{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-topoteretes--cognee","slug":"topoteretes--cognee","name":"cognee","type":"agent","url":"https://www.cognee.ai","page_url":"https://unfragile.ai/topoteretes--cognee","categories":["ai-agents"],"tags":["ai","ai-agents","ai-memory","cognitive-architecture","cognitive-memory","context-engineering","contributions-welcome","good-first-issue","good-first-pr","graph-database","graph-rag","graphrag","help-wanted","knowledge","knowledge-graph","neo4j","open-source","openai","rag","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-topoteretes--cognee__cap_0","uri":"capability://data.processing.analysis.multi.source.document.ingestion.with.automatic.preprocessing","name":"multi-source document ingestion with automatic preprocessing","description":"Accepts unstructured data (documents, text, PDFs, web content) via cognee.add() and automatically routes through a configurable preprocessing pipeline that handles format detection, chunking, and normalization before storage. Uses a task-based execution model where each ingestion step (parsing, cleaning, validation) is a discrete pipeline task with telemetry tracking and error recovery, enabling both synchronous and asynchronous processing modes.","intents":["I want to feed raw documents into my agent's memory without manual preprocessing","I need to ingest multiple document formats (PDFs, markdown, HTML) into a unified knowledge store","I want to track data lineage and provenance as documents flow through the pipeline"],"best_for":["AI agent builders creating long-term memory systems","teams building RAG applications with heterogeneous data sources","developers needing observable, auditable data ingestion pipelines"],"limitations":["Preprocessing pipeline adds latency proportional to document size and complexity","No built-in support for streaming ingestion of very large files (>1GB) without chunking configuration","Custom preprocessing tasks require Python implementation; no visual pipeline builder"],"requires":["Python 3.9+","LLM provider API key (OpenAI, Anthropic, or self-hosted)","Vector database (Weaviate, Qdrant, Milvus) or graph database (Neo4j) configured"],"input_types":["text/plain","application/pdf","text/markdown","text/html","application/json"],"output_types":["structured data points (DataPoint objects)","vector embeddings","graph entities and relationships"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_1","uri":"capability://memory.knowledge.knowledge.graph.generation.from.unstructured.text.via.llm.driven.entity.and.relationship.extraction","name":"knowledge graph generation from unstructured text via llm-driven entity and relationship extraction","description":"Transforms ingested documents into a structured knowledge graph by using LLMs to extract entities, relationships, and semantic triplets (subject-predicate-object) via the cognee.cognify() operation. Implements a multi-stage extraction pipeline: document chunking → entity identification → relationship inference → triplet embedding, with support for custom graph schemas and temporal metadata. The extracted triplets are stored in both a graph database (Neo4j) and vector database simultaneously, enabling both structural and semantic queries.","intents":["I want to automatically extract structured knowledge from unstructured documents without manual annotation","I need to build a queryable knowledge graph that captures both entity relationships and semantic similarity","I want to customize what entities and relationships my agent can reason about"],"best_for":["teams building domain-specific knowledge graphs for enterprise AI agents","researchers working on graph-based RAG systems","developers needing both structural (graph) and semantic (vector) query capabilities"],"limitations":["LLM-based extraction introduces hallucination risk; no built-in deduplication of semantically identical triplets across documents","Extraction quality depends heavily on LLM model choice and prompt engineering; no automatic quality validation","Temporal knowledge graphs add complexity; requires explicit timestamp metadata in source documents"],"requires":["LLM provider with structured output support (OpenAI GPT-4, Anthropic Claude 3+, or compatible)","Graph database (Neo4j 4.4+) for storing entities and relationships","Vector database for storing triplet embeddings","Embedding model (OpenAI, Hugging Face, or local)"],"input_types":["text chunks","structured DataPoint objects from ingestion pipeline"],"output_types":["graph entities (nodes with properties)","relationships (edges with semantic labels)","triplet embeddings (vectors)","temporal metadata (timestamps, version info)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_10","uri":"capability://planning.reasoning.user.feedback.and.interaction.tracking.for.continuous.improvement","name":"user feedback and interaction tracking for continuous improvement","description":"Captures user feedback on search results, agent decisions, and retrieved context via the cognee.improve() operation, storing feedback as graph entities linked to the original queries and results. Feedback is used to improve ranking, identify knowledge gaps, and retrain extraction models. Implements a feedback loop where agents can learn from corrections and improve future performance. Feedback data is queryable, enabling analysis of system performance and user satisfaction.","intents":["I want to capture user feedback on agent responses and use it to improve future performance","I need to identify which knowledge graph entities are most useful vs. irrelevant","I want to track system performance metrics and identify improvement opportunities"],"best_for":["teams building production AI agents requiring continuous improvement","developers implementing human-in-the-loop learning systems","enterprises needing to measure and improve agent quality"],"limitations":["Feedback collection requires explicit user interaction; no automatic quality assessment","Feedback loop introduces latency before improvements are reflected in system behavior","Feedback data can be biased if collection is not representative of all use cases"],"requires":["Mechanism to collect user feedback (UI, API, etc.)","Graph database to store feedback entities","Analysis pipeline to process feedback and improve models"],"input_types":["user ratings and comments","relevance judgments","correction suggestions"],"output_types":["feedback entities in knowledge graph","performance metrics and analytics","improvement recommendations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_11","uri":"capability://image.visual.graph.visualization.and.interactive.exploration","name":"graph visualization and interactive exploration","description":"Generates interactive visualizations of the knowledge graph using network visualization libraries (Pyvis, D3.js), enabling developers and users to explore entity relationships, identify clusters, and understand graph structure. Implements filtering and search capabilities within the visualization, allowing users to focus on subgraphs of interest. Visualizations can be embedded in web interfaces or exported as static images.","intents":["I want to visually explore the knowledge graph to understand what entities and relationships exist","I need to identify clusters or patterns in the knowledge graph","I want to debug knowledge graph quality by inspecting extracted entities and relationships"],"best_for":["developers debugging knowledge graph quality","data scientists analyzing knowledge graph structure","product teams building user-facing graph exploration interfaces"],"limitations":["Large graphs (>10k nodes) become difficult to visualize and interact with; requires filtering or clustering","Visualization rendering can be slow for complex graphs; browser performance depends on graph size","Static visualizations don't scale; real-time updates require WebSocket or polling"],"requires":["Populated knowledge graph","Web browser for interactive visualization","Visualization library (Pyvis, D3.js, etc.)"],"input_types":["knowledge graph entities and relationships"],"output_types":["interactive HTML visualizations","static images (PNG, SVG)","graph statistics and metrics"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_12","uri":"capability://safety.moderation.multi.tenant.access.control.and.data.isolation","name":"multi-tenant access control and data isolation","description":"Implements multi-tenant architecture where each tenant has isolated knowledge graphs, vector databases, and access credentials. Uses tenant IDs to partition data at the database level, ensuring queries from one tenant cannot access another tenant's data. Supports role-based access control (RBAC) with configurable permissions (read, write, delete) per tenant and user. Tenant configuration is managed via environment variables or API, enabling easy onboarding of new tenants.","intents":["I want to run Cognee as a multi-tenant SaaS service with data isolation","I need to control which users can access which knowledge graphs","I want to ensure compliance with data residency and access control requirements"],"best_for":["SaaS platforms offering Cognee as a service to multiple customers","enterprises with multiple business units requiring data isolation","teams building compliance-sensitive applications"],"limitations":["Multi-tenancy adds complexity to database queries (tenant ID filtering on every operation)","Shared infrastructure means one tenant's heavy workload can impact others; requires resource quotas","Cross-tenant analytics or insights require explicit data aggregation logic"],"requires":["Tenant ID management system","Database support for row-level security or tenant ID partitioning","Authentication system to map users to tenants"],"input_types":["tenant IDs","user credentials","access control policies"],"output_types":["tenant-isolated data","access control decisions (allow/deny)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_13","uri":"capability://automation.workflow.custom.pipeline.task.definition.and.composition","name":"custom pipeline task definition and composition","description":"Enables developers to define custom pipeline tasks (cognee/modules/pipelines/tasks/task.py) that can be composed into data processing workflows. Tasks are Python classes that implement a standard interface (execute, validate inputs/outputs) and can be chained together using a pipeline builder. Custom tasks integrate with the telemetry system automatically, enabling observability of custom operations. Supports both synchronous and asynchronous task execution.","intents":["I want to add custom processing steps to the Cognee pipeline (e.g., domain-specific entity extraction)","I need to compose multiple tasks into a reusable workflow","I want to monitor and debug custom pipeline tasks with the same observability as built-in operations"],"best_for":["developers building domain-specific knowledge extraction pipelines","teams implementing custom preprocessing or postprocessing steps","researchers experimenting with different pipeline architectures"],"limitations":["Custom task development requires Python knowledge and understanding of Cognee's task interface","Task composition can become complex for deeply nested pipelines; no visual pipeline builder","Debugging custom tasks requires understanding of async execution and error handling"],"requires":["Python 3.9+","Understanding of Cognee's task interface and pipeline architecture","Knowledge of async/await patterns for async tasks"],"input_types":["task inputs (data, configuration)","pipeline context"],"output_types":["task outputs (processed data)","telemetry traces"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_14","uri":"capability://tool.use.integration.embedding.service.abstraction.with.multiple.model.support","name":"embedding service abstraction with multiple model support","description":"Abstracts embedding generation through a provider-agnostic interface supporting multiple embedding models (OpenAI, Hugging Face, local models). Implements caching of embeddings to avoid recomputation, batch processing for efficiency, and automatic fallback to alternative models if primary provider fails. Developers configure embedding provider via environment variables and Cognee automatically routes all embedding operations through the appropriate service.","intents":["I want to use different embedding models without changing my code","I need to cache embeddings to avoid redundant API calls","I want to use local embedding models for privacy or cost reasons"],"best_for":["teams evaluating different embedding models for quality and cost","developers building privacy-sensitive systems requiring local embeddings","enterprises wanting to optimize embedding costs"],"limitations":["Embedding quality varies significantly by model; no automatic model selection based on use case","Caching adds complexity; cache invalidation requires explicit management","Local embedding models require GPU resources; CPU-only inference is slow"],"requires":["Embedding provider (OpenAI, Hugging Face, local model server)","Cache backend (Redis, in-memory, etc.) for embedding caching","Python 3.9+"],"input_types":["text strings to embed","batch of texts"],"output_types":["embedding vectors","cached embeddings"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_2","uri":"capability://search.retrieval.hybrid.search.combining.graph.traversal.and.vector.semantic.similarity","name":"hybrid search combining graph traversal and vector semantic similarity","description":"Provides multiple search strategies accessible via cognee.recall() that intelligently combine graph-based structural queries with vector-based semantic search. Implements a search router that selects optimal retrieval strategy based on query type: graph traversal for relationship-heavy queries, vector search for semantic similarity, and hybrid fusion for complex multi-faceted queries. Results are ranked and deduplicated using configurable scoring functions that weight structural relevance and semantic similarity.","intents":["I want to retrieve relevant context for my agent using both semantic similarity and explicit relationship paths","I need to find entities connected through multiple relationship hops in the knowledge graph","I want to combine keyword search, semantic search, and graph traversal in a single query"],"best_for":["AI agent builders needing sophisticated context retrieval for reasoning tasks","teams building question-answering systems over structured and unstructured knowledge","developers implementing advanced RAG with multi-hop reasoning"],"limitations":["Hybrid search adds latency (~200-500ms per query) due to parallel graph and vector lookups plus fusion ranking","Search quality depends on knowledge graph quality; garbage in = garbage out for relationship-based queries","No built-in query optimization; complex multi-hop graph queries can timeout on large graphs (>1M nodes)"],"requires":["Populated knowledge graph in Neo4j or compatible graph database","Vector database with indexed triplet embeddings","Query parser and routing logic (built-in to cognee.recall())"],"input_types":["natural language queries (strings)","structured query objects with filters"],"output_types":["ranked list of relevant entities and relationships","context chunks with relevance scores","graph subgraphs showing relationship paths"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_3","uri":"capability://memory.knowledge.agent.memory.persistence.and.recall.via.decorator.pattern","name":"agent memory persistence and recall via decorator pattern","description":"Provides an @agent_memory decorator that automatically captures agent interactions (inputs, outputs, reasoning steps) and persists them to the knowledge graph, enabling agents to build and query their own memory over time. The decorator intercepts function calls, extracts semantic information from execution context, and stores it as graph entities and relationships. Agents can then recall previous interactions via cognee.recall() to inform future decisions, creating a persistent learning loop.","intents":["I want my AI agent to remember past interactions and learn from them","I need to capture agent reasoning steps and decisions for audit and improvement","I want agents to retrieve relevant past experiences when facing similar problems"],"best_for":["teams building long-running AI agents with persistent memory requirements","developers implementing agent learning and adaptation systems","enterprises needing audit trails of agent decision-making"],"limitations":["Decorator adds overhead (~50-100ms per decorated function call) for memory capture and storage","Memory recall quality depends on how well semantic extraction captures relevant context from agent execution","No automatic memory pruning or forgetting; knowledge graphs grow unbounded without explicit cleanup policies"],"requires":["Python 3.9+","Cognee initialized with graph and vector databases configured","LLM provider for semantic extraction from agent execution context"],"input_types":["Python function arguments and return values","execution context (timestamps, metadata)"],"output_types":["memory entities stored in knowledge graph","retrievable context for future agent decisions"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_4","uri":"capability://tool.use.integration.multi.database.adapter.abstraction.for.vector.and.graph.storage","name":"multi-database adapter abstraction for vector and graph storage","description":"Provides pluggable database adapters (cognee/infrastructure/databases/) that abstract away vendor-specific APIs for both vector databases (Weaviate, Qdrant, Milvus, Pinecone) and graph databases (Neo4j, ArangoDB). Developers configure database backends via environment variables or code, and Cognee automatically routes all storage and retrieval operations through the appropriate adapter. Adapters implement a common interface (store, retrieve, delete, update) enabling seamless switching between providers without code changes.","intents":["I want to switch between vector database providers without rewriting my agent code","I need to use multiple databases simultaneously (e.g., Neo4j for graphs, Weaviate for vectors)","I want to avoid vendor lock-in by using a database-agnostic abstraction layer"],"best_for":["teams evaluating multiple database vendors before committing","enterprises with existing database infrastructure wanting to integrate Cognee","developers building portable AI agent frameworks"],"limitations":["Adapter abstraction adds ~10-20ms latency per operation due to indirection layer","Not all database features are exposed through the common interface; vendor-specific optimizations require custom adapter code","Multi-database setups require managing multiple connection pools and credentials"],"requires":["At least one vector database (Weaviate, Qdrant, Milvus, Pinecone) OR one graph database (Neo4j, ArangoDB)","Database connection credentials and endpoint URLs","Python 3.9+"],"input_types":["embeddings (vectors)","graph entities and relationships","metadata and filters"],"output_types":["stored records with IDs","retrieved entities with scores","query results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_5","uri":"capability://tool.use.integration.configurable.llm.provider.abstraction.with.structured.output.support","name":"configurable llm provider abstraction with structured output support","description":"Abstracts LLM interactions through a provider-agnostic interface (cognee/infrastructure/engine/models/) that supports multiple LLM providers (OpenAI, Anthropic, Ollama, local models) with automatic fallback and error recovery. Implements structured output frameworks (JSON schema validation, Pydantic models) ensuring LLM responses conform to expected formats for downstream processing. Developers configure LLM provider via environment variables and Cognee automatically routes all LLM calls through the appropriate adapter with retry logic and token counting.","intents":["I want to use different LLM providers (OpenAI, Anthropic, local) without changing my agent code","I need guaranteed structured output from LLMs for reliable downstream processing","I want to track LLM token usage and costs across multiple providers"],"best_for":["teams evaluating multiple LLM providers for cost and performance","developers building production agents requiring structured LLM outputs","enterprises wanting to use local/private LLMs alongside cloud providers"],"limitations":["Structured output support varies by provider; some require prompt engineering workarounds instead of native schema validation","LLM abstraction adds ~50-100ms per call for adapter routing and response validation","Token counting and cost tracking require provider-specific implementations; not all providers expose token counts"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, etc.) OR local LLM server (Ollama, vLLM)","Python 3.9+","Pydantic 2.0+ for structured output validation"],"input_types":["natural language prompts (strings)","Pydantic model schemas for structured output"],"output_types":["LLM text responses","structured objects (JSON, Pydantic models)","token counts and usage metrics"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_6","uri":"capability://memory.knowledge.temporal.knowledge.graphs.with.version.tracking.and.time.aware.queries","name":"temporal knowledge graphs with version tracking and time-aware queries","description":"Extends the core knowledge graph with temporal metadata (timestamps, version numbers, validity periods) enabling agents to reason about how knowledge evolves over time. Implements time-aware query operations that can retrieve entities and relationships as they existed at specific points in time, track entity evolution across versions, and identify temporal patterns (e.g., 'what changed between date X and Y'). Temporal data is stored as additional properties on graph nodes and edges, enabling both point-in-time and range-based temporal queries.","intents":["I want my agent to understand how information has changed over time","I need to retrieve the state of knowledge as it existed at a specific historical point","I want to track entity evolution and identify when relationships changed"],"best_for":["teams building agents for domains with temporal dynamics (financial markets, scientific research, regulatory compliance)","developers implementing audit trails and change tracking","researchers studying knowledge evolution and temporal reasoning"],"limitations":["Temporal queries add complexity and latency; range-based temporal queries on large graphs can be slow","Requires explicit timestamp metadata in source documents; no automatic temporal inference","Storage overhead increases with version history; no built-in data retention policies for old versions"],"requires":["Graph database with property storage (Neo4j 4.4+)","Source documents with timestamp metadata","Temporal query API (built into cognee.recall())"],"input_types":["timestamped documents","temporal query specifications (point-in-time or range)"],"output_types":["entities and relationships as they existed at specified times","change logs showing entity evolution","temporal patterns and trends"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_7","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.agent.tool.access","name":"mcp server integration for ai agent tool access","description":"Exposes Cognee's core capabilities (add, cognify, recall, improve) as Model Context Protocol (MCP) tools that AI agents (Claude, other MCP-compatible clients) can invoke directly. Implements MCP server that translates agent tool calls into Cognee operations, handles async execution, and returns results in MCP-compatible format. Agents can use Cognee as a memory backend without direct Python integration, enabling use cases like Claude agents with persistent knowledge graphs.","intents":["I want my Claude agent to have persistent memory via Cognee without writing Python code","I need to expose Cognee operations as tools that any MCP-compatible AI agent can use","I want agents to build and query knowledge graphs through standard tool calling"],"best_for":["teams using Claude or other MCP-compatible AI agents","developers building agent systems that need to share a knowledge graph","non-Python environments wanting to integrate Cognee"],"limitations":["MCP protocol adds serialization overhead (~50-100ms per tool call)","Tool calling latency depends on agent's MCP client implementation","Complex operations (large graph queries) may timeout if agent tool call timeout is too short"],"requires":["Cognee Python backend running as MCP server","MCP-compatible AI agent (Claude, etc.)","Network connectivity between agent and MCP server"],"input_types":["MCP tool call requests with JSON parameters"],"output_types":["MCP tool results with JSON-serialized responses","error messages in MCP format"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_8","uri":"capability://tool.use.integration.rest.api.server.for.remote.cognee.access","name":"rest api server for remote cognee access","description":"Exposes Cognee operations (add, cognify, recall, improve) as HTTP REST endpoints enabling remote access from any HTTP client. Implements async request handling, request validation, and response serialization. Developers can deploy Cognee as a microservice and integrate it into larger systems via standard HTTP calls. API includes endpoints for data ingestion, knowledge graph generation, search, and feedback collection.","intents":["I want to integrate Cognee into a microservices architecture via HTTP","I need to expose Cognee's capabilities to non-Python applications","I want to deploy Cognee as a shared service for multiple agents or applications"],"best_for":["teams building microservices architectures with Cognee as a shared service","developers integrating Cognee with non-Python systems","enterprises deploying Cognee in Kubernetes or cloud environments"],"limitations":["HTTP serialization and network latency add 50-200ms per request compared to direct Python calls","Large file uploads (>100MB) may timeout or require chunking","Stateless REST design means complex multi-step operations require multiple round trips"],"requires":["Cognee REST server running (FastAPI-based)","HTTP client library (requests, curl, etc.)","Network connectivity to REST server"],"input_types":["JSON request bodies","multipart form data (for file uploads)","URL query parameters"],"output_types":["JSON responses","HTTP status codes","error messages in JSON format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-topoteretes--cognee__cap_9","uri":"capability://automation.workflow.observability.and.telemetry.with.opentelemetry.integration","name":"observability and telemetry with opentelemetry integration","description":"Instruments all Cognee operations with OpenTelemetry tracing, enabling developers to observe data flow, identify bottlenecks, and debug issues. Automatically captures traces for pipeline execution, database operations, LLM calls, and search queries. Traces include timing information, error details, and custom attributes (e.g., document size, query complexity). Integrates with standard observability backends (Jaeger, Datadog, New Relic) via OpenTelemetry exporters.","intents":["I want to understand where time is spent in my Cognee pipeline","I need to debug why a particular operation is slow or failing","I want to monitor Cognee in production and alert on performance degradation"],"best_for":["teams running Cognee in production requiring observability","developers debugging complex multi-step pipelines","enterprises needing compliance audit trails"],"limitations":["Telemetry collection adds ~5-10% overhead to operation latency","Trace sampling is required for high-volume systems to avoid overwhelming observability backend","Custom attributes require code instrumentation; not all operations are automatically instrumented"],"requires":["OpenTelemetry SDK for Python","Observability backend (Jaeger, Datadog, New Relic, etc.) or OTLP collector","Configuration of OpenTelemetry exporters"],"input_types":["Cognee operations (add, cognify, recall, etc.)"],"output_types":["OpenTelemetry traces with timing and metadata","spans for individual operations","custom attributes and events"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM provider API key (OpenAI, Anthropic, or self-hosted)","Vector database (Weaviate, Qdrant, Milvus) or graph database (Neo4j) configured","LLM provider with structured output support (OpenAI GPT-4, Anthropic Claude 3+, or compatible)","Graph database (Neo4j 4.4+) for storing entities and relationships","Vector database for storing triplet embeddings","Embedding model (OpenAI, Hugging Face, or local)","Mechanism to collect user feedback (UI, API, etc.)","Graph database to store feedback entities","Analysis pipeline to process feedback and improve models"],"failure_modes":["Preprocessing pipeline adds latency proportional to document size and complexity","No built-in support for streaming ingestion of very large files (>1GB) without chunking configuration","Custom preprocessing tasks require Python implementation; no visual pipeline builder","LLM-based extraction introduces hallucination risk; no built-in deduplication of semantically identical triplets across documents","Extraction quality depends heavily on LLM model choice and prompt engineering; no automatic quality validation","Temporal knowledge graphs add complexity; requires explicit timestamp metadata in source documents","Feedback collection requires explicit user interaction; no automatic quality assessment","Feedback loop introduces latency before improvements are reflected in system behavior","Feedback data can be biased if collection is not representative of all use cases","Large graphs (>10k nodes) become difficult to visualize and interact with; requires filtering or clustering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7168828822895115,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-03T12:40:52Z"},"community":{"stars":16996,"forks":1768,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=topoteretes--cognee","compare_url":"https://unfragile.ai/compare?artifact=topoteretes--cognee"}},"signature":"CKYqGphAuMHdUkk9XAM+eLlPg2iayapFTm1r+YTUG07dHyyM9G/4+riLAaXHZLDagRtOqRnKBhZKFV/rHheoCg==","signedAt":"2026-06-22T14:41:05.632Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/topoteretes--cognee","artifact":"https://unfragile.ai/topoteretes--cognee","verify":"https://unfragile.ai/api/v1/verify?slug=topoteretes--cognee","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}