Open-source · Python · Apache 2.0
AI agents that
earn trust before they get it.
organism-core researches its own success criteria before every action, validates them after, and grants autonomy based on a measurable track record.
Pre-action DoD research
Before any tool fires, the framework derives a Definition of Done from six prioritized sources with separate provenance tracking — not a vague prompt, but auditable criteria.
Cross-domain genericity
CI tests enforce identical pipeline structure across three demo domains. What works for code review works for email triage works for research — without per-domain glue.
Score-driven autonomy
Tools start supervised and earn higher autonomy stages — automatically — from a rolling window of validation scores. Quality regressions revoke autonomy the same way.
Five lines, a real quality gate.
Decorate a function, ship it, and let the framework prove it earns autonomy.
from organism_core import Tool, run
@Tool.register("draft_email", autonomy="staged")
def draft_email(to: str, topic: str) -> str:
return llm.generate(f"Email to {to} about {topic}")
# Before execution, organism-core derives a Definition of Done
# from six sources (entity profile, lessons, related entities,
# vector search, domain patterns, user input).
# After execution, it scores the result against that DoD.
# Tools earn autonomy stages from a rolling track record.
result = run("draft_email", to="alice@example.com", topic="Q3 review")
print(result.score, result.stage, result.autonomy_next)Different from LangGraph & CrewAI.
| organism-core | LangGraph | CrewAI | |
|---|---|---|---|
| Pre-action DoD research | ✓ | — | — |
| Post-action validation against derived criteria | ✓ | manual | manual |
| Auto-earned autonomy stages | ✓ | — | — |
| Cross-domain genericity enforced in CI | ✓ | — | — |
| Open-source, self-hostable | ✓ | ✓ | ✓ |
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