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Error reported in the modify Agent plug-in delivery context #570
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Originally created by @hyqf98 on GitHub (Aug 19, 2025).
Self Checks
Dify version
1.7.2
Cloud or Self Hosted
Self Hosted (Docker)
Steps to reproduce
Failed to transform agent message: req_id: 0f1fe59bdd PluginInvokeError: {"args":{},"error_type":"ValueError","message":"Invalid parameters: 3 validation errors for ReActParams\ncontext.0.content\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing\ncontext.0.title\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing\ncontext.0.metadata\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing"}
✔️ Expected Behavior
normal execution
❌ Actual Behavior
Failed to transform agent message: req_id: 0f1fe59bdd PluginInvokeError: {"args":{},"error_type":"ValueError","message":"Invalid parameters: 3 validation errors for ReActParams\ncontext.0.content\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing\ncontext.0.title\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing\ncontext.0.metadata\n Field required [type=missing, input_value={'result': [{'TABLE_COMME...atermark_camera_data'}]}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.11/v/missing"}
@dosubot[bot] commented on GitHub (Aug 19, 2025):
The error happens because the agent plugin is returning a context object that doesn't match the required schema: each context entry must include
content,title, andmetadatafields. Instead, your plugin/tool is returning a dictionary with only aresultkey, which fails Pydantic validation forReActParamsand triggers the error.To fix this, ensure that every context item passed to the agent plugin has the required structure. For example:
If your plugin or tool outputs only a
result, add a transformation step before invoking the agent to wrap the result in an object with these fields. This can be done in your plugin code or by preprocessing the output in your workflow.Also, make sure your plugin/tool is up to date, as some similar issues have been resolved by updating to the latest version or fixing the response structure in the plugin code [source].
If you are customizing the agent or plugin code, look for the transformation logic that builds the context and ensure it always outputs the required fields as described above. This should resolve the validation error and allow normal execution.
To reply, just mention @dosu.
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@hyqf98 commented on GitHub (Aug 19, 2025):
My plugin is the latest version 0.0.23，There was a problem of conversion failure，And when I added the output of a node and then deleted the node, the agent plug-in failed to execute
@dosubot[bot] commented on GitHub (Aug 19, 2025):
This error is usually caused by the agent plugin receiving an unexpected or incomplete context after node output changes. When you delete a node output, any variables or data that downstream nodes (like your agent plugin) depend on may become missing or malformed, leading to validation errors like missing
content,title, ormetadatafields in the context [source].To fix this, make sure every context item passed to the agent plugin matches the required schema—each should be a dictionary with
content,title, andmetadata. If your plugin or tool only outputs aresult, add a transformation step to wrap it in the expected structure before invoking the agent. Also, check your workflow configuration: after deleting a node output, verify that all variable assignments and downstream references are updated and valid.If you’re using custom plugins or MCP integrations, ensure their output format matches Dify’s expectations. Version mismatches between Dify core and plugin-daemon can also cause similar issues, so confirm both are aligned [source].
Troubleshooting steps: review your agent node configuration and variable assignments, check logs for errors during the agent exception phase, and simplify your toolset to isolate the problem. Updating to the latest Dify version and plugin may help if regressions are involved [source].
If you need more targeted help, sharing relevant log snippets and clarifying your agent node’s variable assignments and tool outputs will make it easier to diagnose.
To reply, just mention @dosu.
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