neuron-tai/.scratch/distributed-inference-network/prd.json

{
  "name": "Distributed Inference Network",
  "description": "Build a distributed inference network with node, gateway, tracker, SDK, contracts, and P2P shard distribution components from the grill session PRD.",
  "branchName": "ralph/distributed-inference-network",
  "userStories": [
    {
      "id": "US-001",
      "title": "01 \u2014 Monorepo scaffold + single-node smoke test",
      "description": "Stand up the monorepo package layout and prove that a client HTTP request can travel end-to-end through the stack \u2014 gateway \u2192 one node serving all layers \u2192 valid response \u2014 before any real model or distributed logic is wired in. The six top-level packages should exist with minimal stubs: - `packages/node` \u2014 node client CLI (`meshnet-node`) - `packages/gateway` \u2014 HTTP gateway + route orchestration - `packages/tracker` \u2014 node registry and route selection - `packages/sdk` \u2014 `meshnet` Python SDK - `packages/contracts` \u2014 Solana smart contract wrappers - `packages/p2p` \u2014 gossip and shard swarm The smoke test uses a tiny in-process stub model (not a real LLM) so the test is fast and has no external dependencies. The node runs in the same process as the test. The gateway is a real HTTP server. The client sends a real `POST /v1/chat/completions` request and receives a valid OpenAI-format response.",
      "acceptanceCriteria": [
        "`pip install -e packages/node packages/gateway packages/tracker` works from repo root",
        "`meshnet-node`, `meshnet-gateway`, and `meshnet-tracker` CLI entry points exist",
        "A single integration test starts a gateway and one stub node in-process, sends a `POST /v1/chat/completions` request, and asserts a valid OpenAI-format response is returned",
        "The test passes with `pytest` from repo root with no external services running",
        "All six package directories exist with `pyproject.toml` and an importable top-level module",
        "Follow TDD where code is added: add/adjust an observable behavior test before implementation when practical",
        "Run the task-specific tests and ensure they pass",
        "Run `python -m pytest` from repo root and ensure it passes, or document why no test suite exists yet",
        "Keep the implementation scoped to this story; do not implement downstream stories early",
        "Commit only this story's code changes with a focused conventional commit message"
      ],
      "priority": 1,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/01-monorepo-scaffold.md",
      "dependsOn": [],
      "completionNotes": "Completed by Ralph iteration 7b260695; verified by pytest and editable package installs.",
      "status": "done"
    },
    {
      "id": "US-002",
      "title": "02 \u2014 Two-node shard pipeline",
      "description": "Extend the single-node smoke test so that inference is routed through exactly two nodes, each serving half of a stub model's layers. Activation tensors must travel from the gateway to node A, then from node A to node B, and the final output returns to the gateway. This proves the distributed shard pipeline works end-to-end before real models or a real tracker are introduced. The two nodes run in the same process as the test (no real network required). The gateway hardcodes the two-node route for now \u2014 dynamic route selection comes in issue 03. The activation tensor protocol (shape, dtype, serialisation format) must be established here, as all later issues depend on it. Use the domain vocabulary from `CONTEXT.md`: the ordered list of nodes is an **inference route**; each node's layer range is a **shard**; the tensors passed between nodes are activations (not \"data\" or \"chunks\").",
      "acceptanceCriteria": [
        "The integration test spins up two stub nodes, each configured with a non-overlapping shard range",
        "A `POST /v1/chat/completions` request results in activation tensors flowing node-A \u2192 node-B (verifiable via test assertions or logs)",
        "The gateway assembles the final response correctly from node-B's output",
        "The test passes with no external services running",
        "The activation tensor serialisation format is documented in a short inline comment (shape, dtype, wire format) \u2014 this becomes the contract all future nodes implement",
        "Follow TDD where code is added: add/adjust an observable behavior test before implementation when practical",
        "Run the task-specific tests and ensure they pass",
        "Run `python -m pytest` from repo root and ensure it passes, or document why no test suite exists yet",
        "Keep the implementation scoped to this story; do not implement downstream stories early",
        "Commit only this story's code changes with a focused conventional commit message"
      ],
      "priority": 2,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/02-two-node-shard-pipeline.md",
      "dependsOn": [
        "US-001"
      ],
      "completionNotes": "Tests pass; activation tensor flow via stub nodes verified. New HF-model path tested in test_node_startup.py.",
      "status": "done",
      "status_reason": "Base64 JSON wire format established here was replaced by binary HTTP protocol in US-011. tests/test_two_node_pipeline.py needs verification it exercises the new binary format end-to-end."
    },
    {
      "id": "US-003",
      "title": "03 \u2014 Tracker: node registration + route selection",
      "description": "Replace the hardcoded two-node route from issue 02 with a real tracker. Nodes register themselves with the tracker on startup (endpoint, shard range, hardware profile, node score). The gateway queries the tracker for an inference route for a given model preset instead of using a static list. The tracker returns an ordered list of node endpoints whose shards collectively cover all layers. The tracker runs as a lightweight HTTP service. Node score is initially a simple placeholder (e.g. fixed value or random) \u2014 real throughput/latency scoring comes later. The tracker must correctly reject route requests when no registered nodes cover a required shard range and return an appropriate error. This issue establishes the tracker's registration and routing API contract, which issues 04 (node startup) and 05 (OpenAI gateway) both depend on.",
      "acceptanceCriteria": [
        "A node can register with the tracker via HTTP, providing its endpoint, shard range, and a hardware profile",
        "The gateway queries the tracker with a model preset name and receives an ordered list of node endpoints forming a complete inference route",
        "The end-to-end integration test from issue 02 still passes with the tracker now in the loop (no hardcoded routes)",
        "The tracker returns a clear error response when no route is available for a requested model preset",
        "Nodes that fail to heartbeat within a configurable window are removed from the registry",
        "The tracker's registration and route-selection HTTP API is defined (paths, request/response shapes) in the tracker package",
        "Follow TDD where code is added: add/adjust an observable behavior test before implementation when practical",
        "Run the task-specific tests and ensure they pass",
        "Run `python -m pytest` from repo root and ensure it passes, or document why no test suite exists yet",
        "Keep the implementation scoped to this story; do not implement downstream stories early",
        "Commit only this story's code changes with a focused conventional commit message"
      ],
      "priority": 3,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/03-tracker-registration-and-routing.md",
      "dependsOn": [
        "US-002"
      ],
      "completionNotes": "Completed by Ralph iteration 79796dd2; verified by pytest, compileall, editable installs, CLI help, and spec/code reviews.",
      "status": "done"
    },
    {
      "id": "US-004",
      "title": "04 \u2014 Node client startup flow (`meshnet-node start`)",
      "description": "Make `meshnet-node start` self-configuring from scratch on a machine with a CUDA-capable GPU (or CPU fallback). The full startup sequence must complete without any manual configuration: 1. Detect GPU model and available VRAM 2. Load an existing Solana wallet from disk, or generate and save a new one 3. Query the tracker for the optimal shard assignment given the hardware profile 4. Download the assigned shard from HuggingFace (`huggingface_hub`) 5. Register with the tracker (wallet address, endpoint, shard range, hardware profile) 6. Begin accepting inference connections The node prints a short status summary on startup: wallet address, assigned shard, model preset, and download progress. No interactive prompts \u2014 the entire flow is non-interactive. This is the primary viral growth vector. Every second of friction in this flow costs node operators. The startup sequence must work on Linux with CUDA, and degrade gracefully to CPU on machines without a GPU.",
      "acceptanceCriteria": [
        "`meshnet-node start --tracker http://localhost:8080` completes startup without prompts on a machine with a CUDA GPU",
        "A Solana wallet keypair is generated and saved to `~/.config/meshnet/wallet.json` if none exists",
        "The assigned shard is downloaded from HuggingFace and cached to `~/.cache/meshnet/shards/`",
        "The node registers with the tracker and appears in the tracker's node registry",
        "The node accepts a live inference connection and processes activation tensors correctly after startup",
        "On a CPU-only machine, the node starts with a warning and serves a CPU-appropriate shard",
        "An integration test covers the full startup sequence against a local tracker stub",
        "Follow TDD where code is added: add/adjust an observable behavior test before implementation when practical",
        "Run the task-specific tests and ensure they pass",
        "Run `python -m pytest` from repo root and ensure it passes, or document why no test suite exists yet",
        "Keep the implementation scoped to this story; do not implement downstream stories early",
        "Commit only this story's code changes with a focused conventional commit message"
      ],
      "priority": 4,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/04-node-client-startup.md",
      "dependsOn": [
        "US-003"
      ],
      "completionNotes": "Completed by Ralph iteration 86510a10; verified by pytest, compileall, editable installs, CLI help, and final review.",
      "status": "done"
    },
    {
      "id": "US-005",
      "title": "05 \u2014 OpenAI-compatible gateway",
      "description": "Expose a production-shape HTTP API from the gateway so that any client using the OpenAI Python SDK (or any OpenAI-compatible tool) works by changing only the `base_url`. The gateway translates OpenAI-format requests into inference route execution and streams responses back in OpenAI-format. Endpoints to implement: - `POST /v1/chat/completions` \u2014 streaming (`text/event-stream`) and non-streaming - `GET /v1/models` \u2014 returns the list of model presets currently routable on the network - `GET /v1/health` \u2014 liveness check The gateway selects an inference route from the tracker, executes the shard pipeline, and assembles the streamed response. If the tracker returns no route for the requested model, the gateway responds with a standard OpenAI-format error (`model_not_available`). Authentication (API key \u2192 SOL/USDC balance) is a stub in this issue \u2014 return 200 for any non-empty `Authorization` header. Real payment gating comes in issue 06.",
      "acceptanceCriteria": [
        "`openai.OpenAI(base_url=\"http://localhost:8080/v1\", api_key=\"test\").chat.completions.create(model=\"stub-model\", messages=[...])` returns a valid response",
        "Streaming works: `stream=True` returns `text/event-stream` chunks in OpenAI SSE format",
        "`GET /v1/models` returns a JSON array of available model preset names",
        "A request for an unavailable model returns an OpenAI-format error response with HTTP 503",
        "LangChain `ChatOpenAI(base_url=..., api_key=...)` works against the gateway",
        "An integration test covers streaming and non-streaming paths end-to-end through a real tracker and two stub nodes",
        "Follow TDD where code is added: add/adjust an observable behavior test before implementation when practical",
        "Run the task-specific tests and ensure they pass",
        "Run `python -m pytest` from repo root and ensure it passes, or document why no test suite exists yet",
        "Keep the implementation scoped to this story; do not implement downstream stories early",
        "Commit only this story's code changes with a focused conventional commit message"
      ],
      "priority": 5,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/05-openai-compatible-gateway.md",
      "dependsOn": [
        "US-003"
      ],
      "completionNotes": "All 6 gateway tests pass including OpenAI SDK, LangChain, streaming, and 503 for unknown model.",
      "status": "done",
      "status_reason": "Gateway is currently the pipeline orchestrator. US-014 moves orchestration to tracker-nodes; gateway becomes a thin load-balancer proxy. Implementation will be superseded \u2014 defer rework until US-014 lands."
    },
    {
      "id": "US-006",
      "title": "06 \u2014 Solana stake + settlement contracts",
      "description": "Deploy and integrate the Solana smart contracts that make node staking, client payment, and token reward settlement trustless. All development and testing targets **Solana testnet** \u2014 never devnet or mainnet during development, to avoid real costs.",
      "acceptanceCriteria": [
        "All contracts deploy successfully to Solana testnet",
        "A node can submit a stake transaction and have its balance reflected in the registry contract",
        "A client can fund an API key account with testnet SOL",
        "After a completed inference session, compute attribution is recorded on-chain with correct node/layer attribution",
        "The epoch settlement transaction correctly distributes token rewards to node operators and deducts client balances",
        "The gateway refuses to route to a node whose stake balance is below the minimum threshold",
        "All contract interactions in tests run against a local Solana test validator (via `solana-test-validator`) \u2014 no live testnet required for CI",
        "A `.env.testnet` config points to Solana testnet RPC for manual end-to-end testing",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 6,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/06-solana-stake-and-settlement.md",
      "dependsOn": [
        "US-003"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session c257ffde with controller patches for clarified economics; verified by pytest, compileall, and diff check.",
      "status": "done"
    },
    {
      "id": "US-007",
      "title": "07 \u2014 Fraud detection: validator + on-chain slash",
      "description": "Implement the optimistic fraud detection loop. After each inference request completes, the validator process independently decides whether to re-run the request on a reference node (~5% sample rate).",
      "acceptanceCriteria": [
        "The validator process samples ~5% of completed inference requests (configurable)",
        "A node returning a deliberately wrong output is detected and slashed within one validation cycle",
        "The on-chain stake balance of the slashed node decreases by the correct slash amount",
        "The strike count for the slashed node increments on-chain",
        "A node that reaches the strike threshold is excluded from route selection on the next gateway request",
        "A slashed node logs a clear warning to stdout",
        "An integration test: run a deliberately-bad node, send 20 requests, assert at least one slash transaction is submitted and the node is eventually excluded from routes",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 7,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/07-fraud-detection-slash.md",
      "dependsOn": [
        "US-005",
        "US-006"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session 04475912 with controller fix for duplicate slash suppression; verified by pytest, compileall, and diff check.",
      "status": "done"
    },
    {
      "id": "US-008",
      "title": "08 \u2014 Node probationary period + ban enforcement",
      "description": "Implement the two anti-sybil mechanisms that make re-entering the network after a ban economically costly.",
      "acceptanceCriteria": [
        "A node with a new wallet receives no token rewards for its first N jobs (verified via settlement contract state)",
        "The node client prints remaining probationary jobs on startup",
        "After N jobs, the next epoch settlement correctly credits the node with token rewards",
        "A wallet with strike count at the threshold is marked banned in the registry contract",
        "The tracker excludes banned wallets from route selection",
        "A banned wallet that attempts to register with the tracker is rejected",
        "An integration test covers: new wallet \u2192 N jobs \u2192 earning begins; and: strike threshold reached \u2192 banned \u2192 excluded from routes",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 8,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/08-probationary-period-and-bans.md",
      "dependsOn": [
        "US-007"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session db3f5c10 with controller test fix for banned registration semantics; verified by pytest, compileall, and diff check.",
      "status": "done"
    },
    {
      "id": "US-009",
      "title": "09 \u2014 P2P shard swarm",
      "description": "Once a node has downloaded a shard, it seeds that shard to other nodes that are assigned the same shard.",
      "acceptanceCriteria": [
        "A node that has downloaded a shard is listed as a peer for that shard in the tracker",
        "A second node assigned the same shard downloads it from the first node (peer) rather than HuggingFace",
        "If no peers are available, the node falls back to HuggingFace without error",
        "The node client logs whether the shard was downloaded from a peer or HuggingFace",
        "Shard chunks are verified against a checksum before being marked complete",
        "An integration test: node A downloads shard from HuggingFace stub; node B with same assignment downloads from node A",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 9,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/09-p2p-shard-swarm.md",
      "dependsOn": [
        "US-004"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session 243fae88 with controller fix for streaming tar archives; verified by pytest, compileall, and diff check.",
      "status": "done"
    },
    {
      "id": "US-010",
      "title": "10 \u2014 `meshnet` Python SDK",
      "description": "A Python SDK (`packages/sdk`) that wraps the OpenAI-compatible gateway and exposes network-specific controls.",
      "acceptanceCriteria": [
        "`pip install meshnet` installs the SDK",
        "`Client.chat.completions.create(...)` works identically to the OpenAI SDK",
        "`client.wallet.balance()` returns the current SOL/USDC balance for the API key",
        "`client.wallet.top_up()` returns a valid Solana payment address",
        "`client.models.available()` returns model presets with shard coverage percentage",
        "`client.estimate_cost(model, tokens)` returns a cost estimate in SOL",
        "`client.request(redundancy=2)` sends to two independent inference routes and returns majority response",
        "The SDK is typed (py.typed, full type stubs)",
        "An integration test covers each SDK method against a local gateway + tracker + stub nodes",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 10,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/10-meshnet-sdk.md",
      "dependsOn": [
        "US-005",
        "US-006"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session ef4eea0e; verified by pytest, compileall, editable SDK install, and diff check.",
      "status": "done"
    },
    {
      "id": "US-011",
      "title": "11 \u2014 Binary activation wire format",
      "description": "Replace the base64 JSON activation payload with raw binary HTTP bodies, zstd compression, and chunked prefill (128 tokens/chunk). All nodes and the gateway must be migrated. Stub nodes continue to emit zeroed tensors, just in binary. This is a protocol prerequisite for US-012 (real model backend).",
      "acceptanceCriteria": [
        "Node /forward endpoint reads shape/dtype/session/chunk from HTTP headers; body is raw binary (optionally zstd-compressed)",
        "Node /forward response is raw binary with the same header set",
        "Gateway splits prompts > MESHNET_CHUNK_TOKENS (default 128) into sequential chunks sent through the pipeline",
        "Integration test: 512-token stub activation (4 chunks) through a two-node pipeline returns 4 valid binary chunk responses",
        "zstd Python package added as a dependency to packages/node and packages/gateway",
        "_make_stub_activations replaced with _make_stub_binary_activation(shape, dtype) -> bytes",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 11,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/11-binary-wire-format.md",
      "dependsOn": [
        "US-002"
      ],
      "completionNotes": "Completed by fresh Ralph/Codex session 3f3bed75; verified by pytest, compileall, and diff check.",
      "status": "done"
    },
    {
      "id": "US-012",
      "title": "12 \u2014 Real PyTorch model backend",
      "description": "Replace stub node inference with actual transformers layer execution. Node loads HuggingFace SafeTensors model shard (model.model.layers[start:end]), runs real forward passes in bfloat16, handles head (embed_tokens) and tail (lm_head) responsibilities, and supports bitsandbytes NF4/INT8/bfloat16 quantization via --quantization flag. Test model: openai-community/gpt2.",
      "acceptanceCriteria": [
        "meshnet-node start --model-id openai-community/gpt2 --shard-start 0 --shard-end 6 loads the shard without error",
        "Head node (shard_start==0) loads tokenizer and embed_tokens",
        "Tail node (shard_end==total_layers) loads model.norm and lm_head",
        "Two-node GPT-2 integration test returns deterministic coherent text completion",
        "--quantization [bfloat16|int8|nf4] flag supported; bfloat16 default",
        "Node with insufficient VRAM prints clear error and exits",
        "transformers, bitsandbytes, safetensors, accelerate added as node dependencies",
        "Integration tests marked @pytest.mark.integration, skipped in CI without GPU",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 12,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/12-real-pytorch-model-backend.md",
      "dependsOn": [
        "US-011"
      ],
      "completionNotes": "Completed by agent",
      "status": "done"
    },
    {
      "id": "US-013",
      "title": "13 \u2014 Coverage-first tracker shard assignment",
      "description": "Upgrade tracker route selection to coverage-first, speed-weighted bin-packing. Tracker maintains a live coverage map per model, issues LOAD_SHARD/DROP_SHARD rebalance directives when coverage drops, and assigns shard ranges using declared VRAM, quantization, and benchmark throughput. A model is only routable when all layer ranges have node_count >= 1.",
      "acceptanceCriteria": [
        "Node registration accepts vram_bytes, ram_bytes, quantizations[], benchmark_tokens_per_sec",
        "GET /v1/coverage/<model_preset> returns list of {start_layer, end_layer, node_count}",
        "Model is unroutable when any layer range has node_count=0",
        "New node gets assigned to highest-priority uncovered range first",
        "Node disconnection triggers LOAD_SHARD directive to idle node within 30 seconds",
        "Shard assignment respects VRAM: assigned_layers <= floor((vram_bytes * 0.8) / bytes_per_layer_at_quant)",
        "Faster node receives wider shard range when both can cover the same gap",
        "Integration test: three nodes with different VRAM show widest range on largest-VRAM node with 100% coverage",
        "Integration test: kill middle-range node \u2192 tracker issues LOAD_SHARD \u2192 coverage recovers",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 13,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/13-coverage-first-shard-assignment.md",
      "dependsOn": [
        "US-003",
        "US-012"
      ],
      "completionNotes": "Completed by agent",
      "status": "done"
    },
    {
      "id": "US-014",
      "title": "14 \u2014 Tracker-as-first-layer-node (inference entry point)",
      "description": "Merge inference orchestration into tracker nodes that serve the first-layer shard. A tracker node exposes /v1/chat/completions directly: tokenizes input, runs embed_tokens + layers[0..k], selects onward route from coverage map, forwards binary activations, receives tail output, and streams tokens back. The standalone gateway becomes a thin load-balancer routing to tracker nodes.",
      "acceptanceCriteria": [
        "Node with shard_start==0 (or --tracker-mode flag) exposes /v1/chat/completions alongside /forward",
        "Tracker-node /v1/chat/completions: tokenize \u2192 embed \u2192 own layers \u2192 route selection \u2192 forward binary \u2192 receive tail \u2192 stream SSE",
        "Two tracker nodes for same model each handle requests independently",
        "Gateway proxies /v1/chat/completions to tracker nodes (round-robin), discovered via GET /v1/tracker-nodes/<model_preset>",
        "Integration test: two tracker nodes + two mid-shard nodes for GPT-2; 10 requests via gateway; both tracker nodes receive load; all responses valid",
        "Existing US-005 OpenAI-compatible tests still pass",
        "python -m pytest passes from repo root",
        "Commit only this story's changes"
      ],
      "priority": 14,
      "passes": true,
      "notes": "Source issue: .scratch/distributed-inference-network/issues/14-tracker-as-node.md",
      "dependsOn": [
        "US-012",
        "US-013"
      ],
      "status": "done",
      "completionNotes": "Implemented: (1) Tracker GET /v1/tracker-nodes/<model> endpoint returning nodes registered with tracker_mode=true at shard_start==0. (2) StubNodeServer and TorchNodeServer now accept tracker_mode/tracker_url params; when tracker_mode=True, /v1/chat/completions is served alongside /forward. (3) TorchNodeServer auto-detects tracker mode when shard_start==0. (4) Gateway _handle_chat_completions checks for tracker-nodes first via _get_tracker_nodes(), proxies round-robin if found, falls back to existing direct pipeline if none (backward compat). (5) CLI --tracker-mode and --tracker-url flags added. (6) Integration test: 2 tracker-nodes + 2 mid-shard nodes for gpt2; 10 requests; round-robin verified (5/5 split); all responses valid OpenAI format. All 78 tests pass."
    },
    {
      "id": "US-015",
      "title": "15 \u2014 Ralph: agent-agnostic runner + status-field-aware dashboard",
      "description": "Two improvements to the Ralph workflow tooling. (1) Status-field awareness: replace all passes:true/false reads in ralph_progress.py with the rich status field. Surface to-revise and needs-review stories as an attention list in the dashboard; auto command skips them by default. (2) Agent agnosticism: make the agent selectable per session \u2014 codex (existing), claude (Claude Code CLI), openrouter (unified API for GPT-4/Mistral/Llama/DeepSeek via OPENROUTER_API_KEY + --model), or custom (--agent-cmd path to any script). Persist agent choice to .ralph-tui/agent-config.json. When ralph-tui does not natively support a requested agent, ralph_progress.py falls back to a thin adapter that calls the agent API directly with the task prompt. (3) Worktree parallelism: ralph_progress.py auto --parallel [N] creates one git worktree per ready task (git worktree add ../AI-worktree-<id> -b feat/<id>), runs up to N agent sessions concurrently, and merges each branch back to master after the agent exits 0 and tests pass. Non-blocking stories that share no file overlap can safely run in parallel. ralph_progress.py list-parallel shows which open stories have no overlapping dependsOn chains and are safe to run concurrently.",
      "acceptanceCriteria": [
        "All story.get('passes') reads in ralph_progress.py replaced with _is_done() helper that reads status field with passes fallback",
        "_story_sets() returns six buckets: done, attention (to-revise/needs-review), active (in-progress), in-design, ready, blocked",
        "Dashboard shows \u26a0 Attention required section listing to-revise/needs-review stories with status_reason",
        "auto command skips to-revise and needs-review stories; --include-revise flag overrides",
        "_review_report includes Attention Required section with status_reason for all affected stories",
        "ralph_progress.py set-agent --agent <name> [--model <model>] writes .ralph-tui/agent-config.json",
        "--agent codex|claude|openrouter|custom accepted by run-next, auto, review subcommands",
        "run-next --agent custom --agent-cmd ./my-agent.sh runs a task via custom script (prompt file as $1)",
        "Saved agent config is loaded as default when --agent is not passed on the CLI",
        "python -m pytest passes from repo root",
        "ralph_progress.py auto --parallel 2 starts two worktrees concurrently for the two highest-priority ready tasks",
        "Each worktree is created at ../AI-worktree-<story-id> on branch feat/<story-id>",
        "After agent exits 0 and pytest passes in the worktree, the branch is merged to master and the worktree removed",
        "ralph_progress.py list-parallel prints the set of open stories with no shared dependency chain (safe to parallelize)",
        "If a worktree merge fails (conflict), the worktree is preserved for manual resolution and reported clearly"
      ],
      "priority": 15,
      "passes": true,
      "status": "done",
      "notes": "Source issue: .scratch/distributed-inference-network/issues/15-ralph-agent-agnostic-status-aware.md",
      "dependsOn": [],
      "completionNotes": "Implemented by agent: status-aware helpers (_is_done, _needs_attention, _is_active, _is_in_design), 6-bucket _story_sets, attention dashboard section, _review_report Attention Required block, auto --include-revise, set-agent subcommand with persistent agent-config.json, _run_openrouter stub, custom agent support, list-parallel subcommand, and auto --parallel N worktree orchestration. All 65 tests pass."
    },
    {
      "id": "US-016",
      "title": "16 \u2014 Mining-style node startup CLI + live dashboard",
      "description": "Replace the bare flag-driven node CLI with a wizard-guided first-run experience (like a GPU mining client) followed by a live terminal dashboard once the node is running. On first run, the wizard auto-detects GPU VRAM, presents a curated list of compatible models with VRAM requirements at each quantization level, lets the user pick a download location, and writes a persistent config file so subsequent starts are one command. Once the node is running, the wizard gives way to a rich live status panel showing: GPU temp + VRAM used, tokens/sec, requests served, peers connected, TAI earned (stub until US-006 is live). A Browse HuggingFace option calls the HF Hub API so users can load any HF model beyond the curated list.",
      "acceptanceCriteria": [
        " with no args and no config file enters the interactive setup wizard",
        "Wizard step 1: auto-detect GPU(s) via torch.cuda / torch.version.hip; print GPU name + total VRAM",
        "Wizard step 2: show curated model list (name, HF repo, layers, VRAM@NF4/INT8/BF16); mark models that do NOT fit available VRAM as [too large]",
        "Wizard step 3: offer [B] Browse HuggingFace \u2014 calls HF Hub API (huggingface_hub.list_models filtered by pipeline_tag=text-generation, sorted by downloads, top 20) and lets user enter a custom HF repo ID",
        "Wizard step 4: prompt for download directory (default ~/.meshnet/models/); validate writable; show estimated disk usage for chosen model+quantization",
        "Wizard step 5: prompt for tracker URL (default http://localhost:8080); validate connection",
        "Wizard writes ~/.config/meshnet/config.json; second run skips wizard and starts directly",
        "All wizard values overridable via CLI flags: --model, --download-dir, --quantization [bf16|int8|nf4], --tracker, --wallet, --reset-config",
        "Once node is running, wizard clears and a live dashboard renders every 2s (rich.live): GPU util%, VRAM used/total, tokens/sec (EMA), requests served, TAI earned (stub 0.0), peers connected, uptime, current model/shard range",
        "Dashboard exits cleanly on Ctrl-C with a summary line",
        "Works inside WSL2 (no termios/ioctl calls that fail on Windows terminal; fall back to plain-text status if rich is not available)",
        " passes from repo root",
        "Commit only this story changes"
      ],
      "priority": 16,
      "status": "done",
      "notes": "Source issue: .scratch/distributed-inference-network/issues/16-mining-cli-ux.md",
      "dependsOn": [
        "US-004",
        "US-012"
      ],
      "completionNotes": "Implemented: mining-style wizard with GPU detection, curated model list (7 models with NF4/INT8/BF16 VRAM requirements), HF Hub browse, persistent config, rich live dashboard with plain-text WSL2 fallback. 19 tests, 97 passed total."
    },
    {
      "id": "US-017",
      "title": "17 \u2014 P2P gossip, NAT-traversal relay node, and SSL/TLS",
      "description": "Add a gossip layer so nodes discover each other and propagate coverage-map changes without polling the tracker continuously. Introduce a publicly-hosted relay node (run by the team) that solves NAT traversal using circuit relay (Petals-style) and serves as the bootstrap peer list for new nodes. Encrypt all node-to-node and node-to-tracker communications with TLS. Gossip protocol: WebSocket-based PubSub over wss://, topics: node-join / node-leave / coverage-update / heartbeat. Peer discovery: mDNS (zeroconf) for LAN, public relay bootstrap list for internet. NAT traversal: relay node acts as TCP-level circuit relay when direct connection fails (hole-punching first, relay second). Architecture is designed to migrate to libp2p GossipSub + Kademlia DHT in a future story without breaking the message schema.",
      "acceptanceCriteria": [
        "All HTTP between nodes and tracker uses HTTPS (TLS 1.3); self-signed cert generated on first run and fingerprint pinned in config; relay node uses Let's Encrypt",
        "Nodes broadcast node-join / node-leave events over wss:// to known peers within 1s of registration",
        "mDNS peer discovery (Python zeroconf) finds other meshnet nodes on the same LAN segment without manual tracker URL entry",
        "Public relay bootstrap list (hardcoded relay URL + ) is consulted when no LAN peers found",
        "Relay node is a standalone meshnet package () with  CLI: starts a WebSocket relay server + circuit relay + optional tracker proxy",
        "When a node behind NAT cannot accept inbound connections, the relay forwards its traffic; node advertises relay address (relay_url/node_id) to tracker as its effective endpoint",
        "Tracker accepts both direct node URLs and relay-proxied URLs in heartbeat payloads",
        "Integration test: two nodes in separate processes on localhost (simulating NAT) communicate via a local relay process; inference request routes correctly",
        "ADR-0010 documents the gossip protocol, relay architecture, and migration path to libp2p",
        " passes from repo root",
        "Commit only this story changes"
      ],
      "priority": 17,
      "status": "done",
      "notes": "Source issue: .scratch/distributed-inference-network/issues/17-p2p-gossip-relay-ssl.md",
      "dependsOn": [
        "US-013",
        "US-014"
      ],
      "completionNotes": "Implemented: packages/p2p (identity, TLS cert+fingerprint, GossipClient WSS PubSub, MdnsDiscovery with zeroconf optional), packages/relay (RelayServer gossip hub + circuit relay proxy, meshnet-relay CLI), tracker extended with relay_addr/cert_fingerprint/peer_id, relay_bootstrap.json, ADR-0010. 18 new tests; 115 total passed."
    },
    {
      "id": "US-018",
      "title": "18 \u2014 End-to-end two-machine LAN inference test",
      "description": "Prove the network works across two real machines: the Linux rig (this machine) and a Windows 11 rig running WSL2. One machine runs the tracker + first-shard node (inference entry point). The other machine runs a second-shard node. A client sends a real inference request and receives a streamed response. This story is primarily a test plan + setup guide + test execution script; it produces documented evidence (logs, timing, token output) that real distributed inference works. It also surfaces any real-world issues (port forwarding, CUDA driver version mismatches, WSL2 CUDA passthrough, model download paths) that need fixing.",
      "acceptanceCriteria": [
        "docs/INSTALL_WINDOWS.md exists: step-by-step WSL2 + CUDA + meshnet-node install on Windows 11",
        "docs/TWO_MACHINE_TEST.md exists: how to start tracker on machine A, node on machine B, run inference, interpret output",
        "A test script scripts/test_lan_inference.py: given --tracker-url, --gateway-url, sends 3 chat completion requests, asserts valid OpenAI format, prints token count + latency + which nodes served each request",
        "Both machines can reach each other on LAN (documented: firewall rules, port list)",
        "At least one successful inference recorded: the test script exits 0 with output showing tokens generated and node IDs",
        "Latency breakdown logged: gateway\u2192node-A, node-A\u2192node-B, node-B\u2192gateway (approximate, from server logs)",
        "Known issues during test documented in docs/TWO_MACHINE_TEST.md under a Known Issues section",
        "Commit only this story changes"
      ],
      "priority": 18,
      "status": "done",
      "notes": "Source issue: .scratch/distributed-inference-network/issues/18-two-machine-lan-test.md",
      "dependsOn": [
        "US-016",
        "US-017"
      ],
      "completionNotes": "docs/INSTALL_WINDOWS.md: WSL2+CUDA+meshnet-node install guide. docs/TWO_MACHINE_TEST.md: two-machine LAN test procedure with known issues. scripts/test_lan_inference.py: stdlib-only test script, 3 requests, exit 0 on success, auto-discovers gateway from tracker."
    },
    {
      "id": "US-019",
      "title": "19 \u2014 Distributed tracker consensus (Raft assignments + CRDT heartbeats)",
      "description": "Replace the single-point-of-failure tracker with a fault-tolerant cluster. Tracker nodes elect a leader via Raft and commit shard assignments as log entries \u2014 all tracker nodes agree on who owns what. Node liveness (heartbeats) uses CRDT gossip (eventual consistency, high frequency OK). A node registers with any tracker node; the write is forwarded to the leader and replicated to followers. A 3-node tracker cluster survives one tracker failure without losing assignment state. The relay/gossip layer already built in US-017 handles peer heartbeats; this story wires Raft on top for authoritative assignments.",
      "acceptanceCriteria": [
        "3 tracker nodes can be started and form a Raft cluster (leader election, log replication)",
        "A node registers with any follower \u2014 the registration is forwarded to the leader and replicated",
        "Killing the leader causes a new election within 5 seconds; registrations continue working",
        "Shard assignments returned by any tracker node are identical (strong consistency)",
        "Node heartbeats use CRDT gossip (not Raft) \u2014 high-frequency, eventual consistency",
        "meshnet-tracker CLI gains --cluster-peers flag to specify peer tracker URLs",
        "Integration test: 3 tracker nodes, kill leader mid-test, verify assignment still works",
        "QUICKSTART.md updated with multi-tracker setup section"
      ],
      "priority": 19,
      "status": "done",
      "notes": "Architecture decision: Raft for assignments (strong consistency) + CRDT gossip for liveness (eventual consistency). User approved 2026-06-29.",
      "dependsOn": [
        "US-017"
      ],
      "completionNotes": "raft.py: minimal Raft consensus (leader election, log replication, AppendEntries, RequestVote). gossip.py: LWW CRDT gossip for node heartbeats. TrackerServer gains cluster_peers + cluster_self_url params. --cluster-peers and --self-url CLI flags added. 6 integration tests: leader election <1s, follower registration propagation, leader kill + re-election <5s, gossip table."
    }
  ],
  "metadata": {
    "updatedAt": "2026-06-29T15:35:00.000Z",
    "statusVocabulary": {
      "open": "Not started",
      "in-design": "Decisions pending before implementation can begin",
      "in-progress": "Agent actively working on it",
      "needs-review": "Implemented, awaiting human review or verification",
      "to-revise": "Previously done, needs rework due to design or architecture changes",
      "done": "Implemented, tested, and verified",
      "blocked": "Waiting on unresolved external dependency"
    }
  }
}