Case: 20260426T070743Z__gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked
PASS Trajectory: recovered | Invoked: yes | Match: 100%
gptme --non-interactive --no-confirm --name gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked-warmup --system short --workspace /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/workspace-warmup --tools shell --tool-format tool -m local/qwen3.5:9b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
gptme --non-interactive --no-confirm --name gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked-measured --system short --workspace /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/workspace --tools shell --tool-format tool -m local/qwen3.5:9b 'Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL. '
Consistency: diverged
Use the shell tool. Read search-url.txt, query.txt, and required-token.txt from the current workspace. Call the search endpoint with query parameters: - q=<urlencoded query from query.txt> - format=json - limit=8 The endpoint returns JSON with a results array. Select the result with the highest score among results whose snippet contains the exact required token. Write search-result.json in the current workspace as JSON with exactly these keys: - query - selected_id - selected_url - selected_score If successful, reply only DONE. If command execution fails, reply only FAIL.
{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_score": 0.97, "selected_url": "https://kb.example/d2ef72f27b5a"}
REQUEST_HIT=/search/rDZgrh3w6gcv44P6wI2RZwur
{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_score": 0.97, "selected_url": "https://kb.example/d2ef72f27b5a"}
REQUEST_HIT=yes
--- expected
+++ observed
@@ -1,2 +1,2 @@
{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_score": 0.97, "selected_url": "https://kb.example/d2ef72f27b5a"}
-REQUEST_HIT=/search/rDZgrh3w6gcv44P6wI2RZwur
+REQUEST_HIT=yes
No structured session transcript found.
[gripprobe] process_started_at=2026-04-26T07:13:57+00:00
[07:13:58] Created config file at /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/home/.config/gptme/config.toml
[07:13:58] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/qwen3.5:9b
[07:13:59] Using logdir: /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/runtime/gptme/warmup/state/gptme-logs/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked-warmup
Using workspace: /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/workspace-warmup
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC
````
````query.txt
warmup query 3da0b1b7
````
````required-token.txt
d583ce4281f262e3
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to:
1. Read three files: search-url.txt, query.txt, and required-token.txt
2. Call a search endpoint with the query from query.txt
3. Filter results whose snippet contains the exact token from required-token.txt
4. Select the result with the highest score
5. Write the result to search-result.json
Let me start by reading the files to understand the content.
</think>
</think>
@shell(call_nipw3gcz): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC
warmup query 3da0b1b7
d583ce4281f262e3
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC
warmup query 3da0b1b7
d583ce4281f262e3
```
[07:14:14] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
Assistant: Thinking...
[07:14:26] INFO Reasoning content: The user is asking me to create a 2-4 word title for this conversation. Looking at the conversation, it's about using a shell tool to read files and call a search endpoint. llm_openai.py:321
The main topic is about shell tool usage and file operations for a search task.
Let me create a concise title:
- "Shell tool search task"
- "File reading and search"
- "Search endpoint integration"
- "Shell script search query"
I'll go with something that captures the essence - it's about using shell tools for a search operation.
"Shell search task" or "Search endpoint task" or "File search operation"
Let me pick: "Shell search task"
INFO Auto-generated conversation name: Shell search task chat.py:332
Assistant:
<think>
<think>
Now I have:
- search-url: http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC
- query: warmup query 3da0b1b7
- required-token: d583ce4281f262e3
I need to call the search endpoint with:
- q=warmup%20query%203da0b1b7 (URL encoded)
- format=json
- limit=8
Let me make the curl request.
</think>
</think>
@shell(call_4jwc45dn): {"command":"curl -s \"http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC?q=warmup%20query%203da0b1b7\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC?q=warmup%20query%203da0b1b7&format=json&limit=8"
{"query": "warmup query 3da0b1b7", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-4af4f080", "title": "Weekly planning with checkboxes", "url": "https://kb.example/2324290c432f", "snippet": "Actionable template. Required token: c9672522be41.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token c9672522be41, but less relevant.", "score": 0.8099999999999999, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}System:
Ran command: `curl -s "http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC?q=warmup%20query%203da0b1b7&format=json&limit=8"`
`{"query": "warmup query 3da0b1b7", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-4af4f080", "title": "Weekly planning with
checkboxes", "url": "https://kb.example/2324290c432f", "snippet": "Actionable template. Required token: c9672522be41.", "score": 0.94, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions
token c9672522be41, but less relevant.", "score": 0.8099999999999999, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan
Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang":
"en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries
list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}`
Assistant: Thinking...
Assistant:
<think>
<think>
Now I need to filter results whose snippet contains the exact required token "d583ce4281f262e3".
Looking at the results:
1. doc-intro: "Basic markdown checklist examples." - no token
2. doc-4af4f080: "Actionable template. Required token: c9672522be41." - has c9672522be41, not d583ce4281f262e3
3. doc-alt-token: "Also mentions token c9672522be41, but less relevant." - has c9672522be41, not d583ce4281f262e3
4. doc-ru: "Русскоязычный шаблон." - no token
5. doc-de: "Deutsche Checklisten-Idee." - no token
6. doc-noise-1: "Time-blocking article." - no token
7. doc-noise-2: "Unrelated meeting summary." - no token
8. doc-noise-3: "Groceries list example." - no token
None of the snippets contain the exact required token "d583ce4281f262e3".
Wait, let me re-read the requirement: "Select the result with the highest score among results whose snippet contains the exact required token."
If no results contain the exact required token, then there's no valid result to select. But the task says to write search-result.json with the selected fields.
Hmm, maybe I need to check if any snippet contains "d583ce4281f262e3" exactly. Looking at the snippets again:
- None contain "d583ce4281f262e3"
This is a problem. Let me think about this...
Actually, wait - maybe the endpoint is different? Let me re-read the search-url.txt:
"http://127.0.0.1:34311/search/FEJfalLoO2PuVV67KM7EZjMC"
I used that URL correctly. The required token is "d583ce4281f262e3" and none of the snippets contain it. The results only have "c9672522be41" as a token. This seems like a test case where no results match the required token. I should probably report an error or indicate no match found. But the instructions say to write search-result.json with specific keys. If no results match, what should I do?
Let me re-read the instructions more carefully. The task says to select the result with the highest score among results whose snippet contains the exact required token. If no results contain the token, there's nothing to select. I think I should write a JSON file indicating no match was found, or perhaps use null values. But the instructions say "exactly these keys" without specifying what to do if no match. Let me try a different approach - maybe I should check if the token appears anywhere in the response, not just in snippets. Looking at the full JSON response, I don't see "d583ce4281f262e3" anywhere. I'll write a JSON file with null or empty values for the selected fields to indicate no match was found.
Actually, wait - let me re-read the task one more time. It says "Select the result with the highest score among results whose snippet contains the exact required token." If no results contain the token, then there's no valid selection. But the task also says "If command execution fails, reply only FAIL." This is not a command execution failure - it's a logical result of the search. I think I should still write the JSON file but indicate no match. Let me write a JSON with null values or empty strings for the selected fields.
Actually, I realize I should just write the JSON with the best matching result even if it
[gripprobe] process_finished_at=2026-04-26T07:15:57+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T07:13:57+00:00 [gripprobe] process_finished_at=2026-04-26T07:15:57+00:00 exit_code=124 timeout=true
[gripprobe] process_started_at=2026-04-26T07:15:57+00:00
[07:15:58] Created config file at /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/home/.config/gptme/config.toml
[07:15:58] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/qwen3.5:9b
[07:15:59] Using logdir: /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/runtime/gptme/measured/state/gptme-logs/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked-measured
Using workspace: /work/results/runs/20260426T070743Z/cases/gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked/workspace
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-url.txt, query.txt, and required-token.txt from the current workspace.
Call the search endpoint with query parameters:
- q=<urlencoded query from query.txt>
- format=json
- limit=8
The endpoint returns JSON with a results array.
Select the result with the highest score among results whose snippet contains the exact required token.
Write search-result.json in the current workspace as JSON with exactly these keys:
- query
- selected_id
- selected_url
- selected_score
If successful, reply only DONE.
If command execution fails, reply only FAIL.
````search-url.txt
http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur
````
````query.txt
weekly plan 5a0f84a25f18 checkbox
````
````required-token.txt
d583ce4281f262e3
````
Assistant: Thinking...
Assistant:
<think>
<think>
The user wants me to:
1. Read three files: search-url.txt, query.txt, and required-token.txt
2. Call a search endpoint with the query from query.txt
3. Filter results whose snippet contains the exact token from required-token.txt
4. Select the result with the highest score
5. Write the result to search-result.json
Let me start by reading the files to understand the content.
</think>
</think>
@shell(call_0lbbc6qb): {"command":"cat search-url.txt query.txt required-token.txt"}
http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur
weekly plan 5a0f84a25f18 checkbox
d583ce4281f262e3
System:
Ran allowlisted command: `cat search-url.txt query.txt required-token.txt`
```stdout
http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur
weekly plan 5a0f84a25f18 checkbox
d583ce4281f262e3
```
Assistant: Thinking...
[07:16:14] ERROR exception during auto-name auto_naming.py:100
Traceback (most recent call last):
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/util/auto_naming.py", line 97, in _generate_llm_name
summary_model_name = get_summary_model(current_model.provider)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venvs/gptme/lib/python3.12/site-packages/gptme/llm/models.py", line 576, in get_summary_model
raise ValueError(f"Provider {provider} did not have a summary model")
ValueError: Provider local did not have a summary model
[07:16:26] INFO Reasoning content: The user is asking me to create a 2-4 word title for this conversation. Looking at the conversation, it's about using a shell tool to read files and call a search endpoint. llm_openai.py:321
The main topic is about shell tool usage and file operations for a search task.
Let me create a concise title:
- "Shell tool search task"
- "File reading and search"
- "Search endpoint integration"
- "Shell script search query"
I'll go with something that captures the essence - it's about using shell tools for a search operation.
"Shell search task" or "Search endpoint task" or "File search operation"
Let me pick: "Shell search task"
INFO Auto-generated conversation name: Shell search task chat.py:332
Assistant:
<think>
<think>
Now I have:
- search-url: http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur
- query: weekly plan 5a0f84a25f18 checkbox
- required-token: d583ce4281f262e3
I need to call the search endpoint with:
- q=weekly+plan+5a0f84a25f18+checkbox (URL encoded)
- format=json
- limit=8
Let me make the HTTP request.
</think>
</think>
@shell(call_vf41qifg): {"command":"curl -s \"http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur?q=weekly+plan+5a0f84a25f18+checkbox\u0026format=json\u0026limit=8\""}
Preview
curl -s "http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur?q=weekly+plan+5a0f84a25f18+checkbox&format=json&limit=8"
{"query": "weekly plan 5a0f84a25f18 checkbox", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-1e2c3a65", "title": "Weekly planning with checkboxes", "url": "https://kb.example/d2ef72f27b5a", "snippet": "Actionable template. Required token: d583ce4281f262e3.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet": "Also mentions token d583ce4281f262e3, but less relevant.", "score": 0.84, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}System:
Ran command: `curl -s "http://127.0.0.1:34311/search/rDZgrh3w6gcv44P6wI2RZwur?q=weekly+plan+5a0f84a25f18+checkbox&format=json&limit=8"`
`{"query": "weekly plan 5a0f84a25f18 checkbox", "results": [{"id": "doc-intro", "title": "Markdown planning intro", "url": "https://kb.example/intro", "snippet": "Basic markdown checklist examples.", "score": 0.72, "lang": "en"}, {"id": "doc-1e2c3a65", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/d2ef72f27b5a", "snippet": "Actionable template. Required token: d583ce4281f262e3.", "score": 0.97, "lang": "en"}, {"id": "doc-alt-token", "title": "Alternate planning approach", "url": "https://kb.example/alt", "snippet":
"Also mentions token d583ce4281f262e3, but less relevant.", "score": 0.84, "lang": "en"}, {"id": "doc-ru", "title": "План на неделю", "url": "https://kb.example/ru-weekly", "snippet": "Русскоязычный шаблон.", "score": 0.61, "lang": "ru"}, {"id": "doc-de", "title": "Wochenplan
Vorlage", "url": "https://kb.example/de-weekly", "snippet": "Deutsche Checklisten-Idee.", "score": 0.59, "lang": "de"}, {"id": "doc-noise-1", "title": "General productivity", "url": "https://kb.example/noise-1", "snippet": "Time-blocking article.", "score": 0.44, "lang":
"en"}, {"id": "doc-noise-2", "title": "Meeting notes", "url": "https://kb.example/noise-2", "snippet": "Unrelated meeting summary.", "score": 0.37, "lang": "en"}, {"id": "doc-noise-3", "title": "Shopping checklist", "url": "https://kb.example/noise-3", "snippet": "Groceries
list example.", "score": 0.21, "lang": "en"}], "total": 8, "returned": 8, "error": ""}`
Assistant: Thinking...
Assistant:
<think>
<think>
Now I need to:
1. Filter results whose snippet contains the exact required token "d583ce4281f262e3"
2. Select the result with the highest score
Looking at the results:
- doc-intro: snippet "Basic markdown checklist examples." - does NOT contain "d583ce4281f262e3"
- doc-1e2c3a65: snippet "Actionable template. Required token: d583ce4281f262e3." - CONTAINS the token, score 0.97
- doc-alt-token: snippet "Also mentions token d583ce4281f262e3, but less relevant." - CONTAINS the token, score 0.84
- doc-ru: snippet "Русскоязычный шаблон." - does NOT contain the token
- doc-de: snippet "Deutsche Checklisten-Idee." - does NOT contain the token
- doc-noise-1: snippet "Time-blocking article." - does NOT contain the token
- doc-noise-2: snippet "Unrelated meeting summary." - does NOT contain the token
- doc-noise-3: snippet "Groceries list example." - does NOT contain the token
Results containing the token:
- doc-1e2c3a65: score 0.97
- doc-alt-token: score 0.84
The highest score is 0.97 for doc-1e2c3a65.
Now I need to write search-result.json with:
- query: "weekly plan 5a0f84a25f18 checkbox"
- selected_id: "doc-1e2c3a65"
- selected_url: "https://kb.example/d2ef72f27b5a"
- selected_score: 0.97
</think>
</think>
@shell(call_164i1zb7): {"command":"echo '{\"query\": \"weekly plan 5a0f84a25f18 checkbox\", \"selected_id\": \"doc-1e2c3a65\", \"selected_url\": \"https://kb.example/d2ef72f27b5a\", \"selected_score\": 0.97}' \u003e search-result.json"}
Preview
echo '{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_url": "https://kb.example/d2ef72f27b5a", "selected_score": 0.97}' > search-result.json
System:
Ran command: `echo '{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_url": "https://kb.example/d2ef72f27b5a", "selected_score": 0.97}' > search-result.json`
No output
Skipped 1 hidden system messages, show with --show-hidden
Assistant: Thinking...
Assistant:
<think>
<think>
The file was created successfully. Let me verify it was written correctly.
</think>
</think>
@shell(call_qimcbdda): {"command":"cat search-result.json"}
{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_url": "https://kb.example/d2ef72f27b5a", "selected_score": 0.97}
System:
Ran allowlisted command: `cat search-result.json`
`{"query": "weekly plan 5a0f84a25f18 checkbox", "selected_id": "doc-1e2c3a65", "selected_url": "https://kb.example/d2ef72f27b5a", "selected_score": 0.97}`
Assistant: Thinking...
Assistant:
<think>
<think>
The file was created correctly with all the required fields. The task is complete.
</think>
</think>
DONE
[07:17:40] INFO Auto-reply: Assistant message had no tools. Asking for confirmation... complete.py:138
[07:17:40] [Loop control] <system>No tool call detected in last message. Did you mean to finish? If so, make sure you are comp...
User: <system>No tool call detected in last message. Did you mean to finish? If so, make sure you are completely done and then use the `complete` tool to end the session.</system>
Assistant: Thinking...
Assistant:
<think>
<think>
The user is asking me to use the complete tool to end the session. I have completed the task - I read the files, called the search endpoint, selected the result with the highest score that contains the required token, and wrote the search-result.json file. I should now use the complete tool.
</think>
</think>
@complete(call_h5lgercv): {}
System: Task complete. Autonomous session finished.
[07:17:48] INFO Complete tool call detected, stopping session immediately complete.py:80
INFO Hook 'complete.complete' signaled session completion __init__.py:360
[07:17:48] Autonomous mode: Session completed via complete tool. Exiting.
Goodbye! (resume with: gptme --name gptme__local_qwen3_5_9b__ollama__tool__web_search_json_ranked-measured)
[gripprobe] process_finished_at=2026-04-26T07:17:49+00:00 exit_code=0
[gripprobe] process_started_at=2026-04-26T07:15:57+00:00 [gripprobe] process_finished_at=2026-04-26T07:17:49+00:00 exit_code=0
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM qwen3.5:9b
FROM /usr/share/ollama/.ollama/models/blobs/sha256-dec52a44569a2a25341c4e4d3fee25846eed4f6f0b936278e3a3c900bb99d37c
TEMPLATE {{ .Prompt }}
RENDERER qwen3.5
PARSER qwen3.5
PARAMETER presence_penalty 1.5
PARAMETER temperature 1
PARAMETER top_k 20
PARAMETER top_p 0.95
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