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With OpenAI joining Anthropic in supporting the Model Context Protocol (MCP), we're witnessing a unified standard for language models to interact with external systems. This creates exciting opportunities for multi-LLM architectures where specialized AI applications work in parallel—discovering tools, handing off tasks, and accessing powerful capabilities through standardized interfaces.
We're excited to announce that Instructor now implements the llms.txt specification! You can now find our llms.txt file at python.useinstructor.com/llms.txt. This adoption marks an important step in making our documentation more accessible to AI language models.
AI-assisted coding is changing how we use version control. Many developers now use what I call "vibe coding" — coding with AI help. This creates new challenges with Git. Today I'll share how we're using Cursor rules in Instructor to solve these problems.
We recently migrated to uv from poetry because we wanted to benefit from it's many features such as
caching functionality provided by the Astral teamWe took around 1-2 days to handle the migration and we're happy with the results. On average, for CI/CD, we've seen a huge speed up for our jobs.
Here are some timings for jobs that I took from our CI/CD runs.
In general I'd say that we saw a ~3x speedup with approximately 67% reduction in time needed for the jobs once we implemented caching for the individual uv github actions.
Multimodal Language Models like gpt-4o excel at processing multimodal, enabling us to extract rich, structured metadata from images.
This is particularly valuable in areas like fashion where we can use these capabilities to understand user style preferences from images and even videos. In this post, we'll see how to use instructor to map images to a given product taxonomy so we can recommend similar products for users.
Language Models struggle to generate consistent graphs that have a large number of nodes. Often times, this is because the graph itself is too large for the model to handle. This causes the model to generate inconsistent graphs that have invalid and disconnected nodes among other issues.
In this article, we'll look at how we can get around this limitation by using a two-phase approach to generate complex DAGs with gpt-4o by looking at a simple example of generating a Choose Your Own Adventure story.
Language Models struggle to generate consistent graphs that have a large number of nodes. Often times, this is because the graph itself is too large for the model to handle. This causes the model to generate inconsistent graphs that have invalid and disconnected nodes among other issues.
In this article, we'll look at how we can get around this limitation by using a two-phase approach to generate complex DAGs with gpt-4o by looking at a simple example of generating a Choose Your Own Adventure story.
Messy data exports are a common problem. Whether it's multiple headers in the table, implicit relationships that make analysis a pain or even just merged cells, using instructor with structured outputs makes it easy to convert messy tables into tidy data, even if all you have is just an image of the table as we'll see below.
Let's look at the following table as an example. It makes analysis unnecessarily difficult because it hides data relationships through empty cells and implicit repetition. If we were using it for data analysis, cleaning it manually would be a huge nightmare.
We're excited to announce that instructor now supports Writer's enterprise-grade LLMs, including their latest Palmyra X 004 model. This integration enables structured outputs and enterprise AI workflows with Writer's powerful language models.
First, make sure that you've signed up for an account on Writer and obtained an API key using this quickstart guide. Once you've done so, install instructor with Writer support by running pip install instructor[writer] in your terminal.
Make sure to set the WRITER_API_KEY environment variable with your Writer API key or pass it as an argument to the Writer constructor.
In this post, we'll explore how to use Google's Gemini model with Instructor to generate accurate citations from PDFs. This approach ensures that answers are grounded in the actual content of the PDF, reducing the risk of hallucinations.
We'll be using the Nvidia 10k report for this example which you can download at this link.