{"components":[{"slug":"llamaindex","name":"LlamaIndex","summary":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","kind":"kit","homepage":"https://www.llamaindex.ai","repoUrl":"https://github.com/run-llama/llama_index","docsUrl":"https://docs.llamaindex.ai","publisher":"LlamaIndex","license":"MIT","hasMcp":true,"hasApi":true,"costRange":"freemium","composabilityRationale":"Index / retrieve / query engines compose. Cloud extras are optional slices. Python-first can block a TS fleet.","functions":["retrieve","index","embed","ingest","compose","memory"],"slices":[{"functionId":"index","label":"Vector index","note":"Local or hosted","cost":"OSS free; cloud usage"},{"functionId":"retrieve","label":"Query engine","note":"Ask over indexed docs","cost":"OSS + embedding spend"},{"functionId":"embed","label":"Embed","note":"Data framework for retrieval and agents. 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Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. Documentation is available.","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"observed"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":1,"contribution":0.1,"why":"An MCP surface is declared. An API is declared. Cost is freemium. 3 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-09-12. Freshness is omitted unless observed as a signal.","sources":[{"kind":"github","label":"run-llama/llama_index","url":"https://github.com/run-llama/llama_index","at":"2026-09-12T00:00:00Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"createdAt":"2026-09-11T16:33:39.685Z","updatedAt":"2026-09-13T08:56:02.844Z","tags":["agents"],"recordId":"cmp_llamaindex","claims":{"name":"LlamaIndex","summary":"Data framework for retrieval and agents. 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Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"},{"functionId":"memory","label":"Memory","note":"Data framework for retrieval and agents. Memory and index slices, not a whole 'RAG product' box.","cost":"freemium"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Index / retrieve / query engines compose. Cloud extras are optional slices. Python-first can block a TS fleet."},"interfaces":[{"kind":"mcp"},{"kind":"http"}],"relations":[{"from":"llamaindex","to":"pageindex","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"memini","kind":"fills-same-slot-as","note":"Shared functions compose, embed, memory, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions index, ingest, retrieve. Can fill the same function slot."},{"from":"llamaindex","to":"telemetry-dev","kind":"fills-same-slot-as","note":"Shared functions ingest, retrieve. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-13T08:56:02.844Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-13T08:56:02.844Z"}],"functionProbabilities":[],"claimVerification":[]},{"slug":"build-software-better-together","name":"Build software better, together","summary":"## Here are 48 public repositories matching this topic...","publisher":"github.com","homepage":"https://github.com/topics/company-brain","kind":"mcp","hasApi":true,"hasMcp":true,"costRange":"free","costNotes":"Cost range free taken from intake evidence.","functions":["code","index","ingest","connect","memory","retrieve","route","tool-call"],"slices":[{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"index","label":"index","note":"Inferred from executor","cost":"Unknown"},{"functionId":"ingest","label":"ingest","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"memory","label":"memory","note":"Inferred from executor","cost":"Unknown"},{"functionId":"retrieve","label":"retrieve","note":"Inferred from executor","cost":"Unknown"},{"functionId":"route","label":"route","note":"Inferred from executor","cost":"Unknown"},{"functionId":"tool-call","label":"tool-call","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Heuristic from executor evidence. Grok pass was unavailable or incomplete. No usage metrics were observed.","signals":[],"functionProbabilities":[{"functionId":"code","value":0.92,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"index","value":0.8,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"ingest","value":0.8,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"connect","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"memory","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"retrieve","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"route","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"},{"functionId":"tool-call","value":0.72,"source":"https://github.com/topics/company-brain","method":"lexical","observedAt":"2026-10-16T00:14:24.433Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"api","status":"unverified","asserted":"true","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"cost","status":"unverified","asserted":"free","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://github.com/topics/company-brain","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-16T00:14:25.325Z","updatedAt":"2026-10-16T00:14:25.325Z","sources":[{"kind":"web","label":"executor","url":"https://github.com/topics/company-brain","excerpt":"#\n\n# company-brain\n\nStar\n\n## Here are 48 public repositories matching this topic...\n\n### inkeep / open-knowledge\n\nStar\n 4.6k\n\nBeautiful, AI-native markdown IDE and LLM wiki\n\n* Updated\n  Oct 16, 2026\n* TypeScript\n\n### baryhuang / claude-code","at":"2026-10-16T00:14:25.325Z"}],"recordId":"cmp_build-software-better-together","claims":{"name":"Build software better, together","summary":"## Here are 48 public repositories matching this topic...","kind":"mcp","publisher":"github.com","homepage":"https://github.com/topics/company-brain","costRange":"free","costNotes":"Cost range free taken from intake evidence.","functions":["code","index","ingest","connect","memory","retrieve","route","tool-call"],"slices":[{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"index","label":"index","note":"Inferred from executor","cost":"Unknown"},{"functionId":"ingest","label":"ingest","note":"Inferred from executor","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"memory","label":"memory","note":"Inferred from executor","cost":"Unknown"},{"functionId":"retrieve","label":"retrieve","note":"Inferred from executor","cost":"Unknown"},{"functionId":"route","label":"route","note":"Inferred from executor","cost":"Unknown"},{"functionId":"tool-call","label":"tool-call","note":"Inferred from executor","cost":"Unknown"}],"interfaces":[{"kind":"mcp"},{"kind":"http"}],"composabilityRationale":"Heuristic from executor evidence. Grok pass was unavailable or incomplete. No usage metrics were observed."},"relations":[{"from":"build-software-better-together","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions connect, index, ingest, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions code, connect, index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"cursor","kind":"fills-same-slot-as","note":"Shared functions code, connect, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions index, ingest, memory, retrieve. Can fill the same function slot."},{"from":"build-software-better-together","to":"agentmemory","kind":"fills-same-slot-as","note":"Shared functions code, connect, memory. Can fill the same function slot."},{"from":"build-software-better-together","to":"lore","kind":"fills-same-slot-as","note":"Shared functions connect, memory, retrieve, tool-call. Can fill the same function slot."},{"from":"build-software-better-together","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions ingest, memory, retrieve. Can fill the same function slot."}],"explain":{"score":13,"formula":"100 × sum(weight × value) over observed factors only. 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Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://github.com/topics/company-brain","excerpt":"#\n\n# company-brain\n\nStar\n\n## Here are 48 public repositories matching this topic...\n\n### inkeep / open-knowledge\n\nStar\n 4.6k\n\nBeautiful, AI-native markdown IDE and LLM wiki\n\n* Updated\n  Oct 16, 2026\n* TypeScript\n\n### baryhuang / claude-code","at":"2026-10-16T00:14:25.325Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":13},{"slug":"pageindex","name":"PageIndex","summary":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","kind":"app","homepage":"https://pageindex.ai","repoUrl":"https://github.com/VectifyAI/PageIndex","docsUrl":"https://docs.pageindex.ai","publisher":"","license":"MIT","hasMcp":true,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["index","reason","embed","memory","retrieve"],"functionProbabilities":[{"functionId":"index","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"reason","value":0.8,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"memory","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"},{"functionId":"retrieve","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md","method":"lexical","observedAt":"2026-09-29T00:00:00.000Z"}],"slices":[{"functionId":"index","label":"Index","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. 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First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","at":"2026-09-29T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Cloud — PAGEINDEX_API_KEY; managed indexing/OCR/storage; chat still uses your model; MCP + folders/metadata (Cloud-only features).","at":"2026-09-29T00:00:00.000Z"}],"interfaces":[{"kind":"mcp"}],"origin":"seed","notes":[],"createdAt":"2026-09-29T00:00:00.000Z","updatedAt":"2026-09-29T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_pageindex","claims":{"name":"PageIndex","summary":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","kind":"app","publisher":"","homepage":"https://pageindex.ai","repoUrl":"https://github.com/VectifyAI/PageIndex","docsUrl":"https://docs.pageindex.ai","license":"MIT","costRange":"unknown","functions":["index","reason","embed","memory","retrieve"],"slices":[{"functionId":"index","label":"Index","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"reason","label":"Reason","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"embed","label":"Embed","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"memory","label":"Memory","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"},{"functionId":"retrieve","label":"Retrieve","note":"PageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","cost":"unknown"}],"interfaces":[{"kind":"mcp"}],"composabilityRationale":""},"relations":[{"from":"pageindex","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"cognee","kind":"fills-same-slot-as","note":"Shared functions embed, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"alibaba-model-studio","kind":"fills-same-slot-as","note":"Shared functions embed, reason. Can fill the same function slot."},{"from":"pageindex","to":"octen","kind":"fills-same-slot-as","note":"Shared functions embed, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"build-software-better-together","kind":"fills-same-slot-as","note":"Shared functions index, memory, retrieve. Can fill the same function slot."},{"from":"pageindex","to":"turbopuffer","kind":"fills-same-slot-as","note":"Shared functions embed, memory. Can fill the same function slot."},{"from":"pageindex","to":"local-office","kind":"fills-same-slot-as","note":"Shared functions memory, retrieve. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-29T00:00:00.000Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-29T00:00:00.000Z"}],"claimVerification":[],"explain":{"score":12,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. Documentation is available.","sources":[],"evidence":"observed"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. First-party pitch: build a hierarchical tree index per document, then let an LLM reason through that tree (human-like section navigation) instead of embedding chunks into a vector database.","at":"2026-09-29T00:00:00.000Z"},{"kind":"human","label":"Catalogue resource card","excerpt":"Cloud — PAGEINDEX_API_KEY; managed indexing/OCR/storage; chat still uses your model; MCP + folders/metadata (Cloud-only features).","at":"2026-09-29T00:00:00.000Z"}],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0.3333333333333333,"contribution":0.03333333333333333,"why":"An MCP surface is declared. No API is declared. Cost is unknown. 1 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.22849520230941842,"contribution":0.031989328323318585,"why":"2 sources cover 1 evidence types. The latest dated evidence is 2026-09-29. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-29-vectifyai-pageindex.md\nPageIndex (MIT, Python) is VectifyAI’s vectorless, reasoning-based RAG engine. 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Grok pass was unavailable or incomplete. 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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."},"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. Can fill the same function slot."},{"from":"memini","to":"turbopuffer","kind":"fills-same-slot-as","note":"Shared functions embed, memory, search, store. Can fill the same function slot."},{"from":"memini","to":"lore","kind":"fills-same-slot-as","note":"Shared functions connect, memory, retrieve, search, store. Can fill the same function slot."},{"from":"memini","to":"llamaindex","kind":"fills-same-slot-as","note":"Shared functions compose, embed, memory, retrieve. Can fill the same function slot."}],"explain":{"score":9,"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/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":"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. 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Cost is therefore unknown.","functions":["connect","memory","code","embed","extract","index","ingest","retrieve","search","store","rank"],"slices":[{"functionId":"ingest","label":"Document ingestion","note":"ingest_document accepts text or a file path and chunks documents into the local store.","cost":"Unknown"},{"functionId":"extract","label":"Answer extraction prompt","note":"MCP prompt extract-answer formats cited answers from retrieved chunks.","cost":"Unknown"},{"functionId":"retrieve","label":"RAG retrieval","note":"query_rag_store returns relevant document chunks for a natural-language query.","cost":"Unknown"},{"functionId":"index","label":"CLI and server indexing","note":"local-faiss index and the MCP server write FAISS indexes under a configurable index directory.","cost":"Unknown"},{"functionId":"embed","label":"Sentence embeddings","note":"Configurable Hugging Face sentence-transformers embedding models; default all-MiniLM-L6-v2.","cost":"Unknown"},{"functionId":"memory","label":"Agent memory store","note":"Positioned as local agent memory and drop-in semantic search for Claude, Copilot, and agents.","cost":"Unknown"},{"functionId":"connect","label":"connect","note":"Inferred from executor","cost":"Unknown"},{"functionId":"code","label":"code","note":"Inferred from executor","cost":"Unknown"},{"functionId":"search","label":"Search","note":"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.","cost":"unknown"},{"functionId":"store","label":"Store","note":"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.","cost":"unknown"},{"functionId":"rank","label":"Rank","note":"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.","cost":"unknown"}],"interfaces":[{"kind":"mcp"}],"composabilityRationale":"Declared as a Model Context Protocol server with stdin/stdout MCP tool calls (ingest_document, query_rag_store) and MCP prompts (extract-answer, summarize-documents). 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No non-MCP HTTP API is stated.","signals":[],"functionProbabilities":[{"functionId":"connect","value":0.92,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"memory","value":0.92,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"code","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"embed","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"extract","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"index","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"ingest","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"retrieve","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"search","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"store","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"summarize","value":0.8,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"compose","value":0.72,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"generate","value":0.72,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"rank","value":0.72,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"test","value":0.72,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"},{"functionId":"tool-call","value":0.72,"source":"https://github.com/nonatofabio/local_faiss_mcp","method":"lexical","observedAt":"2026-10-16T06:16:48.523Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://github.com/nonatofabio/local_faiss_mcp","method":"fallback"},{"key":"api","status":"unverified","asserted":"false","evidenceSource":"https://github.com/nonatofabio/local_faiss_mcp","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://github.com/nonatofabio/local_faiss_mcp","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://github.com/nonatofabio/local_faiss_mcp","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-16T06:16:49.468Z","updatedAt":"2026-10-16T06:16:49.468Z","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"}],"recordId":"cmp_local-faiss-mcp-server","claims":{"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.","kind":"mcp","publisher":"nonatofabio","homepage":"https://github.com/nonatofabio/local_faiss_mcp","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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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":"lore","name":"Lore","summary":"Open-source shared persistent memory for AI agents, backed by Notion. 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It does not state a price, usage fee, seat cost, or whether a paid Notion plan is required.","functions":["memory","extract","search","store","connect","evaluate","retrieve","tool-call"],"slices":[{"functionId":"memory","label":"Shared agent memory vault","note":"Persistent shared memory for experiential knowledge, follow-up tasks, decision records, and procedures, stored as related Notion pages.","cost":"Unknown"},{"functionId":"store","label":"Background memory write","note":"Hooks prompt agents to store information and save it in the background. 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Exact tool schemas, authentication, and any non-MCP HTTP API are not specified in the retrieved text.","signals":[],"functionProbabilities":[{"functionId":"memory","value":0.92,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"extract","value":0.8,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"search","value":0.8,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"store","value":0.8,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"connect","value":0.72,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"evaluate","value":0.72,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"retrieve","value":0.72,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"},{"functionId":"tool-call","value":0.72,"source":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"lexical","observedAt":"2026-10-16T00:14:10.454Z"}],"claimVerification":[{"key":"mcp","status":"unverified","asserted":"true","evidenceSource":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"fallback"},{"key":"api","status":"unverified","asserted":"false","evidenceSource":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"fallback"},{"key":"cost","status":"unverified","asserted":"unknown","evidenceSource":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"fallback"},{"key":"license","status":"unverified","asserted":"","evidenceSource":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","method":"fallback"}],"tweets":[],"notes":[],"origin":"intake","createdAt":"2026-10-16T00:14:11.348Z","updatedAt":"2026-10-16T00:14:11.348Z","sources":[{"kind":"web","label":"executor","url":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","excerpt":"All posts\n\nAll posts\n\nPublished August 18, 2026 in Tech\n\n# Building Shared Memory for AI Agents in Notion\n\nBy Hesham Salman\n\nEngineering, Notion\n\nAs software engineering changes, tribal knowledge is increasingly concentrated not only in a s","at":"2026-10-16T00:14:11.348Z"}],"recordId":"cmp_lore","claims":{"name":"Lore","summary":"Open-source shared persistent memory for AI agents, backed by Notion. 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It does not state a price, usage fee, seat cost, or whether a paid Notion plan is required.","functions":["memory","extract","search","store","connect","evaluate","retrieve","tool-call"],"slices":[{"functionId":"memory","label":"Shared agent memory vault","note":"Persistent shared memory for experiential knowledge, follow-up tasks, decision records, and procedures, stored as related Notion pages.","cost":"Unknown"},{"functionId":"store","label":"Background memory write","note":"Hooks prompt agents to store information and save it in the background. 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It contrasts this with harness-local, model-provider, and single-machine file memory. Exact tool schemas, authentication, and any non-MCP HTTP API are not specified in the retrieved text."},"relations":[{"from":"lore","to":"exa","kind":"fills-same-slot-as","note":"Shared functions connect, extract, retrieve, search, tool-call. Can fill the same function slot."},{"from":"lore","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions connect, extract, memory, retrieve, search, store. Can fill the same function slot."},{"from":"lore","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions connect, evaluate, memory, search, store. Can fill the same function slot."},{"from":"lore","to":"dataforseo","kind":"fills-same-slot-as","note":"Shared functions extract, retrieve, search. Can fill the same function slot."},{"from":"lore","to":"litescrape","kind":"fills-same-slot-as","note":"Shared functions extract, retrieve, search. Can fill the same function slot."},{"from":"lore","to":"tikhub","kind":"fills-same-slot-as","note":"Shared functions extract, retrieve, search. Can fill the same function slot."},{"from":"lore","to":"pulsemcp","kind":"fills-same-slot-as","note":"Shared functions connect, retrieve, search, tool-call. Can fill the same function slot."},{"from":"lore","to":"memini","kind":"fills-same-slot-as","note":"Shared functions connect, memory, retrieve, search, store. Can fill the same function slot."}],"explain":{"score":6,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0.3333333333333333,"contribution":0.03333333333333333,"why":"An MCP surface is declared. No API is declared. Cost is unknown. 1 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-10-16. Freshness is omitted unless observed as a signal.","sources":[{"kind":"web","label":"executor","url":"https://www.notion.com/blog/building-shared-memory-for-ai-agents-in-notion","excerpt":"All posts\n\nAll posts\n\nPublished August 18, 2026 in Tech\n\n# Building Shared Memory for AI Agents in Notion\n\nBy Hesham Salman\n\nEngineering, Notion\n\nAs software engineering changes, tribal knowledge is increasingly concentrated not only in a s","at":"2026-10-16T00:14:11.348Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":6,"tags":["agents"]}]}