Models and providers
TabulaFlow supports hundreds of models across more than 25 cloud
and local providers. It uses one model for the main conversation and
another for parallel subagent work. Most users only need to export one provider
API key and choose models in /config.
Quick setup
Choose one provider and configure it before launching TabulaFlow:
export OPENAI_API_KEY="your-api-key"
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export ANTHROPIC_API_KEY="your-api-key"
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export VLLM_BASE_URL="http://127.0.0.1:8000/v1"
# For authenticated endpoints:
# export VLLM_API_KEY="your-api-key"
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export FIREWORKS_API_KEY="your-api-key"
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TabulaFlow automatically selects models for OpenAI, Anthropic, and single-model
vLLM endpoints. Use /config to change them or select among multiple vLLM
models. Credentials are read from the environment and never saved.
vLLM tool calling
TabulaFlow relies on automatic tool calling. Follow the current vLLM tool-calling guide to configure it for your model.
Choose models
Run /config to choose the main model, subagent model, reasoning effort, and
maximum model request rate. The main model handles the conversation and plans
tool use; the subagent model handles parallel extraction, classification, and
other row-wise work.
Model identifiers use the Pydantic AI provider:model format:
openai:gpt-5.6-sol
anthropic:claude-sonnet-5
vllm:Qwen/Qwen3-8B
The picker includes recommended models, the current selection, and compatible
models from the installed provider stack. You can also type a complete custom
identifier. TabulaFlow saves model selections in
~/.tabulaflow/app_config.json, but continues to read credentials from the
environment.
Supported providers
The model picker includes models from all the provider routes below. TabulaFlow
uses Pydantic AI's environment variables and authentication unchanged; it does
not store these credentials. Set the listed variables before launching
TabulaFlow, then select a provider:model identifier in /config when
automatic setup does not apply.
API key providers
| Provider | Prefix | Environment variable |
|---|---|---|
| OpenAI | openai: |
OPENAI_API_KEY |
| Anthropic | anthropic: |
ANTHROPIC_API_KEY |
| Google Gemini | google: |
GOOGLE_API_KEY or GEMINI_API_KEY |
| OpenRouter | openrouter: |
OPENROUTER_API_KEY |
| DeepSeek | deepseek: |
DEEPSEEK_API_KEY |
| xAI | xai: |
XAI_API_KEY |
| Groq | groq: |
GROQ_API_KEY |
| Mistral | mistral: |
MISTRAL_API_KEY |
| Together AI | together: |
TOGETHER_API_KEY |
| Hugging Face | huggingface: |
HF_TOKEN |
| Fireworks AI | fireworks: |
FIREWORKS_API_KEY |
| Cohere | cohere: |
CO_API_KEY |
| Cerebras | cerebras: |
CEREBRAS_API_KEY |
| Alibaba Cloud Model Studio | alibaba: |
ALIBABA_API_KEY or DASHSCOPE_API_KEY |
| Moonshot AI | moonshotai: |
MOONSHOTAI_API_KEY |
| Nebius AI Studio | nebius: |
NEBIUS_API_KEY |
| Z.AI | zai: |
ZAI_API_KEY |
| Crusoe | crusoe: |
CRUSOE_API_KEY |
| OVHcloud AI Endpoints | ovhcloud: |
OVHCLOUD_API_KEY |
Cloud and hosted authentication
| Provider | Prefix | Authentication |
|---|---|---|
| Amazon Bedrock | bedrock: |
AWS credential chain and AWS_DEFAULT_REGION, or AWS_BEARER_TOKEN_BEDROCK |
| Azure OpenAI | azure: |
AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT; OPENAI_API_VERSION for versioned endpoints |
| Google Cloud | google-cloud: |
Application Default Credentials with GOOGLE_CLOUD_PROJECT and optional GOOGLE_CLOUD_LOCATION; or GOOGLE_API_KEY for Vertex AI Express Mode |
| GitHub Copilot | github-copilot: |
GITHUB_COPILOT_API_KEY, GITHUB_COPILOT_API_TOKEN, or COPILOT_GITHUB_TOKEN |
| Vercel AI Gateway | vercel: |
VERCEL_AI_GATEWAY_API_KEY or VERCEL_OIDC_TOKEN |
| Snowflake Cortex | snowflake: |
SNOWFLAKE_ACCOUNT and SNOWFLAKE_TOKEN |
Local and custom endpoints
| Provider | Prefix | Connection settings |
|---|---|---|
| Ollama | ollama: |
OLLAMA_BASE_URL; optional OLLAMA_API_KEY |
| vLLM | vllm: |
VLLM_BASE_URL; optional VLLM_API_KEY |
Provider requirements can change independently of TabulaFlow. Follow the linked Pydantic AI provider page for account setup and provider-specific details. If a required setting is missing, TabulaFlow names it when model activation fails.
Model requirements
The Data Agent sends instructions and tool schemas and expects models to produce dependable tool calls. Choose models with:
- Text generation and tool-calling support
- A large context window
- Reliable structured output for extraction and subagent work
Disabling the LLM in /config turns off conversational analysis while keeping
data connections and the data explorer available.