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from __future__ import annotations
from pathlib import Path
from loguru import logger
from agent.context_manager import ContextManager
from agent.llm_client import BaseLLMClient
from tools.bash_tool import run_bash
from tools.file_tools import run_read, run_write, run_edit
from tools.code_executor import run_create_plugin
from tools.browser_tool import run_browser
from tools.memory_tools import run_recall, run_remember
# Tool definitions sent to the API
TOOLS = [
{
"name": "bash",
"description": "Execute a shell command. Use for: ls, find, grep, git, python, npm, etc.",
"input_schema": {
"type": "object",
"properties": {
"command": {"type": "string", "description": "The shell command to execute"}
},
"required": ["command"],
},
},
{
"name": "read_file",
"description": "Read file contents. Returns UTF-8 text.",
"input_schema": {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path"},
"limit": {"type": "integer", "description": "Max lines to read (default: all)"},
},
"required": ["path"],
},
},
{
"name": "write_file",
"description": "Write content to a file. Creates parent directories if needed.",
"input_schema": {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path"},
"content": {"type": "string", "description": "Content to write"},
},
"required": ["path", "content"],
},
},
{
"name": "edit_file",
"description": "Replace exact text in a file. Finds old_text and replaces with new_text.",
"input_schema": {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path"},
"old_text": {"type": "string", "description": "Exact text to find"},
"new_text": {"type": "string", "description": "Replacement text"},
},
"required": ["path", "old_text", "new_text"],
},
},
{
"name": "remember",
"description": (
"Store information to long-term memory.\n"
"Use for: important information, preferences, decisions, lessons learned.\n"
"Organize by category (e.g., 'python', 'ai', 'project-x')."
),
"input_schema": {
"type": "object",
"properties": {
"content": {"type": "string", "description": "Information to store"},
"category": {"type": "string", "description": "Category for organizing memories"},
},
"required": ["content"],
},
},
{
"name": "recall",
"description": "Search long-term memory for relevant information.",
"input_schema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"},
},
"required": ["query"],
},
},
{
"name": "create_plugin",
"description": (
"Create a new plugin with Python code. The code must define a class "
"inheriting from BasePlugin. Use when you identify repeatable patterns."
),
"input_schema": {
"type": "object",
"properties": {
"code": {"type": "string", "description": "Python plugin code"},
"name": {"type": "string", "description": "Plugin name"},
"description": {"type": "string", "description": "Plugin description"},
},
"required": ["code", "name", "description"],
},
},
{
"name": "browser",
"description": (
"Browser automation. Actions: navigate, click, type, screenshot, extract.\n"
"Maintains cookies and login state across calls."
),
"input_schema": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": ["navigate", "click", "type", "screenshot", "extract"],
"description": "Browser action to perform",
},
"url": {"type": "string", "description": "URL (for navigate)"},
"selector": {"type": "string", "description": "CSS selector (for click/type)"},
"text": {"type": "string", "description": "Text to type (for type action)"},
},
"required": ["action"],
},
},
]
class AgentLoop:
"""Core agent loop: LLM call → tool execution → result append → continue."""
def __init__(
self,
client: BaseLLMClient,
system_prompt: str,
model: str = "claude-sonnet-4-5-20250929",
max_tokens: int = 8000,
context_manager: ContextManager | None = None,
workdir: Path | None = None,
plugin_manager=None,
core_context_provider=None,
):
self.client = client
self.system_prompt = system_prompt
self.model = model
self.max_tokens = max_tokens
self.context_manager = context_manager
self.workdir = workdir
self.plugin_manager = plugin_manager
self.core_context_provider = core_context_provider
async def run(self, messages: list[dict]) -> list[dict]:
"""Run the agent loop until the model stops calling tools.
Args:
messages: Conversation history (modified in place).
Returns:
Updated messages list.
"""
while True:
# Context compaction
if self.context_manager:
messages = await self.context_manager.maybe_compact(messages)
# Gather all tools (built-in + plugin)
all_tools = list(TOOLS)
if self.plugin_manager:
all_tools.extend(self.plugin_manager.get_all_tools())
# Call LLM
try:
response = await self.client.create_message(
model=self.model,
system_prompt=self.system_prompt,
messages=messages,
tools=all_tools,
max_tokens=self.max_tokens,
)
except Exception as e:
logger.error(f"API error: {e}")
messages.append({
"role": "assistant",
"content": [{"type": "text", "text": f"[API Error: {e}]"}],
})
break
# Extract text and tool calls
for block in response.content_blocks:
if isinstance(block, dict) and block.get("type") == "text":
text = block.get("text", "")
if text:
logger.info(f"Assistant: {text[:200]}")
# If no tool calls, conversation turn is done
if response.stop_reason != "tool_use":
messages.append({
"role": "assistant",
"content": response.content_blocks,
})
break
# Execute tools and collect results
results = []
for tc in response.tool_calls:
logger.info(f"Tool: {tc.name}({_preview_args(tc.input)})")
output = await self._execute_tool(tc.name, tc.input)
logger.debug(f"Result: {output[:200]}")
results.append({
"type": "tool_result",
"tool_use_id": tc.id,
"content": output,
})
# Append assistant message and tool results
messages.append({
"role": "assistant",
"content": response.content_blocks,
})
messages.append({
"role": "user",
"content": results,
})
return messages
async def _execute_tool(self, name: str, args: dict) -> str:
"""Dispatch a tool call to the appropriate handler."""
try:
if name == "bash":
return await run_bash(args["command"], workdir=self.workdir)
elif name == "read_file":
return await run_read(
args["path"], workdir=self.workdir, limit=args.get("limit")
)
elif name == "write_file":
return await run_write(args["path"], args["content"], workdir=self.workdir)
elif name == "edit_file":
return await run_edit(
args["path"], args["old_text"], args["new_text"], workdir=self.workdir
)
elif name == "remember":
category = args.get("category")
if category is not None and not isinstance(category, str):
return "Error: category must be a string"
return await run_remember(
args["content"],
category=category,
)
elif name == "recall":
return await run_recall(args["query"])
elif name == "create_plugin":
return await run_create_plugin(
args["code"], args["name"], args["description"]
)
elif name == "browser":
return await run_browser(
args["action"],
url=args.get("url", ""),
selector=args.get("selector", ""),
text=args.get("text", ""),
)
else:
# Try plugin tools
if self.plugin_manager:
result = await self.plugin_manager.execute_plugin_tool(name, args)
if result is not None:
return result
return f"Unknown tool: {name}"
except Exception as e:
logger.error(f"Tool execution error ({name}): {e}")
return f"Error executing {name}: {e}"
def _preview_args(args: dict, max_len: int = 100) -> str:
"""Create a short preview of tool arguments for logging."""
parts = []
for k, v in args.items():
s = str(v)
if len(s) > max_len:
s = s[:max_len] + "..."
parts.append(f"{k}={s}")
return ", ".join(parts)
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