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feat: CodeCortex AI — autonomous terminal coding agent with pip package
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‎.ai-agent/config.toml‎

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This file was deleted.

‎.codecortex-ai/config.toml‎

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hooks_enabled = false
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[model]
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name = "openrouter/hunter-alpha"
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temperature = 0

.ai-agent/tools/test_tool.py renamed to .codecortex-ai/tools/test_tool.py

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‎.gitignore‎

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# Sessions & Logs
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hook.log
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*.log
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sessions/
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# Git
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.git/
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# CodeCortex AI Pip build artifacts
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CodeCortex AI Pip/dist/
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CodeCortex AI Pip/build/
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CodeCortex AI Pip/*.egg-info/
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CodeCortex AI Pip/codecortex/*.egg-info/
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# User config
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.codecortex/

‎CodeCortex AI Pip/README.md‎

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# CodeCortex AI
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**Autonomous coding agent for the terminal** — powered by LLMs.
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## Installation
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```bash
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pip install codecortex-ai
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```
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## Quick Start
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```bash
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codecortex
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```
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On first run, you'll be prompted for:
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- **API Key** (required) — your OpenAI, OpenRouter, or any compatible provider key
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- **Base URL** (optional) — leave blank for OpenAI, or enter a custom endpoint
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Configuration is saved to `~/.codecortex/config.json`.
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## Usage
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```bash
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# Interactive mode
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codecortex
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# Single prompt
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codecortex "refactor this function to use async/await"
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# Specify working directory
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codecortex --cwd ./my-project
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# Reset saved config
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codecortex --reset
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```
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## Supported Providers
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Any OpenAI-compatible API works:
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- **OpenAI** — leave Base URL empty (default)
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- **OpenRouter** — `https://openrouter.ai/api/v1`
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- **Anthropic via proxy** — any compatible endpoint
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- **Local models** (Ollama, LM Studio) — `http://localhost:11434/v1`
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## Commands
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Inside the interactive session:
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| Command | Description |
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|-------------|--------------------------------|
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| `/help` | Show help |
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| `/config` | Show current configuration |
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| `/model` | View or change model |
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| `/approval` | View or change approval policy |
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| `/tools` | List available tools |
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| `/mcp` | List MCP servers |
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| `/stats` | Show session statistics |
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| `/save` | Save current session |
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| `/sessions` | List saved sessions |
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| `/resume` | Resume a saved session |
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| `/clear` | Clear conversation |
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| `/exit` | Quit |
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## License
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MIT

‎CodeCortex AI Pip/codecortex/__init__.py‎

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"""CodeCortex AI — autonomous coding agent for the terminal."""
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__version__ = "0.1.0"

‎CodeCortex AI Pip/codecortex/agent/__init__.py‎

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‎CodeCortex AI Pip/codecortex/agent/agent.py‎

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from __future__ import annotations
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from typing import AsyncGenerator, Awaitable, Callable
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from codecortex.agent.events import AgentEvent, AgentEventType
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from codecortex.agent.session import Session
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from codecortex.client.response import StreamEventType, TokenUsage, ToolCall, ToolResultMessage
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from codecortex.config.config import Config
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from codecortex.prompts.system import create_loop_breaker_prompt
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from codecortex.tools.base import ToolConfirmation
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class Agent:
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def __init__(
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self,
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config: Config,
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confirmation_callback: Callable[[ToolConfirmation], bool] | None = None,
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):
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self.config = config
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self.session: Session | None = Session(self.config)
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self.session.approval_manager.confirmation_callback = confirmation_callback
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async def run(self, message: str):
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await self.session.hook_system.trigger_before_agent(message)
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yield AgentEvent.agent_start(message)
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self.session.context_manager.add_user_message(message)
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final_response: str | None = None
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async for event in self._agentic_loop():
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yield event
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if event.type == AgentEventType.TEXT_COMPLETE:
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final_response = event.data.get("content")
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await self.session.hook_system.trigger_after_agent(message, final_response)
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yield AgentEvent.agent_end(final_response)
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async def _agentic_loop(self) -> AsyncGenerator[AgentEvent, None]:
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max_turns = self.config.max_turns
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for turn_num in range(max_turns):
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self.session.increment_turn()
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response_text = ""
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# check for context overflow
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if self.session.context_manager.needs_compression():
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summary, usage = await self.session.chat_compactor.compress(
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self.session.context_manager
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)
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if summary:
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self.session.context_manager.replace_with_summary(summary)
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self.session.context_manager.set_latest_usage(usage)
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self.session.context_manager.add_usage(usage)
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tool_schemas = self.session.tool_registry.get_schemas()
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tool_calls: list[ToolCall] = []
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usage: TokenUsage | None = None
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async for event in self.session.client.chat_completion(
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self.session.context_manager.get_messages(),
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tools=tool_schemas if tool_schemas else None,
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):
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if event.type == StreamEventType.TEXT_DELTA:
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if event.text_delta:
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content = event.text_delta.content
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response_text += content
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yield AgentEvent.text_delta(content)
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elif event.type == StreamEventType.TOOL_CALL_COMPLETE:
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if event.tool_call:
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tool_calls.append(event.tool_call)
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elif event.type == StreamEventType.ERROR:
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yield AgentEvent.agent_error(
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event.error or "Unknown error occurred.",
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)
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elif event.type == StreamEventType.MESSAGE_COMPLETE:
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usage = event.usage
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self.session.context_manager.add_assistant_message(
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response_text or None,
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(
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[
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{
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"id": tc.call_id,
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"type": "function",
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"function": {
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"name": tc.name,
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"arguments": str(tc.arguments),
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},
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}
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for tc in tool_calls
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]
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if tool_calls
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else None
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),
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)
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if response_text:
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yield AgentEvent.text_complete(response_text)
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self.session.loop_detector.record_action(
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"response",
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text=response_text,
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)
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if not tool_calls:
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if usage:
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self.session.context_manager.set_latest_usage(usage)
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self.session.context_manager.add_usage(usage)
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self.session.context_manager.prune_tool_outputs()
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return
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tool_call_results: list[ToolResultMessage] = []
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for tool_call in tool_calls:
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yield AgentEvent.tool_call_start(
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tool_call.call_id,
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tool_call.name,
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tool_call.arguments,
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)
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self.session.loop_detector.record_action(
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"tool_call",
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tool_name=tool_call.name,
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args=tool_call.arguments,
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)
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result = await self.session.tool_registry.invoke(
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tool_call.name,
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tool_call.arguments,
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self.config.cwd,
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self.session.hook_system,
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self.session.approval_manager,
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)
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yield AgentEvent.tool_call_complete(
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tool_call.call_id,
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tool_call.name,
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result,
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)
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tool_call_results.append(
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ToolResultMessage(
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tool_call_id=tool_call.call_id,
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content=result.to_model_output(),
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is_error=not result.success,
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)
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)
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for tool_result in tool_call_results:
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self.session.context_manager.add_tool_result(
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tool_result.tool_call_id,
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tool_result.content,
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)
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loop_detection_error = self.session.loop_detector.check_for_loop()
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if loop_detection_error:
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loop_prompt = create_loop_breaker_prompt(loop_detection_error)
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self.session.context_manager.add_user_message(loop_prompt)
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if usage:
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self.session.context_manager.set_latest_usage(usage)
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self.session.context_manager.add_usage(usage)
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self.session.context_manager.prune_tool_outputs()
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yield AgentEvent.agent_error(f"Maximum turns ({max_turns}) reached")
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async def __aenter__(self) -> Agent:
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await self.session.initialize()
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return self
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async def __aexit__(
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self,
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exc_type,
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exc_val,
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exc_tb,
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) -> None:
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if self.session and self.session.client and self.session.mcp_manager:
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await self.session.client.close()
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await self.session.mcp_manager.shutdown()
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self.session = None

‎CodeCortex AI Pip/codecortex/agent/events.py‎

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from __future__ import annotations
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from enum import Enum
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from dataclasses import dataclass, field
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from typing import Any
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from codecortex.client.response import TokenUsage
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from codecortex.tools.base import ToolResult
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class AgentEventType(str, Enum):
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# Agent lifecycle
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AGENT_START = "agent_start"
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AGENT_END = "agent_end"
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AGENT_ERROR = "agent_error"
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# Tool calls
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TOOL_CALL_START = "tool_call_start"
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TOOL_CALL_COMPLETE = "tool_call_complete"
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# Text streaming
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TEXT_DELTA = "text_delta"
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TEXT_COMPLETE = "text_complete"
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@dataclass
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class AgentEvent:
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type: AgentEventType
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data: dict[str, Any] = field(default_factory=dict)
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@classmethod
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def agent_start(cls, message: str) -> AgentEvent:
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return cls(
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type=AgentEventType.AGENT_START,
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data={"message": message},
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)
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@classmethod
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def agent_end(
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cls,
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response: str | None = None,
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usage: TokenUsage | None = None,
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) -> AgentEvent:
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return cls(
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type=AgentEventType.AGENT_END,
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data={
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"response": response,
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"usage": usage.__dict__ if usage else None,
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},
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)
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@classmethod
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def agent_error(
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cls,
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error: str,
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details: dict[str, Any] | None = None,
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) -> AgentEvent:
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return cls(
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type=AgentEventType.AGENT_ERROR,
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data={"error": error, "details": details or {}},
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)
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@classmethod
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def text_delta(cls, content: str) -> AgentEvent:
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return cls(
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type=AgentEventType.TEXT_DELTA,
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data={"content": content},
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)
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@classmethod
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def text_complete(cls, content: str) -> AgentEvent:
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return cls(
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type=AgentEventType.TEXT_COMPLETE,
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data={"content": content},
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)
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@classmethod
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def tool_call_start(cls, call_id: str, name: str, arguments: dict[str, Any]):
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return cls(
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type=AgentEventType.TOOL_CALL_START,
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data={
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"call_id": call_id,
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"name": name,
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"arguments": arguments,
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},
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)
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@classmethod
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def tool_call_complete(
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cls,
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call_id: str,
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name: str,
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result: ToolResult,
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):
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return cls(
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type=AgentEventType.TOOL_CALL_COMPLETE,
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data={
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"call_id": call_id,
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"name": name,
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"success": result.success,
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"output": result.output,
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"error": result.error,
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"metadata": result.metadata,
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"diff": result.diff.to_diff() if result.diff else None,
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"truncated": result.truncated,
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"exit_code": result.exit_code,
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},
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)

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