Case: 20260501T144517Z__gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file
PASS Trajectory: recovered | Invoked: yes | Match: 100%
gptme --non-interactive --no-confirm --name gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-warmup --system short --workspace /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/workspace-warmup --tools shell --tool-format tool -m local/aravhawk/qwen3.5-opus-4.6-text:9b 'Use the shell tool. Read search-response.json and required-token.txt from the current workspace. From results in search-response.json, select the item with highest score among snippets that contain 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_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-measured --system short --workspace /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/workspace --tools shell --tool-format tool -m local/aravhawk/qwen3.5-opus-4.6-text:9b 'Use the shell tool. Read search-response.json and required-token.txt from the current workspace. From results in search-response.json, select the item with highest score among snippets that contain 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-response.json and required-token.txt from the current workspace. From results in search-response.json, select the item with highest score among snippets that contain 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 static fixture checkbox", "selected_id": "doc-static-top", "selected_score": 0.98, "selected_url": "https://kb.example/static-top"}
{"query": "weekly plan static fixture checkbox", "selected_id": "doc-static-top", "selected_score": 0.98, "selected_url": "https://kb.example/static-top"}
Expected and observed match.
No structured session transcript found.
[gripprobe] process_started_at=2026-05-01T14:45:19+00:00
[14:45:20] Created config file at /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/runtime/gptme/warmup/home/.config/gptme/config.toml
[14:45:21] WARNING Skipping all confirmation prompts. cli.py:265
[14:45:21] Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
Using logdir: /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/runtime/gptme/warmup/state/gptme-logs/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-warmup
Using workspace: /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/workspace-warmup
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-response.json and required-token.txt from the current workspace.
From results in search-response.json, select the item with highest score among snippets that contain 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-response.json
{"query": "weekly plan static fixture 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-static-top", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/static-top", "snippet": "Actionable template. Required token: static-token-abc123.", "score": 0.98, "lang": "en"}, {"id": "doc-static-alt", "title": "Alternate planning approach", "url": "https://kb.example/static-alt",
"snippet": "Also mentions token static-token-abc123, but less relevant.", "score": 0.91, "lang": "en"}, {"id": "doc-noise", "title": "General productivity", "url": "https://kb.example/noise", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}], "total": 4,
"returned": 4, "error": ""}
````
````required-token.txt
static-token-abc123
````
````search-response.json
{"query": "weekly plan static fixture 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-static-top", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/static-top", "snippet": "Actionable template. Required token: static-token-abc123.", "score": 0.98, "lang": "en"}, {"id": "doc-static-alt", "title": "Alternate planning approach", "url": "https://kb.example/static-alt",
"snippet": "Also mentions token static-token-abc123, but less relevant.", "score": 0.91, "lang": "en"}, {"id": "doc-noise", "title": "General productivity", "url": "https://kb.example/noise", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}], "total": 4,
"returned": 4, "error": ""}
````
Assistant: Thinking...
Assistant:
<think>
<think>
Let me analyze this task step by step:
1. I need to read the required token from `required-token.txt`
2. Find all snippets in `search-response.json` that contain this exact token
3. Among those matching snippets, select the one with the highest score
4. Write the result to `search-result.json`
Let me work through this:
**Step 1: Identify the required token**
From `required-token.txt`: `static-token-abc123`
**Step 2: Check each snippet for the exact token**
- `doc-intro`: "Basic markdown checklist examples." - Does NOT contain the token
- `doc-static-top`: "Actionable template. Required token: static-token-abc123." - Contains the exact token ✓
- `doc-static-alt`: "Also mentions token static-token-abc123, but less relevant." - Contains the exact token ✓
- `doc-noise`: "Time-blocking article." - Does NOT contain the token
**Step 3: Compare scores of matching snippets**
- `doc-static-top`: score = 0.98
- `doc-static-alt`: score = 0.91
The highest score is 0.98 from `doc-static-top`.
**Step 4: Create the output JSON**
```json
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
```
Now I'll write this to search-result.json using the shell tool.
</think>
</think>
@shell(call_qlxvc470): {"command":"cat \u003e search-result.json \u003c\u003c 'EOF'\n{\n \"query\": \"weekly plan static fixture checkbox\",\n \"selected_id\": \"doc-static-top\",\n \"selected_url\": \"https://kb.example/static-top\",\n \"selected_score\": 0.98\n}\nEOF"}
Preview
cat > search-result.json << 'EOF'
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
EOF
System:
Ran command:
```bash
cat > search-result.json << 'EOF'
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
EOF
```
No output
[14:46:30] 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...
Assistant:
<think>
<think>
The file was created successfully. Let me verify its contents to make sure it's correct.
</think>
</think>
@shell(call_w9ik93ym): {"command":"cat search-result.json"}
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
System:
Ran allowlisted command: `cat search-result.json`
```stdout
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
```
Assistant: Thinking...
[14:46:46] INFO Reasoning content: Let me analyze this conversation to create an appropriate title. llm_openai.py:321
The conversation involves:
1. Using a shell tool
2. Reading files from a workspace (search-response.json and required-token.txt)
3. Processing search results by finding snippets containing a specific token
4. Selecting the highest-scoring item
This is a technical task involving file processing, search result filtering, and data selection based on token matching and scoring.
Key elements to capture:
- Shell tool usage
- File processing
- Search result filtering
- Token-based selection
A concise 2-4 word title should capture the essence of this technical task.
INFO Auto-generated conversation name: Shell search result filtering chat.py:332
Assistant:
<think>
<think>
The file was created correctly with all the required fields:
- query: "weekly plan static fixture checkbox"
- selected_id: "doc-static-top"
- selected_url: "https://kb.example/static-top"
- selected_score: 0.98
This is the correct result because:
1. The required token is "static-token-abc123"
2. Two snippets contain this exact token: doc-static-top (score 0.98) and doc-static-alt (score 0.91)
3. doc-static-top has the higher score, so it's the correct selection
The task is complete.
</think>
</think>
DONE
[14:47:00] INFO Auto-reply: Assistant message had no tools. Asking for confirmation... complete.py:138
[14:47:00] [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 asked me to complete a task involving reading files, selecting the best matching result, and writing output. I have:
1. Read the search-response.json file (provided in the prompt)
2. Read the required-token.txt file (provided in the prompt)
3. Identified that doc-static-top has the highest score (0.98) among snippets containing the required token "static-token-abc123"
4. Created search-result.json with the required fields
5. Verified the output by reading it back
The task is complete. I should use the complete tool to end the session.
</think>
</think>
@complete(call_dexyorw7): {}
System: Task complete. Autonomous session finished.
[14:47:13] INFO Complete tool call detected, stopping session immediately complete.py:80
INFO Hook 'complete.complete' signaled session completion __init__.py:360
[14:47:13] Autonomous mode: Session completed via complete tool. Exiting.
Goodbye! (resume with: gptme --name gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-warmup)
[gripprobe] process_finished_at=2026-05-01T14:47:13+00:00 exit_code=0
[gripprobe] process_started_at=2026-05-01T14:45:19+00:00 [gripprobe] process_finished_at=2026-05-01T14:47:13+00:00 exit_code=0
[gripprobe] process_started_at=2026-05-01T14:47:13+00:00
[14:47:15] Created config file at /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/runtime/gptme/measured/home/.config/gptme/config.toml
[14:47:15] WARNING Skipping all confirmation prompts. cli.py:265
Using model: local/aravhawk/qwen3.5-opus-4.6-text:9b
Using logdir:
/work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/runtime/gptme/measured/state/gptme-logs/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-measured
Using workspace: /work/results/runs/20260501T144517Z/cases/gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file/workspace
Skipped 2 hidden system messages, show with --show-hidden
--- ^^^ past messages ^^^ ---
User:
Use the shell tool.
Read search-response.json and required-token.txt from the current workspace.
From results in search-response.json, select the item with highest score among snippets that contain 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-response.json
{"query": "weekly plan static fixture 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-static-top", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/static-top", "snippet": "Actionable template. Required token: static-token-abc123.", "score": 0.98, "lang": "en"}, {"id": "doc-static-alt", "title": "Alternate planning approach", "url": "https://kb.example/static-alt",
"snippet": "Also mentions token static-token-abc123, but less relevant.", "score": 0.91, "lang": "en"}, {"id": "doc-noise", "title": "General productivity", "url": "https://kb.example/noise", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}], "total": 4,
"returned": 4, "error": ""}
````
````required-token.txt
static-token-abc123
````
````search-response.json
{"query": "weekly plan static fixture 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-static-top", "title": "Weekly
planning with checkboxes", "url": "https://kb.example/static-top", "snippet": "Actionable template. Required token: static-token-abc123.", "score": 0.98, "lang": "en"}, {"id": "doc-static-alt", "title": "Alternate planning approach", "url": "https://kb.example/static-alt",
"snippet": "Also mentions token static-token-abc123, but less relevant.", "score": 0.91, "lang": "en"}, {"id": "doc-noise", "title": "General productivity", "url": "https://kb.example/noise", "snippet": "Time-blocking article.", "score": 0.44, "lang": "en"}], "total": 4,
"returned": 4, "error": ""}
````
Assistant: Thinking...
Assistant:
<think>
<think>
Let me analyze this task step by step:
1. I need to read the required token from `required-token.txt`
2. Find all snippets in `search-response.json` that contain this exact token
3. Among those matching snippets, select the one with the highest score
4. Write the result to `search-result.json`
Let me work through this:
**Step 1: Identify the required token**
From `required-token.txt`: `static-token-abc123`
**Step 2: Check each snippet for the exact token**
- `doc-intro`: "Basic markdown checklist examples." - Does NOT contain the token
- `doc-static-top`: "Actionable template. Required token: static-token-abc123." - Contains the exact token ✓
- `doc-static-alt`: "Also mentions token static-token-abc123, but less relevant." - Contains the exact token ✓
- `doc-noise`: "Time-blocking article." - Does NOT contain the token
**Step 3: Compare scores of matching snippets**
- `doc-static-top`: score = 0.98
- `doc-static-alt`: score = 0.91
The highest score is 0.98 from `doc-static-top`.
**Step 4: Create the output JSON**
```json
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
```
Now I'll write this to search-result.json using the shell tool.
</think>
</think>
@shell(call_jasm7vwp): {"command":"cat \u003e search-result.json \u003c\u003c 'EOF'\n{\n \"query\": \"weekly plan static fixture checkbox\",\n \"selected_id\": \"doc-static-top\",\n \"selected_url\": \"https://kb.example/static-top\",\n \"selected_score\": 0.98\n}\nEOF"}
Preview
cat > search-result.json << 'EOF'
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
EOF
System:
Ran command:
```bash
cat > search-result.json << 'EOF'
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
EOF
```
No output
Assistant: Thinking...
[14:47:56] 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:
<think>
<think>
The file was created successfully. Let me verify its contents to make sure it's correct.
</think>
</think>
@shell(call_enyb23fi): {"command":"cat search-result.json"}
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
System:
Ran allowlisted command: `cat search-result.json`
```stdout
{
"query": "weekly plan static fixture checkbox",
"selected_id": "doc-static-top",
"selected_url": "https://kb.example/static-top",
"selected_score": 0.98
}
```
Assistant: Thinking...
[14:48:12] INFO Reasoning content: Let me analyze this conversation to create an appropriate title. llm_openai.py:321
The conversation involves:
1. Using a shell tool
2. Reading files from a workspace (search-response.json and required-token.txt)
3. Processing search results by finding snippets containing a specific token
4. Selecting the highest-scoring item
This is a technical task involving file processing, search result filtering, and data selection based on token matching and scoring.
Key elements to capture:
- Shell tool usage
- File processing
- Search result filtering
- Token-based selection
A concise 2-4 word title should capture the essence of this technical task.
INFO Auto-generated conversation name: Shell search result filtering chat.py:332
Assistant:
<think>
<think>
The file was created correctly with all the required fields:
- query: "weekly plan static fixture checkbox"
- selected_id: "doc-static-top"
- selected_url: "https://kb.example/static-top"
- selected_score: 0.98
This is the correct result because:
1. The required token is "static-token-abc123"
2. Two snippets contain this exact token: doc-static-top (score 0.98) and doc-static-alt (score 0.91)
3. doc-static-top has the higher score, so it's the correct selection
The task is complete.
</think>
</think>
DONE
[14:48:26] INFO Auto-reply: Assistant message had no tools. Asking for confirmation... complete.py:138
[14:48:26] [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 asked me to complete a task involving reading files, selecting the best matching result, and writing output. I have:
1. Read the search-response.json file (provided in the prompt)
2. Read the required-token.txt file (provided in the prompt)
3. Identified that doc-static-top has the highest score (0.98) among snippets containing the required token "static-token-abc123"
4. Created search-result.json with the required fields
5. Verified the output by reading it back
The task is complete. I should use the complete tool to end the session.
</think>
</think>
@complete(call_3a6jk742): {}
System: Task complete. Autonomous session finished.
[14:48:39] INFO Complete tool call detected, stopping session immediately complete.py:80
INFO Hook 'complete.complete' signaled session completion __init__.py:360
[14:48:39] Autonomous mode: Session completed via complete tool. Exiting.
Goodbye! (resume with: gptme --name gptme__local_aravhawk_qwen3_5_opus_4_6_text_9b__ollama__tool__json_rank_from_file-measured)
[gripprobe] process_finished_at=2026-05-01T14:48:39+00:00 exit_code=0
[gripprobe] process_started_at=2026-05-01T14:47:13+00:00 [gripprobe] process_finished_at=2026-05-01T14:48:39+00:00 exit_code=0
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM aravhawk/qwen3.5-opus-4.6-text:9b
FROM /usr/share/ollama/.ollama/models/blobs/sha256-d0ecd80b0e45b0d9e49c8cd1527b7f7d52d8d3bde2c569ab36aac59bb78f53f7
TEMPLATE {{ .Prompt }}
RENDERER qwen3.5
PARSER qwen3.5
PARAMETER num_ctx 32768
PARAMETER presence_penalty 1.5
PARAMETER stop <|im_end|>
PARAMETER stop <|im_start|>
PARAMETER temperature 1
PARAMETER top_k 20
PARAMETER top_p 0.95