Newborn 
PY FOREST is a research agent for long-horizon science, where evidence gets lost, errors compound, and goals drift. It explores ideas, reads literature, forms hypotheses, runs real experiments, analyzes results, revises paths, and builds a full paper—all in an editable graph you can inspect, branch, redirect, with claims and figures linked to evidence
// readme
Give it a research question. Turn it into ideas, real experiments, evidence, and a complete paper.
FOREST is an end-to-end research agent framework for carrying a scientific project from an early question to a finished manuscript.
It can explore and compare research directions, search the literature, turn promising ideas into testable hypotheses, design experiments, run real code on your compute, analyze the results, and decide what to try next. As the project develops, FOREST can revise its plan, abandon weak directions, branch into alternatives, and build the figures, tables, and manuscript around the evidence that survives.
Instead of keeping the research process inside a long chat history, FOREST represents it as an editable research graph. Ideas, hypotheses, experiments, results, decisions, and paper sections remain connected. You can inspect the graph, change a plan, replace a hypothesis, branch from an earlier point, or redirect the research without starting over.
**Idea discovery → literature review → hypothesis formation → experiment design → real execution → result analysis → research-path revision → baselines & ablations → figures & tables → full-paper…
How much a project earns versus how much attention it actually gets. Above 50 means the work is outrunning its audience. Recomputed nightly from commit velocity, contributor effort, issue resolution, fork utility, release cadence, and project maturity — divided by a logarithmic reach factor.
score = signal / reach
signal = 0.25·commit_velocity // commits in last 90 days (cap 30)
+ 0.20·contributor_work // unique authors × velocity (cap 100)
+ 0.20·issue_resolution // closed ÷ total issues
+ 0.20·fork_ratio // forks ÷ stars (proxy for real usage)
+ 0.10·release_cadence // releases in 90 days (cap 3)
+ age_bonus // +0 to +0.30 after 6 months
+ homepage_bonus // +0.05 if homepage is set
reach = log₁₀(stars + watchers + 10)█ Is the project alive and maintained right now? A 0–100 pulse recomputed nightly from commit recency, rhythm, how fast issues close, and how quickly PRs get merged.
health = 0.35·recency // days since last commit (90d decay)
+ 0.25·cadence // commit rhythm consistency
+ 0.20·issue_health // closed ÷ total issues
+ 0.20·pr_health // merged ÷ total PRs█ The colour and label on every card come straight from the health score.
Healthy 80 – 100 active, responsive, regular releases
Stable 60 – 79 maintained, steady, no alarms
Quiet 40 – 59 slowing down — watch this one
At Risk 0 – 39 going dark · candidate for rescue█ Tags are independent behavioral signals computed nightly. A project can hold multiple at once. They drive the home page sections.
solo_builder one person holds > 80% of commits (last 180d)
needs_contributors has open "help wanted" or "good first issue" labels
hidden_gem < 100 stars · active in last 3 months · documented
legacy_hero repo > 5 years old · committed this year
fork_magnet forks/stars > 0.5 · used as template or dependency
release_machine 5+ releases in the last 90 days
under_pressure > 10 open issues · ≤ 2 contributors · health ≥ 60
community_watch watchers > stars · devs tracking before the public
community_hub GitHub Discussions enabled · > 20 discussions
funded maintainer has active funding channel█ Stars are an outcome, not effort. A project with 8 stars and daily commits is doing more interesting work than one coasting on 8k. We measure the building, then divide by the attention already received — so the genuinely undervalued rise to the top.
// stars = lagging indicator
// commits = leading indicator
// we rank by the leading one█