{"name":"clawmind","version":"2.0.0","kind":"agentic-infrastructure","track":"Track 1: Agentic Infrastructure & OpenClaw Lab","manifestValidation":{"valid":true,"errors":[],"warnings":[],"pipelineSteps":8,"modelStrategy":"single_primary_model_route"},"pipeline":[{"id":"memory_retrieval","label":"Memory Retrieval","skill":"persistent-memory-retrieval","model":"all-MiniLM-L6-v2","declaredModel":"local","modelFamily":"Local embeddings","temperature":0,"maxTokens":0,"dependsOn":[],"structuredOutput":false},{"id":"planner","label":"Planner Agent","skill":"task-decomposition","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.2,"maxTokens":1200,"dependsOn":["memory_retrieval"],"structuredOutput":true},{"id":"researcher","label":"Research Agent","skill":"research-extraction","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.3,"maxTokens":1500,"dependsOn":["planner"],"structuredOutput":true},{"id":"risk_agent","label":"Risk Agent","skill":"web3-risk-analysis","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.2,"maxTokens":1500,"dependsOn":["researcher","memory_retrieval"],"structuredOutput":true},{"id":"architect","label":"Architect Agent","skill":"architecture-design","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.3,"maxTokens":1500,"dependsOn":["researcher","risk_agent"],"structuredOutput":true},{"id":"critic","label":"Critic Agent (Adversarial)","skill":"adversarial-review","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.8,"maxTokens":1500,"dependsOn":["planner","researcher","risk_agent","architect"],"structuredOutput":true},{"id":"final_agent","label":"Final Decision Agent","skill":"decision-synthesis","model":"deepseek/deepseek-chat-v3-0324","declaredModel":"deepseek/deepseek-chat-v3-0324","modelFamily":"DeepSeek","temperature":0.1,"maxTokens":2500,"dependsOn":["planner","researcher","risk_agent","architect","critic"],"structuredOutput":true},{"id":"memory_writer","label":"Memory Writer","skill":"persistent-memory-writing","model":"all-MiniLM-L6-v2 + 0G Storage","declaredModel":"all-MiniLM-L6-v2 + 0G Storage","modelFamily":"Local embeddings","temperature":0.1,"maxTokens":500,"dependsOn":["final_agent"],"structuredOutput":true}],"liveEvidence":{"network":{"name":"mainnet","chainId":16661,"explorerBaseUrl":"https://chainscan.0g.ai"},"compute":{"provider":"0G_COMPUTE","isConfigured":true,"multiModelEnsemble":false,"models":["deepseek/deepseek-chat-v3-0324"],"declaredModels":["deepseek/deepseek-chat-v3-0324"],"strategy":"single_primary_model_route","envOverrideActive":true},"storage":{"provider":"0G_STORAGE","isConfigured":true},"onChain":{"configured":true,"contractAddress":"0x08a9c275f5d0764a32f9dda4f50ba6f9a828e2b1","explorerUrl":"https://chainscan.0g.ai/address/0x08a9c275f5d0764a32f9dda4f50ba6f9a828e2b1","latestAnalysis":{"analysisId":40,"rootHash":"0x4a600ed59ac6b9b93c68ce288046a148bb1684b6642d97ce128a53c55ea8efd9","score":40,"recommendation":"INVESTIGATE_MORE","submitter":"0x9A0C8040A8C6aB9F65F544578b891Fba599799F8","timestamp":1778618269,"storageUri":"0g://0x4a600ed59ac6b9b93c68ce288046a148bb1684b6642d97ce128a53c55ea8efd9?tx=0x759490ce869aa3b889e69bac8c1289f6f0c14f0e295a482932823e745cbc4978","taskHash":"0xed4f11d432c54ba9b9df806870e2d21e733504905d02b1b7773fe0dd7bc84c95","signatureVerified":true,"registryMode":"SIGNED_OPERATOR"},"operatorAuthentication":{"mode":"EIP712_OPERATOR_SIGNATURE","signatureVerified":true,"signedBy":"0x9A0C8040A8C6aB9F65F544578b891Fba599799F8"}},"semanticMemory":{"embeddingModel":"all-MiniLM-L6-v2","embeddingDimensions":384,"embeddingReady":true,"retrievalMethod":"cosine_similarity_top_k"}},"rawYaml":"name: clawmind\nversion: 2.0.0\nkind: agentic-infrastructure\ntrack: \"Track 1: Agentic Infrastructure & OpenClaw Lab\"\n\ndescription: >\n  Multi-agent Web3 due diligence powered by 0G Compute for agent inference,\n  0G Storage for reports and memory, and 0G Chain for report anchoring.\n\nruntime:\n  framework: nextjs\n  language: typescript\n  entrypoints:\n    api:\n      analyze: app/api/analyze/route.ts\n      memory: app/api/memory/route.ts\n      retrieve_report: app/api/report/retrieve/route.ts\n    orchestrator: lib/orchestrator/run-analysis.ts\n\norchestration:\n  mode: sequential-multi-agent-pipeline\n  manifest_driven: true\n  state_persistence:\n    primary: 0G_STORAGE\n    fallback: LOCAL_FALLBACK\n  compute:\n    primary: 0G_COMPUTE_ROUTER\n    fallback: LOCAL_DETERMINISTIC_INFERENCE\n  pipeline:\n    - id: memory_retrieval\n      label: Memory Retrieval\n      skill: persistent-memory-retrieval\n      model: local\n      temperature: 0\n      max_tokens: 0\n      input: user_task\n      output: relevant_memories\n      depends_on: []\n      reads:\n        - local_memory_store\n        - zero_g_memory_index\n    - id: planner\n      label: Planner Agent\n      skill: task-decomposition\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.2\n      max_tokens: 1200\n      structured_output: true\n      input:\n        - user_task\n        - relevant_memories\n      output: execution_plan\n      depends_on:\n        - memory_retrieval\n    - id: researcher\n      label: Research Agent\n      skill: research-extraction\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.3\n      max_tokens: 1500\n      structured_output: true\n      input:\n        - user_task\n        - execution_plan\n      output: research_findings\n      depends_on:\n        - planner\n    - id: risk_agent\n      label: Risk Agent\n      skill: web3-risk-analysis\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.2\n      max_tokens: 1500\n      structured_output: true\n      input:\n        - user_task\n        - research_findings\n        - relevant_memories\n      output: risk_map\n      depends_on:\n        - researcher\n        - memory_retrieval\n    - id: architect\n      label: Architect Agent\n      skill: architecture-design\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.3\n      max_tokens: 1500\n      structured_output: true\n      input:\n        - user_task\n        - research_findings\n        - risk_map\n      output: architecture_recommendations\n      depends_on:\n        - researcher\n        - risk_agent\n    - id: critic\n      label: Critic Agent (Adversarial)\n      skill: adversarial-review\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.8\n      max_tokens: 1500\n      structured_output: true\n      input:\n        - execution_plan\n        - research_findings\n        - risk_map\n        - architecture_recommendations\n      output: critique\n      depends_on:\n        - planner\n        - researcher\n        - risk_agent\n        - architect\n    - id: final_agent\n      label: Final Decision Agent\n      skill: decision-synthesis\n      model: deepseek/deepseek-chat-v3-0324\n      temperature: 0.1\n      max_tokens: 2500\n      structured_output: true\n      input:\n        - user_task\n        - relevant_memories\n        - execution_plan\n        - research_findings\n        - risk_map\n        - architecture_recommendations\n        - critique\n      output: structured_decision_report\n      depends_on:\n        - planner\n        - researcher\n        - risk_agent\n        - architect\n        - critic\n    - id: memory_writer\n      label: Memory Writer\n      skill: persistent-memory-writing\n      model: all-MiniLM-L6-v2 + 0G Storage\n      temperature: 0.1\n      max_tokens: 500\n      structured_output: true\n      input:\n        - structured_decision_report\n        - storage_receipt\n      output:\n        - memory_record\n        - zero_g_memory_index_receipt\n        - on_chain_receipt\n      depends_on:\n        - final_agent\n\nskills:\n  - id: persistent-memory-retrieval\n    description: Retrieves prior ClawMind memory records using embedding-based semantic similarity and ranks them against the new task.\n    embedding_model: all-MiniLM-L6-v2\n    retrieval_method: cosine_similarity_top_k\n  - id: task-decomposition\n    description: Breaks ambiguous Web3/AI tasks into analysis work items.\n    model_family: deepseek\n  - id: research-extraction\n    description: Extracts project claims, assumptions, stakeholders, and missing information.\n    model_family: deepseek\n  - id: web3-risk-analysis\n    description: Identifies custody, oracle, governance, tokenomics, security, and automation risks.\n    model_family: deepseek\n  - id: architecture-design\n    description: Proposes agent, infra, storage, and execution-control architecture.\n    model_family: deepseek\n  - id: adversarial-review\n    description: Hostile adversarial review. Runs as a separate high-temperature Critic role on the production 0G Compute route. Challenges assumptions, identifies blind spots, and flags optimism bias.\n    model_family: deepseek\n  - id: decision-synthesis\n    description: Converts agent outputs into a structured score, recommendation, risks, opportunities, and next steps.\n    model_family: deepseek\n  - id: persistent-memory-writing\n    description: Stores distilled analysis memory with embeddings and persists the memory index to 0G Storage.\n    model_family: local_embeddings\n\nartifacts:\n  reports:\n    kind: CLAWMIND_ANALYSIS_REPORT\n    storage: 0G_STORAGE\n    uri_scheme: 0g://\n  memory_index:\n    kind: CLAWMIND_MEMORY_INDEX\n    storage: 0G_STORAGE\n    uri_scheme: 0g://\n    embedding_model: all-MiniLM-L6-v2\n    embedding_dimensions: 384\n    retrieval: cosine_similarity_top_k\n\nsecurity:\n  execution_policy:\n    - LLM agents may reason and recommend.\n    - LLM agents must not directly sign transactions.\n    - Human or deterministic policy layer must gate fund movement.\n  wallet_policy:\n    - Use burner wallets with limited 0G token balance for mainnet operations.\n    - Never commit private keys.\n  fallback_policy:\n    - Fallback mode must be clearly labeled as LOCAL_FALLBACK in receipts.\n\nzero_g_integration:\n  compute:\n    provider: 0G_COMPUTE_ROUTER\n    endpoint: https://router-api.0g.ai/v1/chat/completions\n    models:\n      - id: deepseek/deepseek-chat-v3-0324\n        roles: [planner, researcher, risk_agent, architect, critic, final_agent]\n        family: deepseek\n    strategy: single_primary_model_route\n    strategy_description: >\n      Production inference currently routes all LLM agents through the stable\n      DeepSeek route on 0G Compute. Agent diversity comes from separate roles,\n      role-specific prompts, Critic temperature, and explicit score-adjustment\n      math. Additional 0G Compute models remain configurable in the runtime\n      model router when availability allows.\n  storage:\n    provider: 0G_STORAGE\n    indexer_rpc: https://indexer-storage-turbo.0g.ai\n    evm_rpc: https://evmrpc.0g.ai\n    uri_scheme: 0g://\n    description: Reports and memory indexes are persisted to 0G Storage with verifiable receipts.\n  chain:\n    network: mainnet\n    chain_id: 16661\n    contract: AnalysisRegistry.sol\n    contract_address: \"0x08a9c275f5d0764a32f9dda4f50ba6f9a828e2b1\"\n    explorer: https://chainscan.0g.ai/address/0x08a9c275f5d0764a32f9dda4f50ba6f9a828e2b1\n    description: Each completed analysis is anchored on-chain with root hash, score, recommendation, storage URI, and an EIP-712 signature from an authorized ClawMind operator.\n    authentication: EIP712_OPERATOR_SIGNATURE\n  judge_mode:\n    url: /judge\n    api: /api/judge\n    description: Read-only review surface for hackathon judges. Shows 0G integration evidence, on-chain analysis data, and memory stats.\n","generatedAt":"2026-05-13T14:15:16.726Z"}