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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_…
Check that every key your node returns exactly matches a channel name in your state schema; a typo like returning message when the channel is messages produces this error. Also make sure graph.invoke() is called with the required initial state values rather than an empty dict (TypedDict does not support defaults…
A maintainer confirmed this is expected behavior rather than a leak: the Python SDK keeps the whole run tree (parents, children, inputs/outputs, metadata) in memory while a trace is live, so RSS grows with concurrency and only releases when the root run finishes. For wide fan-outs or long-running apps, reduce pressure…
Long-running Python app using LangGraph/LangChain (asyncio.gather with create_task, one graph per task). Memory grows steadily over time, but only when LangSmith tracing is enabled. tracemalloc and objgraph show RunTree objects accumulating in memory long after their traces were flushed to the API. langsmith 0.4.27, l…
Running a LangGraph plan-and-execute style graph raised "langgraph.errors.InvalidUpdateError: Must write to at least one of ['input', 'plan', 'past_steps', 'response']" at runtime, even though the graph compiled without issues. The node's update dict did not match any channel in the state schema. One reporter's node r…
Pass chunk_size=1 when constructing the embeddings: embeddings = OpenAIEmbeddings(chunk_size=1). This was the confirmed fix in the thread and also works through helpers like vectorstore.from_documents() where you can't pass chunk_size yourself. In langchain.js the equivalent option is batchSize (`new OpenAIEmb…
Using langchain's OpenAIEmbeddings against Azure OpenAI failed with "InvalidRequestError: Too many inputs for model None. The max number of inputs is 1." Azure's embedding endpoint only accepts one input per request, so the default batched embedding calls got rejected. The reporter also noted that OpenAIEmbeddings nee…
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…