a56c6571d2
The docstring incorrectly stated that fetch_webpage uses Jina AI reader. Updated to accurately describe the actual implementation which uses: 1. Exa API (if EXA_API_KEY is available) 2. Trafilatura (fallback) 3. Readability + html2text (final fallback) 🐾 Generated with [Letta Code](https://letta.com) Co-authored-by: Letta <noreply@letta.com>
79 lines
3.7 KiB
Python
79 lines
3.7 KiB
Python
from typing import List, Literal, Optional
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def run_code(code: str, language: Literal["python", "js", "ts", "r", "java"]) -> str:
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"""
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Run code in a sandbox. Supports Python, Javascript, Typescript, R, and Java.
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Args:
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code (str): The code to run.
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language (Literal["python", "js", "ts", "r", "java"]): The language of the code.
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Returns:
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str: The output of the code, the stdout, the stderr, and error traces (if any).
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"""
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raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
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def run_code_with_tools(code: str) -> str:
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"""
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Run code with access to the tools of the agent. Only support python. You can directly invoke the tools of the agent in the code.
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Args:
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code (str): The python code to run.
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Returns:
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str: The output of the code, the stdout, the stderr, and error traces (if any).
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"""
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raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
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async def web_search(
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query: str,
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num_results: int = 10,
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category: Optional[
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Literal["company", "research paper", "news", "pdf", "github", "tweet", "personal site", "linkedin profile", "financial report"]
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] = None,
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include_text: bool = False,
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include_domains: Optional[List[str]] = None,
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exclude_domains: Optional[List[str]] = None,
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start_published_date: Optional[str] = None,
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end_published_date: Optional[str] = None,
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user_location: Optional[str] = None,
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) -> str:
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"""
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Search the web using Exa's AI-powered search engine and retrieve relevant content.
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Examples:
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web_search("Tesla Q1 2025 earnings report", num_results=5, category="financial report")
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web_search("Latest research in large language models", category="research paper", include_domains=["arxiv.org", "paperswithcode.com"])
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web_search("Letta API documentation core_memory_append", num_results=3)
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Args:
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query (str): The search query to find relevant web content.
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num_results (int, optional): Number of results to return (1-100). Defaults to 10.
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category (Optional[Literal], optional): Focus search on specific content types. Defaults to None.
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include_text (bool, optional): Whether to retrieve full page content. Defaults to False (only returns summary and highlights, since the full text usually will overflow the context window).
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include_domains (Optional[List[str]], optional): List of domains to include in search results. Defaults to None.
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exclude_domains (Optional[List[str]], optional): List of domains to exclude from search results. Defaults to None.
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start_published_date (Optional[str], optional): Only return content published after this date (ISO format). Defaults to None.
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end_published_date (Optional[str], optional): Only return content published before this date (ISO format). Defaults to None.
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user_location (Optional[str], optional): Two-letter country code for localized results (e.g., "US"). Defaults to None.
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Returns:
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str: A JSON-encoded string containing search results with title, URL, content, highlights, and summary.
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"""
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raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
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async def fetch_webpage(url: str) -> str:
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"""
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Fetch a webpage and convert it to markdown/text format using Exa API (if available) or trafilatura/readability.
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Args:
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url: The URL of the webpage to fetch and convert
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Returns:
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String containing the webpage content in markdown/text format
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"""
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raise NotImplementedError("This is only available on the latest agent architecture. Please contact the Letta team.")
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