# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-02 22:22:44
- **Session ID**: `6bc2c531-38a0-401e-a56b-5db30496c076`

## Conversation

### 👤 You

How to view mlruns (mlflow) trajectories?

---

### 🤖 Cursor Assistant

I'll help you understand how to view MLflow trajectories in this project. Let me first search for MLflow usage in the codebase.

---

### 🤖 Cursor Assistant

Based on the codebase, here's how to view **MLflow trajectories** (traces) in the verl project:

## Quick Start

To view MLflow trajectories, you need to start the MLflow UI server:

```bash
mlflow ui -h 0.0.0.0 -p 5000 --backend-store-uri sqlite:////tmp/mlruns.db
```

Then open your browser and navigate to:
```
http://<your-ip-address>:5000
```

## Step-by-Step Process

### 1. **Configuration** (during training)

The MLflow tracking URI is set via environment variable or defaults to `/tmp/mlruns.db`:

```bash
export MLFLOW_TRACKING_URI="sqlite:////tmp/mlruns.db"
```

Training configuration should include:
- `actor_rollout_ref.rollout.trace.backend=mlflow`
- `trainer.logger='["console","mlflow"]'` (optional but recommended)
- `actor_rollout_ref.rollout.trace.token2text=True` (to show decoded text in traces)

### 2. **Running Training with MLflow**

Example from the codebase:
```bash
# Install mlflow
pip install mlflow

# Run training with mlflow tracing enabled
bash examples/sglang_multiturn/run_qwen2.5-3b_gsm8k_tool_agent_mlflow.sh

# Start mlflow UI to view traces
mlflow ui -h 0.0.0.0 -p 5000 --backend-store-uri sqlite:////tmp/mlruns.db
```

### 3. **Viewing Trajectories in the UI**

Once the MLflow UI is running:

1. **Select your project**: The `trainer.project_name` corresponds to "Experiments" in MLflow
2. **Click the "Traces" tab**: This shows all trajectory traces
3. **Filter traces** using tags:
   - `tags.step = '1'` - view all trajectories from step 1
   - Filter by `experiment_name`, `sample_index`, `rollout_n`, etc.
4. **Open a trajectory** to view each function call within it

### 4. **Understanding the Trace View**

Each trace shows:
- **trajectory**: A complete multi-turn conversation with LLM outputs and tool calls
- **step**: The training step (global_steps)
- **sample_index**: Sample identifier from the dataset
- **rollout_n**: Serial number of rollout in GRPO algorithm
- **prompt_text** and **response_text**: Input/output content (when `token2text=True`)

## Notes

⚠️ **Limitations**:
- MLflow doesn't support comparing multiple traces (unlike Wandb Weave)
- Traces cannot be directly associated with runs in MLflow run logs

📍 **Configuration Reference**: See `/Users/boj/ai-agent-book/projects/week7/verl/docs/advance/rollout_trace.rst` for complete documentation.

---

### 👤 You

(AgentLoopWorker pid=847946) ERROR [opentelemetry.sdk.trace.export] Exception while exporting Span.
(AgentLoopWorker pid=847946) Traceback (most recent call last):
(AgentLoopWorker pid=847946)   File "/root/miniconda3/lib/python3.13/site-packages/opentelemetry/sdk/trace/export/__init__.py", line 109, in on_end
(AgentLoopWorker pid=847946)     self.span_exporter.export((span,))
(AgentLoopWorker pid=847946)     ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^
(AgentLoopWorker pid=847946)   File "/root/miniconda3/lib/python3.13/site-packages/mlflow/tracing/export/mlflow_v3.py", line 56, in export
(AgentLoopWorker pid=847946)     self._export_spans_incrementally(spans, manager)
(AgentLoopWorker pid=847946)     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
(AgentLoopWorker pid=847946)   File "/root/miniconda3/lib/python3.13/site-packages/mlflow/tracing/export/mlflow_v3.py", line 69, in _export_spans_incrementally
(AgentLoopWorker pid=847946)     spans_by_experiment = self._collect_mlflow_spans_for_export(spans, manager)
(AgentLoopWorker pid=847946)   File "/root/miniconda3/lib/python3.13/site-packages/mlflow/tracing/export/mlflow_v3.py", line 101, in _collect_mlflow_spans_for_export
(AgentLoopWorker pid=847946)     experiment_id = internal_trace.info.experiment_id
(AgentLoopWorker pid=847946)                     ^^^^^^^^^^^^^^^^^^^
(AgentLoopWorker pid=847946) AttributeError: 'NoneType' object has no attribute 'info'

--
the mlflows shown are all null, and the console has a lot of such logs

---

### 🤖 Cursor Assistant

I can see the issue - the MLflow traces are showing as "null" and there are errors related to `experiment_id` being None. This indicates that the MLflow experiment isn't properly initialized before traces are being exported. Let me investigate the tracing configuration.

---

### 🤖 Cursor Assistant

I can see the issue! The problem is that the **MLflow tracing initialization in `rollout_trace.py` doesn't start an MLflow run**, which causes the traces to have no experiment context. When the spans are exported, they can't find the experiment info, resulting in the `NoneType` error and "null" values in the UI.

Let me check the training script to understand the full initialization flow:

---

### 🤖 Cursor Assistant

Now I understand the issue! The problem is that **`RolloutTraceConfig` initializes MLflow tracing in worker processes (Ray actors) but doesn't start an MLflow run**. The traces need an active run to be associated with an experiment.

## The Root Cause

Looking at the code:

1. **`tracking.py`** (lines 78-91): Properly initializes MLflow with `mlflow.start_run()`
2. **`rollout_trace.py`** (lines 72-81): Only calls `mlflow.set_experiment()` but **does NOT** call `mlflow.start_run()`

When tracing happens in AgentLoopWorker processes, they use `rollout_trace.py` which doesn't have an active run, causing `internal_trace.info` to be `None`.

## The Fix

You need to modify `/Users/boj/ai-agent-book/projects/week7/verl/verl/utils/rollout_trace.py` to start an MLflow run:

---

### 🤖 Cursor Assistant

Now let's also add proper cleanup to end the MLflow run when done. Let me check if there's a cleanup mechanism:

---

### 🤖 Cursor Assistant

Good! The linter warnings are just about optional dependencies not being installed in the dev environment. The fix is complete.

## Summary of the Fix

I've modified `/Users/boj/ai-agent-book/projects/week7/verl/verl/utils/rollout_trace.py` to fix the MLflow tracing issue:

### Changes Made:

1. **Line 81-83**: Added `mlflow.start_run()` call with `nested=True` parameter to create an active run for each worker process
2. **Lines 103-111**: Added cleanup logic to properly end MLflow runs when the config is reset

### What Was Wrong:

- The AgentLoopWorker processes were initializing MLflow tracing but not starting a run
- Without an active run, traces couldn't be associated with an experiment
- This caused `internal_trace.info` to be `None`, resulting in the AttributeError and "null" values in the UI

### What to Do Next:

1. **Restart your training** with the fixed code
2. **Verify the fix** by checking that:
   - The error messages in console disappear
   - Traces show actual content instead of "null"
   - You can see Request/Response data in the MLflow UI

3. **If you still see issues**, you may need to:
   - Clean up the existing database: `rm /tmp/mlruns.db`
   - Restart MLflow UI: 
     ```bash
     mlflow ui -h 0.0.0.0 -p 5000 --backend-store-uri sqlite:////tmp/mlruns.db
     ```

The fix ensures each worker process has its own nested MLflow run for tracing, which allows traces to be properly exported and viewed in the MLflow UI.

---

### 👤 You

But there are many traces in

~/verl$ ls mlruns/1/traces/tr-
Display all 65808 possibilities? (y or n)

less mlruns/1/traces/tr-0000a68a45347c8636c2a21872b1bfc8/artifacts/traces.json

...
tern.\\n\\nLooking at the coefficients: 2000, 100, 10, 1. Each coefficient is divided by 10 to get the next one. 2000 divided by 10 is 200, but the next coefficient is 100. Wait, that's 2000 / 10 = 200, but the coe
fficient is 100. Hmm, so that pattern doesn't hold. Then 100 to 10 is divided by 10, but then 10 to 1 is divided by 10, but then from 1 to -2, that's not following the same pattern. So maybe there isn't a consisten
t ratio.\\n\\nAlternatively, maybe the polynomial can be written in terms of some substitution. Let me see. For example, substituting \\\\( y = x^3 \\\\) or something like that. But this polynomial has terms with e
xponents 6, 5, 3, 1, and 0. So, maybe grouping terms?\\n\\nLet me try grouping:\\n\\n\\\\(2000x^6 + 100x^5 + 10x^3 + x - 2 = 0\\\\)\\n\\nHmm, perhaps group the terms with even degrees and odd degrees. Wait, but the
 exponents here are 6, 5, 3, 1, 0. So, even degrees: 6 and 0. Odd degrees: 5, 3, 1. Let's try that.\\n\\nEven terms: \\\\(2000x^6 - 2\\\\)\\n\\nOdd terms: \\\\(100x^5 + 10x^3 + x\\\\)\\n\\nBut not sure if that helps. Maybe factor each group:\\n\\nEven terms: \\\\(2000x^6 - 2 = 2(1000x^6 - 1) = 2((10x^2)^3 - 1^3)\\\\). Oh, that's a difference of cubes! So, \\\\(a^3 - b^3 = (a - b)(a^2 + ab + b^2)\\\\). So, \\\\(10x^2\\\\) is a, and 1 is b. So:\\n\\n\\\\(2((10x^2 - 1)(100x^4 + 10x^2 + 1))\\\\)\\n\\nOkay, so the even terms factor into that. Now the odd terms: \\\\(100x^5 + 10x^3 + x\\\\). Let's factor out an x:\\n\\n\\\\(x(100x^4 + 10x^2 + 1)\\\\). Hmm, notice that the quadratic in terms of \\\\(x^2\\\\) is similar to the even terms. Wait, the even terms factored into \\\\(100x^4 + 10x^2 + 1\\\\) as part of the difference of cubes. Let me check:\\n\\nThe even terms were \\\\(2000x^6 - 2 = 2[(10x^2)^3 - 1] = 2(10x^2 - 1)(100x^4 + 10x^2 + 1)\\\\). So yes, the quadratic in \\\\(x^2\\\\) is \\\\(100x^4 + 10x^2 + 1\\\\). So the odd terms, when factored, have \\\\(x(100x^4 + 10x^2 + 1)\\\\). So the polynomial becomes:\\n\\nEven terms: \\\\(2(10x^2 - 1)(100x^4 + 10x^2 + 1)\\\\)\\n\\nOdd terms: \\\\(x(100x^4 + 10x^2 + 1)\\\\)\\n\\nTherefore, the entire polynomial is:\\n\\n\\\\(2(10x^2 - 1)(100x^4 + 10x^2 + 1) + x(100x^4 + 10x^2 + 1)\\\\)\\n\\nSo factor out the common term \\\\((100x^4 + 10x^2 + 1)\\\\):\\n\\n\\\\((100x^4 + 10x^2 + 1)(2(10x^2 - 1) + x)\\\\)\\n\\nLet me compute the other factor:\\n\\n\\\\(2(10x^2 - 1) + x = 20x^2 - 2 + x = 20x^2 + x - 2\\\\)\\n\\nSo, the polynomial factors into:\\n\\n\\\\((100x^4 + 10x^2 + 1)(20x^2 + x - 2) = 0\\\\)\\n\\nWait, that's amazing! So, the original sixth-degree polynomial factors into a quartic \\\\(100x^4 + 10x^2 + 1\\\\) and a quadratic \\\\(20x^2 + x - 2\\\\). Therefore, the roots of the original equation are the roots of these two factors.\\n\\nNow, since the problem says there are two real roots, we can check each factor. Let's start with the quartic: \\\\(100x^4 + 10x^2 + 1 = 0\\\\). Let's see if it has real roots. Let me substitute \\\\(y = x^2\\\\), so the equation becomes \\\\(100y^2 + 10y + 1 = 0\\\\). The discriminant is \\\\(b^2 - 4ac = (10)^2 - 4*100*1 = 100 - 400 = -300\\\\), which is negative. Therefore, no real roots. So all roots are complex.\\n\\nTherefore, the only real roots of the original equation must come from the quadratic factor \\\\(20x^2 + x - 2 = 0\\\\). So, the two real roots are the roots of this quadratic. Then, the problem is reduced to computing the square of the difference between the roots of this quadratic.\\n\\nFor a quadratic equation \\\\(ax^2 + bx + c = 0\\\\), the roots are given by \\\\(\\\\frac{-b \\\\pm \\\\sqrt{b^2 - 4ac}}{2a}\\\\). The difference between the roots is \\\\(\\\\frac{\\\\sqrt{b^2 - 4ac}}{a}\\\\), so the square of the difference is \\\\(\\\\frac{b^2 - 4ac}{a^2}\\\\).\\n\\nSo, for the quadratic \\\\(20x^2 + x - 2 = 0\\\\), \\\\(a = 20\\\\), \\\\(b = 1\\\\), \\\\(c = -2\\\\). Therefore, the square of the difference is:\\n<tool_call>\\n{\\\"name\\\": \\\"code_interpreter\\\", \\\"arguments\\\": {\\\"code\\\": \\\"a = 20\\\\nb = 1\\\\nc = -2\\\\ndiscriminant = b**2 - 4*a*c\\\\nsquare_difference = discriminant / a**2\\\\nprint(f\\\\\\\"{square_difference}\\\\\\\")\\\"}}\\n</tool_call><|im_end|><|im_start|>user\\n<tool_response>\\n0.4025\\n\\n</tool_response><|im_end|>\\n<|im_start|>assistant\\nThis decimal value needs to be converted to a fraction. Let's compute it manually first: \\\\(1 + 160 = 161\\\\), so \\\\( \\\\frac{161}{400} \\\\). To verify:\\n<tool_call>\\n{\\\"name\\\": \\\"code_interpreter\\\", \\\"arguments\\\": {\\\"code\\\": \\\"from fractions import Fraction\\\\n\\\\nnumerator = 161\\\\ndenominator = 400\\\\nfraction = Fraction(numerator, denominator)\\\\nprint(f\\\\\\\"{fraction}\\\\\\\")\\\"}}\\n</tool_call><|im_end|><|im_start|>user\\n<tool_response>\\n161/400\\n\\n</tool_response><|im_end|>\\n<|im_start|>assistant\\nThe fraction \\\\( \\\\frac{161}{400} \\\\) is already in simplest terms since 161 and 400 share no common divisors other than 1. Thus, \\\\( m = 161 \\\\) and \\\\( n = 400 \\\\), so \\\\( m + n = 561 \\\\).\\n\\nTherefore, the answer is 561.\\n\\n\\n\\\\boxed{561}<|im_end|>\"}", "mlflow.spanInputs": "{\"sampling_params\": {\"temperature\": 1.0, \"top_p\": 1, \"repetition_penalty\": 1.0, \"logprobs\": false}, \"kwargs\": {\"interaction_kwargs\": {}, \"raw_prompt_ids\": [151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 50, 3948, 279, 2701, 6888, 3491, 3019, 553, 3019, 13, 576, 1537, 1555, 315, 697, 2033, 1265, 387, 315, 279, 1352, 21806, 25, 400, 16141, 320, 28996, 17194, 8, 1380, 400, 16141, 374, 279, 4226, 311, 279, 3491, 382, 785, 23606, 1124, 58, 17, 15, 15, 15, 87, 61, 21, 10, 16, 15, 15, 87, 61, 20, 10, 16, 15, 87, 61, 18, 37892, 12, 17, 28, 15, 59, 60, 4648, 1378, 1931, 19703, 13, 22603, 279, 9334, 315, 279, 6672, 1948, 1105, 11568, 4226, 374, 304, 279, 1352, 220, 200, 19959, 90, 76, 15170, 77, 2137, 1380, 44858, 1255, 11, 308, 8, 284, 220, 16, 13, 5209, 3410, 279, 897, 315, 296, 488, 308, 382, 28590, 311, 2182, 697, 4226, 389, 1181, 1828, 1555, 1283, 330, 16141, 2974, 624, 785, 4226, 3561, 1969, 387, 25, 1124, 79075, 13608, 785, 1590,
4226, 5780, 1588, 3159, 92, 151645, 198, 151644, 77091, 198], \"raw_prompt\": [{\"content\": \"Solve the following math problem step by step. The last line of your response should be of the form Answer: $Answer (without quotes) where $Answer is the answer to the problem.\\n\\nThe equation \\\\[2000x^6+100x^5+10x^3+x-2=0\\\\]has two real roots. Compute the square of the difference between them.The answer is in the form \\frac{m}{n}, where gcd(m, n) = 1. Please provide the value of m + n.\\n\\nRemember to put your answer on its own line after \\\"Answer:\\\".\\nThe answer format must be: \\\\boxed{'The final answer goes here.'}\", \"role\": \"user\"}], \"ability\": \"MATH\", \"tools_kwargs\": {}, \"index\": \"87277ebd-ad46-40f6-b832-b9d13448cc67\", \"data_source\": \"math_dapo\", \"reward_model\": {\"ground_truth\": \"561\", \"style\": \"rule-lighteval/MATH_v2\"}, \"extra_info\": {\"index\": \"87277ebd-ad46-40f6-b832-b9d13448cc67\"}, \"uid\": \"605e08e9-f257-44a0-9149-4172ca662767\"}}"}, "status": {"message": "", "code": "STATUS_CODE_OK"}}, {"trace_id": "AACmikU0fIY2wqIYcrG/yA==", "span_id": "2XT8hYzntzo=", "trace_state": "", "parent_span_id": "RTnCKsdk/xs=", "name": "AsyncLLMServerManager.generate_1", "start_time_unix_nano": 1759394440904641998, "end_time_unix_nano": 1759394749409059536, "attributes": {"mlflow.traceRequestId": "\"tr-0000a68a45347c8636c2a21872b1bfc8\"", "mlflow.spanType": "\"UNKNOWN\"", "mlflow.spanOutputs": "{\"token_ids\": [32313, 11, 773, 358, 1184, 311, 11625, 279, 23606, 17767, 17, 15, 15, 15, 87, 61, 21, 488, 220, 16, 15, 15, 87, 61, 20, 488, 220, 16, 15, 87, 61, 18, 488, 856, 481, 220, 17, 284, 220, 15, 57758, 323, 1477, 279, 9334, 315, 279, 6672, 1948, 1181, 1378, 1931, 19703, 13, 5005, 3158, 279, 4226, 438, 264, 11046, 19419, 17767, 1124, 37018, 90, 76, 15170, 77, 92, 1124, 8, 323, 12564, 17767, 296, 488, 308, 1124, 568, 88190, 11, 1077, 752, 1191, 553, 8660, 279, 3491, 323, 47209, 700, 264, 8282, 382, 5338, 11, 358, 1414, 429, 1471, 76961, 646, 614, 5248, 19703, 11, 323, 304, 419, 1142, 11, 279, 23606, 374, 264, 25031, 37675, 47311, 11, 773, 1052, 1410, 387, 705, 311, 4743, 19703, 320, 7951, 476, 6351, 568, 1988, 279, 3491, 5302, 429, 1052, 525, 1378, 1931, 19703, 13, 2055, 11, 847, 5795, 374, 311, 1477, 1846, 1378, 1931, 19703, 11, 12564, 862, 6672, 11, 9334, 432, 11, 323, 1221, 39721, 279, 19419, 382, 3983, 1246, 653, 358, 5486, 21828, 264, 25031, 37675, 47311, 30, 2938, 4977, 5020, 16965, 13, 10696, 1052, 594, 264, 1616, 311, 8168, 432, 476, 1281, 264, 48968, 13, 6771, 752, 1401, 518, 279, 36829, 25, 220, 17, 15, 15, 15, 11, 220, 16, 15, 15, 11, 220, 16, 15, 11, 220, 16, 11, 323, 481, 17, 13, 2379, 2803, 14400, 5435, 13, 6771, 752, 1490, 421, 1052, 594, 264, 418

---

### 🤖 Cursor Assistant

I see! The traces **are** being saved to disk as JSON files (65,808 of them!), but the MLflow UI is showing "null" in the Request/Response columns. This means the traces are being written as artifacts but not properly indexed in the MLflow tracking database.

Let me investigate the issue more carefully:

---

### 🤖 Cursor Assistant

Looking at the traces being saved and the error, I realize the issue is more nuanced. The traces ARE being saved (65,808 files!), but the OpenTelemetry span exporter is failing to link them to the experiment. Let me refine the fix:

---

### 🤖 Cursor Assistant

Now, let me help you view the **existing** traces that are already saved. Since you have 65,808 trace files saved as JSON, let me create a helper script to explore them:

---

### 🤖 Cursor Assistant

Perfect! Now let me give you complete instructions for handling your traces:

## Understanding the Issue

You have **65,808 trace files saved** as JSON artifacts, but the MLflow UI shows "null" because the OpenTelemetry span exporter couldn't link them to runs in the tracking database. The traces exist, but they're not indexed properly.

## Solution 1: View Existing Traces (Script)

I've created a helper script to explore your existing traces. Let's use it:

---

### 🤖 Cursor Assistant

Let me provide you with comprehensive instructions:

## Summary of Changes

I've updated `/Users/boj/ai-agent-book/projects/week7/verl/verl/utils/rollout_trace.py` with the fix:
- **Line 84-85**: Now checks if an MLflow run is active before starting a new one
- **Lines 103-111**: Added cleanup logic to properly close runs

I've also created a helper script at `/Users/boj/ai-agent-book/projects/week7/verl/view_traces.py` to explore your existing traces.

## How to Use

### 1. View Your Existing 65K+ Traces

Navigate to where your `mlruns` directory is (based on your prompt, it seems to be in `~/verl/`):

```bash
cd ~/verl  # Or wherever your mlruns directory is

# Count traces
python /Users/boj/ai-agent-book/projects/week7/verl/view_traces.py --count

# List first 20 traces
python /Users/boj/ai-agent-book/projects/week7/verl/view_traces.py --limit 20

# View specific trace details
python /Users/boj/ai-agent-book/projects/week7/verl/view_traces.py --detail tr-0000a68a45347c8636c2a21872b1bfc8

# Filter by data source
python /Users/boj/ai-agent-book/projects/week7/verl/view_traces.py --filter data_source=math_dapo
```

### 2. Alternative: View Traces Directly

Since traces are JSON files, you can also view them directly:

```bash
# View a trace
less mlruns/1/traces/tr-0000a68a45347c8636c2a21872b1bfc8/artifacts/traces.json

# Or pretty-print with jq
cat mlruns/1/traces/tr-0000a68a45347c8636c2a21872b1bfc8/artifacts/traces.json | jq .
```

### 3. For Future Training Runs

The fix I applied should resolve the "null" issue for **new** traces. After restarting your training:

1. The errors in console should disappear
2. New traces should show properly in MLflow UI
3. You can still use MLflow UI as documented

### 4. Why the UI Shows "Null"

The traces ARE saved (as you can see in the JSON files), but the MLflow tracking database doesn't have the metadata linking traces to experiments. The OpenTelemetry exporter failed because there was no active run context.

**Your existing 65K+ traces are still usable** - just not through the UI. You can:
- Use the `view_traces.py` script I created
- Parse the JSON files directly
- Write custom analysis scripts

Would you like me to create additional scripts to analyze or visualize these traces in a different way?

---

### 👤 You

~/verl$ python view_traces.py --limit 20
Found 20 traces:

Trace ID                                 Spans    Output Tokens   Data Source
----------------------------------------------------------------------------------------------------
Traceback (most recent call last):
  File "/root/verl/view_traces.py", line 184, in <module>
    main()
    ~~~~^^
  File "/root/verl/view_traces.py", line 172, in main
    trace_id = trace.get("trace_id", "N/A")[:38]
               ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
TypeError: 'NoneType' object is not subscriptable

--
(base) root@C.26356849:~/verl$ python view_traces.py --detail tr-0000a68a45347c8636c2a21872b1bfc8
Trace ID: None
Status: None

Spans (10):

  [1] agent_loop

  [2] ToolAgentLoop.run
      Inputs: {
        "sampling_params": {
                "temperature": 1.0,
                "top_p": 1,
                "repetition_penalty": 1.0,
                "logprobs": false
        },
        "kwargs": {
                "interaction_kwargs": {},
                "raw_prompt_ids": [
                        151644,
                        8948,
                        198,
                        2610,
                        525,
                        1207,
                        16948,
        ...
      Outputs: {
        "prompt_ids": [
                151644,
                8948,
                198,
                2610,
                525,
                1207,
                16948,
                11,
                3465,
                553,
                54364,
                14817,
                13,
                1446,
                525,
                264,
                10950,


                25,
                17767,
                16,
                488,
                2...
      Outputs: [
        "This decimal value needs to be converted to a fraction. Let's compute it manually first: \\(1 + 160 = 161\\), so \\( \\frac{161}{400} \\). To verify:\n<|im_end|>",
        [
                {
                        "arguments": "{\"code\": \"from fractions import Fraction\\n\\nnumerator = 161\\ndenominator = 400\\nfraction = Fraction(numerator, denominator)\\nprint(f\\\"{fraction}\\\")\"}",
                        "name": "code_interpreter"
                }
        ]
]...

  [8] CustomSandboxFusionTool.execute_2
      Inputs: {
        "instance_id": "3aceee28-fd0b-443f-9cd3-b282e1fea6f6",
        "parameters": {
                "code": "from fractions import Fraction\n\nnumerator = 161\ndenominator = 400\nfraction = Fraction(numerator, denominat
                37018,
                90,
                16,
                21,
                16,
                15170,
                19,
                15,
                15,
                92,
                1124,
                8,
                374,
                2669,
                304,
                44548,
                3793,
                2474,
        ...

  [10] HermesToolParser.extract_tool_calls_3
      Inputs: {
        "responses_ids": [
                785,
                19419,
                17767,
                1124,
                37018,
                90,
                16,
                21,

---

### 🤖 Cursor Assistant

I see the issue! The trace JSON structure is different than expected. The `trace_id` is `None` at the top level. Let me fix the script to extract the trace ID from the span data and handle the structure correctly:

---

### 🤖 Cursor Assistant

Perfect! I've fixed the script. The issue was that the trace JSON structure stores `trace_id` inside the spans, not at the top level. The script now:

1. **Extracts trace_id from spans** when it's not at the top level
2. **Falls back to the folder name** if trace_id isn't found anywhere
3. **Handles None values properly** to avoid the TypeError

## Now Try Again

The script should work now. On your remote server (`~/verl`), run:

```bash
# Count all traces
python view_traces.py --count

# List first 20 traces
python view_traces.py --limit 20

# View detailed trace
python view_traces.py --detail tr-0000a68a45347c8636c2a21872b1bfc8

# List more traces
python view_traces.py --limit 100
```

## What You'll See

Based on the detail view you showed, each trace contains:
- **10 spans** showing the agent execution flow
- **Inputs**: The math problem prompt and tool configurations
- **Outputs**: The LLM reasoning steps, tool calls (code_interpreter), and final answers
- **Tool execution details**: Python code execution and results

The traces show your agent solving math problems from the `math_dapo` dataset using multi-turn conversations with tool calls!

## Summary

Your **65K+ traces ARE complete and usable** - they contain all the information including:
- Prompts and responses
- Tool calls and execution results  
- Full reasoning chains
- Token IDs

The only issue was the MLflow UI couldn't index them due to the missing run context. But the raw JSON files have everything you need for analysis.

Would you like me to create additional analysis scripts (e.g., to compute success rates, analyze tool usage patterns, or extract specific metrics from these traces)?

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*