N8n: Tool Input Empty In Intermediate Steps

by Alex Johnson 44 views

In the world of workflow automation, precision and visibility are key. When using n8n, especially with its powerful Langchain integration, you expect the system to accurately reflect the steps taken, including the inputs fed into each tool. However, a recent observation has surfaced regarding the intermediateSteps object, specifically within the toolInput field. It appears that this crucial field, which should detail the arguments passed to a tool, is consistently showing up empty, even when the log clearly indicates that input was provided. This can be a significant hurdle for users who rely on these intermediate steps for debugging, analysis, or further processing of tool call data. This article delves into this bug, explores its implications, and provides insights into why this might be happening and what the expected behavior should be. We'll also touch upon the workflow configuration that can lead to this issue and how to potentially work around it while a fix is being developed.

Understanding the Bug: The Empty toolInput Mystery

The core of the problem lies within the intermediateSteps array, which is designed to log the actions taken by the agent during the execution of a workflow. Each step in this array represents a tool call, and within that call, we expect to find details about the tool itself, the arguments it received (under toolInput), and the observation or result it produced. The bug manifests as an empty {} object for toolInput, regardless of the arguments actually passed. This is particularly confusing because the accompanying log field within the same intermediateSteps entry does contain the full, serialized input object. For example, if a Calculator tool is called with the input "5*343", the log might show "input":"5*343", but the toolInput field remains stubbornly empty. This inconsistency makes it difficult to programmatically extract the precise inputs used in tool calls, a feature that is invaluable for complex workflow designs and troubleshooting.

This issue isn't entirely new; it's been observed in older versions of n8n, particularly when dealing with tools that accepted more than one input argument. While the Calculator tool with a single input argument might have worked previously, the problem seems to have become more pervasive, now affecting even the simplest Calculator calls. The user's use case highlights the importance of this data: they need to extract both the inputs and outputs of tool calls, a task made significantly harder when the input data is not readily available in a structured format within intermediateSteps. The desire for an easier way to access these details, perhaps through an "Output Tool Calls" option, underscores the need for this bug to be addressed.

The Broader Impact on n8n and Langchain Users

For users leveraging n8n's powerful integration with Langchain, understanding the flow of data through tool calls is paramount. Langchain agents often rely on a sequence of tool calls to achieve complex tasks, and the intermediateSteps are the primary mechanism for n8n to expose this internal process. When toolInput is empty, it breaks the chain of information, making it challenging to:

  • Debug Workflows: Pinpointing the exact data that caused an issue in a tool call becomes significantly more difficult when the input is not clearly logged in a structured way. You're left cross-referencing the log string, which is less machine-readable and more prone to errors.
  • Analyze Tool Performance: Understanding how different inputs affect tool outputs requires direct access to those inputs. Without it, performance analysis becomes a guesswork.
  • Reconstruct Complex Operations: In scenarios where you might want to re-run a specific tool call with the same parameters or use the inputs for subsequent processing, the absence of this data in toolInput is a major roadblock.
  • Build Advanced Automations: Users building sophisticated workflows that dynamically adjust based on the inputs of previous tool calls will find their options severely limited.

It's particularly frustrating when the log does contain the necessary information, suggesting that the data is available within the system but not being correctly populated into the toolInput field. This points to a potential mapping or serialization issue within the n8n-Langchain integration. The expectation is that intermediateSteps should serve as a comprehensive record, and the toolInput field is a vital component of that record. The current state hinders the ability to fully utilize the power and transparency that n8n and Langchain aim to provide.

Reproducing the Bug: A Practical Example

To better understand and address this bug, it's helpful to look at a concrete example of how it manifests. The provided bug report includes a detailed description and a workflow JSON that reproduces the issue. At its core, the reproduction involves setting up a basic n8n workflow that utilizes OpenAI's language models through the Langchain nodes.

Here’s a breakdown of the workflow structure that leads to the problem:

  1. Chat Trigger (@n8n/n8n-nodes-langchain.chatTrigger): This node acts as the entry point, receiving messages from a chat interface. The input from this trigger is then passed to the agent.
  2. OpenAI Chat Model (@n8n/n8n-nodes-langchain.lmChatOpenAi): This node connects to the OpenAI API, providing the language model that the agent will use.
  3. Calculator Tool (@n8n/n8n-nodes-langchain.toolCalculator): A simple tool designed to perform mathematical calculations. In the example, it's named Calculator1.
  4. Agent Node (@n8n/n8n-nodes-langchain.agent): This is the central piece where the Langchain agent logic resides. It takes the user's prompt (via the chat trigger), uses the OpenAI model, and has access to defined tools like the Calculator. Crucially, the returnIntermediateSteps option is enabled in this node. This setting is what populates the intermediateSteps array in the output, making the bug visible.
  5. Save Result Tool (@n8n/n8n-nodes-langchain.toolWorkflow): This node is configured to act as another tool, specifically designed to save the result and the original equation. It calls another n8n workflow and expects specific inputs.

When this workflow is triggered with a mathematical expression, such as 5 * 231, the agent processes the request. It identifies that the Calculator tool is needed, calls it, and then likely uses the save_result tool to store the equation and its computed value. Because returnIntermediateSteps is enabled in the Agent node, the execution output includes the intermediateSteps array. However, as described, the toolInput field within each step of this array is an empty object {}.

This setup clearly demonstrates that the input data is being processed and logged (as seen in the log field), but it's not being correctly mapped or populated into the structured toolInput field within the intermediateSteps. This reproducibility is vital for developers to identify the exact point of failure in the data pipeline. The configuration, especially the returnIntermediateSteps option, is the key to observing this bug, highlighting that the issue is not in the tool's execution itself, but in how its inputs are reported back through the n8n framework.

Expected Behavior vs. Actual Output

Let's clarify what the intended functionality of the intermediateSteps and toolInput fields should be, and how the current bug deviates from this. The fundamental purpose of intermediateSteps in an agentic workflow is to provide a transparent and detailed trace of the agent's decision-making process. This includes showing which tools were called, with what specific arguments, and what the outcome of those calls was. The toolInput field is a critical component of this trace, as it should contain the exact arguments that were passed to the tool.

Expected Behavior:

Ideally, when a tool is called, the toolInput field within the corresponding intermediateStep should be populated with an object representing the arguments provided to that tool. For instance:

  • If the Calculator1 tool is called with the input "5*343", the toolInput should reflect this, likely as {"input": "5*343"}. This directly shows the key-value pair that the tool received.
  • If multiple arguments were passed to a tool (e.g., toolName(arg1='value1', arg2=123)), the toolInput should contain all of them, such as {"arg1": "value1", "arg2": 123}.

This structured format makes the input data easily accessible and parsable for subsequent actions or analysis within n8n. It aligns with the principle of observability, allowing users to understand precisely how their workflow is executing.

Actual Output (Due to the Bug):

As reported, the toolInput field is consistently an empty object {}. This means that even though the log field might contain a string like "Calling Calculator1 with input: {"input":"5*343","id":"call_9yHJCyXEEWxsRmU7xM56JX2m"}", the structured toolInput field fails to capture this information. The log string itself contains the input details, but it's presented as a raw string within the log message, not as a structured object in toolInput.

Historical Context:

It's worth noting that the format of toolInput has evolved. In older versions, it was sometimes a simple string (e.g., "5*343"). The current expectation is for it to be an object, which is generally more useful for structured data. However, the bug seems to be preventing even this structured object from being populated, regardless of the complexity of the input. The fact that it worked previously for some cases (like the Calculator with a single argument) but fails now, even for that same case, suggests a regression or a change in how inputs are handled that has introduced this defect.

This discrepancy between the expected, structured input data and the actual empty toolInput field is the core of the bug. It obstructs the intended transparency and data accessibility that intermediateSteps are meant to provide.

Debugging Information and Potential Causes

When encountering bugs like the empty toolInput in n8n's Langchain integration, examining the provided debug information is crucial for pinpointing the root cause. The debug output offers valuable insights into the environment and the specific version of n8n being used, which can help developers identify potential conflicts or issues.

Key Debugging Details:

  • n8n Version: The reported version is 2.1.1. This is a specific version, and bugs can often be tied to particular releases. Developers will check if this is a known issue in this version or if a recent change in this version could have introduced the problem.
  • Platform: docker (self-hosted). This indicates the environment where n8n is running. While less likely to be the direct cause of a data mapping bug, environment specifics can sometimes influence behavior.
  • Node.js Version: 22.21.1. Node.js versions can impact how JavaScript code, including n8n's internal logic and dependencies like Langchain, executes.
  • Database: sqlite (default). Similar to the platform, the database type is usually not the culprit for this kind of bug, but it's part of the overall system context.
  • Execution Mode: regular. Standard execution mode.
  • License: enterprise (production). This confirms the n8n edition being used.
  • Client User Agent: mozilla/5.0 (macintosh; intel mac os x 10_15_7) applewebkit/537.36 (khtml, like gecko) chrome/143.0.0.0 safari/537.36. This indicates the browser used to interact with the n8n UI, generally not relevant for backend execution bugs but useful for UI-related issues.

Potential Causes for the Empty toolInput:

  1. Langchain Library Changes: The n8n-nodes-langchain package relies heavily on the underlying Langchain.js (or Python, if applicable) libraries. If there have been recent updates or changes in how Langchain serializes tool calls or exposes intermediate step data, n8n might not be correctly parsing or capturing this information. Specifically, changes in how Langchain handles tool arguments or constructs the AIMessage content could be the cause.
  2. Data Serialization/Deserialization Issues: The process involves taking the input arguments, passing them to the tool, and then capturing the result and the input arguments for the intermediateSteps. There might be a flaw in the serialization logic within the n8n node where the structured input object is either not being created correctly or is being lost before it can be assigned to the toolInput field.
  3. Incorrect Parsing of messageLog: The messageLog field within the intermediateSteps often contains rich information, including tool_calls which detail the arguments. It's possible that the toolInput field is intended to be populated directly from this tool_calls data, and there's a bug in the parsing logic that fails to extract and assign these arguments correctly.
  4. Regression in n8n Core or Langchain Node: As noted, this bug has been observed in previous versions and appears to be present again. This could indicate a regression where a fix was implemented and then inadvertently broken in a subsequent update to either the core n8n platform or the specific Langchain nodes.
  5. Handling of Complex Arguments: While the example focuses on a simple string input, if the bug only appears with specific types of arguments (e.g., nested objects, arrays) or when multiple arguments are present, it points to a more nuanced handling issue within the node.

The presence of the input data in the log field is a strong indicator that the data is being processed. The problem is likely in the final step of mapping this processed data into the structured toolInput field within the intermediateSteps object. Developers will likely need to dive into the source code of the @n8n/n8n-nodes-langchain package to trace the data flow from the tool execution to the final output structure.

Conclusion and Moving Forward

The issue where the toolInput field remains empty within intermediateSteps in n8n's Langchain integration is a significant impediment for users seeking detailed insights into their workflow executions. It hampers debugging, analysis, and the ability to build sophisticated, data-driven automations. The discrepancy between the clearly logged input and the empty structured field points towards a data mapping or serialization bug within the n8n nodes responsible for handling Langchain agent steps.

While the exact cause requires deeper investigation by the n8n development team, understanding the bug's manifestation, its impact, and the reproduction steps is the first critical phase. The debug information provided offers a solid foundation for this investigation, highlighting the specific n8n version and environment.

Moving forward, users encountering this bug are encouraged to:

  1. Report the Bug: Ensure this issue is formally reported on the n8n community forums or GitHub issues page, providing all the details from this report. This helps prioritize the fix.
  2. Check for Updates: Keep an eye on n8n release notes for updates to the Langchain nodes, as this bug is likely to be addressed in a future version.
  3. Consider Workarounds: If immediate access to structured input data is critical, users might explore alternative logging mechanisms within their workflows or attempt to parse the log string programmatically as a temporary workaround, though this is less ideal.

This bug highlights the ongoing development and refinement within powerful automation tools like n8n and Langchain. Transparency and robust logging are fundamental to user trust and effective development. We look forward to a resolution that restores the full visibility and utility of the intermediateSteps feature.

For more information on Langchain and its capabilities, you can visit the official Langchain Documentation. Understanding the underlying Langchain concepts can often provide context for issues encountered within integrations like n8n.