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The root cause: with_fallbacks wraps the model in a Runnable that hid bind_tools, so the agent factory could not find the method. A maintainer linked the fix in PR #22139, which forwards bind_tools through fallbacks: upgrade to langchain-core>=0.2.2. For the AzureChatOpenAI variant, the fix was importing the model fro…
Wrapping a chat model with .with_fallbacks([fallback_model]) and passing the result to create_tool_calling_agent raised ValueError: This function requires a .bind_tools method be implemented on the LLM. The same error also hit AzureChatOpenAI users importing from langchain_community. The code worked fine without with_…
The root cause: with_fallbacks wraps the model in a Runnable that hid bind_tools, so the agent factory could not find the method. A maintainer linked the fix in PR #22139, which forwards bind_tools through fallbacks: upgrade to langchain-core>=0.2.2. For the AzureChatOpenAI variant, the fix was importing the model fro…
Wrapping a chat model with .with_fallbacks([fallback_model]) and passing the result to create_tool_calling_agent raised ValueError: This function requires a .bind_tools method be implemented on the LLM. The same error also hit AzureChatOpenAI users importing from langchain_community. The code worked fine without with_…
The root cause was on the Ollama server side, not in LangChain. A reporter confirmed that updating to the latest Ollama release (which shipped streaming-tool support) fixed streaming with bind_tools, and another confirmed the same behavior: ChatOpenAI pointed at a non-Ollama backend streamed fine with the same code. A…
Streaming chat responses from an Ollama model (e.g. llama3.1:70b via ChatOllama, or ChatOpenAI pointed at Ollama's /v1 endpoint) with tools bound via llm.bind_tools([...]).stream(). With tools bound, streaming produced no tokens at all and the on_llm_new_token callback never fired, so apps relying on token-by-token ou…