{"components":[{"slug":"llamaindex","name":"LlamaIndex","summary":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","kind":"kit","homepage":"https://www.llamaindex.ai","repoUrl":"https://github.com/run-llama/llama_index","docsUrl":"https://docs.llamaindex.ai","publisher":"LlamaIndex","license":"MIT","hasMcp":true,"hasApi":true,"costRange":"freemium","composabilityRationale":"Index / retrieve / query engines compose. Cloud extras are optional slices. Python-first can block a TS fleet.","functions":["retrieve","index","embed","ingest","compose","memory"],"slices":[{"functionId":"index","label":"Vector index","note":"Local or hosted","cost":"OSS free; cloud usage"},{"functionId":"retrieve","label":"Query engine","note":"Ask over indexed docs","cost":"OSS + embedding spend"},{"functionId":"embed","label":"Embed","note":"Data framework for retrieval and agents. 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Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. Documentation is available.","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"observed"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":1,"contribution":0.1,"why":"An MCP surface is declared. An API is declared. Cost is freemium. 3 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-09-12. Freshness is omitted unless observed as a signal.","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"createdAt":"2026-09-11T16:33:39.685Z","updatedAt":"2026-09-13T08:56:02.844Z","tags":["agents"],"recordId":"cmp_llamaindex","claims":{"name":"LlamaIndex","summary":"Data framework for retrieval and agents. 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Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"memory","label":"Memory","note":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Index / retrieve / query engines compose. Cloud extras are optional slices. Python-first can block a TS fleet."},"interfaces":[{"kind":"mcp"},{"kind":"http"}],"relations":[{"from":"llamaindex","to":"pageindex","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"memini","kind":"fills-same-slot-as","note":"Shared functions compose, embed, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions index, ingest, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"telemetry-dev","kind":"fills-same-slot-as","note":"Shared functions ingest, retrieve. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-13T08:56:02.844Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-13T08:56:02.844Z"}],"functionProbabilities":[],"claimVerification":[]},{"slug":"build-software-better-together","name":"Build software better, together","summary":"## Here are 48 public repositories matching this topic...","publisher":"github.com","homepage":"https://github.com/topics/company-brain","kind":"mcp","hasApi":true,"hasMcp":true,"costRange":"free","costNotes":"Cost range free taken from intake evidence.","functions":["code","index","ingest","connect","memory","retrieve","route","tool-call"],"slices":[{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"index","label":"index","note":"Inferred from executor","cost":"Unknown"},{"functionId":"ingest","label":"ingest","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"memory","label":"memory","note":"Inferred from executor","cost":"Unknown"},{"functionId":"retrieve","label":"retrieve","note":"Inferred from executor","cost":"Unknown"},{"functionId":"route","label":"route","note":"Inferred from executor","cost":"Unknown"},{"functionId":"tool-call","label":"tool-call","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Heuristic from executor evidence. Grok pass was unavailable or incomplete. No usage metrics were observed.","signals":[],"functionProbabilities":[{"functionId":"code","value":0.92,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"index","value":0.8,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"ingest","value":0.8,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"connect","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"memory","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"retrieve","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"route","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"tool-call","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"api","status":"unverified","asserted":"true","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"cost","status":"unverified","asserted":"free","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-16T00:14:25.325Z","updatedAt":"2026-10-16T00:14:25.325Z","sources":[{"kind":"web","label":"executor","url":"https://github.com/topics/company-brain","excerpt":"#\n\n# company-brain\n\nStar\n\n## Here are 48 public repositories matching this topic...\n\n### inkeep / open-knowledge\n\nStar\n 4.6k\n\nBeautiful, AI-native markdown IDE and LLM wiki\n\n* Updated\n  Oct 16, 2026\n* TypeScript\n\n### baryhuang / claude-code","at":"2026-10-16T00:14:25.325Z"}],"recordId":"cmp_build-software-better-together","claims":{"name":"Build software better, together","summary":"## Here are 48 public repositories matching this topic...","kind":"mcp","publisher":"github.com","homepage":"https://github.com/topics/company-brain","costRange":"free","costNotes":"Cost range free taken from intake evidence.","functions":["code","index","ingest","connect","memory","retrieve","route","tool-call"],"slices":[{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"index","label":"index","note":"Inferred from executor","cost":"Unknown"},{"functionId":"ingest","label":"ingest","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"memory","label":"memory","note":"Inferred from executor","cost":"Unknown"},{"functionId":"retrieve","label":"retrieve","note":"Inferred from executor","cost":"Unknown"},{"functionId":"route","label":"route","note":"Inferred from executor","cost":"Unknown"},{"functionId":"tool-call","label":"tool-call","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Heuristic from executor evidence. Grok pass was unavailable or incomplete. No usage metrics were observed."},"relations":[{"from":"build-software-better-together","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions connect, index, ingest, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions code, connect, index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"cursor","kind":"fills-same-slot-as","note":"Shared functions code, connect, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"agentmemory","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory. Can fill the same function slot."},{"from":"build-software-better-together","to":"lore","kind":"fills-same-slot-as","note":"Shared functions connect, memory, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions ingest, memory, retrieve. Can fill the same function slot."}],"explain":{"score":13,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":1,"contribution":0.1,"why":"An MCP surface is declared. An API is declared. Cost is free. 3 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-10-16. Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://github.com/topics/company-brain","excerpt":"#\n\n# company-brain\n\nStar\n\n## Here are 48 public repositories matching this topic...\n\n### inkeep / open-knowledge\n\nStar\n 4.6k\n\nBeautiful, AI-native markdown IDE and LLM wiki\n\n* Updated\n  Oct 16, 2026\n* TypeScript\n\n### baryhuang / claude-code","at":"2026-10-16T00:14:25.325Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":13},{"slug":"pageindex","name":"PageIndex","summary":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","kind":"app","homepage":"https://pageindex.ai","repoUrl":"https://github.com/VectifyAI/PageIndex","docsUrl":"https://docs.pageindex.ai","publisher":"","license":"MIT","hasMcp":true,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["index","reason","embed","memory","retrieve"],"functionProbabilities":[{"functionId":"index","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"reason","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"memory","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"retrieve","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"}],"slices":[{"functionId":"index","label":"Index","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. 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First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","at":"2026-09-29T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Cloud — PAGEINDEX_API_KEY; managed indexing/OCR/storage; chat still uses your model; MCP + folders/metadata (Cloud-only features).","at":"2026-09-29T00:00:00.000Z"}],"interfaces":[{"kind":"mcp"}],"origin":"seed","notes":[],"createdAt":"2026-09-29T00:00:00.000Z","updatedAt":"2026-09-29T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_pageindex","claims":{"name":"PageIndex","summary":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","kind":"app","publisher":"","homepage":"https://pageindex.ai","repoUrl":"https://github.com/VectifyAI/PageIndex","docsUrl":"https://docs.pageindex.ai","license":"MIT","costRange":"unknown","functions":["index","reason","embed","memory","retrieve"],"slices":[{"functionId":"index","label":"Index","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"memory","label":"Memory","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"retrieve","label":"Retrieve","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"}],"interfaces":[{"kind":"mcp"}],"composabilityRationale":""},"relations":[{"from":"pageindex","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"alibaba-model-studio","kind":"fills-same-slot-as","note":"Shared functions embed, reason. Can fill the same function slot."},{"from":"pageindex","to":"octen","kind":"fills-same-slot-as","note":"Shared functions embed, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"turbopuffer","kind":"fills-same-slot-as","note":"Shared functions embed, memory. Can fill the same function slot."},{"from":"pageindex","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions memory, retrieve. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-29T00:00:00.000Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-29T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":12,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. Documentation is available.","sources":[],"evidence":"observed"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","at":"2026-09-29T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Cloud — PAGEINDEX_API_KEY; managed indexing/OCR/storage; chat still uses your model; MCP + folders/metadata (Cloud-only features).","at":"2026-09-29T00:00:00.000Z"}],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0.3333333333333333,"contribution":0.03333333333333333,"why":"An MCP surface is declared. No API is declared. Cost is unknown. 1 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.22849520230941842,"contribution":0.031989328323318585,"why":"2 sources cover 1 evidence types. The latest dated evidence is 2026-09-29. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","at":"2026-09-29T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Cloud — PAGEINDEX_API_KEY; managed indexing/OCR/storage; chat still uses your model; MCP + folders/metadata (Cloud-only features).","at":"2026-09-29T00:00:00.000Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":12},{"slug":"otel-desktop-viewer","name":"otel-desktop-viewer","summary":"README: a CLI for exploring OpenTelemetry traces, metrics, and logs locally. Go custom Collector distribution, DuckDB backend, Svelte UI. Receives OTLP on localhost:4317 (gRPC) and localhost:4318 (HTTP). UI at localhost:8000. In-memory by default; --db persists DuckDB.","kind":"app","repoUrl":"https://github.com/CtrlSpice/otel-desktop-viewer","publisher":"","license":"Apache-2.0","hasMcp":false,"hasApi":true,"costRange":"unknown","composabilityRationale":"","functions":["memory"],"functionProbabilities":[{"functionId":"memory","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-08-24-ctrlspice-otel-desktop-viewer.md","method":"lexical","observedAt":"2026-08-24T00:00:00.000Z"}],"slices":[{"functionId":"memory","label":"Memory","note":"README: a CLI for exploring OpenTelemetry traces, metrics, and logs locally. Go custom Collector distribution, DuckDB backend, Svelte UI. Receives OTLP on localhost:4317 (gRPC) and localhost:4318 (HTTP). UI at localhost:8000. In-memory by default; --db persists DuckDB.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-24-ctrlspice-otel-desktop-viewer.md\nREADME: a CLI for exploring OpenTelemetry traces, metrics, and logs locally. Go custom Collector distribution, DuckDB backend, Svelte UI. Receives OTLP on localhost:4317 (gRPC) and localhost:4318 (HTTP). UI at localhost:8000. In-memory by default; --db persists DuckDB.","at":"2026-08-24T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"README: a CLI for exploring OpenTelemetry traces, metrics, and logs locally.","at":"2026-08-24T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Receives OTLP on localhost:4317 (gRPC) and localhost:4318 (HTTP).","at":"2026-08-24T00:00:00.000Z"}],"interfaces":[{"kind":"cli"},{"kind":"http"}],"origin":"seed","notes":[],"createdAt":"2026-08-24T00:00:00.000Z","updatedAt":"2026-08-24T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_otel-desktop-viewer","claims":{"name":"otel-desktop-viewer","summary":"README: a CLI for exploring OpenTelemetry traces, metrics, and logs locally. Go custom Collector distribution, DuckDB backend, Svelte UI. Receives OTLP on localhost:4317 (gRPC) and localhost:4318 (HTTP). UI at localhost:8000. 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The cost of those external services is not stated.","functions":["connect","code","compose","deploy","memory","policy","rank","search","store","embed","retrieve"],"slices":[{"functionId":"memory","label":"Tiered agent memory","note":"Shared remember and recall across working, episodic, semantic, and procedural tiers, with decay, TTLs, namespace isolation, and optional consolidation into durable facts.","cost":"Unknown"},{"functionId":"search","label":"Hybrid vector and keyword search","note":"Hybrid vector and keyword retrieval fused with Reciprocal Rank Fusion.","cost":"Unknown"},{"functionId":"rank","label":"Composite re-ranking","note":"Hybrid results are re-ranked by relevance, access recency, and importance rather than similarity alone.","cost":"Unknown"},{"functionId":"policy","label":"Client connect policy","note":"The MCP server sends a standing policy to every client on connect.","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"compose","label":"compose","note":"Inferred from executor","cost":"Unknown"},{"functionId":"deploy","label":"deploy","note":"Inferred from executor","cost":"Unknown"},{"functionId":"store","label":"Store","note":"Self-hosted persistent memory service for MCP-capable agents. 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It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"},{"functionId":"store","label":"Store","note":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md\nturbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","at":"2026-08-21T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Official SDKs are MIT; the service is paid (Launch minimum $16/month as of the 2026-06 pricing change).","at":"2026-08-21T00:00:00.000Z"}],"interfaces":[{"kind":"sdk"}],"origin":"seed","notes":[],"createdAt":"2026-08-21T00:00:00.000Z","updatedAt":"2026-08-21T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_turbopuffer","claims":{"name":"turbopuffer","summary":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","kind":"app","publisher":"","homepage":"https://turbopuffer.com/","repoUrl":"https://github.com/turbopuffer","docsUrl":"https://turbopuffer.com/docs","costRange":"unknown","functions":["memory","search","embed","store"],"slices":[{"functionId":"memory","label":"Memory","note":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"},{"functionId":"search","label":"Search","note":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"},{"functionId":"store","label":"Store","note":"turbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. 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Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md\nturbopuffer is a managed, commercial search engine from turbopuffer Inc. (Canadian, founded 2023). It is object-storage-first: Amazon S3 / Google Cloud Storage / Azure Blob is the source of truth; NVMe and memory are caches. 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Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-09T00:00:00.000Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-09T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":8,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. 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Can fill the same function slot."},{"from":"lore","to":"tikhub","kind":"fills-same-slot-as","note":"Shared functions extract, retrieve, search. Can fill the same function slot."},{"from":"lore","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions connect, retrieve, search, tool-call. Can fill the same function slot."},{"from":"lore","to":"memini","kind":"fills-same-slot-as","note":"Shared functions connect, memory, retrieve, search, store. Can fill the same function slot."}],"explain":{"score":6,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0.3333333333333333,"contribution":0.03333333333333333,"why":"An MCP surface is declared. No API is declared. Cost is unknown. 1 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-10-16. Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","excerpt":"All posts\n\nAll posts\n\nPublished August 18, 2026 in Tech\n\n# Building Shared Memory for AI Agents in Notion\n\nBy Hesham Salman\n\nEngineering, Notion\n\nAs software engineering changes, tribal knowledge is increasingly concentrated not only in a s","at":"2026-10-16T00:14:11.348Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":6,"tags":["agents"]},{"slug":"atomic-agent","name":"Atomic Agent","summary":"Local-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","kind":"agent","homepage":"https://atomicagent.io","repoUrl":"https://github.com/AtomicBot-ai/atomic-agent","publisher":"","license":"MIT","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["browser","memory","connect","tool-call"],"functionProbabilities":[{"functionId":"browser","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md","method":"lexical","observedAt":"2026-07-23T00:00:00.000Z"},{"functionId":"memory","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md","method":"lexical","observedAt":"2026-07-23T00:00:00.000Z"},{"functionId":"connect","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md","method":"lexical","observedAt":"2026-07-23T00:00:00.000Z"},{"functionId":"tool-call","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md","method":"lexical","observedAt":"2026-07-23T00:00:00.000Z"}],"slices":[{"functionId":"browser","label":"Browser","note":"Local-first AI agent whose control loop and all state run on the machine. 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Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","cost":"unknown"},{"functionId":"tool-call","label":"Tool-call","note":"Local-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md\nLocal-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","at":"2026-07-23T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-07-23T00:00:00.000Z","updatedAt":"2026-07-23T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_atomic-agent","claims":{"name":"Atomic Agent","summary":"Local-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. 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Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","cost":"unknown"},{"functionId":"connect","label":"Connect","note":"Local-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","cost":"unknown"},{"functionId":"tool-call","label":"Tool-call","note":"Local-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[{"from":"atomic-agent","to":"vellum-assistant","kind":"alternative-to","note":"Shared functions browser, memory, tool-call. Same kind and overlapping capability."},{"from":"atomic-agent","to":"agent-desktop","kind":"fills-same-slot-as","note":"Shared functions browser, tool-call. Can fill the same function slot."},{"from":"atomic-agent","to":"agentmemory","kind":"fills-same-slot-as","note":"Shared functions connect, memory. Can fill the same function slot."},{"from":"atomic-agent","to":"browserbase","kind":"fills-same-slot-as","note":"Shared functions browser, connect. Can fill the same function slot."},{"from":"atomic-agent","to":"monetization-gateway","kind":"fills-same-slot-as","note":"Shared functions connect, tool-call. Can fill the same function slot."},{"from":"atomic-agent","to":"openship","kind":"fills-same-slot-as","note":"Shared functions connect, tool-call. Can fill the same function slot."},{"from":"atomic-agent","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions connect, memory, tool-call. Can fill the same function slot."},{"from":"atomic-agent","to":"lore","kind":"fills-same-slot-as","note":"Shared functions connect, memory, tool-call. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-07-23T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":5,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.15,"contribution":0.027,"why":"A licence is declared.","sources":[],"evidence":"observed"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md\nLocal-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","at":"2026-07-23T00:00:00.000Z"}],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0,"contribution":0,"why":"No MCP surface is declared. No API is declared. Cost is unknown. 0 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-07-23. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-07-23-atomic-agent-local-first.md\nLocal-first AI agent whose control loop and all state run on the machine. Drives browser, files, approved shell, documents, memory across sessions, scheduled tasks, and MCP tools. Default path is llama.cpp (their TurboQuant fork) so small quantized models stay useful for long multi-step work on consumer hardware.","at":"2026-07-23T00:00:00.000Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":5,"tags":["documents"]},{"slug":"agentmemory","name":"AgentMemory","summary":"An open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. The repo positions it as agent-agnostic infrastructure that can work across Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, and other MCP or HTTP-capable agents.","kind":"resource","repoUrl":"https://github.com/rohitg00/agentmemory","publisher":"","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["code","memory","connect"],"functionProbabilities":[{"functionId":"code","value":0.92,"source":"06 Knowledge Library/Resources/coding/2026-05-08-agentmemory-persistent-memory-for-coding-agents.md","method":"lexical","observedAt":"2026-05-08T00:00:00.000Z"},{"functionId":"memory","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-05-08-agentmemory-persistent-memory-for-coding-agents.md","method":"lexical","observedAt":"2026-05-08T00:00:00.000Z"},{"functionId":"connect","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-05-08-agentmemory-persistent-memory-for-coding-agents.md","method":"lexical","observedAt":"2026-05-08T00:00:00.000Z"}],"slices":[{"functionId":"code","label":"Code","note":"An open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. 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The repo positions it as agent-agnostic infrastructure that can work across Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, and other MCP or HTTP-capable agents.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-05-08-agentmemory-persistent-memory-for-coding-agents.md\nAn open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. The repo positions it as agent-agnostic infrastructure that can work across Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, and other MCP or HTTP-capable agents.","at":"2026-05-08T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-05-08T00:00:00.000Z","updatedAt":"2026-05-08T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_agentmemory","claims":{"name":"AgentMemory","summary":"An open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. 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The repo positions it as agent-agnostic infrastructure that can work across Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, and other MCP or HTTP-capable agents.","cost":"unknown"},{"functionId":"connect","label":"Connect","note":"An open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. The repo positions it as agent-agnostic infrastructure that can work across Claude Code, OpenClaw, Codex CLI, Cursor, Gemini CLI, and other MCP or HTTP-capable agents.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[{"from":"agentmemory","to":"executor","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"agentmemory","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory. Can fill the same function slot."},{"from":"agentmemory","to":"rea","kind":"alternative-to","note":"Shared functions code, connect. Same kind and overlapping capability."},{"from":"agentmemory","to":"atomic-agent","kind":"fills-same-slot-as","note":"Shared functions connect, memory. Can fill the same function slot."},{"from":"agentmemory","to":"openai-codex","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"agentmemory","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory. Can fill the same function slot."},{"from":"agentmemory","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory. Can fill the same function slot."},{"from":"agentmemory","to":"claude-code","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."}],"signals":[],"claimVerification":[],"explain":{"score":3,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-05-08-agentmemory-persistent-memory-for-coding-agents.md\nAn open source memory system for coding agents that captures session activity, compresses it into searchable memory, and re-injects relevant context into future sessions. 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Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","cost":"unknown"},{"functionId":"memory","label":"Memory","note":"Vellum Assistant is a personal AI assistant product (OSS + managed cloud) in the same broad class as Hermes / OpenClaw / NanoClaw: multi-channel agent, skills, memory, computer use, OAuth-connected tools, managed or self-hosted runtime. Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","cost":"unknown"},{"functionId":"auth","label":"Auth","note":"Vellum Assistant is a personal AI assistant product (OSS + managed cloud) in the same broad class as Hermes / OpenClaw / NanoClaw: multi-channel agent, skills, memory, computer use, OAuth-connected tools, managed or self-hosted runtime. Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","cost":"unknown"},{"functionId":"tool-call","label":"Tool-call","note":"Vellum Assistant is a personal AI assistant product (OSS + managed cloud) in the same broad class as Hermes / OpenClaw / NanoClaw: multi-channel agent, skills, memory, computer use, OAuth-connected tools, managed or self-hosted runtime. Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","cost":"unknown"}],"interfaces":[{"kind":"skill"}],"composabilityRationale":""},"relations":[{"from":"vellum-assistant","to":"atomic-agent","kind":"alternative-to","note":"Shared functions browser, memory, tool-call. Same kind and overlapping capability."},{"from":"vellum-assistant","to":"agent-desktop","kind":"fills-same-slot-as","note":"Shared functions browser, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"asp-net-core","kind":"fills-same-slot-as","note":"Shared functions auth, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"railcode","kind":"fills-same-slot-as","note":"Shared functions auth, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"model-context-protocol","kind":"fills-same-slot-as","note":"Shared functions auth, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"mcp-ai-gateway","kind":"fills-same-slot-as","note":"Shared functions auth, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions memory, tool-call. Can fill the same function slot."},{"from":"vellum-assistant","to":"camofox-browser-server","kind":"fills-same-slot-as","note":"Shared functions auth, browser. Can fill the same function slot."}],"signals":[],"claimVerification":[],"explain":{"score":3,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-07-28-vellum-assistant-oauth-integrations.md\nVellum Assistant is a personal AI assistant product (OSS + managed cloud) in the same broad class as Hermes / OpenClaw / NanoClaw: multi-channel agent, skills, memory, computer use, OAuth-connected tools, managed or self-hosted runtime. Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","at":"2026-07-28T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"ntegrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","at":"2026-07-28T00:00:00.000Z"}],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0,"contribution":0,"why":"No MCP surface is declared. No API is declared. Cost is unknown. 0 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.22849520230941842,"contribution":0.031989328323318585,"why":"2 sources cover 1 evidence types. The latest dated evidence is 2026-07-28. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-07-28-vellum-assistant-oauth-integrations.md\nVellum Assistant is a personal AI assistant product (OSS + managed cloud) in the same broad class as Hermes / OpenClaw / NanoClaw: multi-channel agent, skills, memory, computer use, OAuth-connected tools, managed or self-hosted runtime. Entry capture was the OAuth Integrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","at":"2026-07-28T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"ntegrations key-concept doc: browser OAuth2 instead of pasting API keys; each integration is a bundled skill with tools (e.g.","at":"2026-07-28T00:00:00.000Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":3}]}