freshcrate
Skin:/
Home > Testing > autonomous-agentic-research-swarm

autonomous-agentic-research-swarm

File-based autonomous agentic research swarm template (Planner/Worker/Judge) with contracts, workstreams, and deterministic quality gates.

Why this rank:Recent releaseHealthy release cadenceStrong adoption

Description

File-based autonomous agentic research swarm template (Planner/Worker/Judge) with contracts, workstreams, and deterministic quality gates.

README

Autonomous Agentic Research Swarm

This repository is a repo-native research operating system for the current L2-to-L1 rent analysis project. v1 is designed to carry one real project from definition lock to a reproducible working-paper release.

Locked empirical artifact DAG

registry -> raw snapshots/manifests -> processed datasets/manifests -> validation -> figures/tables -> Quarto paper -> release manifest

The current project is not releaseable until that full path exists.

Default execution paths

  • Local swarm (scripts/swarm.py + .orchestrator/) is the default engine for routine repo task execution, deterministic gates, and normal multi-agent delivery.
  • Reviewed staged-workflow-runner path is for high-stakes Operator-owned synthesis work such as architecture rewrites, major replans, and release assessments.
  • The paper substrate is Quarto-backed Markdown under reports/paper/.

Four-role operating model

  • Operator — runtime preflight, worktree/tmux supervision, repair handling, sweeps, run/review/release logging, catalog refresh, and release assembly.
  • Planner — task decomposition, dependency wiring, workstream ownership, and lifecycle projection.
  • Worker — one assigned task, one isolated worktree, one explicit output contract.
  • Judge — reruns gates, verifies outputs and provenance, and is the only role allowed to mark work done.

Current battle-test queue

  1. T025 — populate registry/rollup_registry_v1.csv with evidence-backed in-scope rows.
  2. T030 — pull growthepie snapshots, write raw manifests, normalize the vendor panel, and commit a tiny deterministic sample.
  3. T035 — build the authoritative on-chain L1 rent path, write processed manifests, and materialize the canonical daily_rollup_panel.
  4. T040 — lock STR math in src/analysis/metrics_str.py with sample-only tests.
  5. T050 — validate the canonical panel, L1 rent decomposition, and cross-source reconciliation.
  6. T060 — generate release figures and tables from validated artifacts only.
  7. T070 — write Quarto manuscript source and confirm a draft render path.
  8. T080 — Operator release assembly: compile reports/catalog.yaml, render final paper artifacts, and write the release manifest.

What counts as a release candidate

A release candidate must include all of the following:

  • an evidence-backed registry/rollup_registry_v1.csv
  • raw manifests for growthepie and the on-chain L1 rent pull
  • processed manifests for the vendor panel, L1 rent decomposition, and canonical rollup panel
  • validation JSON/Markdown outputs
  • release figures and tables
  • Quarto paper source plus rendered HTML/PDF and render_manifest.json
  • reports/catalog.yaml compiled from successful run manifests
  • reports/status/releases/release_<YYYY-MM-DD>.json

A sample figure alone is not battle-test success.

Repository map

  • .orchestrator/ — file-based control plane, task queue, templates, and handoffs
  • contracts/ — project instance contract, framework policy, empirical definitions, and hybrid/modeling interfaces
  • docs/ — protocol lock, runbooks, and role prompts
  • registry/ — versioned rollup universe contract
  • data/raw_manifest/ and data/processed_manifest/ — tracked provenance for raw and processed artifacts
  • src/etl/, src/validation/, src/analysis/, src/model/ — code split by responsibility
  • reports/ — validation outputs, figures, tables, Quarto paper source/build, catalog, and release manifests
  • tests/ — fast offline tests on tracked samples

Quickstart

  1. Read AGENTS.md.
  2. Review docs/protocol.md, contracts/project.yaml, and contracts/framework.json.
  3. Inspect .orchestrator/workstreams.md and the live backlog under .orchestrator/backlog/.
  4. Run make gate and make test on the base branch.
  5. Use docs/runbook_swarm.md for manual execution or docs/runbook_swarm_automation.md for the default local swarm path.
  6. When upstream tasks are done, run the Operator release path with python scripts/release_assembly.py --release-date YYYY-MM-DD --check.

Mode coverage

  • Empirical is the active mode for this repo and the only mode that may be claimed as end-to-end ready after the battle-test release succeeds.
  • Modeling remains contract-ready through contracts/model_spec.md, contracts/instances/, and contracts/experiments/, but it is not yet battle-tested here.
  • Hybrid remains contract-ready through contracts/hybrid_interface_v1.yaml; modeling tasks may consume only explicit instance manifests, not ad hoc empirical CSV paths.

Release History

VersionChangesUrgencyDate
main@2026-07-11Latest activity on main branchMedium7/11/2026
v0.1.0Latest release: v0.1.0High4/11/2026

Dependencies & License Audit

Loading dependencies...

Similar Packages

octobenchBenchmark and compare LLM tool, configuration, and prompt setups using a shared case framework with automated scoring and telemetry.main@2026-07-19
multi-agent-ralph-loopAutonomous orchestration framework for Claude Code with MemPalace-inspired memory (4-layer stack, 818-token wake-up), parallel-first Agent Teams (6 teammates), Aristotle First Principles methodology, main@2026-07-26
meerkatMeerkat - A modular, high-performance agent harness built in Rust.v0.8.7
taktTAKT: TAKT Agent Koordination Topology - AI Agent Workflow Orchestrationmain@2026-07-24
automagik-genieSelf-evolving AI agent orchestration framework with Model Context Protocol supportv5.260720.10

More in Testing

multi-agent-ralph-loopAutonomous orchestration framework for Claude Code with MemPalace-inspired memory (4-layer stack, 818-token wake-up), parallel-first Agent Teams (6 teammates), Aristotle First Principles methodology,
trulensEvaluation and Tracking for LLM Experiments and AI Agents
ObservalObserval is an AI agent registry with first in class observabilty and eval framework
pilot#1 Terminal Benchmark 2.0 — AI that ships your tickets.