Datasette 1.0a17 is the latest Datasette 1.0 alpha release, with bug fixes and small feature improvements from the last few months.

Python 3.13 was released today. Datasette 1.0a16 is compatible with Python 3.13, but Datasette 0.64.8 was not. The new Datasette 0.65 release fixes compatibility with the new version of Python.

Datasette 1.0a14 includes some breaking changes to how metadata works for plugins, described in detail in the new upgrade guide. See also the annotated release notes that accompany this release.

Datasette 1.0a10 is a focused alpha that changes some internal details about how Datasette handles transactions. The datasette.execute_write_fn() internal method now wraps the function in a database transaction unless you pass transaction=False.

Datasette 1.0a9 adds basic alter table support to the JSON API, tweaks how permissions works and introduces some new plugin debugging utilities.

Datasette 1.0a8 introduces several new plugin hooks, a JavaScript plugin system and moves plugin configuration from metadata.yaml to datasette.yaml. Read more about the release in the annotated release notes for 1.0a8.

Datasette Enrichments is a new feature for Datasette that supports enriching data by running custom code against every selected row in a table. Read Datasette Enrichments: a new plugin framework for augmenting your data for more details, plus a video demo of enrichments for geocoding addresses and processing text and images using GPT-4.

datasette-comments is a new plugin by Alex Garcia which adds collaborative commenting to Datasette. Alex built the plugin for Datasette Cloud, but it's also available as an open source package for people who are hosting their own Datasette instances. See Annotate and explore your data with datasette-comments on the Datasette Cloud blog for more details.

Datasette 1.0a4 has a fix for a security vulnerability in the Datasette 1.0 alpha series: the API explorer interface exposed the names of private databases and tables in public instances that were protected by a plugin such as datasette-auth-passwords, though not the actual content of those tables. See the security advisory for more details and workarounds for if you can't upgrade immediately. The latest edition of the Datasette Newsletter also talks about this issue.

datasette-write-ui: a Datasette plugin for editing, inserting, and deleting rows introduces a new plugin adding add/edit/delete functionality to Datasette, developed by Alex Garcia. Alex built this for Datasette Cloud, and this post is the first announcement made on the new Datasette Cloud blog - see also Welcome to Datasette Cloud.

Datasette 1.0a3 is an alpha release of Datasette that previews the new default JSON API design that’s coming in version 1.0 - the single most significant change planned for that 1.0 release.

New tutorial: Data analysis with SQLite and Python. This tutorial, originally presented at PyCon 2023, includes a 2h45m video and an extensive handout that should be useful with or without the video. Topics covered include Python's sqlite3 module, sqlite-utils, Datasette, Datasette Lite, advanced SQL patterns and more.

I built a ChatGPT plugin to answer questions about data hosted in Datasette describes a new experimental Datasette plugin to enable people to query data hosted in a Datasette interface via ChatGPT, asking human language questions that are automatically converted to SQL and used to generate a readable response.

Using Datasette in GitHub Codespaces is a new tutorial showing how Datasette can be run in GitHub's free Codespaces browser-based development environments, using the new datasette-codespaces plugin.

Examples of sites built using Datasette now includes screenshots of Datasette deployments that illustrate a variety of problems that can be addressed using Datasette and its plugins.

• llm models --options now shows keys and environment variables for models that use API keys. Thanks, Steve Morin. #903
• Added py.typed marker file so LLM can now be used as a dependency in projects that use mypy without a warning. #887
• $ characters can now be used in templates by escaping them as $$. Thanks, @guspix. #904
• LLM now uses pyproject.toml instead of setup.py. #908

• Upgraded for compatibility with recent Pandas and Click libraries. Thanks, Luighi Viton-Zorrilla. #99

• Fixed a bug on Windows with the new llm -t path/to/file.yaml feature. #901

• Templates can now be specified as a path to a file on disk, using llm -t path/to/file.yaml. This makes them consistent with how -f fragments are loaded. #897
• llm logs backup /tmp/backup.db command for backing up your logs.db database. #879

Support for fragments to help assemble prompts for long context models. Improved support for templates to support attachments and fragments. New plugin hooks for providing custom loaders for both templates and fragments. See Long context support in LLM 0.24 using fragments and template plugins for more on this release.

The new llm-docs plugin demonstrates these new features. Install it like this:

llm install llm-docs

Now you can ask questions of the LLM documentation like this:

llm -f docs: 'How do I save a new template?'

The docs: prefix is registered by the plugin. The plugin fetches the LLM documentation for your installed version (from the docs-for-llms repository) and uses that as a prompt fragment to help answer your question.

Two more new plugins are llm-templates-github and llm-templates-fabric.

llm-templates-github lets you share and use templates on GitHub. You can run my Pelican riding a bicycle benchmark against a model like this:

llm install llm-templates-github
llm -t gh:simonw/pelican-svg -m o3-mini

This executes this pelican-svg.yaml template stored in my simonw/llm-templates repository, using a new repository naming convention.

To share your own templates, create a repository on GitHub under your user account called llm-templates and start saving .yaml files to it.

llm-templates-fabric provides a similar mechanism for loading templates from Daniel Miessler's fabric collection:

llm install llm-templates-fabric
curl https://simonwillison.net/2025/Apr/6/only-miffy/ | \
  llm -t f:extract_main_idea

Major new features:

• New fragments feature. Fragments can be used to assemble long prompts from multiple existing pieces - URLs, file paths or previously used fragments. These will be stored de-duplicated in the database avoiding wasting space storing multiple long context pieces. Example usage: llm -f https://llm.datasette.io/robots.txt 'explain this file'. #617
• The llm logs file now accepts -f fragment references too, and will show just logged prompts that used those fragments.
• register_template_loaders() plugin hook allowing plugins to register new prefix:value custom template loaders. #809
• register_fragment_loaders() plugin hook allowing plugins to register new prefix:value custom fragment loaders. #886
• llm fragments family of commands for browsing fragments that have been previously logged to the database.
• The new llm-openai plugin provides support for o1-pro (which is not supported by the OpenAI mechanism used by LLM core). Future OpenAI features will migrate to this plugin instead of LLM core itself.

Improvements to templates:

• llm -t $URL option can now take a URL to a YAML template. #856
• Templates can now store default model options. #845
• Executing a template that does not use the $input variable no longer blocks LLM waiting for input, so prompt templates can now be used to try different models using llm -t pelican-svg -m model_id. #835
• llm templates command no longer crashes if one of the listed template files contains invalid YAML. #880
• Attachments can now be stored in templates. #826

Other changes:

• New llm models options family of commands for setting default options for particular models. #829
• llm logs list, llm schemas list and llm schemas show all now take a -d/--database option with an optional path to a SQLite database. They used to take -p/--path but that was inconsistent with other commands. -p/--path still works but is excluded from --help and will be removed in a future LLM release. #857
• llm logs -e/--expand option for expanding fragments. #881
• llm prompt -d path-to-sqlite.db option can now be used to write logs to a custom SQLite database. #858
• llm similar -p/--plain option providing more human-readable output than the default JSON. #853
• llm logs -s/--short now truncates to include the end of the prompt too. Thanks, Sukhbinder Singh. #759
• Set the LLM_RAISE_ERRORS=1 environment variable to raise errors during prompts rather than suppressing them, which means you can run python -i -m llm 'prompt' and then drop into a debugger on errors with import pdb; pdb.pm(). #817
• Improved --help output for llm embed-multi. #824
• llm models -m X option which can be passed multiple times with model IDs to see the details of just those models. #825
• OpenAI models now accept PDF attachments. #834
• llm prompt -q gpt -q 4o option - pass -q searchterm one or more times to execute a prompt against the first model that matches all of those strings - useful for if you can't remember the full model ID. #841
• OpenAI compatible models configured using extra-openai-models.yaml now support supports_schema: true, vision: true and audio: true options. Thanks @adaitche and @giuli007. #819, #843

• New Fragments feature. #617
• register_fragment_loaders() plugin hook. #809

• Fix some warnings.
• Drop support for Python versions prior to 3.9.

• shot-scraper javascript can now optionally load scripts hosted on GitHub via the new gh: prefix to the shot-scraper javascript -i/--input option. #173

Scripts can be referenced as gh:username/repo/path/to/script.js or, if the GitHub user has created a dedicated shot-scraper-scripts repository and placed scripts in the root of it, using gh:username/name-of-script.

For example, to run this readability.js script against any web page you can use the following:

bash shot-scraper javascript -i gh:simonw/readability \ https://simonwillison.net/2025/Mar/24/qwen25-vl-32b/

• Fix for compatibility with datasette-extract. #13

• Alpha release with experimental register_template_loaders() plugin hook. #809

• Fixed a bug where strip-tags -t meta still removed <meta> tags from the <head> because the entire <head> element was removed first. #32
• Kept <meta> tags now default to keeping their content and property attributes.
• The CLI -m/--minify option now also removes any remaining blank lines. #33
• A new strip_tags(remove_blank_lines=True) option can be used to achieve the same thing with the Python library function.

Support for schemas, for getting supported models to output JSON that matches a specified JSON schema. See also Structured data extraction from unstructured content using LLM schemas for background on this feature. #776

• New llm prompt --schema '{JSON schema goes here} option for specifying a schema that should be used for the output from the model. The schemas documentation has more details and a tutorial.
• Schemas can also be defined using a concise schema specification, for example llm prompt --schema 'name, bio, age int'. #790
• Schemas can also be specified by passing a filename and through several other methods. #780
• New llm schemas family of commands: llm schemas list, llm schemas show, and llm schemas dsl for debugging the new concise schema language. #781
• Schemas can now be saved to templates using llm --schema X --save template-name or through modifying the template YAML. #778
• The llm logs command now has new options for extracting data collected using schemas: --data, --data-key, --data-array, --data-ids. #782
• New llm logs --id-gt X and --id-gte X options. #801
• New llm models --schemas option for listing models that support schemas. #797
• model.prompt(..., schema={...}) parameter for specifying a schema from Python. This accepts either a dictionary JSON schema definition or a Pydantic BaseModel subclass, see schemas in the Python API docs.
• The default OpenAI plugin now enables schemas across all supported models. Run llm models --schemas for a list of these.
• The llm-anthropic and llm-gemini plugins have been upgraded to add schema support for those models. Here's documentation on how to add schema support to a model plugin.

Other smaller changes:

• GPT-4.5 preview is now a supported model: llm -m gpt-4.5 'a joke about a pelican and a wolf' #795
• The prompt string is now optional when calling model.prompt() from the Python API, so model.prompt(attachments=llm.Attachment(url=url))) now works. #784
• extra-openai-models.yaml now supports a reasoning: true option. Thanks, Kasper Primdal Lauritzen. #766
• LLM now depends on Pydantic v2 or higher. Pydantic v1 is no longer supported. #520

Alpha release adding support for schemas, for getting supported models to output JSON that matches a specified JSON schema. #776

• llm prompt --schema '{JSON schema goes here} option for specifying a schema that should be used for the output from the model, see schemas in the CLI docs.
• model.prompt(..., schema={...}) parameter for specifying a schema from Python. This accepts either a dictionary JSON schema definition of a Pydantic BaseModel subclass, see schemas in the Python API docs.
• The default OpenAI plugin now supports schemas across all models.
• Documentation on how to add schema support to a model plugin.
• LLM now depends on Pydantic v2 or higher. Pydantic v1 is no longer supported. #520

• New options for the shot-scraper har command: --wait, --wait-for and --javascript - similar to those same options on shot-scraper shot. #171
• Documentation now recommends using shot-scraper multi --har-zip for larger collections of files to avoid a Playwright crash if the .har JSON grows too large. #170