distribird 0.1.1

Literature-informed Prior distributions for Bayesian model calibration

Automated Bayesian prior construction from scientific literature

---

Distribird turns a parameter name and description into a fully cited, publication-ready prior distribution. It searches Semantic Scholar, OpenAlex, and LLM deep-research agents in parallel, extracts numerical values from papers, and fits the best-matching scipy.stats distribution via AIC selection.

"I need a prior for maximum leaf area index of maize."

➜ truncated_normal(mu=5.2, sigma=1.5, a=0, b=12) — fitted from 6 peer-reviewed sources with full citations.

Why Distribird?

Bayesian calibration requires informative priors, but building them from literature is tedious. Researchers default to flat priors, losing valuable domain knowledge. Distribird closes that gap: describe your parameter, get a defensible prior in seconds.

Architecture

                        ┌──────────────────────────────┐
                        │        LangGraph DAG         │
                        └──────────────────────────────┘

START ─► Enrich ─► QueryGen ─► Search ─► RelevanceJudge ──┬► CrossEnrich ───┐
                                 ▲                        │                 │
                                 │                        └► FetchFulltext ◄┘
                          RefineSearch                            │
                                 ▲                            Extract
                                 │                                │
                                 │   RefineExtraction ◄─── QualityGate
                                 │         │                │     │
                                 │         └────────────────┘     │
                                 │                                ▼
                                 └─────────────────────────  Synthesize ─► END

Multi-agent search — Semantic Scholar, OpenAlex, and LLM deep-research agents run concurrently; a moderator LLM selects the best papers via deliberation.

Relevance scoring — An LLM-based relevance judge scores each paper before extraction. When multiple high-relevance papers are found, the pipeline routes through cross-enrichment (citation snowballing + follow-up queries) to discover additional sources.

Feedback loops — A quality gate inspects extraction results and can trigger search refinement (new queries) or extraction refinement (web-assisted re-extraction) before falling through to synthesis.

Budget-bounded — IterationBudget caps every loop to guarantee termination.

Live progress — The pipeline streams node-by-node updates to the UI, showing which step is running, paper/value counts, and per-parameter progress bars.

Quickstart

Install

pip install distribird

Development install

git clone https://github.com/HUN-REN-AI1Science/Distribird.git
cd distribird
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest                     # 148 tests, all passing

Configure

Distribird reads configuration from environment variables (prefix DISTRIBIRD_) or a .env file in the project root.

# .env (or export these in your shell)
DISTRIBIRD_LLM_BASE_URL="http://localhost:4000"   # any OpenAI-compatible endpoint
DISTRIBIRD_LLM_API_KEY="your-key"
DISTRIBIRD_LLM_MODEL="gpt-4o"
DISTRIBIRD_SEMANTIC_SCHOLAR_API_KEY=""            # optional, increases rate limits

Sidebar behaviour in the Streamlit UI:

• Settings provided in .env are used automatically — no manual input needed.
• Settings not provided in .env appear as required fields in the sidebar; the user must fill them in before generation can start.
• An "Override configured settings" toggle lets users temporarily replace .env values without editing the file.
• Literature source toggles (Semantic Scholar, OpenAlex, LLM Web Search, LLM Deep Research) are always visible and control which connection fields are required.

Use

Python

import asyncio
from distribird.agent.pipeline import run_parameter
from distribird.models import ParameterInput, ConstraintSpec

result = asyncio.run(run_parameter(
    ParameterInput(
        name="max_lai",
        description="Maximum leaf area index of maize",
        unit="m2/m2",
        domain_context="Biome-BGCMuSo maize crop modeling",
        constraints=ConstraintSpec(lower_bound=0, upper_bound=12),
    )
))

print(result.prior.display_name())   # truncated_normal(mu=5.2, sigma=1.5, a=0, b=12)
print(result.prior.n_sources)        # 6
print(result.prior.confidence.value) # high

REST API

distribird-api                          # starts on :8000

curl -u demo:distribird2026 -X POST http://localhost:8000/api/v1/parameter \
  -H "Content-Type: application/json" \
  -d '{"name":"max_lai","description":"Maximum leaf area index of maize","unit":"m2/m2"}'

Streamlit UI

Try the hosted version at distribird.streamlit.app, or run locally:

streamlit run src/distribird/ui/app.py

Prior Fitting Strategy

Evidence	Method	Confidence
5+ values	AIC across Normal, Truncated Normal, Gamma, Log-Normal, Beta	High
2 – 4 values	Moment matching with widened σ	Medium
1 value	Wide Normal centered on value	Low
0 values	Jeffreys / wide uninformative prior	None

All fitted distributions respect user-specified physical constraints (bounds).

Export Formats

from distribird.export.json_export import export_json
from distribird.export.r_export import export_r
from distribird.export.python_export import export_python

Format	Output
JSON	Parameter name, family, params, citations, confidence
R	Executable R script with distribution calls
Python	scipy.stats code ready for MCMC samplers

Demo

A complete worked example using five Biome-BGCMuSo maize parameters:

python examples/maize_bgcmuso/demo.py

Testing

pytest                 # 148 tests
ruff check src/ tests/ # lint
mypy src/distribird/      # type checking (strict)