{"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. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"ingest","label":"Ingest","note":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"compose","label":"Compose","note":"Data framework for retrieval and agents. 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"}],"tweets":[],"sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"origin":"seed","sector":"memory-rag","notes":[],"score":18,"explain":{"score":18,"formula":"100 × sum(weight × value) over observed factors only. 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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. Memory and index slices, not a whole 'RAG product' box.","kind":"kit","publisher":"LlamaIndex","homepage":"https://www.llamaindex.ai","repoUrl":"https://github.com/run-llama/llama_index","docsUrl":"https://docs.llamaindex.ai","license":"MIT","costRange":"freemium","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. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"ingest","label":"Ingest","note":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"compose","label":"Compose","note":"Data framework for retrieval and agents. 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":"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":"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":"casdoor","name":"Casdoor","summary":"An open-source, self-hosted identity and access management platform and authentication server with a web console, supporting SSO, multiple identity protocols, policy-based authorization, APIs, SDKs, and MCP.","publisher":"Casdoor","homepage":"https://github.com/casdoor/casdoor","kind":"app","hasApi":true,"hasMcp":true,"costRange":"unknown","costNotes":"Unknown: The page describes open-source self-hosting and a hosted Casdoor Cloud option with flat monthly pricing and $20 in trial credit, but it does not state actual prices.","functions":["auth","code","compose","embed","identity","policy","search","connect"],"slices":[{"functionId":"auth","label":"Application authentication and SSO","note":"Acts as an OAuth 2.0/OIDC authorization server and supports SAML 2.0, CAS, WebAuthn, TOTP, MFA, password, and email/SMS-code sign-in.","cost":"Unknown"},{"functionId":"identity","label":"Identity provider and user directory","note":"Stores users, issues tokens, manages organizations and providers, and exposes the same directory through several identity protocols.","cost":"Unknown"},{"functionId":"policy","label":"Policy-based authorization","note":"Supports Casbin-based ACL, RBAC, ABAC, and custom authorization models.","cost":"Unknown"},{"functionId":"connect","label":"Application and identity-provider integration","note":"Connects applications and providers using OAuth, OIDC, SAML, CAS, LDAP, SCIM, SDKs, a Public API, Terraform, and MCP.","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"compose","label":"compose","note":"Inferred from executor","cost":"Unknown"},{"functionId":"embed","label":"embed","note":"Inferred from executor","cost":"Unknown"},{"functionId":"search","label":"search","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Casdoor can compose with applications and identity providers through OAuth 2.0, OIDC, SAML 2.0, CAS, LDAP, SCIM 2.0, SDKs, a Public API, Terraform, and MCP. The retrieved page does not specify the MCP capability surface or detailed API limits and pricing.","signals":[],"functionProbabilities":[{"functionId":"auth","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"code","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"compose","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"embed","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"identity","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"policy","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"search","value":0.8,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"connect","value":0.72,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"deploy","value":0.72,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"},{"functionId":"evaluate","value":0.72,"source":"https://github.com/casdoor/casdoor","method":"lexical","observedAt":"2026-11-04T06:18:14.693Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://github.com/casdoor/casdoor","method":"fallback"},{"key":"api","status":"unverified","asserted":"true","evidenceSource":"https://github.com/casdoor/casdoor","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://github.com/casdoor/casdoor","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://github.com/casdoor/casdoor","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-11-04T06:18:17.529Z","updatedAt":"2026-11-04T06:18:17.529Z","sources":[{"kind":"web","label":"executor","url":"https://github.com/casdoor/casdoor","excerpt":"BranchesTags\n\n## Repository files navigation\n\n### An open-source, self-hosted identity and access management platform\n\nCasdoor is a single sign-on (SSO) and authentication server with a web console.  \nIt speaks **OAuth 2.0**, **OIDC**, **SA","at":"2026-11-04T06:18:17.529Z"}],"recordId":"cmp_casdoor","claims":{"name":"Casdoor","summary":"An open-source, self-hosted identity and access management platform and authentication server with a web console, supporting SSO, multiple identity protocols, policy-based authorization, APIs, SDKs, and MCP.","kind":"app","publisher":"Casdoor","homepage":"https://github.com/casdoor/casdoor","costRange":"unknown","costNotes":"Unknown: The page describes open-source self-hosting and a hosted Casdoor Cloud option with flat monthly pricing and $20 in trial credit, but it does not state actual prices.","functions":["auth","code","compose","embed","identity","policy","search","connect"],"slices":[{"functionId":"auth","label":"Application authentication and SSO","note":"Acts as an OAuth 2.0/OIDC authorization server and supports SAML 2.0, CAS, WebAuthn, TOTP, MFA, password, and email/SMS-code sign-in.","cost":"Unknown"},{"functionId":"identity","label":"Identity provider and user directory","note":"Stores users, issues tokens, manages organizations and providers, and exposes the same directory through several identity protocols.","cost":"Unknown"},{"functionId":"policy","label":"Policy-based authorization","note":"Supports Casbin-based ACL, RBAC, ABAC, and custom authorization models.","cost":"Unknown"},{"functionId":"connect","label":"Application and identity-provider integration","note":"Connects applications and providers using OAuth, OIDC, SAML, CAS, LDAP, SCIM, SDKs, a Public API, Terraform, and MCP.","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"compose","label":"compose","note":"Inferred from executor","cost":"Unknown"},{"functionId":"embed","label":"embed","note":"Inferred from executor","cost":"Unknown"},{"functionId":"search","label":"search","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Casdoor can compose with applications and identity providers through OAuth 2.0, OIDC, SAML 2.0, CAS, LDAP, SCIM 2.0, SDKs, a Public API, Terraform, and MCP. The retrieved page does not specify the MCP capability surface or detailed API limits and pricing."},"relations":[{"from":"casdoor","to":"invisible-playwright-mcp","kind":"fills-same-slot-as","note":"Shared functions auth, code, connect, identity, policy, search. Can fill the same function slot."},{"from":"casdoor","to":"memini","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, embed, policy, search. Can fill the same function slot."},{"from":"casdoor","to":"self","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, embed, search. Can fill the same function slot."},{"from":"casdoor","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, policy, search. Can fill the same function slot."},{"from":"casdoor","to":"mcp-ai-gateway","kind":"fills-same-slot-as","note":"Shared functions auth, connect, identity, policy. Can fill the same function slot."},{"from":"casdoor","to":"camofox-browser-server","kind":"fills-same-slot-as","note":"Shared functions auth, compose, policy, search. Can fill the same function slot."},{"from":"casdoor","to":"tinyfish-search-api","kind":"fills-same-slot-as","note":"Shared functions auth, code, compose, search. Can fill the same function slot."},{"from":"casdoor","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."}],"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,"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":0.6666666666666666,"contribution":0.06666666666666667,"why":"An MCP surface is declared. An API is declared. Cost is unknown. 2 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 2026-11-04. Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://github.com/casdoor/casdoor","excerpt":"BranchesTags\n\n## Repository files navigation\n\n### An open-source, self-hosted identity and access management platform\n\nCasdoor is a single sign-on (SSO) and authentication server with a web console.  \nIt speaks **OAuth 2.0**, **OIDC**, **SA","at":"2026-11-04T06:18:17.529Z"}],"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":9},{"slug":"cognee","name":"cognee","summary":"Open-source memory system for agents and teams. It connects company sources such as Slack, GitHub, and Linear so agents can recall shared context through one memory API, an SDK, or an MCP server, with an optional Cognee Cloud for managed scale.","publisher":"cognee","homepage":"https://www.cognee.ai","kind":"runtime","hasApi":true,"hasMcp":true,"costRange":"unknown","costNotes":"Pricing is not stated. The page says to start with the open-source package (pip install cognee) and move to Cognee Cloud for managed scale. 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No plans, prices, or free-tier limits are given.","functions":["memory","code","connect","embed","retrieve"],"slices":[{"functionId":"memory","label":"Agent memory","note":"Memory for agents, teams, and people so agents can recall what a company knows and what they just did.","cost":"Unknown"},{"functionId":"connect","label":"Source and agent connections","note":"Connect Slack, GitHub, and Linear, with first-party integrations for Claude Code, Cursor, LangGraph, OpenClaw, and more.","cost":"Unknown"},{"functionId":"retrieve","label":"Memory read","note":"Agents recall stored context through the memory API or MCP read access.","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"embed","label":"embed","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"The page states one memory API and an SDK, plus an MCP server so compatible agents can read and write Cognee memory without custom glue. 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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":0.6666666666666666,"contribution":0.06666666666666667,"why":"An MCP surface is declared. An API is declared. Cost is unknown. 2 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://www.cognee.ai","excerpt":"# Memory for Agents, Teams, and People\n\nConnect Slack, GitHub, Linear to Cognee and help agents recall what your company knows.\n\nStart building\n\nWorks withClaude CodeCodexSkillMCPOpenClawHermes\n\nWorks with\n\nClaude Code\n\nCodex\n\nSkill\n\nMCP\n\nO","at":"2026-10-15T16:29:15.555Z"}],"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":9,"tags":["agents"]},{"slug":"fx","name":"fx","summary":"fx is a Vercel Labs coding-agent harness and CLI written in Zig. 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It stores and recalls memories in a tiered store using hybrid vector and keyword retrieval, ships as a single Go binary with SQLite or Postgres, and exposes REST, MCP, and an embedded admin UI.","cost":"unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"The page states REST plus MCP over stdio and Streamable HTTP, sharing one service layer, with an embedded web UI. Most listed agent integrations read MEMINI_BASE_URL and MEMINI_API_KEY; some clients use their own config or a fixed local MCP URL. Authentication can be enabled with an API key. Embeddings are not built in and must be supplied by an external endpoint whose dimensions match the store. An LLM is opt-in for background deduplication, contradiction handling, and distillation; without one, marker heuristics run the lifecycle. Pricing, quotas, and hosted multi-tenant terms are not stated.","signals":[],"functionProbabilities":[{"functionId":"connect","value":0.92,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"code","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"compose","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"deploy","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"memory","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"policy","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"rank","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"search","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"store","value":0.8,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"auth","value":0.72,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"embed","value":0.72,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"retrieve","value":0.72,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"},{"functionId":"tool-call","value":0.72,"source":"https://github.com/eleboucher/memini","method":"lexical","observedAt":"2026-10-16T06:17:46.735Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://github.com/eleboucher/memini","method":"fallback"},{"key":"api","status":"unverified","asserted":"true","evidenceSource":"https://github.com/eleboucher/memini","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://github.com/eleboucher/memini","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://github.com/eleboucher/memini","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-16T06:17:47.632Z","updatedAt":"2026-10-16T06:17:47.632Z","sources":[{"kind":"web","label":"executor","url":"https://github.com/eleboucher/memini","excerpt":"BranchesTags\n\n## Repository files navigation\n\n# memini\n\n> A shared, persistent memory service for AI agents.\n\n`memini` gives any MCP-capable agent (Claude Code, Cursor, Codex, opencode, Hermes, OpenClaw, Open WebUI) one place to `remember` ","at":"2026-10-16T06:17:47.632Z"}],"recordId":"cmp_memini","claims":{"name":"memini","summary":"Self-hosted persistent memory service for MCP-capable agents. 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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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Pricing, quotas, and hosted multi-tenant terms are not stated."},"relations":[{"from":"memini","to":"self","kind":"alternative-to","note":"Shared functions code, compose, connect, deploy, embed, rank, search. Same kind and overlapping capability."},{"from":"memini","to":"local-faiss-mcp-server","kind":"alternative-to","note":"Shared functions code, connect, embed, memory, rank, retrieve, search, store. Same kind and overlapping capability."},{"from":"memini","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, memory, policy, search, store. Can fill the same function slot."},{"from":"memini","to":"casdoor","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, embed, policy, search. Can fill the same function slot."},{"from":"memini","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed, memory, retrieve. 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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":0.6666666666666666,"contribution":0.06666666666666667,"why":"An MCP surface is declared. An API is declared. Cost is unknown. 2 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/eleboucher/memini","excerpt":"BranchesTags\n\n## Repository files navigation\n\n# memini\n\n> A shared, persistent memory service for AI agents.\n\n`memini` gives any MCP-capable agent (Claude Code, Cursor, Codex, opencode, Hermes, OpenClaw, Open WebUI) one place to `remember` ","at":"2026-10-16T06:17:47.632Z"}],"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":9,"tags":["agents"]},{"slug":"self","name":"Self","summary":"Self-Hosted AIEnterprise Search18 min readPublished May 22, 2026","publisher":"onyx.app","homepage":"https://onyx.app/insights/self-hosted-rag","kind":"mcp","hasApi":true,"hasMcp":true,"costRange":"unknown","costNotes":"Unknown: pricing was not stated in the intake evidence.","functions":["index","code","compose","embed","rank","search","connect","deploy"],"slices":[{"functionId":"index","label":"index","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":"embed","label":"embed","note":"Inferred from executor","cost":"Unknown"},{"functionId":"rank","label":"rank","note":"Inferred from executor","cost":"Unknown"},{"functionId":"search","label":"search","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"deploy","label":"deploy","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":"index","value":0.92,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"code","value":0.8,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"compose","value":0.8,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"embed","value":0.8,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"rank","value":0.8,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"search","value":0.8,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"connect","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"deploy","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"evaluate","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"generate","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"ingest","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"memory","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"observe","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"retrieve","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"},{"functionId":"store","value":0.72,"source":"https://onyx.app/insights/self-hosted-rag","method":"lexical","observedAt":"2026-10-15T16:28:23.244Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://onyx.app/insights/self-hosted-rag","method":"fallback"},{"key":"api","status":"unverified","asserted":"true","evidenceSource":"https://onyx.app/insights/self-hosted-rag","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://onyx.app/insights/self-hosted-rag","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://onyx.app/insights/self-hosted-rag","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-15T16:28:24.104Z","updatedAt":"2026-10-15T16:28:24.104Z","sources":[{"kind":"web","label":"executor","url":"https://onyx.app/insights/self-hosted-rag","excerpt":"All Insights\n\nSelf-Hosted AIEnterprise Search18 min readPublished May 22, 2026\n\n# Self-Hosted RAG in 2026: The Complete Guide to Running Retrieval-Augmented Generation On Your Own Infrastructure\n\nBy Roshan Desai\n\n---\n\n> **TL;DR:** Self-host","at":"2026-10-15T16:28:24.104Z"}],"recordId":"cmp_self","claims":{"name":"Self","summary":"Self-Hosted AIEnterprise Search18 min readPublished May 22, 2026","kind":"mcp","publisher":"onyx.app","homepage":"https://onyx.app/insights/self-hosted-rag","costRange":"unknown","costNotes":"Unknown: pricing was not stated in the intake evidence.","functions":["index","code","compose","embed","rank","search","connect","deploy"],"slices":[{"functionId":"index","label":"index","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":"embed","label":"embed","note":"Inferred from executor","cost":"Unknown"},{"functionId":"rank","label":"rank","note":"Inferred from executor","cost":"Unknown"},{"functionId":"search","label":"search","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"deploy","label":"deploy","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":"self","to":"memini","kind":"alternative-to","note":"Shared functions code, compose, connect, deploy, embed, rank, search. Same kind and overlapping capability."},{"from":"self","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed, index, rank, search. Can fill the same function slot."},{"from":"self","to":"casdoor","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, embed, search. Can fill the same function slot."},{"from":"self","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect, index, search. Can fill the same function slot."},{"from":"self","to":"claude-code","kind":"fills-same-slot-as","note":"Shared functions code, compose, connect. Can fill the same function slot."},{"from":"self","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"from":"self","to":"pen-dev","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"from":"self","to":"grok-xai","kind":"fills-same-slot-as","note":"Shared functions code, connect, search. Can fill the same function slot."}],"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,"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":0.6666666666666666,"contribution":0.06666666666666667,"why":"An MCP surface is declared. An API is declared. Cost is unknown. 2 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/insights/self-hosted-rag","excerpt":"All Insights\n\nSelf-Hosted AIEnterprise Search18 min readPublished May 22, 2026\n\n# Self-Hosted RAG in 2026: The Complete Guide to Running Retrieval-Augmented Generation On Your Own Infrastructure\n\nBy Roshan Desai\n\n---\n\n> **TL;DR:** Self-host","at":"2026-10-15T16:28:24.104Z"}],"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":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":"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. It does approximate vector search (SPFresh), BM25 full-text, filters, and hybrid fusion.","cost":"unknown"}],"interfaces":[{"kind":"sdk"}],"composabilityRationale":""},"relations":[{"from":"turbopuffer","to":"octen","kind":"fills-same-slot-as","note":"Shared functions embed, search. Can fill the same function slot."},{"from":"turbopuffer","to":"memini","kind":"fills-same-slot-as","note":"Shared functions embed, memory, search, store. Can fill the same function slot."},{"from":"turbopuffer","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, memory, search, store. Can fill the same function slot."},{"from":"turbopuffer","to":"lore","kind":"fills-same-slot-as","note":"Shared functions memory, search, store. Can fill the same function slot."},{"from":"turbopuffer","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions memory, search, store. Can fill the same function slot."},{"from":"turbopuffer","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, memory. 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":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions embed, memory. 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":"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"}],"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":"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"}],"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,"tags":["text"]},{"slug":"alibaba-model-studio","name":"Alibaba Cloud Model Studio","publisher":"Alibaba Cloud","kind":"app","summary":"Model APIs for text, vision, speech and embeddings, with compatible developer interfaces.","homepage":"https://www.alibabacloud.com/","docsUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","functions":["generate","reason","embed"],"costRange":"unknown","recordId":"cmp_alibaba-model-studio","hasMcp":false,"hasApi":true,"costNotes":"Model, region, access and billing support vary; review the selected model and compatible API contract.","composabilityRationale":"Use its declared http, sdk surface for the listed capability slices. Source review does not establish integration success or observed usage.","interfaces":[{"kind":"http","specUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio"},{"kind":"sdk","specUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio"}],"origin":"intake","tweets":[],"notes":[],"sources":[{"kind":"docs","label":"Alibaba Cloud Model Studio official documentation","url":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"slices":[{"functionId":"generate","label":"Generate","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"}],"claims":{"name":"Alibaba Cloud Model Studio","summary":"Model APIs for text, vision, speech and embeddings, with compatible developer interfaces.","kind":"app","publisher":"Alibaba Cloud","homepage":"https://www.alibabacloud.com/","docsUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","costRange":"unknown","costNotes":"Model, region, access and billing support vary; review the selected model and compatible API contract.","functions":["generate","reason","embed"],"slices":[{"functionId":"generate","label":"Generate","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Model, region, access and billing support vary; review the selected model and compatible API contract.","cost":"unknown"}],"interfaces":[{"kind":"http","specUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio"},{"kind":"sdk","specUrl":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio"}],"composabilityRationale":"Use its declared http, sdk surface for the listed capability slices. Source review does not establish integration success or observed usage."},"relations":[{"from":"alibaba-model-studio","to":"minimax","kind":"alternative-to","note":"Shared functions generate, reason. Same kind and overlapping capability."},{"from":"alibaba-model-studio","to":"byteplus-modelark","kind":"alternative-to","note":"Shared functions generate, reason. Same kind and overlapping capability."},{"from":"alibaba-model-studio","to":"41-open-source-llms-evaluated-on-19-benchmarks","kind":"fills-same-slot-as","note":"Shared functions generate, reason. Can fill the same function slot."},{"from":"alibaba-model-studio","to":"pageindex","kind":"fills-same-slot-as","note":"Shared functions embed, reason. Can fill the same function slot."},{"from":"alibaba-model-studio","to":"grok-xai","kind":"fills-same-slot-as","note":"Shared functions generate, reason. Can fill the same function slot."}],"signals":[],"functionProbabilities":[],"claimVerification":[],"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":[{"kind":"docs","label":"Alibaba Cloud Model Studio official documentation","url":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"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":[{"kind":"docs","label":"Alibaba Cloud Model Studio official documentation","url":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"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 undated. Freshness is omitted unless observed as a signal.","sources":[{"kind":"docs","label":"Alibaba Cloud Model Studio official documentation","url":"https://www.alibabacloud.com/help/en/model-studio/what-is-model-studio","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"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},{"slug":"local-faiss-mcp-server","name":"Local FAISS MCP Server","summary":"Local FAISS vector store exposed as an MCP server for document ingestion, embeddings, persistent indexes, semantic search, optional re-ranking, and RAG-style retrieval with MCP prompts.","publisher":"nonatofabio","homepage":"https://github.com/nonatofabio/local_faiss_mcp","kind":"mcp","hasApi":false,"hasMcp":true,"costRange":"unknown","costNotes":"No price, license fee, or paid plan is stated on the retrieved page. Installation is described as pip install local-faiss-mcp from PyPI or from source; optional pandoc is mentioned for extra document formats. 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Installation is described as pip install local-faiss-mcp from PyPI or from source; optional pandoc is mentioned for extra document formats. 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Can fill the same function slot."},{"from":"local-faiss-mcp-server","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed, memory, retrieve. Can fill the same function slot."},{"from":"local-faiss-mcp-server","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions code, connect, index, memory, search, store. Can fill the same function slot."},{"from":"local-faiss-mcp-server","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions embed, index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"local-faiss-mcp-server","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions connect, index, ingest, retrieve, search. Can fill the same function slot."}],"explain":{"score":6,"formula":"100 × sum(weight × value) over observed factors only. 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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/nonatofabio/local_faiss_mcp","excerpt":"BranchesTags\n\n## Latest commit\n\n## History\n\n37 Commits\n\n## Folders and files\n\n## Repository files navigation\n\n# Local FAISS MCP Server\n\nA Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for ","at":"2026-10-16T06:16:49.468Z"}],"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},{"slug":"octen","name":"Octen","publisher":"Octen","kind":"app","summary":"Search infrastructure for web, image and video retrieval, with related embedding and research capabilities.","homepage":"https://octen.ai/","docsUrl":"https://docs.octen.ai/overview/introduction","functions":["search","retrieve","embed"],"costRange":"unknown","recordId":"cmp_octen","hasMcp":false,"hasApi":true,"costNotes":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","composabilityRationale":"Use its declared http surface for the listed capability slices. Source review does not establish integration success or observed usage.","interfaces":[{"kind":"http","specUrl":"https://docs.octen.ai/overview/introduction"}],"origin":"intake","tweets":[],"notes":[],"sources":[{"kind":"docs","label":"Octen official documentation","url":"https://docs.octen.ai/overview/introduction","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"slices":[{"functionId":"search","label":"Search","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"},{"functionId":"retrieve","label":"Retrieve","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"}],"claims":{"name":"Octen","summary":"Search infrastructure for web, image and video retrieval, with related embedding and research capabilities.","kind":"app","publisher":"Octen","homepage":"https://octen.ai/","docsUrl":"https://docs.octen.ai/overview/introduction","costRange":"unknown","costNotes":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","functions":["search","retrieve","embed"],"slices":[{"functionId":"search","label":"Search","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"},{"functionId":"retrieve","label":"Retrieve","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"Freshness, latency, benchmark and service-level statements remain vendor claims; no performance was measured.","cost":"unknown"}],"interfaces":[{"kind":"http","specUrl":"https://docs.octen.ai/overview/introduction"}],"composabilityRationale":"Use its declared http surface for the listed capability slices. Source review does not establish integration success or observed usage."},"relations":[{"from":"octen","to":"keenable","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"crunchbase","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"dataforseo","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"litescrape","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"tendata","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"tikhub","kind":"alternative-to","note":"Shared functions retrieve, search. Same kind and overlapping capability."},{"from":"octen","to":"turbopuffer","kind":"fills-same-slot-as","note":"Shared functions embed, search. Can fill the same function slot."},{"from":"octen","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, retrieve. Can fill the same function slot."}],"signals":[],"functionProbabilities":[],"claimVerification":[],"explain":{"score":6,"formula":"100 × sum(weight × value) over observed factors only. 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Cost is unknown. 1 of 3 checklist fields are complete.","sources":[{"kind":"docs","label":"Octen official documentation","url":"https://docs.octen.ai/overview/introduction","checkedAt":"2026-11-07T13:46:44.976Z","basis":"vendor-claim"}],"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 undated. 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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). 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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. 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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":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"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","kind":"fills-same-slot-as","note":"Shared functions diagram, render. 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":"casdoor","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. Can fill the same function slot."},{"from":"pen-dev","to":"self","kind":"fills-same-slot-as","note":"Shared functions code, connect, embed. 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":"Catalogue 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":"Catalogue 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":"Catalogue 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":"Catalogue 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":"Catalogue 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":"Catalogue 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":[{"from":"needle-3","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, extract. 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/2026-09-18-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":"Catalogue 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,"tags":["automation","text"]}]}