{"components":[{"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. 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"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium 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":"Compendium 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":"turbopuffer","kind":"fills-same-slot-as","note":"Shared functions embed, memory. Can fill the same function slot."},{"from":"pageindex","to":"slack-enterprise-search","kind":"fills-same-slot-as","note":"Shared functions memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"onyx","kind":"fills-same-slot-as","note":"Shared functions embed, index. 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":"Compendium 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":"Compendium 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":"Compendium 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":"Compendium 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":"fx","name":"fx","summary":"fx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","kind":"harness","homepage":"https://fx.sh/","repoUrl":"https://github.com/vercel-labs/fx","docsUrl":"https://fx.sh/docs","publisher":"","license":"Apache-2.0","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["embed"],"functionProbabilities":[{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-08-19-vercel-labs-fx.md","method":"lexical","observedAt":"2026-08-19T00:00:00.000Z"}],"slices":[{"functionId":"embed","label":"Embed","note":"fx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-19-vercel-labs-fx.md\nfx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","at":"2026-08-19T00:00:00.000Z"},{"kind":"human","label":"Compendium resource card","excerpt":"fx is a Vercel Labs coding-agent harness and CLI written in Zig.","at":"2026-08-19T00:00:00.000Z"}],"interfaces":[{"kind":"cli"}],"origin":"seed","notes":[],"createdAt":"2026-08-19T00:00:00.000Z","updatedAt":"2026-08-19T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_fx","claims":{"name":"fx","summary":"fx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","kind":"harness","publisher":"","homepage":"https://fx.sh/","repoUrl":"https://github.com/vercel-labs/fx","docsUrl":"https://fx.sh/docs","license":"Apache-2.0","costRange":"unknown","functions":["embed"],"slices":[{"functionId":"embed","label":"Embed","note":"fx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","cost":"unknown"}],"interfaces":[{"kind":"cli"}],"composabilityRationale":""},"relations":[],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-08-19T00:00:00.000Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-08-19T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":9,"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":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-19-vercel-labs-fx.md\nfx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","at":"2026-08-19T00:00:00.000Z"},{"kind":"human","label":"Compendium resource card","excerpt":"fx is a Vercel Labs coding-agent harness and CLI written in Zig.","at":"2026-08-19T00:00:00.000Z"}],"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 2026-08-19. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-08-19-vercel-labs-fx.md\nfx is a Vercel Labs coding-agent harness and CLI written in Zig. Official copy positions it as a tiny, open, native agent originally used internally, now open sourced for research and embedding in larger systems. The durable object is the product at fx.sh / vercel-labs/fx, not the tweet.","at":"2026-08-19T00:00:00.000Z"},{"kind":"human","label":"Compendium resource card","excerpt":"fx is a Vercel Labs coding-agent harness and CLI written in Zig.","at":"2026-08-19T00:00:00.000Z"}],"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":9},{"slug":"turbopuffer","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","homepage":"https://turbopuffer.com/","repoUrl":"https://github.com/turbopuffer","docsUrl":"https://turbopuffer.com/docs","publisher":"","hasMcp":false,"hasApi":true,"costRange":"unknown","composabilityRationale":"","functions":["memory","search","embed","store"],"functionProbabilities":[{"functionId":"memory","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md","method":"lexical","observedAt":"2026-08-21T00:00:00.000Z"},{"functionId":"search","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md","method":"lexical","observedAt":"2026-08-21T00:00:00.000Z"},{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md","method":"lexical","observedAt":"2026-08-21T00:00:00.000Z"},{"functionId":"store","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-08-21-turbopuffer.md","method":"lexical","observedAt":"2026-08-21T00:00:00.000Z"}],"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. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium 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":"Compendium 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. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"}],"interfaces":[{"kind":"sdk"}],"composabilityRationale":""},"relations":[{"from":"turbopuffer","to":"how-hopx-achieves-100ms-cold-starts","kind":"fills-same-slot-as","note":"Shared functions memory, store. Can fill the same function slot."},{"from":"turbopuffer","to":"pageindex","kind":"fills-same-slot-as","note":"Shared functions embed, memory. Can fill the same function slot."},{"from":"turbopuffer","to":"slack-enterprise-search","kind":"fills-same-slot-as","note":"Shared functions memory, search. Can fill the same function slot."},{"from":"turbopuffer","to":"daytona-alternatives-4-ai-agent-sandbox-platforms-compared-2026","kind":"fills-same-slot-as","note":"Shared functions memory, store. Can fill the same function slot."},{"from":"turbopuffer","to":"onyx","kind":"fills-same-slot-as","note":"Shared functions embed, search. Can fill the same function slot."}],"signals":[{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-08-21T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":9,"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":"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":"Compendium 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":"Compendium 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"}],"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":"No MCP surface is declared. An 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 2026-08-21. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Compendium 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":"Compendium 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"}],"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":9},{"slug":"pen-dev","name":"pen.dev","summary":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","kind":"app","homepage":"https://www.pen.dev/","docsUrl":"https://www.pen.dev/","publisher":"","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["code","connect","diagram","embed","render"],"functionProbabilities":[{"functionId":"code","value":0.8,"source":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md","method":"lexical","observedAt":"2026-08-01T00:00:00.000Z"},{"functionId":"connect","value":0.72,"source":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md","method":"lexical","observedAt":"2026-08-01T00:00:00.000Z"},{"functionId":"diagram","value":0.72,"source":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md","method":"lexical","observedAt":"2026-08-01T00:00:00.000Z"},{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md","method":"lexical","observedAt":"2026-08-01T00:00:00.000Z"},{"functionId":"render","value":0.72,"source":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md","method":"lexical","observedAt":"2026-08-01T00:00:00.000Z"}],"slices":[{"functionId":"code","label":"Code","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"connect","label":"Connect","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"diagram","label":"Diagram","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"render","label":"Render","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md\nPencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","at":"2026-08-01T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-08-01T00:00:00.000Z","updatedAt":"2026-08-01T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_pen-dev","claims":{"name":"pen.dev","summary":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","kind":"app","publisher":"","homepage":"https://www.pen.dev/","docsUrl":"https://www.pen.dev/","costRange":"unknown","functions":["code","connect","diagram","embed","render"],"slices":[{"functionId":"code","label":"Code","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"connect","label":"Connect","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"diagram","label":"Diagram","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"},{"functionId":"render","label":"Render","note":"Pencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[{"from":"pen-dev","to":"executor","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"pen-dev","to":"agentmemory","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"pen-dev","to":"rea","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"pen-dev","to":"tldraw-offline","kind":"fills-same-slot-as","note":"Shared functions diagram, render. Can fill the same function slot."},{"from":"pen-dev","to":"onyx","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"from":"pen-dev","to":"comparing-ai-agent-sandbox-platforms-e2b-modal-daytona-and-more","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"pen-dev","to":"gosearch-for-slack-agentic-enterprise-search-custom-ai-agents-and-workflow-au","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."}],"signals":[{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-08-01T00:00:00.000Z"}],"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":"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":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md\nPencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","at":"2026-08-01T00:00:00.000Z"}],"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 2026-08-01. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-08-01-pen-dev-pencil-infinite-canvas.md\nPencil / pen.dev (site banner: “pencil.dev is now pen.dev”) is an AI-native infinite design canvas positioned as “Dream on canvas. Land in code.” Backed by a16z Speedrun (site badge). Integrates with Cursor, VS Code, Claude Code, OpenAI Codex; emphasizes bi-directional MCP vector canvas, open file format, Figma bring-over, brand kits, and design-as-code.","at":"2026-08-01T00:00:00.000Z"}],"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},{"slug":"quiverai","name":"QuiverAI","summary":"QuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","kind":"resource","homepage":"https://quiver.ai/","docsUrl":"https://docs.quiver.ai/","publisher":"","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["embed"],"functionProbabilities":[{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/design/2026-09-18-quiverai-arrow-2.md","method":"lexical","observedAt":"2026-09-18T00:00:00.000Z"}],"slices":[{"functionId":"embed","label":"Embed","note":"QuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-09-18-quiverai-arrow-2.md\nQuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","at":"2026-09-18T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-09-18T00:00:00.000Z","updatedAt":"2026-09-18T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_quiverai","claims":{"name":"QuiverAI","summary":"QuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","kind":"resource","publisher":"","homepage":"https://quiver.ai/","docsUrl":"https://docs.quiver.ai/","costRange":"unknown","functions":["embed"],"slices":[{"functionId":"embed","label":"Embed","note":"QuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[],"signals":[{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-18T00:00:00.000Z"}],"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":"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":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-09-18-quiverai-arrow-2.md\nQuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","at":"2026-09-18T00:00:00.000Z"}],"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 2026-09-18. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/design/2026-09-18-quiverai-arrow-2.md\nQuiverAI ships foundational models for generating, editing, and animating editable vector/SVG assets (logos, illustrations, diagrams, line work; typography & animations marked coming soon on marketing site). Arrow 2 is the Sep 2026 model line called out as higher quality and faster, in App and API.","at":"2026-09-18T00:00:00.000Z"}],"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},{"slug":"needle-3","name":"Needle 3","summary":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","kind":"model","homepage":"https://cactuscompute.com/needle","repoUrl":"https://github.com/cactus-compute/needle","publisher":"","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["embed","extract"],"functionProbabilities":[{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md","method":"lexical","observedAt":"2026-09-18T00:00:00.000Z"},{"functionId":"extract","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md","method":"lexical","observedAt":"2026-09-18T00:00:00.000Z"}],"slices":[{"functionId":"embed","label":"Embed","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"},{"functionId":"extract","label":"Extract","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-09-18T00:00:00.000Z","updatedAt":"2026-09-18T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_needle-3","claims":{"name":"Needle 3","summary":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","kind":"model","publisher":"","homepage":"https://cactuscompute.com/needle","repoUrl":"https://github.com/cactus-compute/needle","costRange":"unknown","functions":["embed","extract"],"slices":[{"functionId":"embed","label":"Embed","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"},{"functionId":"extract","label":"Extract","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[],"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":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"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 2026-09-18. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Compendium resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"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},{"slug":"onyx","name":"Onyx","summary":"Open-source enterprise search and AI assistant that connects to internal knowledge sources, indexes them with source permissions, and supplies humans and agents with answers and context. It is described as self-hosted in the customer's cloud, VPC, Kubernetes, or air-gapped enviro","publisher":"Onyx","homepage":"https://onyx.app","kind":"app","hasApi":false,"hasMcp":false,"costRange":"unknown","costNotes":"Unknown: The page offers Book a Demo and Try for Free but does not state a price, seat fee, or usage rate. An in-page example compares one answer's time and dollar cost with another search method and is not a price list.","functions":["code","index","route","search","test","connect","deploy","embed"],"slices":[{"functionId":"connect","label":"Source connectors","note":"Continuously syncs company tools, with 50+ connectors named as a class and GitHub, Drive, Slack, and Confluence shown as indexed sources.","cost":"Unknown"},{"functionId":"index","label":"Permissioned index","note":"Indexes synced sources and keeps the index inside the customer's infrastructure.","cost":"Unknown"},{"functionId":"embed","label":"Embeddings","note":"States that embeddings stay in the customer's infrastructure.","cost":"Unknown"},{"functionId":"search","label":"Parallel enterprise search","note":"Searches indexed sources in parallel rather than one source at a time.","cost":"Unknown"},{"functionId":"route","label":"Model routing","note":"Swap model providers, route teams to different models, or run open weights on the customer's GPUs. Anthropic is named as a routing target.","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"test","label":"test","note":"Inferred from executor","cost":"Unknown"},{"functionId":"deploy","label":"deploy","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[],"composabilityRationale":"No public HTTP API or MCP server is declared. The page says Onyx syncs 50+ connectors, enforces source permissions at query time, routes teams to different models or self-hosted open weights, and provides context to humans and agents. How an external agent invokes Onyx is not specified. A contrast with one-source-at-a-time MCP search does not state that Onyx itself exposes MCP.","signals":[],"functionProbabilities":[{"functionId":"code","value":0.8,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"index","value":0.8,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"route","value":0.8,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"search","value":0.8,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"test","value":0.8,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"connect","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"deploy","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"embed","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"evaluate","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"retrieve","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"},{"functionId":"tool-call","value":0.72,"source":"https://onyx.app","method":"lexical","observedAt":"2026-10-15T07:03:36.665Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"false","evidenceSource":"https://onyx.app","method":"fallback"},{"key":"api","status":"unverified","asserted":"false","evidenceSource":"https://onyx.app","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://onyx.app","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://onyx.app","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-15T07:03:37.498Z","updatedAt":"2026-10-15T07:03:37.498Z","sources":[{"kind":"web","label":"executor","url":"https://onyx.app","excerpt":"# Complete Context for Your Humans and Agents\n\nOnyx connects to your enterprise knowledge and gives humans and agents accurate, fast, and token-efficient context.\n\nBook a Demo\n\nTry for Free\n\nThe world's top teams trust Onyx to be their prim","at":"2026-10-15T07:03:37.498Z"}],"recordId":"cmp_onyx","claims":{"name":"Onyx","summary":"Open-source enterprise search and AI assistant that connects to internal knowledge sources, indexes them with source permissions, and supplies humans and agents with answers and context. It is described as self-hosted in the customer's cloud, VPC, Kubernetes, or air-gapped enviro","kind":"app","publisher":"Onyx","homepage":"https://onyx.app","costRange":"unknown","costNotes":"Unknown: The page offers Book a Demo and Try for Free but does not state a price, seat fee, or usage rate. An in-page example compares one answer's time and dollar cost with another search method and is not a price list.","functions":["code","index","route","search","test","connect","deploy","embed"],"slices":[{"functionId":"connect","label":"Source connectors","note":"Continuously syncs company tools, with 50+ connectors named as a class and GitHub, Drive, Slack, and Confluence shown as indexed sources.","cost":"Unknown"},{"functionId":"index","label":"Permissioned index","note":"Indexes synced sources and keeps the index inside the customer's infrastructure.","cost":"Unknown"},{"functionId":"embed","label":"Embeddings","note":"States that embeddings stay in the customer's infrastructure.","cost":"Unknown"},{"functionId":"search","label":"Parallel enterprise search","note":"Searches indexed sources in parallel rather than one source at a time.","cost":"Unknown"},{"functionId":"route","label":"Model routing","note":"Swap model providers, route teams to different models, or run open weights on the customer's GPUs. Anthropic is named as a routing target.","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"test","label":"test","note":"Inferred from executor","cost":"Unknown"},{"functionId":"deploy","label":"deploy","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[],"composabilityRationale":"No public HTTP API or MCP server is declared. The page says Onyx syncs 50+ connectors, enforces source permissions at query time, routes teams to different models or self-hosted open weights, and provides context to humans and agents. How an external agent invokes Onyx is not specified. A contrast with one-source-at-a-time MCP search does not state that Onyx itself exposes MCP."},"relations":[{"from":"onyx","to":"rtk","kind":"fills-same-slot-as","note":"Shared functions code, search, test. Can fill the same function slot."},{"from":"onyx","to":"rea","kind":"fills-same-slot-as","note":"Shared functions code, connect, test. Can fill the same function slot."},{"from":"onyx","to":"best-code-execution-sandbox-for-ai-agents-in-2026","kind":"fills-same-slot-as","note":"Shared functions code, deploy, route, test. Can fill the same function slot."},{"from":"onyx","to":"gosearch-for-slack-agentic-enterprise-search-custom-ai-agents-and-workflow-au","kind":"fills-same-slot-as","note":"Shared functions code, connect, deploy, search. Can fill the same function slot."},{"from":"onyx","to":"pen-dev","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"from":"onyx","to":"comparing-ai-agent-sandbox-platforms-e2b-modal-daytona-and-more","kind":"fills-same-slot-as","note":"Shared functions code, connect, deploy. Can fill the same function slot."},{"from":"onyx","to":"executor","kind":"fills-same-slot-as","note":"Shared functions code, connect. Can fill the same function slot."},{"from":"onyx","to":"magika","kind":"fills-same-slot-as","note":"Shared functions route, test. Can fill the same function slot."}],"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":[],"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 2026-10-15. Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://onyx.app","excerpt":"# Complete Context for Your Humans and Agents\n\nOnyx connects to your enterprise knowledge and gives humans and agents accurate, fast, and token-efficient context.\n\nBook a Demo\n\nTry for Free\n\nThe world's top teams trust Onyx to be their prim","at":"2026-10-15T07:03:37.498Z"}],"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}]}