Bag of words — deploy an AI Analyst in minutes

Connect any LLM to any data source through a unified context layer (instructions, docs, dbt, code) with full observability.

Built for reliable quick answers and deeper investigations like root-cause analysis.

Features:

• Agentic loop - chat with data, ask complex questions, run multi-step analysis with tools, reasoning, and reflection
• Any LLM ↔ Any warehouse - OpenAI, Anthropic, Gemini, Ollama ↔ Snowflake, BigQuery, Postgres, Redshift
• Git repo indexing - use AGENTS.md, dbt, LookML, Dataform, Tableau, and code as live context
• Centralized instructions - one place for definitions, business terms, and rules with approval workflows
• Self-learning - learns from usage and surfaces instruction and semantic-layer updates, review-gated
• Reliability - full observability: agent traces, plans, guardrails, LLM judges, evals
• Auth & deploy - run via Docker/Compose/k8s with SSO, RBAC, warehouse inherited permissions, and audit trail

Deploy anywhere

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Quick Start 🚀

# runs with SQLite (default)
docker run -p 3000:3000 bagofwords/bagofwords

Or, run with a ready PostgreSQL instance

docker run -p 3000:3000 \
  -e BOW_DATABASE_URL=postgresql://user:password@localhost:5432/dbname \
  bagofwords/bagofwords

Other deployment options

docker compose and Kubernete deployments are also available (and recommended for servers). See the docs.

Product Overview

Chat with any data

Create reports, deep analysis or quick visuals with an AI interface powered by an agentic-loop with tools, reasoning and reflection built in.

Create and customize AI instructions and rules

Manage your AI rules and instructions with review process and control

Connect dbt, Tableau, and more for better AI context

Enrich your AI context with dbt models, Tableau data sources, AGENTS.md and your git repo

Save data and queries to the Catalog

Leverage the catalog to store, share, and explore reusable queries and datasets. This feature also improves discoverability and searchability for AI, contributing to smarter AI decisions.

Monitor AI and data operations

Full observability into queries, feedback, and context — powering self-learning and high quality AI results

Architecture

Bag of words acts as a context-aware analytics layer that connects to any database or service, works with any LLM, and enriches queries with docs, BI models, or code.

The architecture is fully flexible: plug in any data source, any model, and any interface — giving your team maximum freedom of choice, without sacrificing governance or reliability.

Integrations

Supported LLM Integrations

Bag of words supports a wide range of LLM providers out of the box. You can bring your own API key for any of the following:

Provider	Supported Models / APIs	Notes
OpenAI	GPT-5, GPT-4.1, o-models, etc.	Any OpenAI-compatible endpoint (including self-hosted, vLLM, etc.)
Azure OpenAI	GPT-5, GPT-4.1, o-models, etc.	Azure resource/endpoint support, including model deployment names
Google Gemini	Gemini 2.5, Flash versions, etc.	Requires Google Cloud API key
Anthropic	Claude, Sonnet, Haiku	Just provide the API key
Any OpenAI-compatible	vLLM, LM Studio, Ollama, etc.	Just provide the base URL and API key

Tip: You can configure multiple providers and models, set defaults, and more.

Data Sources

Supported Data Sources

Below is a list of all data sources supported by Bag of words, as defined in the data source registry. Each entry is marked as either a Database/Warehouse or a Service.

Title	Kind
PostgreSQL	Database/Warehouse
Snowflake	Database/Warehouse
Google BigQuery	Database/Warehouse
NetSuite	Service
MySQL	Database/Warehouse
AWS Athena	Database/Warehouse
MariaDB	Database/Warehouse
DuckDB	Database/Warehouse
Salesforce	Service
Microsoft SQL Server	Database/Warehouse
ClickHouse	Database/Warehouse
Azure Data Explorer	Database/Warehouse
AWS Cost Explorer	Service
Vertica	Database/Warehouse
AWS Redshift	Database/Warehouse
Tableau	Service
Presto	Database/Warehouse
Apache Pinot	Database/Warehouse
Oracle DB	Database/Warehouse
MongoDB	Database/Warehouse

Note: Some data sources (like NetSuite) may be marked as inactive or beta in the registry. "Service" refers to APIs or SaaS platforms, while "Database/Warehouse" refers to systems that store and query structured data.

🔒 Security & Privacy

We take data security and privacy seriously.

Telemetry

By default, Bag of words captures basic usage stats of self-hosted instances to a centralized server. The data helps us improve the product.

You can disable by setting in bow-config.yaml

telemetry
  enabled: false

You can also disable the Intercom chat for support

intercom
  enabled: false