Summary

# Summary

PromptTrail is a library that provides a Unified LLM Interface + DSL for agents with developer-friendly functionality. The usage looks like this:

```python
import os

from prompttrail.agent.runners import CommandLineRunner
from prompttrail.agent.templates import *
from prompttrail.agent.tools.builtin import *
from prompttrail.agent.user_interface import CLIInterface
from prompttrail.core import Session
from prompttrail.models.anthropic import AnthropicConfig, AnthropicModel

# 🤖 Multiple LLM models are supported through a unified API
model = AnthropicModel(
  configuration=AnthropicConfig(
    api_key=os.environ["ANTHROPIC_API_KEY"],
    model_name="claude-3-5-sonnet-latest",
    max_tokens=4096,
    tools=[ExecuteCommand(), ReadFile(), CreateOrOverwriteFile(), EditFile()]))

# 🏗️ Create your agent with an intuitive Domain-Specific Language (DSL)
templates = LinearTemplate([
  # ✨ Generate dynamic messages using Jinja2 templating
  SystemTemplate(content=
    "You're a smart coding agent! Type END if you want to end conversation. Follow rules: {{clinerules}}"),
  # 🔄 Supports all standard control flows (while/for, if/else, functions)
  LoopTemplate([
    # 💬 Handle user interactions seamlessly
    UserTemplate(description="Input: "),
    # 🛠️ Integrate powerful built-in tools for function calling and automation
    ToolingTemplate(tools=[ExecuteCommand(),ReadFile(),CreateOrOverwriteFile(),EditFile()])],
    # 🧩 Easily construct complex control flow logic
    exit_condition=lambda session: session.messages[-1].content == "END")])

# 📦 Use metadata to efficiently pass, store, and retrieve information within your agent
initial_session = Session(metadata={"clinerules": open(".clinerules").read()})

# 🚀 Deploy your agent anywhere - terminal, server, or other environments
runner = CommandLineRunner(model=model, template=templates, user_interface=CLIInterface())
runner.run(session=initial_session)

# ===== Start =====
# From: 📝 system
# message:  "You're a smart coding agent! Type END if you want to end conversation. Follow rules: ..."
# metadata:  {'clinerules': '...'}
# =================
# From: 👤 user
# Input:
```

# Important files

- src/prompttrail/core/__init__.py: Implementation of core functionality (Model, Config, Message, Session)
- src/prompttrail/agent/templates/_core.py: Implementation of agent template
- src/prompttrail/agent/runner.py: Implementation of agent runner

# Project Setup
- This project is managed with rye and pyproject.toml.
  - requirements.lock and requirements-dev.lock files should not be edited directly.
  - Edit pyproject.toml and run `rye sync`
- The project uses the following development tools: black, pautoflake, isort, mypy, flake8, pytest
  - You can run all these checks at once by running `rye run all`.
- Your environment is already configured with the necessary API keys.
- For debugging purposes, the library uses `logger = logging.getLogger(__name__)`.
- Set `logging.basicConfig(level=logging.DEBUG)` to see debug information.

# Important Rules
- If a diff application fails, re-read the file first and check it as it may have been updated.
- If type matching fails repeatedly, you can add #type: ignore and proceed.
- To run tests for specific files, use `rye run pytest [test files]`. Always run `rye run all` after successfully passing related tests.
- After your knowledge cutoff, there are new models. Use `gpt-4o-mini` or `claude-3-5-haiku-latest` for easy tasks or tests. Use `gpt-4o` or `claude-3-5-sonnet-latest` for complex tasks.
- If you're writing a proposal, place it in proposal-docs