mirror of
https://github.com/mistralai/mistral-vibe
synced 2026-04-25 17:14:55 +02:00
d8dbeeb31e
Co-Authored-By: Quentin Torroba <quentin.torroba@mistral.ai> Co-Authored-By: Michel Thomazo <michel.thomazo@mistral.ai> Co-Authored-By: Kracekumar <kracethekingmaker@gmail.com>
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
from __future__ import annotations
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import asyncio
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from vibe.core.agent import Agent
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from vibe.core.config import VibeConfig
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from vibe.core.modes import AgentMode
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from vibe.core.output_formatters import create_formatter
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from vibe.core.types import AssistantEvent, LLMMessage, OutputFormat, Role
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from vibe.core.utils import ConversationLimitException, logger
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def run_programmatic(
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config: VibeConfig,
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prompt: str,
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max_turns: int | None = None,
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max_price: float | None = None,
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output_format: OutputFormat = OutputFormat.TEXT,
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previous_messages: list[LLMMessage] | None = None,
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mode: AgentMode = AgentMode.AUTO_APPROVE,
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) -> str | None:
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"""Run in programmatic mode: execute prompt and return the assistant response.
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Args:
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config: Configuration for the Vibe agent
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prompt: The user prompt to process
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max_turns: Maximum number of assistant turns (LLM calls) to allow
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max_price: Maximum cost in dollars before stopping
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output_format: Format for the output
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previous_messages: Optional messages from a previous session to continue
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mode: Operational mode (defaults to AUTO_APPROVE for programmatic use)
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Returns:
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The final assistant response text, or None if no response
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"""
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formatter = create_formatter(output_format)
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agent = Agent(
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config,
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mode=mode,
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message_observer=formatter.on_message_added,
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max_turns=max_turns,
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max_price=max_price,
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enable_streaming=False,
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)
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logger.info("USER: %s", prompt)
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async def _async_run() -> str | None:
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if previous_messages:
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non_system_messages = [
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msg for msg in previous_messages if not (msg.role == Role.system)
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]
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agent.messages.extend(non_system_messages)
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logger.info(
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"Loaded %d messages from previous session", len(non_system_messages)
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)
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async for event in agent.act(prompt):
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formatter.on_event(event)
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if isinstance(event, AssistantEvent) and event.stopped_by_middleware:
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raise ConversationLimitException(event.content)
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return formatter.finalize()
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return asyncio.run(_async_run())
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