bigquery-agent-analytics 0.4.0

SDK for analyzing and evaluating agent traces stored in BigQuery.

BigQuery Agent Analytics SDK

An open-source Python SDK for analyzing, evaluating, and curating agent traces stored in BigQuery. Built on top of the BigQuery Agent Analytics, it provides a consumption-layer toolkit for agent observability, analysis, evaluation, and advanced capabilities like the Agent Context Graph — extracting decision traces from your agent's context graph — at scale.

Overview

The BigQuery Agent Analytics SDK connects your AI agent telemetry in BigQuery to a rich set of evaluation, observability, and analytics capabilities. It is designed for ML engineers, data scientists, and platform teams who run agents in production and need to understand agent behavior, measure quality, and detect regressions — all through BigQuery SQL or Python.

Key Features

Observability

• Trace reconstruction and DAG visualization
• Per-event-type BigQuery views
• Observability dashboards (SQL and BigFrames)

Evaluation

• Code-based metrics (latency, turn count, error rate, token efficiency, cost)
• LLM-as-Judge scoring (correctness, hallucination, sentiment)
• Trajectory matching (exact, in-order, any-order)
• Multi-trial evaluation with pass@k / pass^k
• Grader composition (weighted, binary, majority strategies)
• Eval suite lifecycle management with graduation and saturation detection
• Static quality validation (ambiguous tasks, class imbalance, suspicious thresholds)

AI/ML Integration

• BigQuery AI.GENERATE, AI.EMBED, AI.CLASSIFY
• Anomaly detection and latency forecasting
• Categorical (Hatteras-style) evaluation via BigFrames

Advanced Analytics

• Agent Context Graph — extract decision traces from your agent's context graph: the requests an agent handled, the options it weighed, and the outcomes it committed, materialized into a queryable BigQuery property graph (GQL traversal, scheduled refresh via bqaa context-graph)
• Long-horizon cross-session memory
• Multi-stage agent insights pipeline
• Drift detection for golden vs production question distributions

CLI (bq-agent-sdk)

• 12+ commands for diagnostics, evaluation, and CI/CD integration

Deployment Surfaces

• Remote Function (BigQuery SQL via Cloud Run)
• Python UDF scoring kernels
• Streaming evaluation (Cloud Scheduler + Cloud Run)
• Continuous query templates

Usage Telemetry

• Every job the SDK submits is labeled (sdk, sdk_version, sdk_surface, sdk_feature, and sdk_ai_function where relevant) so operators can attribute spend, latency, and adoption directly from INFORMATION_SCHEMA.JOBS_BY_PROJECT. No extra telemetry pipeline is required. See docs/sdk_usage_tracking.md for the label schema and ready-to-run tracking queries.

Prerequisites

• Python 3.10+
• A Google Cloud project with BigQuery enabled
• Agent traces stored in BigQuery via the ADK BigQuery Trace Exporter

Installation

pip install bigquery-agent-analytics

With optional LLM judge support:

pip install bigquery-agent-analytics[llm]

With BigFrames support:

pip install bigquery-agent-analytics[bigframes]

Quick Start

from bigquery_agent_analytics import Client

client = Client(project_id="my-project", dataset_id="analytics")
trace = client.get_trace("trace-abc-123")
trace.render()

See SDK.md for the full API walkthrough with code examples for every feature.

Try it: extract decision traces (Agent Context Graph, ~10 minutes)

Deploy a context graph, seed sample agent events, extract the decision traces, and query one in GQL — entirely from your terminal:

export PROJECT_ID="your-project" DATASET="agent_analytics_demo"
gcloud config set project "$PROJECT_ID"
bq --location=US mk --dataset "$PROJECT_ID:$DATASET"

# 1. Deploy the context graph (one-time DDL: tables, then the property graph).
cd examples/context_graph/codelab
envsubst < table_ddl.sql      | bq query --use_legacy_sql=false
envsubst < property_graph.sql | bq query --use_legacy_sql=false

# 2. Seed five sample agent sessions into agent_events.
bqaa seed-events --project-id "$PROJECT_ID" --dataset-id "$DATASET" --sessions 5

# 3. Extract decision traces from the deployed graph
#    (read back via INFORMATION_SCHEMA.PROPERTY_GRAPHS — no SQL file passed).
bqaa context-graph --project-id "$PROJECT_ID" --dataset-id "$DATASET" \
    --graph agent_decisions_graph --lookback-hours 24 --format json

# 4. Query a decision trace: what did the agent weigh, and how did it resolve?
bq query --use_legacy_sql=false "
SELECT * FROM GRAPH_TABLE(
  $DATASET.agent_decisions_graph
  MATCH (req:DecisionRequest)-[eo:evaluatesOption]->(opt:DecisionOption),
        (req)-[ri:resultedIn]->(out:DecisionOutcome)
  COLUMNS (req.request_text AS question, opt.option_label AS considered,
           out.status AS outcome, out.rationale AS rationale))"

Expect "ok": true with 5 sessions materialized, and fifteen GQL rows — three options weighed per request, each with the committed outcome and rationale. The Agent Context Graph codelab is the guided version of these steps (plus backfill and production scheduling), and examples/context_graph/ is the worked example with a runnable ADK agent.

Documentation

Resource	Description
SDK Feature Reference	Complete API walkthrough with working code examples
Agent Context Graph Codelab	Extract decision traces from your agent's context graph, end to end (~35 min)
Scheduled Deploy Runbook	Keep the context graph fresh on a Cloud Run + Cloud Scheduler cron
Design Documents	Architecture decisions and design rationale
Examples	Notebooks, SQL scripts, and demos
Deployment Guides	Four deployment surfaces for Google Cloud

Architecture

src/bigquery_agent_analytics/
│
├── Core
│   ├── client.py                  # High-level SDK client
│   ├── trace.py                   # Trace reconstruction & visualization
│   ├── views.py                   # Per-event-type BigQuery view management
│   ├── event_semantics.py         # Canonical event type helpers & predicates
│   ├── serialization.py           # Uniform serialization layer
│   └── formatter.py               # Output formatting (json/text/table)
│
├── Evaluation
│   ├── evaluators.py              # SystemEvaluator + LLMAsJudge
│   ├── trace_evaluator.py         # Trajectory matching & replay
│   ├── multi_trial.py             # Multi-trial runner + pass@k
│   ├── grader_pipeline.py         # Grader composition pipeline
│   ├── eval_suite.py              # Eval suite lifecycle management
│   └── eval_validator.py          # Static validation checks
│
├── AI/ML
│   ├── ai_ml_integration.py       # BigQuery AI/ML capabilities
│   ├── bigframes_evaluator.py     # BigFrames DataFrame evaluator
│   ├── categorical_evaluator.py   # Hatteras categorical evaluation
│   └── categorical_views.py       # Categorical metric views
│
├── Analytics
│   ├── insights.py                # Multi-stage insights pipeline
│   ├── feedback.py                # Drift detection & question distribution
│   └── memory_service.py          # Long-horizon agent memory
│
├── Agent Context Graph
│   ├── context_graph.py           # Decision-trace extraction & GQL traversal
│   ├── materialize_window.py      # Scheduled materialization (bqaa context-graph)
│   └── property_graph_spec.py     # Derive the spec from your deployed property graph
│
└── CLI & Deploy
    ├── cli.py                     # CLI entry point (bq-agent-sdk)
    ├── udf_kernels.py             # Python UDF scoring kernels
    └── udf_sql_templates.py       # UDF SQL generation

Related Projects

• Google ADK — Agent Development Kit for building AI agents
• BigQuery — Google Cloud analytics data warehouse
• BigQuery AI Functions — AI.GENERATE, AI.EMBED, AI.CLASSIFY, and more

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Format code
pyink --config pyproject.toml src/ tests/
isort src/ tests/

Contributing

See CONTRIBUTING.md for guidelines.

License

Apache License 2.0 — see LICENSE for details.