A Prolog library for interfacing with LLMs

A new SWI-Prolog library called pllm allows developers to interface with large language models (LLMs) via OpenAI-compatible chat/completions endpoints. The library provides a simple predicate llm/2 that posts prompts and returns model responses, supporting services like OpenAI and local Ollama instances. It also offers a reverse-prompt feature that generates a prompt from a desired response.

Use LLMs inside Prolog pllm is a minimal SWI-Prolog helper that exposes llm/2 . The predicate posts a prompt to an HTTP LLM endpoint and unifies the model's response text with the second argument. The library currently supports any OpenAI-compatible chat/completions endpoint. ?- pack install pllm . Some services require an API key for authentication. Set the LLM API KEY environment variable to your API key. You can do the following in your shell before starting SWI-Prolog: echo LLM API KEY="sk-..." .env set -a && source .env && set +a Configure the endpoint and default model before calling llm/2 or llm/3 : ?- config "https://api.openai.com/v1/chat/completions", "gpt-4o-mini" . You can override the configured model per call with llm/3 options. Fill in .env with your settings set -a && souce .env && set +a swipl ?- prolog/llm . ?- llm "Say hello in French.", Output . Output = "Bonjour ". ?- llm "Say hello in French.", Output, model "gpt-4o-mini" , timeout 30 . Output = "Bonjour ". ?- llm Prompt, "Dog" . Prompt = "What animal is man's best friend?", ... This library expects an OpenAI-compatible chat/completions endpoint. Below are common providers and endpoints you can try. OpenAI - Endpoint: https://api.openai.com/v1/chat/completions - Example: ?- config "https://api.openai.com/v1/chat/completions", "gpt-4o-mini" . Ollama local - Endpoint: http://localhost:11434/v1/chat/completions - Example: ?- config "http://localhost:11434/v1/chat/completions", "llama3.1" . If you call llm/2 with an unbound first argument and a concrete response, the library first asks the LLM to suggest a prompt that would ideally produce that response, binds it to your variable, and then sends a second request that wraps the suggested prompt in a hard constraint "answer only with ..." . This costs two API calls and is still best-effort; the model may ignore the constraint, in which case the predicate simply fails.