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Tracing OpenCode

MLflow Tracing provides automatic tracing for OpenCode, a terminal-based agentic coding tool that supports multiple LLM providers including Claude, OpenAI, Google, and local models.

After setting up the MLflow plugin, MLflow will automatically capture traces of your OpenCode conversations and log them to the specified MLflow experiment. The trace automatically captures information such as:

User prompts and assistant responses
Tool usage (file operations, bash commands, code edits, etc.)
Conversation timing and duration per turn
Token usage (input, output, and total tokens)

Setup

OpenCode tracing is configured using the @mlflow/opencode plugin and environment variables.

Requirements

OpenCode CLI installed
Node.js runtime (for the plugin)
The @mlflow/opencode npm package
An MLflow tracking server running

Step 1: Install the Plugin

bash

npm install @mlflow/opencode

Step 2: Configure OpenCode

Add the plugin to your opencode.json file:

json

{
  "plugin": ["@mlflow/opencode"]
}

Step 3: Set Environment Variables

Create a .env file in your project root with the MLflow connection settings. OpenCode automatically loads .env files on startup, so the configuration persists across sessions:

bash

MLFLOW_TRACKING_URI=http://localhost:5000
MLFLOW_EXPERIMENT_ID=123456

Alternatively, you can set them in your shell:

bash

export MLFLOW_TRACKING_URI=http://localhost:5000
export MLFLOW_EXPERIMENT_ID=123456

Step 4: Start MLflow Server (if not already running)

You can start an MLflow server using Docker Compose, which doesn't require a Python environment:

bash

docker compose up -d

Alternatively, if you have Python and mlflow installed:

bash

mlflow server

Step 5: Run OpenCode

bash

# Run OpenCode normally - tracing happens automatically
opencode

Traces will be automatically created when your OpenCode session becomes idle (after each conversation turn).

Configuration Reference

The plugin is configured entirely via environment variables:

Environment Variable	Required	Description
`MLFLOW_TRACKING_URI`	Yes	MLflow tracking server URI (e.g., `http://localhost:5000`)
`MLFLOW_EXPERIMENT_ID`	Yes	Experiment ID to log traces to
`MLFLOW_OPENCODE_DEBUG`	No	Set to `true` to enable debug logging to stderr

Configuration Examples

bash

# Local MLflow server
export MLFLOW_TRACKING_URI=http://localhost:5000
export MLFLOW_EXPERIMENT_ID=0

# Remote MLflow server
export MLFLOW_TRACKING_URI=http://mlflow.example.com:5000
export MLFLOW_EXPERIMENT_ID=123456

# Databricks MLflow
export MLFLOW_TRACKING_URI=databricks
export MLFLOW_EXPERIMENT_ID=123456789

Token Usage

MLflow automatically tracks the token usage for each LLM call within OpenCode conversations. The token usage for each LLM call is logged in the mlflow.chat.tokenUsage attribute. The token usage and cost will be displayed in the Overview dashboard and the trace detail page.

Session and User Tracking

The plugin automatically tracks session and user metadata for each trace:

Session ID: Each OpenCode session is tagged with mlflow.trace.session, allowing you to group and filter traces by session.
User: The current system user is tagged with mlflow.trace.user, making it easy to identify who initiated each conversation.

You can use these metadata fields to filter traces in the MLflow UI or via the search API. For more details on session and user tracking, see Track Users and Sessions.

Troubleshooting

Debug Logging

Enable debug logging to see detailed information about trace creation:

bash

export MLFLOW_OPENCODE_DEBUG=true
opencode

Debug output is written to stderr to avoid interfering with OpenCode's TUI.

Common Issues

Traces not appearing:

Verify environment variables are set correctly:

bash

echo $MLFLOW_TRACKING_URI
echo $MLFLOW_EXPERIMENT_ID

Check that the MLflow server is accessible:

bash

curl $MLFLOW_TRACKING_URI/api/2.0/mlflow/experiments/list

Enable debug logging to see any errors:
bash
```
export MLFLOW_OPENCODE_DEBUG=true
```

Plugin not loading:

Verify the plugin is installed:
bash
```
npm ls @mlflow/opencode
```
Check that opencode.json contains the plugin:
json
```
{
  "plugin": ["@mlflow/opencode"]
}
```

Missing token usage:

Token usage is only available when the LLM provider reports it. Some providers or configurations may not include token counts in their responses.

Disable Tracing

To stop automatic tracing, remove the plugin from your opencode.json:

json

{
  "plugin": []
}

Or unset the environment variables:

bash

unset MLFLOW_TRACKING_URI
unset MLFLOW_EXPERIMENT_ID

Existing traces are preserved - disabling only stops new traces from being created.