Agnes — AI Data Analyst

Agnes is an open-source data distribution platform for AI analytical systems. It extracts data from configured sources into DuckDB, serves it via a FastAPI backend, and distributes Parquet files to analysts who query them locally using Claude Code and DuckDB.

Each data source produces a self-describing extract.duckdb file. The SyncOrchestrator attaches all extract databases into a master analytics.duckdb, making every table available through a unified view layer without copying data unnecessarily.

Architecture: extract.duckdb Contract

Every connector produces the same output structure:

/data/extracts/{source_name}/
├── extract.duckdb          ← _meta table + views
└── data/                   ← parquet files (local sources only)

The orchestrator scans /data/extracts/*/extract.duckdb, attaches each into analytics.duckdb, and creates master views.

┌──────────────┐  ┌──────────────┐  ┌──────────────┐
│   Keboola    │  │   BigQuery   │  │   Jira       │
│  extractor   │  │  extractor   │  │  webhooks    │
│ (DuckDB ext) │  │ (remote BQ)  │  │ (incremental)│
└──────┬───────┘  └──────┬───────┘  └──────┬───────┘
       │                 │                 │
       ▼                 ▼                 ▼
   extract.duckdb    extract.duckdb    extract.duckdb
   + data/*.parquet  (views → BQ)      + data/*.parquet
       │                 │                 │
       └─────────────────┼─────────────────┘
                         ▼
              SyncOrchestrator.rebuild()
              ATTACH → master views in analytics.duckdb
                         │
              ┌──────────┼──────────┐
              ▼          ▼          ▼
          FastAPI      CLI
          (serve)    (agnes pull)

Supported Data Sources

Mode	Distribution	Sources	Use when
Batch pull (local)	Parquet on disk, scheduled	Keboola	Source has a native bulk-export and the table fits on disk
Materialized SQL (materialized)	Parquet on disk, scheduled query	BigQuery, Keboola	Source table is too large to mirror as-is; you want a curated subset / aggregate on disk
Remote attach (remote)	View only, no download	BigQuery	Table is too large to materialize; latency cost of remote query is acceptable
Real-time push	Incremental parquet	Jira	Source is event-driven and you need sub-minute freshness

The first three modes are what agnes pull distributes to analysts. The fourth is server-side only — analysts query Jira data through the same agnes pull-distributed parquets.

Admins manage per-source registrations through the /admin/tables UI (per-connector tabs for BigQuery / Keboola / Jira) or the agnes admin register-table CLI; per-row "Manage access" deep-links to /admin/access for granting tables to user groups via resource_grants(group, ResourceType.TABLE, table_id).

Analysts get a closed loop with Claude Code: agnes init writes <workspace>/.claude/settings.json with SessionStart (agnes pull --quiet) and SessionEnd (agnes push --quiet) hooks so every Claude Code session starts with fresh RBAC-filtered parquets and ends with the session log uploaded back.

Adding a new source means creating connectors/<name>/extractor.py that produces extract.duckdb with a _meta table (table_name, description, rows, size_bytes, extracted_at, query_mode). The orchestrator attaches it automatically.

Quick Start with Docker

# Clone the repository
git clone https://github.com/keboola/agnes-the-ai-analyst.git
cd agnes-the-ai-analyst

# Copy and edit configuration
cp config/instance.yaml.example config/instance.yaml
cp config/.env.template .env
# Edit both files for your environment

# Start the app and scheduler
docker compose up

# Start with all optional services (Telegram bot, etc.)
docker compose --profile full up

# Start with TLS (Caddy on :443 with corporate-CA certs from /data/state/certs)
docker compose -f docker-compose.yml -f docker-compose.prod.yml -f docker-compose.tls.yml \
    --profile tls up -d

Once running, the FastAPI app is available at http://localhost:8000 (or https://$DOMAIN in TLS mode). See docs/DEPLOYMENT.md for cert provisioning + auto-rotation via scripts/ops/agnes-tls-rotate.sh. Trigger a manual sync:

curl -X POST http://localhost:8000/api/sync/trigger

Local sync & auto-update

Analysts run Claude Code against a local DuckDB built from RBAC-filtered parquets pulled from the server. agnes pull is the distribution path:

agnes pull             # delta-pull: manifest → MD5 compare → download changed → rebuild views
agnes pull --quiet     # same, no progress output (for hooks/cron)
agnes push  # push session jsonl + CLAUDE.local.md back to the server

agnes init writes Claude Code lifecycle hooks into <workspace>/.claude/settings.json:

• SessionStart → agnes pull --quiet — fresh data on every session
• SessionEnd → agnes push --quiet — uploads notes and session log

Hooks live at workspace level so they only fire in this analyst workspace, not in unrelated Claude Code sessions on the same machine.

Admin: which tables auto-sync to whom

The auto-sync set per analyst is the intersection of:

1. Tables with query_mode IN ('local', 'materialized') — these have parquets on disk and end up in the manifest
2. Tables granted to one of the analyst's groups via resource_grants(group, ResourceType.TABLE, table_id) (see docs/RBAC.md)

To enroll a new table for auto-sync, register it (or update its query_mode) and grant it to the relevant groups in /admin/access. New analysts get the same set on their next agnes pull.

For BigQuery, register a query_mode='materialized' table with a SQL body:

agnes admin register-table orders_90d \
    --source-type bigquery \
    --query-mode materialized \
    --query @docs/queries/orders_90d.sql \
    --schedule "every 6h"

The scheduler runs the query through the DuckDB BigQuery extension on each tick that's due, writes the result as a parquet, and the analyst picks it up on the next agnes pull. Cost guardrail: data_source.bigquery.max_bytes_per_materialize (default 10 GiB) — operations exceeding the BQ dry-run estimate are skipped.

Development Setup

# Create and activate virtual environment
python3 -m venv .venv && source .venv/bin/activate

# Install dependencies
uv pip install ".[dev]"

# Run FastAPI locally with hot reload
uvicorn app.main:app --reload

# Run the test suite
pytest tests/ -v

Project Structure

├── src/                    # Core engine
│   ├── db.py               # DuckDB schema (system.duckdb, analytics.duckdb)
│   ├── orchestrator.py     # SyncOrchestrator — ATTACHes extract.duckdb files
│   ├── repositories/       # DuckDB-backed CRUD (sync_state, table_registry, users, etc.)
│   ├── profiler.py         # Data profiling
│   └── catalog_export.py   # OpenMetadata catalog export
├── app/                    # FastAPI application
│   ├── main.py             # App setup, router registration
│   ├── api/                # REST API (sync, data, catalog, admin, auth)
│   ├── auth/               # Auth providers (Google OAuth, email magic link, desktop JWT)
│   └── web/                # HTML dashboard routes
├── connectors/             # Data source connectors (extract.duckdb contract)
│   ├── keboola/            # Keboola: extractor.py (DuckDB extension) + client.py (fallback)
│   ├── bigquery/           # BigQuery: extractor.py (remote-only via DuckDB BQ extension)
│   └── jira/               # Jira: webhook + incremental parquet → extract.duckdb
├── cli/                    # CLI tool (`agnes pull`, `agnes query`, `agnes admin`)
├── services/               # Standalone services (scheduler, telegram_bot, ws_gateway, etc.)
├── scripts/                # Utility + migration scripts
├── config/                 # Configuration templates (instance.yaml.example)
├── docs/                   # Documentation + metric YAML definitions
└── tests/                  # Test suite (633 tests)

Configuration

File	Purpose
config/instance.yaml	Instance-specific settings: branding, data source type, auth provider, Google domain
.env	Secrets and environment variables — never committed
system.duckdb table_registry table	Table definitions managed via POST /api/admin/register-table (or PUT /api/admin/registry/{id} to update) or the web UI

Copy the example to get started:

cp config/instance.yaml.example config/instance.yaml

See config/instance.yaml.example for all available options.

Documentation

• Hackathon TL;DR — condensed deploy + dev playbooks (for both humans and AI agents)
• Onboarding Guide — end-to-end Terraform deployment into a GCP project (recommended for production)
• Deployment Guide — chooses between Terraform and Docker Compose; covers OSS self-host
• Configuration Reference — instance.yaml, env vars, per-instance options
• Architecture — orchestrator, extractors, DB layout
• Quickstart — local development

Contributing

1. Fork the repository and create a feature branch.
2. Run pytest tests/ -v to verify all tests pass before opening a pull request.
3. Keep commits focused and messages concise.
4. Open a pull request against main with a clear description of the change.

For bugs and feature requests, open a GitHub issue.

License

This project is licensed under the MIT License.