prompt-build-tool 0.1.8

A dbt-inspired prompt orchestration tool for LLMs

pbt — prompt-build-tool

A dbt-inspired prompt orchestration tool for LLMs.

Write modular prompts in Jinja2, reference the output of other prompts with ref(), and let pbt resolve dependencies.

---

Quick start

1. Install

pip install prompt-build-tool

# Also install the SDK for your LLM provider:
# pip install google-genai      # Gemini
# pip install openai            # OpenAI
# pip install anthropic         # Anthropic

2. Generate example

pbt init --provider anthropic
# pbt init --provider openai
# pbt init --provider gemini

3. Set your API key

export ANTHROPIC_API_KEY=your_key_here
# export OPENAI_API_KEY=your_key_here
# export GEMINI_API_KEY=your_key_here

4. Run

pbt run

5. Extend prompt models

In the models/ directory write .prompt files:

models/
  topic.prompt
  outline.prompt
  article.prompt

Use ref('model_name') to inject the output of another model:

{# models/outline.prompt #}
Based on this topic, create a detailed outline:

{{ ref('topic') }}

All standard Jinja2 syntax works too:

{# models/comparison.prompt #}
{% set languages = ['Python', 'Go', 'Rust'] %}
Compare these languages for building CLI tools:
{% for lang in languages %}
- {{ lang }}
{% endfor %}

Context from previous analysis:
{{ ref('initial_analysis') }}

---

Concepts (if you are familiar with data build tool)

pbt concept	dbt analogy
.prompt file	.sql model file
ref('model')	{{ ref('model') }}
models/ directory	models/ directory
SQLite runs table	dbt run_results.json
SQLite model_results table	dbt model timing artifacts

---

Commands

pbt run

Execute all prompt models in dependency order.

pbt run

pbt ls

List discovered models and their dependency graph.

pbt ls

pbt test

Run tests/*.prompt files against the latest run's outputs. Each test passes when the LLM returns {"results": "pass"}.

pbt test

pbt serve

Start the pbt HTTP server and open the docs page in the browser.

pbt serve
# pbt serve --host 0.0.0.0 --port 8000

pbt docs

Generate a self-contained HTML report of all previous runs with expandable model details and a DAG diagram.

pbt docs                        # writes to .pbt/docs/index.html
pbt docs --open                 # also opens in the browser
pbt docs --output my/report.html

---

Python API

pbt can be used directly from Python without the CLI:

import pbt

results = pbt.run("path/to/models")

for r in results:
    print(r.model_name, r.status, r.llm_output)

pbt.run()

pbt.run(
    models_dir="models",       # path to *.prompt files
    select=["article"],        # optional: run only these models
    llm_call=my_llm_fn,        # optional: custom LLM backend
    rag_call=my_rag_fn,        # optional: custom RAG function
    promptdata={"tone": "formal"},   # optional: variables injected via promptdata()
    validation_dir="validation", # optional: per-model validation functions
)

Parameter	Type	Description
models_dir	str	Directory containing *.prompt files
select	list[str] | None	Run only these models (upstream outputs loaded from DB)
llm_call	(prompt: str) -> str | None	Override LLM backend. Falls back to client.py (next to models/)
rag_call	(*args) -> list | str | None	Override RAG function. Falls back to rag.py (next to models/) do_RAG
promptdata	dict | None	Variables injected into every template, accessed via {{ promptdata('key') }}
promptfiles	dict | None	File paths by name, provided to models that declare promptfiles: in their config block
validation_dir	str	Directory with per-model validate(prompt, result) -> bool files

Returns a list of ModelRunResult objects with fields: model_name, status, prompt_rendered, llm_output, error, execution_ms, cached.

---

Passing variables to templates (promptdata())

Inject runtime variables into templates using the promptdata("name") function — similar to how dbt's source() and ref() work.

pbt run --promptdata tone=formal --promptdata audience=engineers

pbt.run("models", promptdata={"tone": "formal", "audience": "engineers"})

Access them in any .prompt file:

Write an article in a {{ promptdata("tone") }} tone for {{ promptdata("audience") }}.

{% if promptdata("topic") %}
Topic: {{ promptdata("topic") }}
{% else %}
Choose a fascinating topic of your choice.
{% endif %}

promptdata("name") returns None if the variable was not provided, so {% if promptdata("x") %} is always safe.

---

Customising the LLM backend (client.py)

pbt is unopinionated about which LLM you use. Create client.py at the project root (alongside your models/ directory) and define an llm_call function — usually 5 lines:

# client.py (Anthropic example)
import anthropic

def llm_call(prompt: str) -> str:
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}],
    )
    return message.content[0].text

pbt will automatically discover and use this file. Run pbt init --provider <anthropic|openai|gemini> to scaffold a starter client.py for your chosen provider. If the file exists but does not define llm_call, pbt raises an error at startup.

---

RAG inside prompts (rag.py)

pbt has very little to say about RAG and leaves that up to you - you do this through the return_list_RAG_results(*args) function pbt give you access to in the .prompt template. pbt will pass this call to the do_RAG function you define in rag.py (at the project root, alongside your models/ directory):

# rag.py
def do_RAG(*args) -> list[str] | str:
    query = args[0]
    # your vector search, keyword lookup, etc.
    return ["Relevant document 1", "Relevant document 2"]

do_RAG receives whatever arguments you pass to return_list_RAG_results in the template. It can return a list[str] or a bare str (wrapped automatically). Return False or None to signal no results.

Use it in any .prompt file:

{% set hits = return_list_RAG_results(ref('topic')) %}
{% if hits[0] %}
A related article in our library: "{{ hits[0] }}"

Write a paragraph explaining how the topic below connects to it:
{{ ref('topic') }}
{% else %}
Write a paragraph introducing this topic as a fresh subject:
{{ ref('topic') }}
{% endif %}

If rag.py is absent and a template calls return_list_RAG_results, pbt raises a clear error at render time.

---

Passing files to models (promptfiles)

Models can receive files (PDFs, images, etc.) alongside the text prompt. Declare the files a model needs via config(), then provide the actual paths at runtime.

1. Declare in config:

{{ config(promptfiles="my_document") }}
Summarise the attached document in 3 bullet points.

Multiple files are comma-separated:

{{ config(promptfiles="report,chart_image") }}

2. Provide file paths at runtime:

pbt run --promptfile my_document=report.pdf
pbt run --promptfile report=annual.pdf --promptfile chart_image=q4.png

pbt.run("models", promptfiles={"my_document": "report.pdf"})
pbt.run("models", promptfiles={"report": "annual.pdf", "chart_image": "q4.png"})

3. Custom llm_call with file and config support:

Accept optional files and/or config parameters in your client.py — pbt passes them if the signature declares them:

# client.py
def llm_call(prompt: str, files: list[str] | None = None, config: dict | None = None) -> str:
    # files  — resolved file paths declared via config(promptfiles=...)
    # config — the full config dict for this model, e.g. {"output_format": "json"}
    ...

Both parameters are optional and independent — declare either, both, or neither.

---

Output format config (config())

Call config() at the top of a .prompt file to declare the expected output format:

{{ config(output_format="json") }}
Return a JSON object with keys "title" and "summary".

When output_format: json is set, pbt validates the LLM output as JSON (stripping optional ```json ``` fences) and passes the parsed dict/list to downstream models via ref(), for example enabling {{ ref('model').title }} access.

---

Validation (validation/)

Create a validation/ directory with Python files matching model names. Each file must define validate(prompt, result) -> bool. If it returns False, the model is marked as an error and stops it use in downstream models.

# validation/article.py
import json
from pydantic import BaseModel, ValidationError


class Article(BaseModel):
    content: str
    author: str
    audience: str


def validate(prompt: str, result: str) -> bool:
    """Article output must be valid JSON matching the Article model."""
    try:
        data = json.loads(result)
        article = Article(**data)
    except (json.JSONDecodeError, ValidationError):
        return False
    return len(article.content) >= 200

Run with pbt run — validation fires automatically after each model's LLM call.

Typed hints in validation

Use Pydantic models to define the expected shape of your model's JSON output:

# validation/summaries.py
import json
from pydantic import BaseModel, ValidationError


class SummaryItem(BaseModel):
    title: str
    summary: str
    key_points: list[str]


class Summaries(BaseModel):
    summaries: list[SummaryItem]


def validate(prompt: str, result: str) -> bool:
    """Summaries output must be valid JSON matching the Summaries model."""
    try:
        data = json.loads(result)
        summaries = Summaries(**data)
    except (json.JSONDecodeError, ValidationError):
        return False
    return len(summaries.summaries) >= 1 and len(summaries.summaries[0].key_points) >= 1

Run pbt type-hints to generate jinja-lsp context stubs from your validation classes, enabling autocomplete for ref() inside .prompt templates in VS Code.

---

HTTP server (utils/server)

Deploy over to run and return LLM response to .prompt pipeline over HTTP. Runs a lightweight FastAPI server and manages pipeline execution and return (requires pip install fastapi uvicorn):

python -m utils.server --models-dir models --port 8000

POST /run   body: {"promptdata": {"tone": "formal"}, "select": ["article"]}
            returns: {"outputs": {"topic": "...", "article": "..."}}

GET  /health

Or use the factory in Python:

from utils.server import create_app
import uvicorn

app = create_app(models_dir="models")
uvicorn.run(app, host="0.0.0.0", port=8000)

---

How to dynamically skip a model

Use {{ skip_and_set_to_value("value") }} to skip the LLM call during Jinja rendering and provide the output directly:

{% if "no action needed" in ref('previous_model') %}
{{ skip_and_set_to_value("No action needed.") }}
{% else %}
Summarise the following: {{ ref('previous_model') }}
{% endif %}

The model is recorded as a successful run, downstream templates can detect it with was_skipped('model_name'), and downstream ref() calls receive the value you provided.