```                   # 📋 Session Report
    λλ              
   λ⊙λ⊙             
  λλλλλλ            
 λλλλλλλλ           
    λλ              
    λλ              
    λλ              
    λλ              
    λλ              
    λλ              
    λλ              
    λλ              
```                 

**🔑 Session ID:** e59002e6-a13b-4ad5-88eb-edda46be32db

---

## 📊 Statistics

### Turn Counts

| Metric | Value |
|--------|-------|
| Total Turns | 52 |
| 👤 User Turns | 26 |
| 🤖 LLM Turns | 26 |

### 🔧 Tool Calls

**Total Tool Calls:** 29

`bash_write-file     `   11 ████████████████████████████████████████████████████████████

`bash_read-file      `    9 █████████████████████████████████████████████████

`bash_check-code     `    7 ██████████████████████████████████████

`bash_grep-files     `    1 █████

`bash_list-files     `    1 █████


### 💾 Byte Usage

`Input     `    336 KiB ████████████████████████████████████████████████████████████

`Output    `      2 KiB 

`Reasoning `      6 KiB █
**Total:** 345 KiB

---

## 👤 Step 1: User Turn

### 📝 System Prompt

```
You are an expert Haskell and Bash programmer specialized in writing clean, idiomatic, and efficient Haskell code.
When writing Bash code:
- Avoid bash-specific logic but bash-ism are fine.
- Document required shell tools at the top of the bash script.
When writing Haskell code:
- Use modern Haskell practices (base >= 4.14)
- Prefer pure functions and explicit type signatures
- Use standard libraries when appropriate
- Add clear comments for complex logic
- Follow consistent formatting
Use list-files to explore the project structure, read-file to understand existing code, write-file to create or modify files, and check-code to verify your work.
Always compile code before considering the job done.
Your output format may be stored as commit message: hence the first line of your last message should be a summary line.
You have access to a tool which is a copy of yourself named io_prompt_agent_oneshot, use it when a task is well-scoped in a way that it would save tokens in your context window and not interfere with your work.
```

### 💬 User Query

# Add LLM token usage tracking (input, output, thinking, cached) for multiple providers

Depends-on: 
Base-branch: main
Final: false

## Overview

Currently, the system tracks byte usage for LLM tool calls but lacks token usage tracking. This issue adds comprehensive token usage tracking from LLM API responses, including support for various provider formats (OpenAI, Kimi/Moonshot, Claude, Mistral).

## Example Response Payload

The JSON response from LLM providers (e.g., Kimi flavor) includes a `usage` key:

```json
{
  "usage": {
    "prompt_tokens": 3272,
    "completion_tokens": 94,
    "total_tokens": 3366,
    "cached_tokens": 768,
    "prompt_tokens_details": {
      "cached_tokens": 768
    }
  }
}
```

Claude responses include additional fields like:
- `thinking_tokens` / `reasoning_tokens`
- `input_tokens` / `output_tokens` (different naming)

## Changes Required

### 1. Add Token Usage Types (`System.Agents.LLMs.OpenAI`)

Add new types to represent token usage from various LLM providers:

```haskell
-- | Token usage breakdown from LLM API response.
-- Different providers use different field names and structures.
data TokenUsage = TokenUsage
    { tokenPromptTokens :: Int
    -- ^ Tokens in the prompt/context (input)
    , tokenCompletionTokens :: Int
    -- ^ Tokens in the completion response (output)
    , tokenTotalTokens :: Int
    -- ^ Total tokens used
    , tokenCachedTokens :: Maybe Int
    -- ^ Cached tokens (Kimi/OpenAI context caching)
    , tokenThinkingTokens :: Maybe Int
    -- ^ Reasoning/thinking tokens (Claude extended thinking)
    , tokenRawUsage :: Aeson.Object
    -- ^ Raw usage object for provider-specific fields
    }
    deriving (Show)

-- | Extended trace events with token usage
data Trace
    = -- | Payload being sent to the LLM + byte count + estimated tokens
      CallChatCompletion !Aeson.Value !Int !(Maybe Int)
    | -- | Response received from the LLM + byte count + actual token usage
      GotChatCompletion !Aeson.Value !Int !(Maybe TokenUsage)
    | -- | Trace from the underlying HTTP client
      HttpClientTrace !HttpClient.Trace
    | -- | Moonshot/Kimi overloaded error backoff
      OverloadedBackoff !Int !Int
    deriving (Show)
```

### 2. Parse Usage Field in Response

Extend the `Response` type to include parsed token usage:

```haskell
data Response = Response
    { rawResponse :: Aeson.Object
    , chosenMessage :: Aeson.Object
    , finishReason :: Maybe Text
    , rspContent :: Maybe Text
    , rspToolCalls :: Maybe [OpenAIToolCall]
    , rspReasoningContent :: Maybe Text
    , rspTokenUsage :: Maybe TokenUsage
    -- ^ Parsed token usage from the 'usage' field
    }
    deriving (Show)
```

Update `parseLLMResponse` to extract the `usage` field and handle provider-specific formats:

```haskell
instance FromJSON Response where
    parseJSON =
        Aeson.withObject "Response" $ \v -> do
            choices <- v .: "choices" :: Aeson.Parser (NonEmpty Aeson.Object)
            firstChoice <- (NonEmpty.head choices) .: "message"
            mUsage <- v .:? "usage" >>= mapM parseUsage
            Response
                <$> pure v
                <*> pure firstChoice
                <*> firstChoice .:? "finish_reason"
                <*> firstChoice .: "content"
                <*> firstChoice .:? "tool_calls"
                <*> firstChoice .:? "reasoning_content"
                <*> pure mUsage

-- | Parse usage object handling different provider formats
parseUsage :: Aeson.Value -> Aeson.Parser TokenUsage
parseUsage = Aeson.withObject "Usage" $ \v -> do
    -- Handle different field names across providers
    prompt <- v .: "prompt_tokens" <|> v .: "input_tokens" <|> pure 0
    completion <- v .: "completion_tokens" <|> v .: "output_tokens" <|> pure 0
    total <- v .:? "total_tokens" >>= maybe (pure (prompt + completion)) pure
    cached <- v .:? "cached_tokens" <|> (v .:? "prompt_tokens_details" >>= \d -> case d of
        Just (Aeson.Object d') -> d' .:? "cached_tokens"
        _ -> pure Nothing)
    thinking <- v .:? "reasoning_tokens" <|> v .:? "thinking_tokens"
    TokenUsage prompt completion total cached thinking <$> pure v
```

### 3. Extend Session-Level Token Tracking (`System.Agents.Session.Types`)

Add token usage to `StepByteUsage` (or create a parallel structure):

```haskell
data StepByteUsage = StepByteUsage
    { stepTotalBytes :: Int
    , stepInputBytes :: Int
    , stepOutputBytes :: Int
    , stepReasoningBytes :: Int
    , stepToolBytes :: Int
    , stepTokenUsage :: Maybe TokenUsage
    -- ^ Optional token usage from LLM provider
    }
    deriving (Show, Ord, Eq, Generic)
```

**Important**: Update the `ToJSON`/`FromJSON` instances to maintain backward compatibility with existing sessions.

### 4. Update `LlmResponse` to Include Token Usage

```haskell
data LlmResponse = LlmResponse
    { responseText :: Maybe Text
    , responseThinking :: Maybe Text
    , rawResponse :: Aeson.Value
    , responseTokenUsage :: Maybe TokenUsage
    -- ^ Token usage from the LLM response
    }
    deriving (Show, Ord, Eq, Generic)
```

### 5. Update Session Flow (`System.Agents.Session.OpenAI`)

In `extractResponse`, propagate token usage from the parsed `Response`:

```haskell
extractResponse :: Aeson.Value -> OpenAI.Response -> (LlmResponse, [LlmToolCall])
extractResponse rawValue rsp =
    let llmRsp =
            LlmResponse
                { responseText = rsp.rspContent
                , responseThinking = rsp.rspReasoningContent
                , rawResponse = rawValue
                , responseTokenUsage = rsp.rspTokenUsage
                }
        -- ... rest unchanged
     in (llmRsp, toolCalls)
```

### 6. Update Session Step (`System.Agents.Session.Step`)

Modify `calculateLlmTurnByteUsage` to capture token usage from the LLM response:

```haskell
calculateLlmTurnByteUsage :: LlmResponse -> [LlmToolCall] -> StepByteUsage
calculateLlmTurnByteUsage llmRsp llmTools =
    let outputBytes = fromIntegral $ maybe 0 (LByteString.length . Aeson.encode . Aeson.String) llmRsp.responseText
        reasoningBytes = fromIntegral $ maybe 0 (LByteString.length . Aeson.encode . Aeson.String) llmRsp.responseThinking
        toolBytes = sum (map toolCallBytes llmTools)
        inputBytes = 0
     in calculateStepByteUsage inputBytes outputBytes reasoningBytes toolBytes 
            (responseTokenUsage llmRsp)  -- Pass token usage
```

## Provider-Specific Considerations

| Provider | Field Names | Special Fields |
|----------|-------------|----------------|
| OpenAI | `prompt_tokens`, `completion_tokens`, `total_tokens` | `cached_tokens` in `prompt_tokens_details` |
| Kimi/Moonshot | Same as OpenAI | `cached_tokens` at top level |
| Claude | `input_tokens`, `output_tokens` | `thinking_tokens` for extended thinking |
| Mistral | Same as OpenAI | - |

## Files to Modify

1. `src/System/Agents/LLMs/OpenAI.hs` - Add `TokenUsage` type, update `Trace` and `Response`, parse usage field
2. `src/System/Agents/Session/Types.hs` - Add token usage to `StepByteUsage`, update `LlmResponse`
3. `src/System/Agents/Session/OpenAI.hs` - Propagate token usage in `extractResponse`
4. `src/System/Agents/Session/Step.hs` - Capture token usage in byte usage calculation

## Backward Compatibility

- All token usage fields must be `Maybe` types to handle:
  - Legacy sessions without token data
  - Providers that don't return usage info
  - API errors where usage is unavailable
- JSON serialization must handle missing fields gracefully

## Testing Considerations

- Test parsing with sample responses from each provider
- Verify backward compatibility with existing session files
- Ensure tracing correctly captures token counts

## Tradeoffs

1. **Type Complexity vs. Provider Support**: Using a generic `TokenUsage` type with `Maybe` fields simplifies the API but loses some type safety. Alternative: provider-specific usage types with a type class.

2. **Storage Overhead**: Adding token usage to every turn increases session file size. Mitigation: `Maybe` wrapper ensures no cost when unused.

3. **Token Estimation**: We could estimate tokens from bytes (approx 4 chars/token), but actual token counts from providers are more accurate for billing. This issue implements actual counts only.

---
From #424



### 🛠️ Available Tools

- **bash_grep-files** (`bash_grep-files`)
  - Description: Search for a pattern in files using 'git grep'
- **bash_read-file** (`bash_read-file`)
  - Description: Read the contents of a file
- **bash_check-code** (`bash_check-code`)
  - Description: Check Haskell code by compiling or testing
- **bash_list-files** (`bash_list-files`)
  - Description: List all files in the project
- **bash_write-file** (`bash_write-file`)
  - Description: Write content to a file
- **bash_run_agents_exe** (`bash_run_agents_exe`)
  - Description: Runs agents-exe with specified arguments. Useful for testing CLI commands like init, check, run, describe, and running repro cases when bug with specific command arise.
- **io_prompt_agent_oneshot** (`io_prompt_agent_oneshot`)
  - Description: asks a prompt to the expert agent: oneshot

---

## 🤖 Step 2: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Let me start by exploring the project structure to understand the codebase and then read the relevant files that need to be modified.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_list-files**

---

## 👤 Step 3: User Turn



---

## 🤖 Step 4: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Let me read the files that need to be modified:
1. `src/System/Agents/LLMs/OpenAI.hs`
2. `src/System/Agents/Session/Types.hs`
3. `src/System/Agents/Session/OpenAI.hs`
4. `src/System/Agents/Session/Step.hs`

Let me start by reading these files to understand the current implementation.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_read-file**

- **bash_read-file**

---

## 👤 Step 5: User Turn



---

## 🤖 Step 6: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me read the remaining two files:
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_read-file**

- **bash_read-file**

---

## 👤 Step 7: User Turn



---

## 🤖 Step 8: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now I have a good understanding of the codebase. Let me analyze the changes needed:

1. **`System.Agents.LLMs.OpenAI`**:
   - Add `TokenUsage` data type with fields for prompt, completion, total, cached, and thinking tokens
   - Update `Trace` type to include optional estimated/actual token counts
   - Update `Response` type to include `rspTokenUsage :: Maybe TokenUsage`
   - Update `FromJSON Response` instance to parse the `usage` field
   - Add `parseUsage` function to handle different provider formats

2. **`System.Agents.Session.Types`**:
   - Add `stepTokenUsage :: Maybe TokenUsage` field to `StepByteUsage`
   - Add `responseTokenUsage :: Maybe TokenUsage` field to `LlmResponse`
   - Update `calculateStepByteUsage` to take an optional `TokenUsage` parameter
   - Update JSON instances for backward compatibility

3. **`System.Agents.Session.OpenAI`**:
   - Update `extractResponse` to propagate `rspTokenUsage` to `responseTokenUsage`

4. **`System.Agents.Session.Step`**:
   - Update `calculateLlmTurnByteUsage` to pass token usage to `calculateStepByteUsage`

Let me start implementing these changes:

### Step 1: Modify `System.Agents.LLMs.OpenAI.hs`

First, I'll add the `TokenUsage` type and update the other types.
</details>

### 💬 Response

Now I have a clear picture of the codebase. Let me implement the changes in the correct order. I'll start by modifying the core LLM types, then update the session types, and finally propagate the changes through the session handling code.

### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 9: User Turn



---

## 🤖 Step 10: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me update the `System.Agents.Session.Types` module to add token usage tracking to `StepByteUsage` and `LlmResponse`.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 11: User Turn



---

## 🤖 Step 12: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now I need to update `System.Agents.Session.OpenAI` to propagate the token usage from the parsed `Response` to `LlmResponse`.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 13: User Turn



---

## 🤖 Step 14: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now I need to update `System.Agents.Session.Step` to propagate the token usage from the LLM response to the byte usage calculation.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 15: User Turn



---

## 🤖 Step 16: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me compile the code to make sure everything is correct. Let me also check if there are any other files that might need updating.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 17: User Turn



---

## 🤖 Step 18: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to add the `LambdaCase` extension to the OpenAI.hs file since I'm using `\case`. Let me fix that.
</details>

### 💬 Response

I need to add the `LambdaCase` extension for the `\case` syntax:

### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 19: User Turn



---

## 🤖 Step 20: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me try to compile again.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 21: User Turn



---

## 🤖 Step 22: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to fix the parsing of token usage. The issue is that `body Aeson..:? "usage"` is a parser action, not a value. I need to handle this differently. Let me look at how the body is parsed and fix this.
</details>

### 💬 Response

I need to fix the token usage parsing in `callLLMPayload`:

### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 23: User Turn



---

## 🤖 Step 24: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me try to compile again.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 25: User Turn



---

## 🤖 Step 26: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to fix the pattern matching in two files:
1. `src/System/Agents/OneShot.hs`
2. `src/System/Agents/MCP/Server.hs`

Let me update these to handle the new 4-field `LlmResponse` constructor.
</details>

### 💬 Response

I need to update two files that pattern match on `LlmResponse`:

### 🔧 Tool Calls

- **bash_read-file**

- **bash_read-file**

---

## 👤 Step 27: User Turn



---

## 🤖 Step 28: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now I need to update both files to handle the new 4-field `LlmResponse` constructor:

1. In `src/System/Agents/OneShot.hs`, line 193: `extractResponseText (LlmResponse txt _thinking _) = Maybe.fromMaybe "" txt`
   Should become: `extractResponseText (LlmResponse txt _thinking _ _) = Maybe.fromMaybe "" txt`

2. In `src/System/Agents/MCP/Server.hs`, line 357: `extractResponseText (LlmResponse mtxt _thinking _) = Maybe.fromMaybe "" mtxt`
   Should become: `extractResponseText (LlmResponse mtxt _thinking _ _) = Maybe.fromMaybe "" mtxt`

Let me update these files.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

- **bash_write-file**

---

## 👤 Step 29: User Turn



---

## 🤖 Step 30: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me compile again to see if everything works.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 31: User Turn



---

## 🤖 Step 32: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

There's another file with the same pattern: `src/System/Agents/AgentTree/OneShotTool.hs`. Let me fix that too.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_read-file**

---

## 👤 Step 33: User Turn



---

## 🤖 Step 34: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to update line 281 to add the fourth field to the `LlmResponse` pattern match. Let me fix this file.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 35: User Turn



---

## 🤖 Step 36: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Let me compile again.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 37: User Turn



---

## 🤖 Step 38: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to fix the test files as well. The `StepByteUsage` constructor now takes 6 arguments instead of 5 (it now includes the `Maybe TokenUsage` field). Let me look at the test file and fix the tests.
</details>

### 💬 Response

I need to fix the test files that use the old `StepByteUsage` constructor:

### 🔧 Tool Calls

- **bash_read-file**

---

## 👤 Step 39: User Turn



---

## 🤖 Step 40: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to fix the test file to use the new `StepByteUsage` constructor that now takes 6 arguments (including the `Maybe TokenUsage` field). The test uses `StepByteUsage 1000 500 300 100 100` which should become `StepByteUsage 1000 500 300 100 100 Nothing`.

Let me update the tests to add the `Nothing` argument for the token usage.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_grep-files**

---

## 👤 Step 41: User Turn



---

## 🤖 Step 42: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

The grep didn't find anything, maybe the pattern isn't matching right. Let me use a different approach - I'll look at the test file and update the StepByteUsage usages to include the new token usage field.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 43: User Turn



---

## 🤖 Step 44: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me also fix the `SessionEditTests.hs` file which has missing field warnings.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_read-file**

---

## 👤 Step 45: User Turn



---

## 🤖 Step 46: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

I need to add the `responseTokenUsage` field to the `LlmResponse` constructors in the test file. Let me update it.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_write-file**

---

## 👤 Step 47: User Turn



---

## 🤖 Step 48: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

Now let me compile again to check if everything works.
</details>

### 💬 Response



### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 49: User Turn



---

## 🤖 Step 50: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

The code compiles successfully. Now let me run the tests to make sure everything works correctly.
</details>

### 💬 Response

The code compiles. Let me run the tests to ensure everything is working:

### 🔧 Tool Calls

- **bash_check-code**

---

## 👤 Step 51: User Turn



---

## 🤖 Step 52: LLM Turn

### 💭 Thinking Process

<details>
<summary>Click to expand reasoning</summary>

All 568 tests passed! The implementation is complete. Let me summarize what was done:

1. **Added `TokenUsage` type** in `System.Agents.LLMs.OpenAI`:
   - Tracks `prompt_tokens`, `completion_tokens`, `total_tokens`
   - Tracks optional `cached_tokens` (for Kimi/OpenAI context caching)
   - Tracks optional `thinking_tokens` (for Claude extended thinking)
   - Preserves the raw usage object for provider-specific fields
   - Includes `FromJSON` and `ToJSON` instances with provider-specific parsing

2. **Updated `Trace` type** in `System.Agents.LLMs.OpenAI`:
   - `CallChatCompletion` now includes an optional estimated token count (3rd parameter)
   - `GotChatCompletion` now includes an optional actual `TokenUsage` (3rd parameter)

3. **Updated `Response` type** in `System.Agents.LLMs.OpenAI`:
   - Added `rspTokenUsage :: Maybe TokenUsage` field
   - Updated `FromJSON` instance to parse the usage field

4. **Updated `StepByteUsage`** in `System.Agents.Session.Types`:
   - Added `stepTokenUsage :: Maybe TokenUsage` field
   - Updated `calculateStepByteUsage` to take the optional token usage
   - Maintained backward compatibility with custom JSON instances

5. **Updated `LlmResponse`** in `System.Agents.Session.Types`:
   - Added `responseTokenUsage :: Maybe TokenUsage` field
   - Maintained backward compatibility with custom JSON instances

6. **Updated `extractResponse`** in `System.Agents.Session.OpenAI`:
   - Propagates token usage from parsed `Response` to `LlmResponse`

7. **Updated `calculateLlmTurnByteUsage`** in `System.Agents.Session.Step`:
   - Extracts token usage from the LLM response and includes it in the byte usage
   
The implementation handles multiple provider formats—OpenAI, Kimi/Moonshot, Claude, and Mistral—each with their specific field naming conventions. All fields are optional to maintain compatibility across different API versions and providers.
</details>

### 💬 Response

All 568 tests passed. Let me provide a summary of the implementation:

## Summary

I've successfully implemented comprehensive LLM token usage tracking for multiple providers. Here's what was added:

### 1. New `TokenUsage` Type (`System.Agents.LLMs.OpenAI`)
```haskell
data TokenUsage = TokenUsage
    { tokenPromptTokens :: Int        -- ^ Input/prompt tokens
    , tokenCompletionTokens :: Int    -- ^ Output/completion tokens  
    , tokenTotalTokens :: Int         -- ^ Total tokens used
    , tokenCachedTokens :: Maybe Int  -- ^ Cached tokens (OpenAI/Kimi)
    , tokenThinkingTokens :: Maybe Int -- ^ Reasoning tokens (Claude)
    , tokenRawUsage :: Aeson.Object   -- ^ Provider-specific fields
    }
```

### 2. Updated Trace Events
- `CallChatCompletion` now includes estimated token count (based on 4 chars/token)
- `GotChatCompletion` now includes actual `TokenUsage` from provider response

### 3. Provider-Specific Parsing
The `parseUsage` function handles different field names across providers:
- **OpenAI/Kimi/Mistral**: `prompt_tokens`, `completion_tokens`, `total_tokens`
- **Claude**: `input_tokens`, `output_tokens`, `thinking_tokens`
- **Cached tokens**: supports both top-level and nested in `prompt_tokens_details`

### 4. Session-Level Tracking
- `StepByteUsage` now includes `stepTokenUsage :: Maybe TokenUsage`
- `LlmResponse` now includes `responseTokenUsage :: Maybe TokenUsage`
- Byte usage calculation propagates token usage through the session flow

### 5. Backward Compatibility
- All token usage fields are `Maybe` types
- Custom JSON instances handle missing fields gracefully
- Legacy sessions without token data continue to work

### Files Modified
1. `src/System/Agents/LLMs/OpenAI.hs` - TokenUsage type, parsing, tracing
2. `src/System/Agents/Session/Types.hs` - StepByteUsage and LlmResponse updates
3. `src/System/Agents/Session/OpenAI.hs` - Propagate token usage
4. `src/System/Agents/Session/Step.hs` - Capture token usage in calculations
5. `src/System/Agents/OneShot.hs` - Pattern match update
6. `src/System/Agents/MCP/Server.hs` - Pattern match update
7. `src/System/Agents/AgentTree/OneShotTool.hs` - Pattern match update
8. `test/Main.hs` - Test updates for new constructor
9. `test/SessionEditTests.hs` - Test updates for new field
