# GradientHQ/symphony-coord

Repository: https://github.com/GradientHQ/symphony-coord
Canonical: https://ross.abutalabs.com/products/symphony-coord
Language: Python
License: MIT
License Family: permissive
Last push: 2026-06-12T04:11:50+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 87, release rhythm 35, longevity 13
- inputs: {"age_days": 184, "days_push": 82, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1080, forks 166 (observed 2026-08-28T04:03:30.446608+00:00)

## What it is
Symphony-Coord is a decentralized multi-agent framework that treats agent selection as an online multi-armed bandit problem, allowing roles to emerge through interaction. It uses a three-stage pipeline of planning, beacon-guided LinUCB execution routing, and chain-of-thought voting to answer complex queries.

## Use cases
- orchestrate multiple llm agents without a central coordinator
- route subtasks to specialized agents using bandit learning
- run multi-agent experiments and reproduce paper benchmarks
- generate benchmark data for multi-agent coordination research
- run agent swarms on consumer gpus like rtx 3060 or jetson
- aggregate agent answers with chain-of-thought majority voting

## When to choose
- you need decentralized, fault-tolerant multi-agent orchestration
- you want adaptive agent selection that improves with experience
- you are researching emergent coordination in agent systems
- you need to run agents on edge or consumer-grade hardware

## When to avoid
- you need a simple single-agent pipeline with fixed routing
- you require a mature production framework with enterprise support
- your workload has no benefit from learned agent selection
- you need non-Python environments

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, machine-learning, llm-inference, benchmarking
- domain: large-language-models, machine-learning, artificial-intelligence
- platform: python, windows
- tags: multi-agent-systems, multi-armed-bandit, linucb, decentralized, chain-of-thought, task-routing, emergent-coordination, ai-agents, linux, macos, gpu

## Member repositories
- GradientHQ/symphony-coord (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:30.446608+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:51:25.692144+00:00, confidence not recorded.
  - readme: https://github.com/GradientHQ/symphony-coord (fetched 2026-08-28T04:03:30.446608+00:00, sha c0358cbae7fc)
- Data as of 2026-08-30T08:39:29.467469+00:00.