LLM integration for Datasette plugins.

This plugin provides a standard interface for Datasette plugins to use LLM models via the llm library, with:

• Model management: Control which models are available, with filtering and defaults
• API key management: Integration with datasette-secrets for secure key storage
• Hooks for extensibility: Track usage, enforce policies, implement accounting

Install this plugin in the same environment as Datasette:

datasette install datasette-llm

You'll also need at least one LLM model plugin installed:

# For OpenAI models
datasette install llm

# For Anthropic models
datasette install llm-anthropic

# For testing without API calls
datasette install llm-echo

Configure the plugin in your datasette.yaml:

plugins:
  datasette-llm:
    # Default model when none specified
    default_model: gpt-5.4-mini

    # Purpose-specific configuration
    purposes:
      enrichments:
        model: gpt-5.4-nano      # Default model for bulk operations
      sql-assistant:
        model: gpt-5.4           # Smarter for complex queries
        models:                  # Only these models for SQL assistance
        - gpt-5.4
        - gpt-5.4-mini
      extract:
        model: claude-sonnet-4.6
        blocked_models:          # Block specific models for extraction
        - gpt-5.4-pro
      chat:
        model: claude-sonnet-4.6

    # Model availability (optional)
    models:                      # Allowlist - only these models available
    - gpt-5.4
    - gpt-5.4-mini
    - gpt-5.4-nano
    - claude-sonnet-4.6

    # Or use a blocklist instead
    blocked_models:
    - gpt-5.4-pro                # Too expensive

    # Only show models with API keys configured (default: true)
    require_keys: true

Anywhere a model name string is accepted in configuration (default_model, purposes.<name>.model, or entries in purposes.<name>.models), you can use a dictionary with model and key fields instead. The key value is a datasette-secrets secret name that will be used as the API key for that model.

plugins:
  datasette-llm:
    # Simple case: default model with a custom key
    default_model:
      model: gpt-5.4-mini
      key: CUSTOM_OPENAI_KEY

    purposes:
      # Pin a purpose to one model with its own billing key
      query-assistant:
        model:
          model: gpt-5.4-mini
          key: QUERY_ASSISTANT_KEY

      # Multiple models, each with their own key —
      # even two models from the same provider can use different keys
      enrichments:
        model:
          model: gpt-5.4-nano
          key: ENRICHMENTS_NANO_KEY
        models:
          - model: gpt-5.4
            key: ENRICHMENTS_GPT5_KEY
          - model: gpt-5.4-mini
            key: ENRICHMENTS_MINI_KEY
          - claude-sonnet-4.6      # Falls through to default key resolution

The key field is resolved through datasette-secrets, so you can set it via environment variables:

export DATASETTE_SECRETS_QUERY_ASSISTANT_KEY=sk-...
export DATASETTE_SECRETS_ENRICHMENTS_NANO_KEY=sk-...

When a model is used for a purpose, key resolution follows this order:

1. Key from the purpose's configuration (a matching dict in model or models)
2. Key from the default_model config (if it's a dict and matches)
3. Standard datasette-secrets resolution (<PROVIDER>_API_KEY)
4. llm's key resolution (keys.json, environment variables)

The models (global allowlist) and blocked_models fields remain plain string lists — custom keys are only supported in default_model, purposes.<name>.model, and purposes.<name>.models.

The models and blocked_models keys control which models are available. Use models to define an allowlist (only these models will be available) or blocked_models to define a blocklist (all models except these will be available). If both are set, the allowlist is applied first and the blocklist removes from the result. The default_model is automatically included in the allowlist, so you don't need to repeat it there.

Plugins register purposes to describe what they use LLM models for (e.g. "extract", "enrichments", "sql-assistant"). Each purpose can have its own configuration under purposes.<name>:

• model: The default model for this purpose, used when a plugin calls await llm.model(purpose="extract") without specifying a model ID.
• models: An allowlist of models for this purpose. When set, this overrides the global models allowlist — so a model can be available for a specific purpose even if it is not in the global list. The purpose's default model is automatically included in this allowlist, so you don't need to repeat it. This filtering is applied when a plugin calls await llm.models(purpose="extract").
• blocked_models: A blocklist of models for this purpose. These are removed even if the model is globally allowed.

When no purpose-specific models list is set, the global models allowlist applies. The global blocked_models always applies regardless of purpose configuration.

datasette-llm integrates with datasette-secrets for API key management. Keys are automatically registered for all installed model providers.

1. Via environment variables (recommended for deployment):

   export DATASETTE_SECRETS_OPENAI_API_KEY=sk-...
   export DATASETTE_SECRETS_ANTHROPIC_API_KEY=sk-ant-...

2. Via the web interface: Navigate to /-/secrets (requires manage-secrets permission)

3. Via llm CLI (fallback): Keys set with llm keys set openai are also used

1. datasette-secrets (env var DATASETTE_SECRETS_<PROVIDER>_API_KEY or encrypted database)
2. llm's keys.json (~/.config/io.datasette.llm/keys.json)
3. llm's environment variables (e.g., OPENAI_API_KEY)

All examples below assume you have created an LLM instance:

from datasette_llm import LLM

llm = LLM(datasette)

async def model(
    model_id: Optional[str] = None,
    purpose: Optional[str] = None,
    actor: Optional[dict] = None,
) -> WrappedAsyncModel

Get an async model wrapped with hook support. Returns a WrappedAsyncModel that invokes hooks around prompts.

Parameters:

• model_id (Optional[str]): The model to use, e.g. "gpt-5.4-mini" or "claude-sonnet-4.6". If not provided, the default model is resolved from configuration — first checking the purpose-specific model setting, then the global default_model.
• purpose (Optional[str]): Identifies what this model will be used for, e.g. "enrichments", "sql-assistant". This selects the purpose-specific default model and is passed through to hooks like llm_prompt_context for auditing and accounting.
• actor (Optional[dict]): The Datasette actor dictionary for the current user. Pass this to enable two things: per-user model filtering, where plugins using the llm_filter_models or llm_default_model hooks can restrict or customize models based on who is making the request; and audit logging, where auditing plugins that implement llm_prompt_context can record which actor ran which prompts.

async def my_plugin_view(datasette, request):
    llm = LLM(datasette)

    # Get the default model
    model = await llm.model()

    # Specify a model explicitly
    model = await llm.model("gpt-5.4-mini")

    # With purpose and actor for auditing and filtering
    model = await llm.model(
        purpose="sql-assistant",
        actor=request.actor,
    )

    # Execute a prompt
    response = await model.prompt("What is the capital of France?")
    text = await response.text()

async def models(
    actor: Optional[dict] = None,
    purpose: Optional[str] = None,
) -> List

Get available models, filtered by configuration, API key availability, and hooks. Returns a list of model objects.

Parameters:

• actor (Optional[dict]): The Datasette actor dictionary. When provided, the llm_filter_models hook can use this to return only models the actor is allowed to use — for example, restricting anonymous users to cheaper models, or looking up per-user model allowlists in a database. Auditing plugins can also use this to log which actors are querying model availability.
• purpose (Optional[str]): When provided, purpose-specific models and blocked_models configuration is applied. A purpose-specific models allowlist overrides the global allowlist, so models can be made available for a specific purpose even if they aren't globally listed.

llm = LLM(datasette)

# Get all available models (filtered by config and key availability)
models = await llm.models()
for model in models:
    print(model.model_id)

# Filter by actor (for per-user permissions)
models = await llm.models(actor=request.actor)

# Filter by purpose (applies purpose-specific models/blocked_models config)
models = await llm.models(purpose="enrichments")

# Both together — e.g. to populate a model picker for the current user
models = await llm.models(actor=request.actor, purpose="sql-assistant")

model = await llm.model("gpt-5.4-mini")
response = await model.prompt("Tell me a story")

# Non-streaming - wait for complete response
text = await response.text()

# Streaming - process chunks as they arrive
async for chunk in response:
    print(chunk, end="", flush=True)

Use group() for batch operations where multiple prompts are logically related:

async def enrich_rows(datasette, rows):
    llm = LLM(datasette)

    # Model determined by purpose configuration
    async with llm.group(purpose="enrichments") as model:
        results = []
        for row in rows:
            response = await model.prompt(f"Summarize: {row['content']}")
            text = await response.text()
            results.append(text)

    # All responses guaranteed complete here
    return results

Benefits of group():

• Transactional semantics: All responses forced to complete on exit
• Shared context: Hooks can treat grouped prompts together (e.g., shared budget reservation)
• Cleanup: The llm_group_exit hook is called for settlement/logging

datasette-llm provides hooks for other plugins to extend LLM operations.

Wrap prompt execution with custom logic. The same hook is used for direct prompt() calls and for each response yielded by chain():

from datasette import hookimpl
from contextlib import asynccontextmanager

@hookimpl
def llm_prompt_context(datasette, model_id, prompt, purpose, actor):
    @asynccontextmanager
    async def wrapper(result):
        # Before the prompt executes
        actor_id = actor.get("id") if actor else None
        print(f"Starting prompt to {model_id} by {actor_id}")

        yield

        # After prompt() returns or chain() is initialized
        async def on_complete(response):
            usage = await response.usage()
            print(f"Used {usage.input} input, {usage.output} output tokens")

        await result.on_response_done(on_complete)

    return wrapper

result.response continues to expose the first response, while result.responses contains all responses seen so far. The await result.on_response_done(callback) helper attaches a callback to all existing responses and any future responses produced by a chain.

Called when a group() context manager exits:

@hookimpl
def llm_group_exit(datasette, group):
    # Can return a coroutine for async cleanup
    async def cleanup():
        print(f"Group for {group.purpose} completed")
        print(f"Processed {len(group._responses)} prompts")
    return cleanup()

Register purpose strings that your plugin uses, along with documentation explaining what they mean.

from datasette import hookimpl
from datasette_llm import Purpose

@hookimpl
def register_llm_purposes(datasette):
    return [
        Purpose(
            name="query-assistant",
            description="Assists users with writing SQL queries",
        ),
        Purpose(
            name="suggest-table-names",
            description="Suggests names for tables based on imported CSV files",
        ),
    ]

Registered purposes can be retrieved by other plugins (e.g., to build an admin UI for model assignment):

from datasette_llm import get_purposes

purposes = get_purposes(datasette)
for purpose in purposes:
    print(f"{purpose.name}: {purpose.description}")

If multiple plugins register the same purpose name, the first registration wins.

Influence the models that are returned from the await llm.models() method. Plugins can use this to add custom logic informing which models are available, taking into account both the actor and the purpose of the prompt.

• models is a list of available model objects from all of the installed LLM plugins.
• actor is an actor dictionary or None
• purpose is a purpose string or None

The actor and purpose are the ones that were passed to the llm.models(actor=..., purpose=...) method.

@hookimpl
async def llm_filter_models(datasette, models, actor, purpose):
    if not actor:
        # Anonymous users get limited models
        return [m for m in models if m.model_id == "gpt-5.4-mini"]

    # Check database for user's allowed models
    db = datasette.get_database()
    result = await db.execute(
        "SELECT model_id FROM user_models WHERE user_id = ?",
        [actor["id"]]
    )
    allowed = {row["model_id"] for row in result.rows}
    return [m for m in models if m.model_id in allowed]

This plugin hook is used when await llm.model() is called without any arguments - or with a purpose and/or actor specified. Plugins can use this to control which default model is used, including for a given purpose.

@hookimpl
async def llm_default_model(datasette, purpose, actor):
    if actor:
        # Check user's preferred model
        db = datasette.get_database()
        result = await db.execute(
            "SELECT preferred_model FROM user_prefs WHERE user_id = ?",
            [actor["id"]]
        )
        row = result.first()
        if row:
            return row["preferred_model"]
    return None  # Use config defaults

• datasette-secrets: Secure API key storage (required dependency)
• datasette-llm-accountant: Budget management and cost tracking

To set up this plugin locally:

cd datasette-llm
uv sync

# Confirm the plugin is visible
uv run datasette plugins

To run the tests:

uv run pytest

The test suite uses the llm-echo model which echoes back prompts without making API calls.