m2 (AI Agent)
14b5543ebf
Working title m2-market. Seeds: unified concept paper (work store, Solutions, credits, Solution Scout, cargstore-as-storefront) and a verified map of how m2-gpt (bifrost gateway, tenancy/metering), agent.memory.system (memory-api/ Qdrant/BGE-M3), and the m2o Coolify fleet (Hermes primary agent, herdr, RDP, openclaw-open) actually connect. specs/ awaits discovery output. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
6.8 KiB
System Map — how the existing stack connects (verified 2026-07-01)
The marketplace composes three live systems plus the m2o fleet. All run as Coolify apps on
the m2 host, on the shared coolify Docker network — services reach each other by Docker
DNS alias, humans/external agents via Traefik-routed domains.
┌────────────────────────── m2 host (Coolify) ──────────────────────────┐
│ │
operator browser ──► Guacamole (m2o.machinemachine.ai) │
│ │ VNC/RDP via per-desktop guacd:4822 │
│ ▼ │
│ m2o desktops (primus: chris/matrix/m2bd/erlengrund · │
│ agent-latest: sdjs/nasr/parlobyg/peter/gunnar) │
│ │ each: Hermes gateway (supervised) + herdr + [openclaw on legacy] │
│ │ │
│ ├── LLM calls ──► m2-gpt gateway (gpt.machinemachine.ai/v1) │
│ │ Bifrost/FastAPI, multi-tenant keys, per-tenant │
│ │ budgets/metering, "subconscious" middleware: │
│ │ injects m2.* memory tools + ambient context ──┐ │
│ │ │ │
│ └── memory ops ──► memory-api:8000 (agent.memory.system) ◄──────┘ │
│ FastAPI + Qdrant (BGE-M3 hybrid) + memgraph │
│ + TEI embeddings + redis; agent_id partitions; │
│ public: memory.machinemachine.ai │
│ │
│ Forgejo (git.machinemachine.ai) — org m2; fedlearn m2/m2-core (WIP) │
└────────────────────────────────────────────────────────────────────────┘
The three repos
1. m2-gpt — github.com/machine-machine/m2-gpt (local: /home/m2/m2-gpt/m2-gpt)
Multi-tenant OpenAI-compatible gateway with a subconscious layer. Any OpenAI-speaking
harness (Hermes, OpenClaw, Claude Code) points base_url at https://gpt.machinemachine.ai/v1
with a tenant key; the gateway routes to upstreams (SGLang on spark cluster, OpenRouter, GLM)
and injects m2.* memory tool-calls + ambient sensation into the prompt. Live:
{"status":"healthy","gateway":"bifrost"}; two gateway containers (staging+prod pattern).
Marketplace relevance: tenant identity, per-tenant budgets and token metering (the natural
substrate/federation point for m2-ledger), admin APIs/UI (React/shadcn — reusable patterns
for a market admin), fleet_standards cascade resolver.
2. agent.memory.system — github.com/machine-machine/agent.memory.system (local: /home/m2/agent.memory.system)
The memory stack behind memory-api. Python FastAPI over Qdrant (BGE-M3 dense+sparse
hybrid) + memgraph (graph) + TEI embeddings + redis; working/episodic/semantic memory with
consolidation + importance scoring. Deployed twice via Coolify (stacks z1rlou… and vc00o…
— the known split-brain; fedlearn task T13 is resolving auth + live-endpoint discovery now).
Consumed by: desktops (m2-memory skill, openclaw memory-engine plugin), m2-gpt backings
(backings/ HTTP client), fedlearn partitions (fedlearn:submissions|clusters|core-index).
Marketplace relevance: the semantic catalog (market:catalog partition), pricing-evidence
recall, Scout intent-matching — all one more partition on proven infra.
3. m2o — github.com/machine-machine/m2o (local: /home/m2/m2o)
The desktop platform: Guacamole gateway + primus image (desktop/) + provision.sh +
fleet.json. Every desktop ships Hermes as primary agent (supervised gateway, config
rendered on first boot pointing at m2-gpt with M2_GPT_API_KEY injected at provision) +
herdr baked fleet-wide (v4.4+) + RDP standard (v4.5). Legacy agent-latest desktops
additionally run openclaw (m2-custom fork) with the memory-engine plugin.
Marketplace relevance: the execution surface (installs land in /agent_home volumes via
the fedlearn sync path), the co-driving wedge (Guacamole VNC/RDP), Scout host, cargstore host.
Agent posture
- Hermes = the gateway-managed primary agent (every primus desktop; supervised; m2-gpt
backed). The marketplace's conversational surface (
market-proposeskill) targets Hermes FIRST. - OpenClaw = open option (legacy desktops run it; m2-gpt explicitly serves both). Design marketplace touchpoints harness-agnostic where cheap: anything speaking OpenAI-wire through m2-gpt inherits the subconscious/memory layer, so Scout/propose logic should live behind the gateway or as skills, not inside one harness.
Related moving parts
- fedlearn MVP (in herd execution now): builds the rails the marketplace rides —
schemas, Forgejo
m2/m2-core, memory-api auth hardening, capture CLI, curator+veto PRs,m2-core-syncapply path. Plan:/home/m2/m2o/.planning/federated-learning/PLAN.md. - cargstore (github.com/machine-machine/cargstore): Electron+React desktop app store (Flatpak backend, JSON catalog, one-click installs, agent WebSocket) → evolve into M2 Store.
- Coolify (cool.machinemachine.ai): deploys everything above; new marketplace services
(m2-ledger, catalog indexer, Scout) should be Coolify apps on the
coolifynetwork. Gotcha: the local registry route self-redirects (302), so primus-style images build local-only. - spark cluster (spark1–6): SGLang upstreams for m2-gpt; future Resource inventory.
Hard constraints inherited
- Tenant isolation (sdjs=GST etc.): client data never crosses into shared catalog/core.
- No secrets in repos/images; keys injected at runtime (M2_GPT_API_KEY pattern).
- Mixed images (primus vs agent-latest): runtime-sync is the only universal install path.
- Host fragility: canary-first rollouts, idempotent + reversible applies, no fleet-wide blast.