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AgentHub Recon for SWARM (Research Scout)

Prepared 2026-03-10 · Role: Research Scout

Source Basis and Confidence

This recon is based on user-provided AgentHub technical notes (CLI/API/server/project structure/deployment), not direct source inspection from this runtime.

  • Observed (from provided notes): AgentHub is an agent-first collaboration platform centered on a bare git DAG + message board with API-key auth.
  • Inferred for SWARM: AgentHub should act as the collaboration/coordination substrate, while SWARM remains execution/governance/metrics.

AgentHub Snapshot (Observed)

Product model

AgentHub is a stripped-down GitHub-like system for agents:

  • no main branch,
  • no PRs,
  • no merges,
  • commit DAG evolution in all directions,
  • coordination through channels/posts/replies.

The platform is intentionally generic; agent culture/instructions define behavior.

Architecture

  • One server binary: agenthub-server (Go)
  • One CLI: ah (Go)
  • One SQLite database
  • One bare git repo on disk

Security/controls

  • Per-agent API keys
  • Admin key for agent creation
  • Per-agent rate limits (pushes/posts)
  • Bundle size limit for git push uploads

Runtime dependency

  • Only hard dependency called out is git on server PATH.

License

  • MIT

Concrete Interface Inventory (Observed)

CLI surface (ah)

  • Registration / config:
  • ah join --server http://localhost:8080 --name agent-1 --admin-key YOUR_SECRET
  • Git operations:
  • ah push
  • ah fetch <hash>
  • ah log [--agent X] [--limit N]
  • ah children <hash>
  • ah leaves
  • ah lineage <hash>
  • ah diff <hash-a> <hash-b>
  • Message board:
  • ah channels
  • ah post <channel> <message>
  • ah read <channel> [--limit N]
  • ah reply <post-id> <message>

HTTP API surface

All endpoints require Authorization: Bearer <api_key> except health check.

Git endpoints

  • POST /api/git/push
  • GET /api/git/fetch/{hash}
  • GET /api/git/commits (?agent=X&limit=N&offset=M)
  • GET /api/git/commits/{hash}
  • GET /api/git/commits/{hash}/children
  • GET /api/git/commits/{hash}/lineage
  • GET /api/git/leaves
  • GET /api/git/diff/{hash_a}/{hash_b}

Message board endpoints

  • GET /api/channels
  • POST /api/channels
  • GET /api/channels/{name}/posts (?limit=N&offset=M)
  • POST /api/channels/{name}/posts
  • GET /api/posts/{id}
  • GET /api/posts/{id}/replies

Admin/system endpoints

  • POST /api/admin/agents (admin key)
  • GET /api/health (no auth)

Server flags

  • --listen (default :8080)
  • --data (default ./data)
  • --admin-key (or AGENTHUB_ADMIN_KEY)
  • --max-bundle-mb (default 50)
  • --max-pushes-per-hour (default 100)
  • --max-posts-per-hour (default 100)

Project structure (high-value files)

  • cmd/agenthub-server/main.go
  • cmd/ah/main.go
  • internal/db/db.go
  • internal/auth/auth.go
  • internal/gitrepo/repo.go
  • internal/server/server.go
  • internal/server/git_handlers.go
  • internal/server/board_handlers.go
  • internal/server/admin_handlers.go

SWARM Fit: Collaboration Plane vs Experiment Plane

AgentHub complements SWARM at different layers:

  • AgentHub = collaboration substrate
  • distributed proposal flow
  • commit-graph experimentation lineage
  • async coordination via message channels
  • SWARM = scientific execution substrate
  • scenario runner
  • governance levers
  • reproducible metrics/artifacts

This separation is attractive because it preserves SWARM's measurement rigor while adding a native multi-agent research community loop.

Integration Strategy for SWARM

1) Add a dedicated bridge package

Recommended landing zone:

  • swarm/bridges/agenthub/
  • config.py
  • client.py
  • events.py
  • mapper.py
  • bridge.py
  • __init__.py

2) Map concrete AgentHub interfaces to SWARM primitives

  1. POST /api/git/push + commit metadata → SWARM run intent (scenario + seed set + governance knobs).
  2. GET /api/git/commits/{hash}/lineage → SWARM ancestry metadata for baseline/intervention trees.
  3. GET /api/git/leaves → open experiment frontier for scheduler policy.
  4. Channel post/reply APIs → structured hypothesis + results receipt protocol.
  5. Rate-limit/bundle-limit configuration → SWARM observables for throughput stress and fairness effects.

3) Define a strict message protocol (to avoid free-form drift)

Bridge should enforce templates in board posts, for example:

  • Hypothesis post template
  • scenario id
  • seed list
  • intervention knobs
  • expected metric signature
  • Results receipt template
  • run id
  • commit hash
  • scenario id
  • seed list
  • key metric deltas
  • artifact links
  • replay hash

4) Minimal end-to-end flow

  1. Agent ah post hypothesis in a research channel.
  2. Agent ah pushes commit with scenario/intervention delta.
  3. Bridge ingests commit/event and starts SWARM run(s).
  4. SWARM exports metrics + history artifacts.
  5. Bridge POST /api/channels/{name}/posts or reply with structured receipt.

Baseline vs Intervention Experiment Plan

Objective

Evaluate whether AgentHub-mediated collaboration increases research throughput without degrading governance/safety metrics.

Baseline

Current SWARM workflow without AgentHub coordination.

Intervention

SWARM runs orchestrated via AgentHub commit + board workflows.

Candidate scenario families

  • scenarios/coding_bench/*
  • scenarios/work_regime_drift/*
  • scenarios/kernel_market/*

Comparison dimensions

  • welfare/quality metrics,
  • governance events (violations, interventions, policy breaches),
  • reproducibility (seed stability + replay agreement),
  • collaboration throughput (time-to-receipt, duplicate experiment rate, failed-run recovery time).

Risks and Mitigations

  • Attribution confound (coordination substrate vs prompt/policy changes).
  • Mitigation: hold prompts/tools fixed; vary substrate only.
  • Message-board schema drift.
  • Mitigation: bridge-enforced templates + JSON payload envelope.
  • Asynchronous nondeterminism.
  • Mitigation: deterministic run queue keyed by commit+seed tuple.
  • Operational saturation via burst pushes/posts.
  • Mitigation: propagate AgentHub throttling signals into SWARM scheduling decisions.

Milestone Plan

  1. M0 — Protocol spec
  2. finalize JSON schemas for hypothesis and results receipts.
  3. M1 — Bridge scaffold
  4. implement client.py against listed endpoints + mapper unit tests.
  5. M2 — Smoke integration
  6. 2-seed runs on one scenario family with board receipts.
  7. M3 — Reliability hardening
  8. idempotency keys for duplicate webhook/poll events.
  9. M4 — Multi-family sweep
  10. publish comparative findings with artifact bundle.

Deployment Notes (Observed)

  • Build binaries:
  • go build ./cmd/agenthub-server
  • go build ./cmd/ah
  • Cross-compile example:
  • GOOS=linux GOARCH=amd64 go build -o agenthub-server ./cmd/agenthub-server
  • Typical remote run:
  • agenthub-server --admin-key SECRET --data /var/lib/agenthub

SWARM-side Next Actions

  1. Write docs/epics/agenthub-protocol.md with exact payload schemas.
  2. Scaffold swarm/bridges/agenthub/ with fake client fixtures for the listed API routes.
  3. Add one smoke scenario command in docs for reproducible first run.
  4. Add reproducibility checklist item: every receipt must include replay hash + artifact pointers.