SLS DataAgent

SLS DataAgent is an AI assistant that autonomously understands natural language and automatically completes data acquisition, processing, analysis, and visualization. It acts as your "fully automated data analyst": you ask a question in natural language, and it fetches, cleans, analyzes the data, and returns conclusions and visualizations.

It is invoked through the SLS DataAgent OpenAPI; the session streams the analysis process and conclusions back over SSE.

Capabilities

• Natural language understanding: turn colloquial data questions into well-defined analysis tasks.
• Data acquisition: automatically locate and pull relevant data (logs, metrics, etc.) within the SLS project.
• Data processing: cleaning, aggregation, transformation.
• Data analysis: statistics, trends, comparisons, anomalies / insights.
• Visualization: generate charts and structured conclusion reports.

Digital Employee (agentId)

A session is handled by a "digital employee" (digital employee / agentId).

• Generic and customizable: --digital-employee \x3Cname> > SLS_DATA_AGENT_EMPLOYEE env var > script default apsara-ops.
• The console assistantId is the digital employee in use.

Built-in Skill (capability routing)

Use --skill to select a DataAgent built-in skill; omit it to use the general capability.

Logstore context

Use --logstore to specify the target logstore, giving the analysis an explicit data source scope.

Environment Variables

Credentials — uses the Alibaba Cloud Credentials SDK default chain (STS / RAM role / CLI profile ~/.aliyun/config.json / env vars ALIBABA_CLOUD_ACCESS_KEY_ID+ALIBABA_CLOUD_ACCESS_KEY_SECRET).

Pre-flight check:

missing=""
[ -z "$SLS_DATA_AGENT_PROJECT" ] && missing="$missing SLS_DATA_AGENT_PROJECT"
[ -z "$SLS_DATA_AGENT_REGION" ]  && missing="$missing SLS_DATA_AGENT_REGION"
if [ -n "$missing" ]; then echo "ERROR: Missing required environment variables:$missing" >&2; exit 1; fi
echo "OK: PROJECT=$SLS_DATA_AGENT_PROJECT REGION=$SLS_DATA_AGENT_REGION"

If a required value is empty, stop and ask the user; do not substitute a placeholder such as example-project.

Invocation

--pipe is mandatory: it guarantees stdout emits the THREAD line and the === DATA AGENT ANSWER BEGIN/END === delimiters, so downstream can parse reliably.

pip3 install -r scripts/requirements.txt

# First question (auto-creates a session; most queries need --logstore)
python3 scripts/call_sls_data_agent.py \
  --logstore my_logstore \
  --question "\x3Cfull data-analysis request>" --pipe

# Follow-up (reuse the THREAD ID from the previous step to keep context)
python3 scripts/call_sls_data_agent.py \
  --thread "\x3Cthread_id>" --question "\x3Cfollow-up>" --pipe

# Specify a built-in skill
python3 scripts/call_sls_data_agent.py \
  --skill builtin.sls.sls-sql-generation \
  --logstore my_logstore \
  --question "number of error logs in the last 5 minutes" --pipe

Example output (pipe):

THREAD: thread-abc123-xyz
DATA_AGENT_URL: https://starops.console.aliyun.com/chat?threadId=thread-abc123-xyz&assistantId=apsara-ops
=== DATA AGENT ANSWER BEGIN ===
(analysis conclusion / visualization description)
=== DATA AGENT ANSWER END ===

Behavioral Notes

1. A DataAgent call is a long task: a single session may trigger multi-step internal analysis and take several minutes; the default timeout is 30 minutes.
2. Provide the full context in one go: SLS project, time range, target logstore/fields, the metrics to analyze, and the expected chart/conclusion form.
3. Always reuse --thread for follow-ups, otherwise context and intermediate results are lost.
4. Always pass --pipe: tool status and intermediate progress go to stderr; stdout keeps only the reusable thread id and the final answer.
5. Conclusions must be based on the real return between the BEGIN / END delimiters: cite concrete data points, do not fabricate. If the answer is empty, retry once with the same --thread; if still empty, state plainly that DataAgent returned no valid data.

Troubleshooting

1. HTTP 401/403 — credentials did not resolve to an authorized identity. Confirm the default chain resolves valid credentials and that the identity is allowed to call the SLS DataAgent OpenAPI. See references/ram-policies.md.
2. HTTP 404 — the digital employee (--digital-employee, default apsara-ops) or the SLS project (SLS_DATA_AGENT_PROJECT) does not exist. Verify both.
3. ConfigError: Missing required SLS DataAgent configuration — one of SLS_DATA_AGENT_PROJECT / SLS_DATA_AGENT_REGION is empty.
4. CredentialError — the default chain found no valid credential source.
5. Idle timeout — no SSE event within --idle-timeout seconds. Increase the value if needed, or retry with the same --thread.
6. ModuleNotFoundError — run pip3 install -r scripts/requirements.txt first.

API Surface

The script calls the SLS DataAgent OpenAPI directly (ACS3-HMAC-SHA256 signing); the endpoint defaults to starops.cn-beijing.aliyuncs.com and can be overridden via SLS_DATA_AGENT_ENDPOINT:

• Create a session (thread)
• Start a conversation and stream the analysis process and conclusions (SSE)

See references/api-reference.md and references/ram-policies.md for details.

Observability

Every HTTP request carries a user-agent header for tracing:

AlibabaCloud-Agent-Skills/alibabacloud-sls-data-agent/{session-id}

• alibabacloud-sls-data-agent — fixed skill identifier (matches the name field).
• {session-id} — a 32-character hex string, read from the SKILL_SESSION_ID environment variable. If the env var is not set, the script falls back to generating uuid.uuid4().hex. The same session-id is shared across all requests in a single invocation (CreateThread + CreateChat), enabling correlation of API calls in server-side logs.