Max treats a chat bot as a correctness problem. Every message, whichever platform it arrives on, lands in one immutable canonical ledger with a single typed message IR; deliveries — including mirroring one conversation across platforms — go through a durable outbox that degrades content deliberately instead of dropping it; and the LLM's context is a rebuildable, integrity-checked projection of that ledger, not a sliding window. The interesting decisions are written down in the ADRs.
chat platforms
│ adapters normalize losslessly; nothing degrades at ingest
▼
canonical message ledger ────┬──▶ mirror deliveries: native where a (one phase-indexed IR, │ platform can, readable text where
PostgreSQL, durable outbox, │ it can't - never silently dropped
echo reconciliation) ├──▶ agent turns: LLM, tools, sandbox,
│ browser, files, media
└──▶ context projections: episodes,
memories, unified recall
- One message IR, capability-tiered delivery. Faces, cards, files, replies, and mentions keep their structure (and raw payloads for native round-trips) all the way to the ledger. Each endpoint declares native/text/drop per feature; a single lowering pass folds whatever an endpoint can't carry into readable text and records every degradation as an auditable note. (ADR 003)
- Mirrors that don't lie. One canonical row per semantic message, per-endpoint durable deliveries with leases and idempotency keys, and ambiguous sends parked until an echo proves the outcome — a mirrored conversation neither drops nor duplicates.
- Context as a database, not a window. Raw messages are immutable; quiet-period episodes carry tiered summaries with exact, hash-checked source coverage; prompts are token-planned projections that degrade deterministically under budget and expand back to raw text on demand. (ADR 001)
- Memory with an audit trail. Conversation-scoped memories are versioned CAS records with evidence links and actor permissions — even the nightly consolidation pass must justify every change. Unified recall spans memories, episodes, raw history, pins, and media captions, lexical and semantic, with embedding provenance checked in SQL.
- It reads its own source. An allowlisted snapshot of this repository ships inside the binary; the bot answers questions about itself by searching and reading the exact deployed code, ADRs, and schema.
- Plus the table stakes: concurrent turns with streaming, cancellation, and mid-turn feedback; multimodal input; persistent per-group sandboxes and browser automation; skills, reminders, proactive participation, and an authenticated local admin panel.
direnv allow # or: nix develop --impure
cp .env.example .env
cp max.yaml.example max.yaml
devenv up # PostgreSQL on 127.0.0.1:5433
docker compose up -d napcat
cabal run maxOpen http://localhost:6099 to log the bot account into QQ. Migrations and
derived-data backfills run automatically. Build sandbox-image/ and
browser-image/ only when those tools are needed.
Configuration is layered as CLI flags, environment variables, then YAML;
one LLM API key (OpenAI-compatible, OpenAI Responses, or Anthropic-native)
is the only required value, and optional feature sections stay disabled
when absent. For a real deployment, nix/module.nix
ships the whole thing as a systemd service. Max runs as a single
production instance for its author — a personal agent with
framework-grade plumbing, not a framework.
cabal test max-test
MAX_TEST_DB_URL=postgresql://127.0.0.1:5433/max_test cabal test max-test-db
cabal build all| Document | Contents |
|---|---|
| features.md | behaviour and configuration semantics |
| architecture.md | runtime, context/memory design, and durability |
| platforms.md | platform operations, mirroring, and cutover invariants |
| ADR 001 | context/memory invariants and privacy boundaries |
| ADR 002 | partial plans, adaptive elaboration, and safe deoptimization |
| ADR 003 | the message IR, capability-tiered lowering, and prior-art survey |
| ADR 004 | canonical handles for the model, and the identity it addresses |
| ADR 005 | turn continuity: durable traces, journal projections, verbatim replay |
| development.md | tests, evaluation, versioning, and debugging |
| prompt-flow.md | generated prompt and tool-round wire examples |
MIT © Hank Hogan