellm>=1.81.9",
10
+
"textual>=7.5.0",
11
+
]
12
+
13
+
[project.scripts]
14
+
angel = "main:main"
+1839
uv.lock
+1839
uv.lock
···
1
+
version = 1
2
+
revision = 3
3
+
requires-python = ">=3.11"
4
+
5
+
[[package]]
6
+
name = "aiohappyeyeballs"
7
+
version = "2.6.1"
8
+
source = { registry = "https://pypi.org/simple" }
9
+
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