qwen-code/docs/users/features/headless.md

Headless Mode

Headless mode allows you to run Qwen Code programmatically from command line scripts and automation tools without any interactive UI. This is ideal for scripting, automation, CI/CD pipelines, and building AI-powered tools.

Overview

The headless mode provides a headless interface to Qwen Code that:

• Accepts prompts via command line arguments or stdin
• Returns structured output (text or JSON)
• Supports file redirection and piping
• Enables automation and scripting workflows
• Provides consistent exit codes for error handling
• Can resume previous sessions scoped to the current project for multi-step automation

Basic Usage

Direct Prompts

Use the --prompt (or -p) flag to run in headless mode:

qwen --prompt "What is machine learning?"

Stdin Input

Pipe input to Qwen Code from your terminal:

echo "Explain this code" | qwen

Combining with File Input

Read from files and process with Qwen Code:

cat README.md | qwen --prompt "Summarize this documentation"

Resume Previous Sessions (Headless)

Reuse conversation context from the current project in headless scripts:

# Continue the most recent session for this project and run a new prompt
qwen --continue -p "Run the tests again and summarize failures"

# Resume a specific session ID directly (no UI)
qwen --resume 123e4567-e89b-12d3-a456-426614174000 -p "Apply the follow-up refactor"

Note

• Session data is project-scoped JSONL under ~/.qwen/projects/<sanitized-cwd>/chats.
• Restores conversation history, tool outputs, and chat-compression checkpoints before sending the new prompt.

Customize the Main Session Prompt

You can change the main session system prompt for a single CLI run without editing shared memory files.

Override the Built-in System Prompt

Use --system-prompt to replace Qwen Code's built-in main-session prompt for the current run:

qwen -p "Review this patch" --system-prompt "You are a terse release reviewer. Report only blocking issues."

Append Extra Instructions

Use --append-system-prompt to keep the built-in prompt and add extra instructions for this run:

qwen -p "Review this patch" --append-system-prompt "Be terse and focus on concrete findings."

You can combine both flags when you want a custom base prompt plus an extra run-specific instruction:

qwen -p "Summarize this repository" \
  --system-prompt "You are a migration planner." \
  --append-system-prompt "Return exactly three bullets."

Note

• --system-prompt applies only to the current run's main session.
• Loaded memory and context files such as QWEN.md are still appended after --system-prompt.
• --append-system-prompt is applied after the built-in prompt and loaded memory, and can be used together with --system-prompt.

Output Formats

Qwen Code supports multiple output formats for different use cases:

Text Output (Default)

Standard human-readable output:

qwen -p "What is the capital of France?"

Response format:

The capital of France is Paris.

JSON Output

Returns structured data as a JSON array. All messages are buffered and output together when the session completes. This format is ideal for programmatic processing and automation scripts.

The JSON output is an array of message objects. The output includes multiple message types: system messages (session initialization), assistant messages (AI responses), and result messages (execution summary).

Example Usage

qwen -p "What is the capital of France?" --output-format json

Output (at end of execution):

[
  {
    "type": "system",
    "subtype": "session_start",
    "uuid": "...",
    "session_id": "...",
    "model": "qwen3-coder-plus",
    ...
  },
  {
    "type": "assistant",
    "uuid": "...",
    "session_id": "...",
    "message": {
      "id": "...",
      "type": "message",
      "role": "assistant",
      "model": "qwen3-coder-plus",
      "content": [
        {
          "type": "text",
          "text": "The capital of France is Paris."
        }
      ],
      "usage": {...}
    },
    "parent_tool_use_id": null
  },
  {
    "type": "result",
    "subtype": "success",
    "uuid": "...",
    "session_id": "...",
    "is_error": false,
    "duration_ms": 1234,
    "result": "The capital of France is Paris.",
    "usage": {...}
  }
]

Stream-JSON Output

Stream-JSON format emits JSON messages immediately as they occur during execution, enabling real-time monitoring. This format uses line-delimited JSON where each message is a complete JSON object on a single line.

qwen -p "Explain TypeScript" --output-format stream-json

Output (streaming as events occur):

{"type":"system","subtype":"session_start","uuid":"...","session_id":"..."}
{"type":"assistant","uuid":"...","session_id":"...","message":{...}}
{"type":"result","subtype":"success","uuid":"...","session_id":"..."}

When combined with --include-partial-messages, additional stream events are emitted in real-time (message_start, content_block_delta, etc.) for real-time UI updates.

qwen -p "Write a Python script" --output-format stream-json --include-partial-messages

Input Format

The --input-format parameter controls how Qwen Code consumes input from standard input:

• text (default): Standard text input from stdin or command-line arguments
• stream-json: JSON message protocol via stdin for bidirectional communication

Note: Stream-json input mode is currently under construction and is intended for SDK integration. It requires --output-format stream-json to be set.

File Redirection

Save output to files or pipe to other commands:

# Save to file
qwen -p "Explain Docker" > docker-explanation.txt
qwen -p "Explain Docker" --output-format json > docker-explanation.json

# Append to file
qwen -p "Add more details" >> docker-explanation.txt

# Pipe to other tools
qwen -p "What is Kubernetes?" --output-format json | jq '.response'
qwen -p "Explain microservices" | wc -w
qwen -p "List programming languages" | grep -i "python"

# Stream-JSON output for real-time processing
qwen -p "Explain Docker" --output-format stream-json | jq '.type'
qwen -p "Write code" --output-format stream-json --include-partial-messages | jq '.event.type'

Configuration Options

Key command-line options for headless usage:

Option	Description	Example
--prompt, -p	Run in headless mode	qwen -p "query"
--output-format, -o	Specify output format (text, json, stream-json)	qwen -p "query" --output-format json
--input-format	Specify input format (text, stream-json)	qwen --input-format text --output-format stream-json
--include-partial-messages	Include partial messages in stream-json output	qwen -p "query" --output-format stream-json --include-partial-messages
--system-prompt	Override the main session system prompt for this run	qwen -p "query" --system-prompt "You are a terse reviewer."
--append-system-prompt	Append extra instructions to the main session system prompt for this run	qwen -p "query" --append-system-prompt "Focus on concrete findings."
--debug, -d	Enable debug mode	qwen -p "query" --debug
--all-files, -a	Include all files in context	qwen -p "query" --all-files
--include-directories	Include additional directories	qwen -p "query" --include-directories src,docs
--yolo, -y	Auto-approve all actions	qwen -p "query" --yolo
--approval-mode	Set approval mode	qwen -p "query" --approval-mode auto_edit
--continue	Resume the most recent session for this project	qwen --continue -p "Pick up where we left off"
--resume [sessionId]	Resume a specific session (or choose interactively)	qwen --resume 123e... -p "Finish the refactor"
--max-session-turns	Cap the number of user/model/tool turns in the run	qwen -p "..." --max-session-turns 30
--max-wall-time	Wall-clock budget; accepts 90 (s), 30s, 5m, 1h, 1.5h	qwen -p "..." --max-wall-time 10m
--max-tool-calls	Cumulative tool-call budget for the run	qwen -p "..." --max-tool-calls 50

For complete details on all available configuration options, settings files, and environment variables, see the Configuration Guide.

Safety in unattended runs

Headless / CI runs combined with --yolo (or --approval-mode=yolo) auto-approve every tool call, including shell, write, and edit. --yolo does not enable a sandbox — those tools run at the host process's privilege level. When Qwen Code detects this combination with no sandbox configured, it prints a one-line warning to stderr at startup. Suppress the warning with QWEN_CODE_SUPPRESS_YOLO_WARNING=1 once you've reviewed the trade-off.

Run-level budgets

Qwen Code can abort an unattended run when it crosses one of the following thresholds. Each is -1 (unlimited) by default; setting any one is enough to bound runaway behavior. They are enforced cooperatively against the same AbortController that already carries SIGINT, so a budget abort emits a structured FatalBudgetExceededError (exit code 55) — distinct from the turn-cap exit code 53 and SIGINT's 130 so CI scripts can branch on the reason.

Flag	Settings key	What it bounds
--max-wall-time	model.maxWallTimeSeconds	Wall-clock duration of the whole run. Flag accepts 90 (s), 30s, 5m, 1h, 1.5h (fractional units supported). Minimum 1s — sub-second values are rejected as typos. Settings is seconds.
--max-tool-calls	model.maxToolCalls	Cumulative top-level tool calls dispatched by the main run loop (counts successes and failures — the model still consumes tokens on errors). See "Scope" below for subagent / structured-output exemptions.
--max-session-turns	model.maxSessionTurns	Number of user/model/tool turns; pre-existing. Exits with code 53 on overrun (distinct from budget exit 55).

Scope

• --max-tool-calls counts top-level dispatches only. When the model calls the agent tool, the dispatch counts as 1; inner tool calls performed by the spawned subagent are not counted. A model that funnels work through subagents can do unbounded inner work under a small top-level budget. Combine with --exclude-tools agent if you need a tighter cap.
• structured_output is exempt from --max-tool-calls. Under --json-schema, the model's terminal structured_output call is the "I'm done" contract, not real work — it doesn't count against --max-tool-calls so a budget-edge completion isn't aborted as a false positive. The exemption is unconditional (including failed Ajv validations), so a model stuck in a malformed-output retry loop is NOT bounded by --max-tool-calls; combine with --max-session-turns or --max-wall-time to cap retries.
• structured_output is NOT exempt from --max-session-turns. That counter is pre-existing and bumps for every turn including the terminal contract. Size --max-session-turns to N+1 if you want to allow N real-work turns under --json-schema.
• Single-shot vs --input-format stream-json: in stream-json input mode the daemon resets the budget counters at the start of every user message; the budget is per-message, not per-process.
• qwen serve / ACP sessions: the daemon ACP session path does NOT currently consult --max-wall-time / --max-tool-calls from settings.json. These budgets only apply to single-shot qwen -p runs and to --input-format stream-json sessions. (qwen serve does emit the YOLO-no-sandbox warning at boot if tools.approvalMode: 'yolo' is set in settings.)

Recommended combinations

• Trusted, isolated environment (ephemeral CI runner, container): qwen -p "..." --yolo --max-session-turns N --max-wall-time 10m --output-format json. Pin a turn budget and a wall-clock budget so a stuck agent can't burn through your CI minutes, and capture --output-format json for post-run usage / tool-call auditing.
• Local machine or shared infra: also pass --sandbox (or set QWEN_SANDBOX=1) so shell / write / edit tools run inside the sandbox image.
• Long-running CI with retry-on-rate-limit: combine QWEN_CODE_UNATTENDED_RETRY=1 with --max-wall-time. The retry env keeps the run alive past transient 429 / 529 responses; the wall-clock budget ensures a persistently-failing provider can't extend the job indefinitely.
• Bounded auditing / exploration: for read-only tasks, --max-tool-calls 25 caps how aggressively the model can grep / read. Combine with --exclude-tools shell,write,edit to make the bound meaningful.

Examples

Code review

cat src/auth.py | qwen -p "Review this authentication code for security issues" > security-review.txt

Generate commit messages

result=$(git diff --cached | qwen -p "Write a concise commit message for these changes" --output-format json)
echo "$result" | jq -r '.response'

API documentation

result=$(cat api/routes.js | qwen -p "Generate OpenAPI spec for these routes" --output-format json)
echo "$result" | jq -r '.response' > openapi.json

Batch code analysis

for file in src/*.py; do
    echo "Analyzing $file..."
    result=$(cat "$file" | qwen -p "Find potential bugs and suggest improvements" --output-format json)
    echo "$result" | jq -r '.response' > "reports/$(basename "$file").analysis"
    echo "Completed analysis for $(basename "$file")" >> reports/progress.log
done

PR code review

result=$(git diff origin/main...HEAD | qwen -p "Review these changes for bugs, security issues, and code quality" --output-format json)
echo "$result" | jq -r '.response' > pr-review.json

Log analysis

grep "ERROR" /var/log/app.log | tail -20 | qwen -p "Analyze these errors and suggest root cause and fixes" > error-analysis.txt

Release notes generation

result=$(git log --oneline v1.0.0..HEAD | qwen -p "Generate release notes from these commits" --output-format json)
response=$(echo "$result" | jq -r '.response')
echo "$response"
echo "$response" >> CHANGELOG.md

Model and tool usage tracking

result=$(qwen -p "Explain this database schema" --include-directories db --output-format json)
total_tokens=$(echo "$result" | jq -r '.stats.models // {} | to_entries | map(.value.tokens.total) | add // 0')
models_used=$(echo "$result" | jq -r '.stats.models // {} | keys | join(", ") | if . == "" then "none" else . end')
tool_calls=$(echo "$result" | jq -r '.stats.tools.totalCalls // 0')
tools_used=$(echo "$result" | jq -r '.stats.tools.byName // {} | keys | join(", ") | if . == "" then "none" else . end')
echo "$(date): $total_tokens tokens, $tool_calls tool calls ($tools_used) used with models: $models_used" >> usage.log
echo "$result" | jq -r '.response' > schema-docs.md
echo "Recent usage trends:"
tail -5 usage.log

Persistent Retry Mode

When Qwen Code runs in CI/CD pipelines or as a background daemon, a brief API outage (rate limiting or overload) should not kill a multi-hour task. Persistent retry mode makes Qwen Code retry transient API errors indefinitely until the service recovers.

How it works

• Transient errors only: HTTP 429 (Rate Limit) and 529 (Overloaded) are retried indefinitely. Other errors (400, 500, etc.) still fail normally.
• Exponential backoff with cap: Retry delays grow exponentially but are capped at 5 minutes per retry.
• Heartbeat keepalive: During long waits, a status line is printed to stderr every 30 seconds to prevent CI runners from killing the process due to inactivity.
• Graceful degradation: Non-transient errors and interactive mode are completely unaffected.

Activation

Set the QWEN_CODE_UNATTENDED_RETRY environment variable to true or 1 (strict match, case-sensitive):

export QWEN_CODE_UNATTENDED_RETRY=1

Important

Persistent retry requires an explicit opt-in. CI=true alone does not activate it — silently turning a fast-fail CI job into an infinite-wait job would be dangerous. Always set QWEN_CODE_UNATTENDED_RETRY explicitly in your pipeline configuration.

Examples

GitHub Actions

- name: Automated code review
  env:
    QWEN_CODE_UNATTENDED_RETRY: '1'
  run: |
    qwen -p "Review all files in src/ for security issues" \
      --output-format json \
      --yolo > review.json

Overnight batch processing

export QWEN_CODE_UNATTENDED_RETRY=1
qwen -p "Migrate all callback-style functions to async/await in src/" --yolo

Background daemon

QWEN_CODE_UNATTENDED_RETRY=1 nohup qwen -p "Audit all dependencies for known CVEs" \
  --output-format json > audit.json 2> audit.log &

Monitoring

During persistent retry, heartbeat messages are printed to stderr:

[qwen-code] Waiting for API capacity... attempt 3, retry in 45s
[qwen-code] Waiting for API capacity... attempt 3, retry in 15s

These messages keep CI runners alive and let you monitor progress. They do not appear in stdout, so JSON output piped to other tools remains clean.

Resources

• CLI Configuration - Complete configuration guide
• Authentication - Setup authentication
• Commands - Interactive commands reference
• Tutorials - Step-by-step automation guides