Chat with CourtListener Case Law via a CustomGPT

[rlfordon]

Headline

Chat with CourtListener's Case Law via a CustomGPT

What is the Feature?

A custom GPT inside OpenAI’s ChatGPT platform that allows users to input their CourtListener API key and interact with case law using natural language. Users could ask legal questions, get summaries of cases, retrieve relevant precedents, and explore judicial opinions conversationally—powered by CourtListener’s database and API.

What Problem Might it Solve?

Using CourtListener data with generative AI tools currently involves a lot of manual effort—copying and pasting text between websites, formatting case law for input, and working around token limits. This creates friction for users who want to explore legal content in a conversational way. A CustomGPT that connects directly to CourtListener via API would eliminate that hassle, letting users ask questions and get answers without leaving the chat. It simplifies access to case law for researchers, students, journalists, and others who may not have the time or skills to work with raw data or build custom tools.

Describe a Scenario in Which the Feature Might be Used

A law student studying First Amendment cases wants to better understand key precedents. Instead of running multiple searches and reading full-length opinions, they log into the CourtListener CustomGPT, enter their API key, and ask: “What are the main holdings in Supreme Court free speech cases from the last 10 years?” The GPT returns summaries with citations, links to opinions on CourtListener, and follow-up questions the student might consider. This saves time and deepens engagement with the material.

Technical Requirements

• How hard is it to make, subjectively? Medium
• Best guess, how long would it take to make, roughly? 3–4 weeks
• What would it require that we do technically?
  • Build a CustomGPT that accepts secure user input of an API key (or perhaps requires the user to log in to the CourtListener account)
  • Use the API key to make authenticated calls to CourtListener’s semantic search API (once available)
  • Write functions (via OpenAI’s function calling tools) to handle search queries, case lookups, opinion retrieval, and summarization
  • Implement guardrails to ensure ethical use and prevent misuse of legal data
  • Test for reliability, edge cases, and proper citation formats

Existing Systems or Alternatives?

There are a few CustomGPTs that connect to Caselaw Access Project (so they only go through 2020 or so). For example US Case Law Research Assistant. There are also a few CustomGPTs (for example, Better Call GPT) that search case law via the web. Finally, midpage has a CustomGPT that allows the user to interact with its databases through chat (it requires a midpage account).

There are few if any publicly available systems offering real-time conversational access to CourtListener case law. Some law firms and legal tech companies have proprietary AI search tools, but these are not open or user-configurable like a CustomGPT. This would stand out by being low-barrier, customizable, and integrated directly into an already-popular AI interface.

Any Additional Information?

• This could support legal clinics, pro se litigants, journalists, and educators.
• It could integrate with other FLP tools, like citation extraction or alert subscriptions.
• This could be a fun launch alongside or semantic search API, as an immediate way to show its power. (The downside being if we didn't get it right, it could make people not trust the API, I guess).

[mlissner]

Interesting. A few scattered thoughts:

1. Is it the right design to build into OpenAI's platform as opposed to our own?

2. Compared to building our own RAG system, how does this differ? Is it just easier to build?

3. How do you promote it if it's living on somebody else's platform?

[rlfordon]

Is it the right design to build into OpenAI's platform as opposed to our own?

I would see it as maybe an intermediary step. I suspect building our own chatbot could take a while? This could also let users try it out and tell us what kind of features they want (or let us try out different features) without having to invest a bunch of time upfront.

Compared to building our own RAG system, how does this differ? Is it just easier to build?

much easier, much cheaper. It might only be a day or two developer time, no changes to website, and wouldn't require us to call OpenAI ourselves, so no token cost.

How do you promote it if it's living on somebody else's platform?

Link to it from CL, and people may find it through discovery on the ChatGPT's customGPT page. They would need an API key, so maybe it would even bring new users to CL (though requirement to get an API key is also a barrier). Also, results would link to CL so would bring people to CL that way.

So overall yeah not as good as building our own, but perhaps still useful if building our own is far on the future?