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https://github.com/crewAIInc/crewAI.git
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@@ -67,33 +67,72 @@ class FilteredStream(io.TextIOBase):
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self._lock = threading.Lock()
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with self._lock:
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# Filter out extraneous messages from LiteLLM
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lower_s = s.lower()
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# Skip common noisy LiteLLM banners and any other lines that contain "litellm"
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if (
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"Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
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in s
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or "LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()`"
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in s
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"give feedback / get help" in lower_s
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or "litellm.info:" in lower_s
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or "litellm" in lower_s
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or "Consider using a smaller input or implementing a text splitting strategy" in lower_s
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):
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return 0
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return self._original_stream.write(s)
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def flush(self):
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with self._lock:
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return self._original_stream.flush()
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def __getattr__(self, name):
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"""Delegate attribute access to the wrapped original stream.
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This ensures compatibility with libraries (e.g., Rich) that rely on
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attributes such as `encoding`, `isatty`, `buffer`, etc., which may not
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be explicitly defined on this proxy class.
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"""
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return getattr(self._original_stream, name)
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# Delegate common properties/methods explicitly so they aren't shadowed by
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# the TextIOBase defaults (e.g., .encoding returns None by default, which
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# confuses Rich). These explicit pass-throughs ensure the wrapped Console
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# still sees a fully-featured stream.
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@property
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def encoding(self):
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return getattr(self._original_stream, "encoding", "utf-8")
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def isatty(self):
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return self._original_stream.isatty()
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def fileno(self):
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return self._original_stream.fileno()
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def writable(self):
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return True
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# Apply the filtered stream globally so that any subsequent writes containing the filtered
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# keywords (e.g., "litellm") are hidden from terminal output. We guard against double
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# wrapping to ensure idempotency in environments where this module might be reloaded.
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if not isinstance(sys.stdout, FilteredStream):
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sys.stdout = FilteredStream(sys.stdout)
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if not isinstance(sys.stderr, FilteredStream):
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sys.stderr = FilteredStream(sys.stderr)
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LLM_CONTEXT_WINDOW_SIZES = {
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# openai
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"gpt-4": 8192,
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"gpt-4o": 128000,
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"gpt-4o-mini": 128000,
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"gpt-4o-mini": 200000,
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"gpt-4-turbo": 128000,
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"gpt-4.1": 1047576, # Based on official docs
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"gpt-4.1-mini-2025-04-14": 1047576,
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"gpt-4.1-nano-2025-04-14": 1047576,
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"o1-preview": 128000,
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"o1-mini": 128000,
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"o3-mini": 200000, # Based on official o3-mini specifications
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"o3-mini": 200000,
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"o4-mini": 200000,
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# gemini
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"gemini-2.0-flash": 1048576,
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"gemini-2.0-flash-thinking-exp-01-21": 32768,
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@@ -208,7 +247,7 @@ LLM_CONTEXT_WINDOW_SIZES = {
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}
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DEFAULT_CONTEXT_WINDOW_SIZE = 8192
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CONTEXT_WINDOW_USAGE_RATIO = 0.75
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CONTEXT_WINDOW_USAGE_RATIO = 0.85
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@contextmanager
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@@ -219,12 +258,7 @@ def suppress_warnings():
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"ignore", message="open_text is deprecated*", category=DeprecationWarning
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)
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# Redirect stdout and stderr
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with (
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redirect_stdout(FilteredStream(sys.stdout)),
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redirect_stderr(FilteredStream(sys.stderr)),
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):
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yield
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yield
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class Delta(TypedDict):
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@@ -44,7 +44,7 @@ def render_text_description_and_args(
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tools: Sequence[Union[CrewStructuredTool, BaseTool]],
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) -> str:
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"""Render the tool name, description, and args in plain text.
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search: This tool is used for search, args: {"query": {"type": "string"}}
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calculator: This tool is used for math, \
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args: {"expression": {"type": "string"}}
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@@ -309,7 +309,7 @@ def handle_context_length(
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"""
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if respect_context_window:
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printer.print(
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content="Context length exceeded. Summarizing content to fit the model context window.",
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content="Context length exceeded. Summarizing content to fit the model context window. Might take a while...",
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color="yellow",
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)
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summarize_messages(messages, llm, callbacks, i18n)
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@@ -337,15 +337,22 @@ def summarize_messages(
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callbacks: List of callbacks for LLM
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i18n: I18N instance for messages
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"""
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messages_string = " ".join([message["content"] for message in messages])
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messages_groups = []
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for message in messages:
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content = message["content"]
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cut_size = llm.get_context_window_size()
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for i in range(0, len(content), cut_size):
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messages_groups.append({"content": content[i : i + cut_size]})
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cut_size = llm.get_context_window_size()
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for i in range(0, len(messages_string), cut_size):
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messages_groups.append({"content": messages_string[i : i + cut_size]})
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summarized_contents = []
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for group in messages_groups:
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total_groups = len(messages_groups)
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for idx, group in enumerate(messages_groups, 1):
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Printer().print(
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content=f"Summarizing {idx}/{total_groups}...",
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color="yellow",
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)
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summary = llm.call(
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[
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format_message_for_llm(
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