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ZORGAX’s repeated list_automations call was not shown to be an Ollama model failure or proof that Open WebUI discarded the empty result. Controlled tests established three narrower facts: Ollama can stop after receiving {"automations":[],"total":0}, Open WebUI can serialize the assistant call and matching tool message, and an end-to-end run succeeds when only one harmless tool is enabled. The original loop still has an unresolved integration variable. The strongest lead is the combination of roughly 35 exposed tools and a possibly constrained context, but neither has been proven causal. The reliable way to debug it is to trace execution, message transport, and the model’s next decision separately, while limiting iterations so a runaway agent cannot continue indefinitely.
What exactly happened to ZORGAX?
ZORGAX is a local DevOps agent in the MyZubster ecosystem, built around a custom model derived from Qwen2.5 3B, Ollama, Open WebUI, and Docker. The reported symptom was a call to list_automations that correctly returned an empty object, followed by another call to the same function instead of a user-facing answer.
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“Building an AI agent is not just about making a language model call tools. It is also about ensuring the model knows when to stop.”
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What has been proved, and what remains a hypothesis?
The evidence separates capabilities that are often conflated. A successful isolated test does not prove that the production conversation is being assembled identically, and a loop does not by itself prove that a particular component dropped data.
- Ollama can handle the result. A synthetic
/api/chatconversation containing an assistant tool call and the matching tool result producedContent: Non ci sono automazioni attive al momento. Tool calls: [] Done: True. The prompt and schema were simplified, so this demonstrates model behavior, not the failing integration’s cause. - Open WebUI’s conversion function can preserve the exchange. A harmless fixture passed through
convert_output_to_messages()and produced an assistant message with thelist_automationscall plus arole: toolmessage carrying the sametool_call_idand the empty JSON result. - The mediated path can complete. In a separate Open WebUI instance with only the harmless custom tool enabled, a structural proxy captured the user request, an assistant call, and the tool message. The empty result reached Ollama, which returned the “no automations active” answer.
- A low iteration limit contains the symptom. Setting
CHAT_RESPONSE_MAX_TOOL_CALL_ITERATIONS: "3"stopped the bounded run with “Tool-call iteration limit reached (3).” This is a safety guardrail, not a correction to the underlying decision. - The cause is not established. One captured request exposed approximately 35 built-in tools, while the successful control exposed only
list_automations. Tool count is therefore a strong debugging lead, but the runs also differed in prompts, schemas, model parameters, and sampling. Those variables were not held constant.
How does a tool-call loop form?
Each turn crosses three boundaries. A failure or ambiguity at any one can make the next model decision look irrational.
1. Did the tool execute?
First verify the real function or fixture returned the expected payload. For this case, the important value is an actual JSON object with an empty automations array and total equal to zero, not a timeout, a stringified error, or a missing response.
2. Did the result reach the next model request?
Open WebUI must append a tool-role message after the assistant’s call. The message needs the original tool-call ID, the tool name or schema context expected by the adapter, and the result content. If that message is absent, attached to a different ID, or omitted from the subsequent request, the model may reasonably behave as if the call never completed.
3. What did the model decide after receiving it?
Once the result is present, capture the next response category: a final answer, another call, invalid arguments, or an error. A repeated call with the same valid empty result points to model or prompt behavior; a request with no tool message points to transport or conversion; malformed arguments indicate schema or generation problems.
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These checks explain why adding “do not repeat the call” to a system prompt is not a sufficient fix. The instruction cannot restore a message that was never sent, repair a mismatched ID, or compensate for context that was truncated.
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Use a disposable Open WebUI deployment, a synthetic read-only function, and a temporary proxy. Keep operational tools disabled while you establish the message sequence.
- Create the fixture. Implement a harmless
list_automationssubstitute that always returns{"automations":[],"total":0}. Do not connect it to production credentials or mutation endpoints. - Constrain the run. Configure
CHAT_RESPONSE_MAX_TOOL_CALL_ITERATIONS: "3"in the diagnostic Open WebUI instance. A three-iteration ceiling makes a runaway cycle observable without allowing hundreds of repeated calls. - Capture structure, not secrets. Put a temporary HTTP proxy between Open WebUI and Ollama. Record the model name, message roles, tool-call IDs, tool names, and whether the empty result is present. Deliberately exclude full prompts, authorization headers, credentials, and tool arguments that could contain sensitive data.
- Run the one-tool control. Expose only the fixture and submit the user request. The expected trace is a user message, one assistant tool call, a tool-role message with the matching ID and empty result, and a final assistant response.
- Classify the outcome. If the final response arrives, the basic path works. If the limit message appears, inspect whether the same ID and result preceded every repeated call. If the tool message is missing, stop investigating model behavior and fix the adapter or transport first.
- Expand one variable at a time. Repeat with the same model, prompt, schemas, generation settings, and context length while changing only the number of enabled tools. Then perform separate runs for context size, prompt wording, and sampling options.
This approach gives you a replayable trace while preserving the production system as a control. It also makes a later regression test possible instead of relying on screenshots or subjective impressions.
What did the experiments show when compared directly?
| Comparison axis | Observed control | Investigated or bounded path | Interpretation |
|---|---|---|---|
| Model endpoint | Direct Ollama /api/chat with a synthetic conversation |
Open WebUI-mediated request forwarded to Ollama | Both paths can be tested independently; direct success does not validate the integration. |
| Enabled tools | Only list_automations |
Approximately 35 built-in tools appeared in one captured request | Tool availability is a lead, not a demonstrated cause. |
| Message handling | Hand-built assistant call and tool result | Open WebUI conversion fixture plus network continuation | The isolated converter preserved the call and result; production continuation still needs tracing. |
| Iteration policy | Not the focus of the direct control | Diagnostic limit of 3; reported default of 256 in the investigated build | The low limit contains damage but does not change the model’s choice. |
| Context setting | Successful single-tool test reported a 4096-token allocation | No explicit num_ctx was captured in diagnostic requests |
Inherited or silently selected context remains a variable to measure. |
The figures in this table are run-specific observations or configuration values, not population statistics or performance benchmarks.
Did Open WebUI drop the empty tool result?
The available checks do not support that conclusion. The isolated convert_output_to_messages() test generated the correct assistant and tool messages, and the successful single-tool end-to-end capture showed the empty result arriving at Ollama.
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That does not prove every failing request contained the result. The conversion fixture was harmless and isolated, whereas the loop occurred in a larger, changing environment. For a failing trace, inspect the exact sequence immediately before the repeated call: verify that the tool-role message exists, that its tool_call_id equals the assistant call’s ID character for character, and that its content is the expected JSON rather than an empty string or serialized exception.
Did exposing many built-in tools cause the repetition?
Not yet proven. Approximately 35 tools were present in one captured request, while the successful control advertised only the harmless function. More schemas increase the amount of information the model must interpret and can create competing choices, so reducing the tool set is a sensible diagnostic experiment.
However, the two runs changed more than tool count. Prompts, function schemas, model parameters, and sampling may have differed. To establish causality, hold those values constant and add tools incrementally. Record the first run in which the model changes from a final answer to a repeated call; that is evidence of a threshold or interaction, not proof until the result reproduces across repeated trials.
Could a small Ollama context window be truncating the conversation?
Yes, it is plausible, but the ZORGAX data does not prove it. Open WebUI documentation states that num_ctx overrides OLLAMA_CONTEXT_LENGTH. If the context is too small, the prompt can be silently truncated; native function calling is particularly exposed because tool schemas consume context. The documented symptoms include blank output or raw tool-call JSON.
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Open WebUI’s documented default selection is 4096 tokens below 24 GiB of available memory, 32768 tokens from 24 to 48 GiB, and 262144 tokens at 48 GiB or above. The successful ZORGAX single-tool diagnostic reported a 4096-token allocation, while captured diagnostic requests did not include an explicit num_ctx. That is a configuration fact, not evidence that truncation caused the loop.
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Measure rather than infer: log the effective context setting for every A/B run, keep the tool schemas unchanged, and compare a deliberately larger allocation with the same prompt and generation options. If the loop disappears only when context increases, inspect the serialized prompt size and the point at which earlier tool messages vanish.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the next controlled experiment test?
Use a matrix in which one factor changes per comparison. Keep the model version, system prompt, tool schemas, generation options, and context setting fixed unless that factor is the one under test.
- Tool-count series: one harmless tool, a small fixed subset, then the full advertised set.
- Context series: an explicit
num_ctxvalue below, at, and above the known successful allocation, with prompt size recorded. - Message-integrity series: replay a valid empty result, a transport or execution error, and invalid tool arguments.
- Prompt series: retain the same schemas and tools while changing only the stop instruction, so wording effects are not confused with serialization effects.
- Sampling series: hold everything else constant and vary generation settings one at a time.
For each run, save a redacted structural trace and classify the next model response. A useful regression suite should assert that a valid empty result leads to one matching tool message and a final answer, that an execution error is surfaced rather than retried forever, and that invalid arguments produce a bounded, diagnosable failure.
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How should you contain a loop in a deployed agent?
Keep the iteration ceiling enabled even after the root cause is found. A limit of three was effective for diagnosis; the investigated build’s reported default of 256 leaves far more room for repeated calls and higher operational cost. Choose a limit appropriate to the workflow, but treat it as a circuit breaker, not as evidence that the agent is correct.
Separate read-only discovery tools from mutating operations, and enable the smallest tool set needed for a task. Log IDs and message roles at the Open WebUI-to-Ollama boundary, redact credentials and full prompts, and alert when the same tool and arguments recur without an intervening state change. If the breaker trips, preserve the trace and return a clear failure to the operator instead of silently continuing.
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What is the defensible conclusion about ZORGAX?
The investigation established that the stack can carry an empty tool result all the way to Ollama and obtain a final answer. It also established that a bounded iteration setting can contain a runaway cycle. It did not establish that Open WebUI dropped the result, that approximately 35 tools caused the loop, or that context truncation was responsible.
The practical conclusion is methodological: test execution, transport, and post-result model behavior as separate boundaries. As Ioni put it, “We therefore have a strong debugging lead, not a definitive explanation.” That distinction keeps a plausible clue from becoming an unfounded fix.
Frequently Asked Questions
What fields should a redacted tool trace retain?
Keep timestamps, message roles, tool names, tool-call IDs, result-presence flags, model identifier, effective context setting, and the iteration number. Hash or remove prompt text, credentials, headers, and arguments that may contain operational data; the goal is to prove sequence and identity, not to archive secrets.
How can I tell a retry from a genuinely new automation request?
Compare the tool name, normalized arguments, and state-change evidence across consecutive calls. Identical arguments and an unchanged empty result indicate repetition, while a changed filter or newly observed state may represent a legitimate follow-up.
Should an execution error be retried automatically?
Only when the error is classified as transient and the retry budget is explicit. Return validation failures and permission errors to the model or operator as structured errors, and cap retries separately from the overall tool-call iteration limit.
Why can a direct Ollama test pass while the UI path fails?
A direct test supplies a hand-built conversation with a known schema and no UI-side conversion, extra tools, or inherited settings. The mediated path adds serialization, tool discovery, prompt assembly, and context management, so success at the endpoint isolates capability but does not validate those integration layers.
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