From 5b29fbe4db8fe25c66cc4141dd888f5a3c617000 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?V=C3=ADctor=20Mayoral=20Vilches?= Date: Wed, 2 Apr 2025 16:48:23 +0200 Subject: [PATCH] Fix docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Víctor Mayoral Vilches --- docs/agents.md | 2 +- docs/mcp.md | 4 +- docs/ref/agent.md | 2 +- docs/ref/mcp/server.md | 2 +- docs/ref/mcp/util.md | 2 +- examples/cai/simple_one_tool_test.py | 2 +- examples/cai/simple_one_tool_test_streamed.py | 2 +- pyproject.toml | 2 + .../agents/models/openai_chatcompletions.py | 9 +- uv.lock | 1210 +++++++++-------- 10 files changed, 674 insertions(+), 563 deletions(-) diff --git a/docs/agents.md b/docs/agents.md index 49c19132..518fd725 100644 --- a/docs/agents.md +++ b/docs/agents.md @@ -142,6 +142,6 @@ Supplying a list of tools doesn't always mean the LLM will use a tool. You can f !!! note - To prevent infinite loops, the framework automatically resets `tool_choice` to "auto" after a tool call. This behavior is configurable via [`agent.reset_tool_choice`][agents.agent.Agent.reset_tool_choice]. The infinite loop is because tool results are sent to the LLM, which then generates another tool call because of `tool_choice`, ad infinitum. + To prevent infinite loops, the framework automatically resets `tool_choice` to "auto" after a tool call. This behavior is configurable via [`agent.reset_tool_choice`][cai.sdk.agents.agent.Agent.reset_tool_choice]. The infinite loop is because tool results are sent to the LLM, which then generates another tool call because of `tool_choice`, ad infinitum. If you want the Agent to completely stop after a tool call (rather than continuing with auto mode), you can set [`Agent.tool_use_behavior="stop_on_first_tool"`] which will directly use the tool output as the final response without further LLM processing. diff --git a/docs/mcp.md b/docs/mcp.md index e279a25e..efc486de 100644 --- a/docs/mcp.md +++ b/docs/mcp.md @@ -13,7 +13,7 @@ Currently, the MCP spec defines two kinds of servers, based on the transport mec 1. **stdio** servers run as a subprocess of your application. You can think of them as running "locally". 2. **HTTP over SSE** servers run remotely. You connect to them via a URL. -You can use the [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse] classes to connect to these servers. +You can use the [`MCPServerStdio`][cai.sdk.agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][cai.sdk.agents.mcp.server.MCPServerSse] classes to connect to these servers. For example, this is how you'd use the [official MCP filesystem server](https://www.npmjs.com/package/@modelcontextprotocol/server-filesystem). @@ -42,7 +42,7 @@ agent=Agent( ## Caching -Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to both [`MCPServerStdio`][agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][agents.mcp.server.MCPServerSse]. You should only do this if you're certain the tool list will not change. +Every time an Agent runs, it calls `list_tools()` on the MCP server. This can be a latency hit, especially if the server is a remote server. To automatically cache the list of tools, you can pass `cache_tools_list=True` to both [`MCPServerStdio`][cai.sdk.agents.mcp.server.MCPServerStdio] and [`MCPServerSse`][cai.sdk.agents.mcp.server.MCPServerSse]. You should only do this if you're certain the tool list will not change. If you want to invalidate the cache, you can call `invalidate_tools_cache()` on the servers. diff --git a/docs/ref/agent.md b/docs/ref/agent.md index 49b6546c..6b00b1c9 100644 --- a/docs/ref/agent.md +++ b/docs/ref/agent.md @@ -1,3 +1,3 @@ # `Agents` -::: cai.sdk.agents.agent +::: cai.sdk.agents.agent \ No newline at end of file diff --git a/docs/ref/mcp/server.md b/docs/ref/mcp/server.md index e58efab2..3bab05fb 100644 --- a/docs/ref/mcp/server.md +++ b/docs/ref/mcp/server.md @@ -1,3 +1,3 @@ # `MCP Servers` -::: agents.mcp.server +::: cai.sdk.agents.mcp.server diff --git a/docs/ref/mcp/util.md b/docs/ref/mcp/util.md index b3f7db25..fecd90ed 100644 --- a/docs/ref/mcp/util.md +++ b/docs/ref/mcp/util.md @@ -1,3 +1,3 @@ # `MCP Util` -::: agents.mcp.util +::: cai.sdk.agents.mcp.util diff --git a/examples/cai/simple_one_tool_test.py b/examples/cai/simple_one_tool_test.py index d7578f9c..a4949612 100644 --- a/examples/cai/simple_one_tool_test.py +++ b/examples/cai/simple_one_tool_test.py @@ -39,7 +39,7 @@ async def main(): print(f"Using model: {os.getenv('CAI_MODEL', 'default')}") # Run the agent with a simple test message - result = await Runner.run(agent, "Hello! Can you introduce yourself and explain what you can do?") + result = await Runner.run(agent, "Hello! Can you list the files in the current directory?") # Print the result print("\nAgent response:") diff --git a/examples/cai/simple_one_tool_test_streamed.py b/examples/cai/simple_one_tool_test_streamed.py index 9708b3d4..9fc3bfa3 100644 --- a/examples/cai/simple_one_tool_test_streamed.py +++ b/examples/cai/simple_one_tool_test_streamed.py @@ -45,7 +45,7 @@ async def main(): print("Agent: ", end="", flush=True) # Run the agent with a simple test message in streaming mode - result = Runner.run_streamed(agent, "Hello! Can you introduce yourself and explain what you can do?") + result = Runner.run_streamed(agent, "Hello! Can you list the files in the current directory?") # Process the streaming response async for event in result.stream_events(): diff --git a/pyproject.toml b/pyproject.toml index c49933a1..c7315122 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,8 @@ dependencies = [ "litellm>=1.63.7", "mako>=1.3.9", "mcp; python_version >= '3.10'", + "mkdocs>=1.6.0", + "mkdocs-material>=9.6.0", ] classifiers = [ "Typing :: Typed", diff --git a/src/cai/sdk/agents/models/openai_chatcompletions.py b/src/cai/sdk/agents/models/openai_chatcompletions.py index de5e80ad..8563c758 100644 --- a/src/cai/sdk/agents/models/openai_chatcompletions.py +++ b/src/cai/sdk/agents/models/openai_chatcompletions.py @@ -113,7 +113,8 @@ class OpenAIChatCompletionsModel(Model): self._client = openai_client # Check if we're using OLLAMA models self.is_ollama = os.getenv('OLLAMA') is not None and os.getenv('OLLAMA').lower() != 'false' - + self.empty_content_error_shown = False + def _non_null_or_not_given(self, value: Any) -> Any: return value if value is not None else NOT_GIVEN @@ -741,7 +742,11 @@ class OpenAIChatCompletionsModel(Model): # Handle Anthropic error for empty text content blocks elif ("text content blocks must be non-empty" in str(e) or "cache_control cannot be set for empty text blocks" in str(e)): # noqa - print(f"Error: {str(e)}") + + # Print the error message only once + print(f"Error: {str(e)}") if not self.empty_content_error_shown else None + self.empty_content_error_shown = True + # Fix for empty content in messages for Anthropic models kwargs["messages"] = [ msg if msg.get("content") not in [None, ""] else diff --git a/uv.lock b/uv.lock index 50b8b90b..3835ab13 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 1 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.10'", @@ -16,7 +17,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.11.14" +version = "3.11.16" source = { registry = "https://pypi.org/simple" } 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