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instructor
Python2023–2026
567-labs / instructor

instructor

structured outputs for llms

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
22Distinct people
25Mentions
0Explicit recommenders
14.1KGitHub stars
REPOSITORY AT A GLANCE

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What it is

structured outputs for llms

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year25 dated mentions

Observed mentions: 2023-06-15 00:32:08 UTC to 2026-05-19 21:12:43 UTC.

0510152023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 7 mentions; 2 submissions, 5 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 14 mentions; 4 submissions, 10 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC725
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC14410
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC110
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC303

What Hacker News says

25 sources
resiros·Jun 18, 2024
If you look into the instructor code(https://github.com/jxnl/instructor/blob/06a49e7824729b8df1f7...). Here is the core code snippet they use: message = dedent( f""" As a genius expert, your task is to understand the content and provide the parsed objects in json that match the following json_schema:\n {json.dumps(response_model.model_json_schema(), indent=2)} Make sure to return an instance of the JSON, not the schema itself """ ) Then depending on the mode, either they add another message `Return the correct JSON response within a ```json codeblock. not the JSON_SCHEMA` or they set the response format to json.
In Every Way to Get Structured Output from LLMs
resiros·Jun 18, 2024
I expected to read about the methods used by the libraries to get the structured output and not a comparison of the language compatibility for each. Fortunately the same author have a blog post (https://www.boundaryml.com/blog/type-definition-prompting-ba...) explaining how their approach works and how it compares to instructor (https://github.com/jxnl/instructor). Basically these libraries provide two things: 1. A way to prompt the LLM 2. A way to get a valid JSON For 1. instructor does it through the json schema definition, BAML's innovation is that they use a simplified lossless schema definition that uses less tokens. For 2. instructor does it through reprompting until they receive a valid JSON. BAML's innovation is a fuzzy parser able to to parse non-perfect JSON. Personally I think that there is no need to all these abstractions to
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get structured outputs from LLMs. A simple .to_prompt() function that takes a pydantic and translate it into some prompt block you can add to your prompt and a retry is sufficient to get the same results.
In Every Way to Get Structured Output from LLMs
hamelsmu·Jun 2, 2024
Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work. However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article): 1. https://www.honeycomb.io/blog/introducing-query-assistant 2. https://www.youtube.com/watch?v=B_DMMlDuJB0 Other co-authors have worked on significant bodies of work: Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients) Eugene Yan works wit
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h LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon) I believe you might find these worth looking at.
In What we've learned from a year of building with LLMs
mloncode·Jun 2, 2024
Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work. However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article): 1. https://www.honeycomb.io/blog/introducing-query-assistant 2. https://www.youtube.com/watch?v=B_DMMlDuJB0 Other co-authors have worked on significant bodies of work: Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients) Eugene Yan works wit
Read the rest
h LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon) I believe you might find these worth looking at.
In What we've learned from a year of building with LLMs
h0rv·May 20, 2024
instructor-go is a port of the popular Python package https://github.com/jxnl/instructor. This implementation uses `jsonschema` and Go struct tags to send data and schema information to the model to return the appropriate response schema. Currently, OpenAI and Anthropic are supported, and you can see all examples of different capabilities here: https://github.com/instructor-ai/instructor-go/tree/main/exa.... This is in early development and would love some feedback. Thanks for checking it out!
In instructor-go – Structured LLM Outputs in Go
hunterbrooks·May 9, 2024Submission

Show HN: Ellipsis – Automated PR reviews and bug fixes

Hi HN, hunterbrooks and nbrad here from Ellipsis (https://www.ellipsis.dev). Ellipsis automatically reviews your PRs when opened and on each new commit. If you tag @ellipsis-dev in a comment, it can make changes to the PR (via direct commit or side PR) and answer questions, just like a human. Demo video: https://www.youtube.com/watch?v=X61NGZpaNQA So far, we have dozens of open source projects and companies using Ellipsis. We seem to have landed in a kind of sweet spot where there’s a good match between the current capabilities of AI tools and the actual needs of software engineers - this doesn’t replace human review, but it saves you time by catching/fixing lots of small silly stuff. Here’s an example in the wild: https://github.com/relari-ai/continuous-eval/pull/38. Ellipsis (1) adds a PR summary; (2) finds a bug and adds a review comme
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nt; (3) after a (human) user comments, generates a side PR with the fix; and (4) after a (human) user merges the side PR and adds another commit, re-reviews the PR and approves it Here’s another example: https://github.com/SciPhi-AI/R2R/pull/350#pullrequestreview-..., where Ellipsis adds several comments with inline suggestions that were directly merged by the developer. You can configure Ellipsis in natural language to enforce custom rules, style guides, or conventions. For example, here’s how the `jxnl/instructor` repo uses natural language rules to make sure that docs are kept in sync: https://github.com/jxnl/instructor/blob/main/ellipsis.yaml#L..., and here’s an example PR that Ellipsis came up with based on those rules: https://github.com/jxnl/instructor/pull/346. Installing into your repo takes 2 clicks at https://www.ellipsis.dev. You do have to sign up to try it out because we need you to authorize our GitHub app to read your code. Don’t worry, your code is never stored or used to train models (https://docs.ellipsis.dev/security). We’d really appreciate your feedback, thoughts, and ideas!
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