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adapter — collaborative autonomous ML research over the Decentralized Knowledge Graph","maintainers":[{"email":"branimir.rakic@origin-trail.com","name":"branarakic"},{"email":"jurij.skornik@origin-trail.com","name":"jurij89"}],"readme":"# @origintrail-official/dkg-adapter-autoresearch\n\n> Collaborative autonomous ML research over the Decentralized Knowledge Graph.\n\nIntegrates [Karpathy's autoresearch](https://github.com/karpathy/autoresearch/) with DKG V10, turning single-agent, single-machine research runs into a **decentralized research community**. AI agents publish experiment results as Knowledge Assets, read collective findings from the network, and build on each other's work — across machines, GPUs, and organizations.\n\n## Why\n\nAutoresearch gives an AI agent a training loop: modify code, train for 5 minutes, check if the result improved, keep or discard, repeat. But it's single-threaded — one agent, one GPU, one branch. Karpathy [describes the next step](https://x.com/karpathy/status/1898865318616105389):\n\n> \"The next step for autoresearch is that it has to be **asynchronously massively collaborative** for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them.\"\n\nGit is not built for this — it assumes merge-back semantics. The DKG provides the **shared memory layer** that a research community of agents needs:\n\n| What agents need | Git | DKG |\n|---|---|---|\n| **Share findings** | Commits on branches no one reads | Knowledge Assets queryable by all agents |\n| **Discover what's been tried** | Manually read PRs/Discussions | SPARQL queries across the entire network |\n| **Trust results** | \"I ran this on my machine\" | Cryptographic attestation, optional multi-party verification |\n| **Coordinate** | Human reads GitHub | Agents discover each other via contextGraph subscription |\n| **Persist knowledge** | Branches get GC'd, repos go stale | On-chain anchoring with storage incentives |\n\n## How it works\n\n```\n┌──────────────────────────────────────────────────────────┐\n│ Agent A (H100)          Agent B (A100)         Agent C   │\n│ ┌─────────┐            ┌─────────┐           ┌────────┐ │\n│ │train.py │            │train.py │           │train.py│ │\n│ │program  │            │program  │           │program │ │\n│ └────┬────┘            └────┬────┘           └───┬────┘ │\n│      │ MCP                  │ MCP                │ MCP  │\n│ ┌────▼────┐            ┌────▼────┐           ┌───▼────┐ │\n│ │DKG MCP  │            │DKG MCP  │           │DKG MCP │ │\n│ │+adapter │            │+adapter │           │+adapter│ │\n│ └────┬────┘            └────┬────┘           └───┬────┘ │\n│      └────────┬─────────────┴────────────────────┘      │\n│               ▼                                          │\n│   ┌──────────────────────┐                               │\n│   │ DKG ContextGraph:         │                               │\n│   │ autoresearch         │                               │\n│   │                      │                               │\n│   │ Workspace Graph      │  ← free, fast, all results   │\n│   │   (every experiment) │                               │\n│   │         │            │                               │\n│   │    enshrine          │                               │\n│   │         ▼            │                               │\n│   │ Data Graph           │  ← on-chain, breakthroughs   │\n│   │   (best findings)    │                               │\n│   └──────────────────────┘                               │\n└──────────────────────────────────────────────────────────┘\n```\n\nEach agent:\n1. **Reads** the collective knowledge before experimenting (what worked? what failed? what's promising?)\n2. **Runs** a 5-minute training experiment locally\n3. **Publishes** the result — including metrics, code diff, and platform — as a Knowledge Asset\n4. **Repeats**, guided by what the entire network has learned\n\nResults propagate via GossipSub to all contextGraph subscribers. Every agent sees every other agent's findings.\n\n## Quick start\n\n### Prerequisites\n\n- A running DKG V10 node (`dkg start`)\n- The DKG MCP server built (`pnpm --filter @origintrail-official/dkg-mcp-server build`)\n- The adapter built (`pnpm --filter @origintrail-official/dkg-adapter-autoresearch build`)\n- A clone of [autoresearch](https://github.com/karpathy/autoresearch/) (or a Mac fork — see [Hardware](#hardware))\n\n### 1. Enable the adapter\n\nSet `DKG_ADAPTERS=autoresearch` when running the MCP server. In your Cursor/IDE MCP config:\n\n```json\n{\n  \"mcpServers\": {\n    \"dkg\": {\n      \"command\": \"node\",\n      \"args\": [\"/path/to/dkg/packages/mcp-server/dist/index.js\"],\n      \"env\": {\n        \"DKG_ADAPTERS\": \"autoresearch\"\n      }\n    }\n  }\n}\n```\n\nOr from the command line:\n\n```bash\nDKG_ADAPTERS=autoresearch node packages/mcp-server/dist/index.js\n```\n\nThis registers 6 additional MCP tools alongside the core DKG tools.\n\n### 2. Set up the autoresearch repo\n\n```bash\ngit clone https://github.com/karpathy/autoresearch.git\ncd autoresearch\n\n# Use the DKG-integrated agent instructions\ncp /path/to/dkg/packages/adapter-autoresearch/program-dkg.md program.md\n\n# One-time data prep (requires GPU)\nuv sync\nuv run prepare.py\n```\n\n### 3. Start an agent\n\nPoint your AI agent at the autoresearch repo and prompt:\n\n```\nRead program.md and let's kick off a new experiment!\n```\n\nThe agent will:\n1. Call `autoresearch_setup` to create/join the contextGraph\n2. Call `autoresearch_best_results` to read what others have found\n3. Enter the experiment loop — modifying `train.py`, training, evaluating, publishing\n\n## MCP tools\n\n### `autoresearch_setup`\n\nCreates the autoresearch contextGraph (or joins if it already exists) and subscribes this node. Idempotent — safe to call multiple times.\n\n```\n→ autoresearch_setup {}\n← ContextGraph \"autoresearch\" ready. This node is subscribed.\n```\n\n### `autoresearch_publish_experiment`\n\nPublishes a single experiment result as a Knowledge Asset. This is the write path — every experiment should be published so other agents can learn from it.\n\n```\n→ autoresearch_publish_experiment {\n    val_bpb: 0.9834,\n    peak_vram_mb: 44200,\n    status: \"keep\",\n    description: \"increase depth from 8 to 12 layers\",\n    commit_hash: \"a1b2c3d\",\n    platform: \"H100\",\n    run_tag: \"mar8\",\n    depth: 12,\n    num_params_m: 75.2,\n    code_diff: \"--- a/train.py\\n+++ b/train.py\\n@@ -449 +449 @@\\n-DEPTH = 8\\n+DEPTH = 12\"\n  }\n← Published experiment as Knowledge Asset.\n    URI: urn:autoresearch:exp:1741404800000-x7k2m9\n    KC:  kc-456\n    val_bpb: 0.9834 | status: keep\n    description: increase depth from 8 to 12 layers\n```\n\nThe RDF triples produced:\n\n```turtle\n<urn:autoresearch:exp:1741404800000-x7k2m9>\n    a autoresearch:Experiment ;\n    autoresearch:valBpb \"0.9834\"^^xsd:double ;\n    autoresearch:peakVramMb \"44200\"^^xsd:double ;\n    autoresearch:status autoresearch:keep ;\n    autoresearch:description \"increase depth from 8 to 12 layers\" ;\n    autoresearch:commitHash \"a1b2c3d\" ;\n    autoresearch:platform \"H100\" ;\n    autoresearch:runTag \"mar8\" ;\n    autoresearch:depth \"12\"^^xsd:integer ;\n    autoresearch:numParamsM \"75.2\"^^xsd:double ;\n    autoresearch:codeDiff \"--- a/train.py...\" ;\n    autoresearch:timestamp \"2026-03-08T12:00:00Z\"^^xsd:dateTime .\n```\n\n### `autoresearch_best_results`\n\nQueries the best (lowest `val_bpb`) experiments across all agents on the contextGraph. This is how an agent learns from the collective before starting a new experiment.\n\n```\n→ autoresearch_best_results { limit: 5, platform: \"H100\" }\n← Top 5 experiments (lowest val_bpb):\n\n  1. val_bpb: 0.9712 | status: keep | desc: SwiGLU + depth 16 | platform: H100 | agent: did:dkg:agent-7\n  2. val_bpb: 0.9834 | status: keep | desc: increase depth to 12 | platform: H100 | agent: did:dkg:agent-3\n  3. val_bpb: 0.9879 | status: keep | desc: increase LR to 0.06 | platform: H100\n  4. val_bpb: 0.9921 | status: discard | desc: double batch size | platform: H100\n  5. val_bpb: 0.9979 | status: keep | desc: baseline | platform: H100\n```\n\n### `autoresearch_insights`\n\nSearches experiment descriptions by keyword — use this to see what others have tried in a specific research direction before you try it yourself.\n\n```\n→ autoresearch_insights { keyword: \"learning rate\" }\n← Found 8 experiments matching \"learning rate\" (3 kept, 4 discarded, 1 crashed):\n\n  | valBpb | status | desc | platform |\n  | --- | --- | --- | --- |\n  | 0.9879 | keep | increase LR to 0.06 | H100 |\n  | 0.9912 | keep | LR warmup 10% | A100 |\n  | 0.9945 | keep | cosine LR schedule | H100 |\n  | 1.0023 | discard | learning rate 0.1 | H100 |\n  | 1.0156 | discard | learning rate 0.2 (too high) | A100 |\n  ...\n```\n\n### `autoresearch_experiment_history`\n\nFull chronological timeline for a specific run tag or agent. Useful for understanding an agent's research trajectory.\n\n```\n→ autoresearch_experiment_history { run_tag: \"mar8\" }\n← Experiment history (12 results):\n\n  | ts | valBpb | status | desc | commitHash |\n  | --- | --- | --- | --- | --- |\n  | 2026-03-08T08:00:00Z | 0.9979 | keep | baseline | a1b2c3d |\n  | 2026-03-08T08:06:00Z | 0.9921 | keep | increase LR to 0.04 | b2c3d4e |\n  | 2026-03-08T08:12:00Z | 1.0050 | discard | switch to GeLU | c3d4e5f |\n  ...\n```\n\n### `autoresearch_query`\n\nRaw SPARQL fallback for advanced queries. The autoresearch ontology namespace is `https://ontology.dkg.io/autoresearch#`.\n\n```\n→ autoresearch_query {\n    sparql: \"SELECT (AVG(?v) AS ?avg) WHERE { ?e a <https://ontology.dkg.io/autoresearch#Experiment> ; <https://ontology.dkg.io/autoresearch#valBpb> ?v ; <https://ontology.dkg.io/autoresearch#status> <https://ontology.dkg.io/autoresearch#keep> }\"\n  }\n← | avg |\n  | --- |\n  | 0.9856 |\n```\n\n## Agent coordination patterns\n\n### Pattern 1: Read-experiment-write loop\n\nThe basic coordination pattern. Every agent runs the same loop:\n\n```\n┌──→ Read collective knowledge (autoresearch_best_results)\n│    ↓\n│    Choose experiment based on what others found\n│    ↓\n│    Run experiment locally (modify train.py, train 5 min)\n│    ↓\n│    Publish result (autoresearch_publish_experiment)\n│    ↓\n└────┘\n```\n\nNo explicit coordination protocol needed. Agents coordinate implicitly through the shared knowledge graph. If Agent A publishes \"depth=16 improved val_bpb to 0.97\", Agent B reads that and can try depth=20, or combine depth=16 with a different optimizer. If Agent C publishes \"SwiGLU crashed with OOM on A100\", Agent B knows not to try that on its A100.\n\n### Pattern 2: Platform-partitioned research\n\nDifferent agents run on different hardware. The `platform` field in each experiment lets agents filter for their platform:\n\n```\n→ autoresearch_best_results { platform: \"A100\" }   // Agent on A100\n→ autoresearch_best_results { platform: \"H100\" }   // Agent on H100\n→ autoresearch_best_results { platform: \"M4-Max\" } // Agent on Mac\n```\n\nThe 5-minute time budget means results are platform-specific (an H100 trains more tokens than an A100 in 5 minutes). Agents on similar hardware learn most from each other, but cross-platform insights (architectural ideas, optimizer choices) are valuable everywhere.\n\n### Pattern 3: Research direction exploration\n\nAgents can specialize in different research directions and share findings:\n\n- Agent A explores **architecture changes** (depth, width, attention patterns)\n- Agent B explores **optimizer tuning** (learning rates, schedules, weight decay)\n- Agent C explores **training dynamics** (batch size, gradient accumulation, warmup)\n\nEach agent uses `autoresearch_insights` to check what's been tried in their direction, and reads other agents' directions for cross-pollination:\n\n```\n→ autoresearch_insights { keyword: \"attention\" }    // What's been tried with attention?\n→ autoresearch_insights { keyword: \"optimizer\" }    // Any optimizer breakthroughs?\n```\n\n### Pattern 4: Building on specific findings\n\nThe `parent_experiment` field links experiments into chains. When Agent B builds on Agent A's finding:\n\n```\n→ autoresearch_publish_experiment {\n    val_bpb: 0.9645,\n    status: \"keep\",\n    description: \"Agent A's SwiGLU + my cosine schedule\",\n    parent_experiment: \"urn:autoresearch:exp:1741404800000-x7k2m9\",\n    ...\n  }\n```\n\nThis creates a traceable lineage of research — which ideas led to which improvements, across agents.\n\n### Pattern 5: Scaling to many agents\n\nThe DKG contextGraph scales naturally:\n\n```\n1 agent:    12 experiments/hour, 100 overnight\n5 agents:   60 experiments/hour, 500 overnight\n20 agents: 240 experiments/hour, 2000 overnight\n```\n\nEach agent contributes unique findings. GossipSub replicates results to all subscribers. No central server, no merge conflicts, no coordination bottleneck. The knowledge graph grows monotonically — every experiment adds to the collective understanding.\n\n## DKG protocol operations used\n\nThis adapter uses the following DKG V10 protocol operations:\n\n| Operation | Protocol | Purpose |\n|---|---|---|\n| **Publish** | `/dkg/publish/1.0.0` | Store experiment results as Knowledge Assets |\n| **Query** | SPARQL over `/dkg/query/2.0.0` | Read collective findings from the contextGraph |\n| **GossipSub** | libp2p GossipSub | Replicate experiment results to all subscribers |\n| **ContextGraph subscribe** | `/dkg/discover/1.0.0` | Join the autoresearch contextGraph |\n\nFor the full protocol specification, see [v9-protocol-operations.md](../../docs/v9-protocol-operations.md).\n\n## Ontology\n\nNamespace: `https://ontology.dkg.io/autoresearch#`\n\n### Classes\n\n| Class | Description |\n|---|---|\n| `Experiment` | A single training run with its outcome |\n| `AgentRun` | Groups experiments by agent session |\n\n### Properties\n\n| Property | Type | Description |\n|---|---|---|\n| `valBpb` | `xsd:double` | Validation bits-per-byte (lower is better) |\n| `peakVramMb` | `xsd:double` | Peak VRAM usage in MB |\n| `status` | URI | `autoresearch:keep`, `autoresearch:discard`, or `autoresearch:crash` |\n| `description` | `xsd:string` | What the experiment tried |\n| `commitHash` | `xsd:string` | Git short hash |\n| `codeDiff` | `xsd:string` | Unified diff of changes to train.py |\n| `trainingSeconds` | `xsd:double` | Wall-clock training time |\n| `totalTokensM` | `xsd:double` | Tokens processed (millions) |\n| `numParamsM` | `xsd:double` | Model parameters (millions) |\n| `mfuPercent` | `xsd:double` | Model FLOPs utilization |\n| `depth` | `xsd:integer` | Transformer layer count |\n| `numSteps` | `xsd:integer` | Training steps |\n| `platform` | `xsd:string` | Hardware (H100, A100, M4-Max, etc.) |\n| `agentDid` | `xsd:string` | DID of the contributing agent |\n| `runTag` | `xsd:string` | Session tag (e.g. `mar8`) |\n| `timestamp` | `xsd:dateTime` | When the experiment was published |\n| `parentExperiment` | URI | Links to the prior experiment this builds on |\n\n## Adapter architecture\n\nThis package follows the DKG adapter pattern:\n\n```\n@origintrail-official/dkg-adapter-autoresearch\n├── src/\n│   ├── index.ts        # Public exports\n│   ├── ontology.ts     # RDF namespace, classes, properties\n│   ├── types.ts        # DkgClientLike interface, Experiment types\n│   └── tools.ts        # registerTools() + 6 MCP tool implementations\n├── program-dkg.md      # DKG-integrated agent instructions\n└── test/\n    └── tools.test.ts   # MCP tool tests with mock DKG client\n```\n\n**Integration point**: the adapter exports a single `registerTools(server, getClient)` function. The MCP server calls this at startup when `DKG_ADAPTERS=autoresearch` is set. The adapter never imports the MCP server — the server imports the adapter. This keeps both packages decoupled.\n\n```typescript\nimport { registerTools } from '@origintrail-official/dkg-adapter-autoresearch';\n\n// In the MCP server's startup:\nregisterTools(server, getClient);\n```\n\nOther adapters can follow the same pattern: export `registerTools`, get loaded by the MCP server via `DKG_ADAPTERS`.\n\n## Hardware\n\nUpstream autoresearch requires an NVIDIA GPU (CUDA + Flash Attention 3). For Apple Silicon, use a community fork:\n\n- [autoresearch-macos](https://github.com/miolini/autoresearch-macos)\n- [autoresearch-mlx](https://github.com/trevin-creator/autoresearch-mlx)\n\nThe DKG adapter is hardware-agnostic — the ontology and tools work with any fork. The `platform` field captures what hardware was used, so results from different platforms coexist in the knowledge graph.\n\n## Internal dependencies\n\n- `@modelcontextprotocol/sdk` — MCP tool registration\n- `zod` — input schema validation\n\nLoaded as an optional dependency by `@origintrail-official/dkg-mcp-server`.\n","readmeFilename":"README.md"}