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Uses a provider registry pattern with standardized request/response schemas that normalize differences in API signatures, token counting, and streaming behavior across proprietary and open-source models.","intents":["Switch between LLM providers without rewriting agent logic","Use local Ollama models as fallback when cloud APIs are unavailable","Compare model outputs across providers for the same prompt","Reduce vendor lock-in by abstracting provider-specific APIs"],"best_for":["Teams building multi-model agent systems","Developers wanting GDPR-compliant local-first inference","Organizations evaluating different LLM providers"],"limitations":["Provider-specific features (vision, function calling) may not be fully normalized across all 19 providers","Token counting accuracy varies by provider; local estimation may differ from actual usage","Streaming response handling adds ~50-100ms latency due to normalization layer"],"requires":["Python 3.12+","API keys for cloud providers (OpenAI, Anthropic, Google) or local Ollama instance","Network access to provider endpoints or local model files"],"input_types":["text prompts","structured messages with role/content","system instructions"],"output_types":["text completions","streaming token chunks","structured JSON responses"],"categories":["tool-use-integration","multi-llm-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_1","uri":"capability://tool.use.integration.mcp.tool.registry.with.145.pre.integrated.tools","name":"mcp tool registry with 145 pre-integrated tools","description":"Cognithor implements a Model Context Protocol (MCP) tool registry that exposes 145 pre-built tools (web search, file operations, database queries, API calls, etc.) as callable functions within agent workflows. Uses a schema-based function registry pattern where tools are defined with JSON schemas for input validation, and agents invoke them via standardized function-calling APIs supported by OpenAI, Anthropic, and other providers.","intents":["Enable agents to call external APIs and services without custom integration code","Validate tool inputs against schemas before execution","Compose multi-step workflows that chain tool outputs to subsequent tool inputs","Extend agent capabilities by adding custom MCP tools to the registry"],"best_for":["Developers building autonomous agents that interact with external systems","Teams standardizing on MCP for tool integration across multiple agents","Organizations needing auditable tool execution with schema validation"],"limitations":["Tool execution is synchronous; no built-in parallelization of independent tool calls","Error handling and retry logic must be implemented at the agent level, not in the tool registry","Tool schemas must be manually maintained if underlying APIs change","145 tools cover common use cases but may require custom tool development for domain-specific integrations"],"requires":["Python 3.12+","MCP server running (local or remote)","API credentials for external services that tools depend on","JSON schema knowledge for defining custom tools"],"input_types":["JSON-formatted tool parameters","structured function call requests","agent-generated function invocations"],"output_types":["tool execution results (JSON, text, binary)","error messages with execution context","structured response objects"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_10","uri":"capability://memory.knowledge.hierarchical.knowledge.graph.construction.and.reasoning","name":"hierarchical knowledge graph construction and reasoning","description":"Cognithor builds and maintains knowledge graphs that represent entities, relationships, and hierarchies extracted from documents and agent interactions. Agents can traverse knowledge graphs to reason about entity relationships, perform multi-hop reasoning, and answer questions that require understanding connections between concepts, rather than relying solely on semantic similarity.","intents":["Build knowledge graphs from unstructured documents to enable relationship-based reasoning","Answer questions requiring multi-hop reasoning across entity relationships","Identify gaps in knowledge by analyzing graph structure","Perform entity disambiguation and relationship extraction at scale"],"best_for":["Organizations with complex domain knowledge requiring relationship reasoning","Teams building knowledge-intensive applications (research, intelligence analysis)","Developers needing to reason about entity hierarchies and dependencies"],"limitations":["Knowledge graph construction requires manual schema definition or expensive LLM-based extraction; no automatic schema inference","Graph traversal for reasoning adds latency; multi-hop queries can take 500ms-2s depending on graph size","Entity linking and disambiguation are error-prone; incorrect entity merging can corrupt graph structure","Knowledge graphs require ongoing maintenance; outdated relationships must be identified and removed"],"requires":["Python 3.12+","Knowledge graph database (Neo4j, ArangoDB, or similar)","Entity extraction and relationship extraction models (LLM-based or ML models)","Schema definition for domain entities and relationships"],"input_types":["documents for entity/relationship extraction","structured entity/relationship data","graph queries (Cypher, SPARQL, or custom)"],"output_types":["knowledge graph nodes and edges","traversal results","relationship chains","reasoning explanations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_2","uri":"capability://memory.knowledge.6.tier.hierarchical.memory.system.with.knowledge.synthesis","name":"6-tier hierarchical memory system with knowledge synthesis","description":"Cognithor implements a multi-level memory architecture combining short-term context windows, episodic memory (conversation history), semantic memory (vector embeddings), knowledge graphs, and persistent vaults for long-term retention. Uses hierarchical retrieval patterns where agents query appropriate memory tiers based on query type: recent context for immediate relevance, embeddings for semantic similarity, knowledge graphs for relationship reasoning, and vaults for archival data.","intents":["Maintain conversation context across multiple agent interactions without token overflow","Retrieve semantically similar past interactions or documents for few-shot learning","Build and query knowledge graphs to reason about entity relationships and dependencies","Archive and retrieve long-term information without losing it to context window limits"],"best_for":["Long-running agents that need persistent memory across sessions","Teams building knowledge-intensive applications (research assistants, domain experts)","Organizations requiring GDPR-compliant local-first data storage"],"limitations":["Hierarchical retrieval adds latency; querying all 6 tiers for a single query can take 200-500ms depending on vault size","Knowledge graph construction requires manual schema definition or expensive LLM-based entity extraction","Vector embeddings require periodic re-indexing as new data arrives; no incremental indexing optimization","No built-in deduplication across memory tiers; duplicate information may be stored and retrieved"],"requires":["Python 3.12+","Vector database (e.g., Chroma, Weaviate, or local FAISS)","Knowledge graph database (e.g., Neo4j) for graph-based memory tier","Persistent storage backend (local filesystem, S3, or database)"],"input_types":["text queries","conversation messages","documents for indexing","entity/relationship data for knowledge graphs"],"output_types":["retrieved context snippets","ranked search results","knowledge graph traversal results","synthesized summaries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_3","uri":"capability://tool.use.integration.18.channel.communication.integration.with.unified.message.routing","name":"18-channel communication integration with unified message routing","description":"Cognithor integrates agents with 18 communication channels (Discord, Telegram, Slack, email, webhooks, etc.) through a unified message routing layer that normalizes channel-specific message formats, user identities, and authentication into a standardized internal message protocol. Agents receive normalized messages regardless of source channel and can respond to any channel without channel-specific code.","intents":["Deploy a single agent across multiple communication platforms without duplicating logic","Route agent responses to the correct channel based on incoming message source","Normalize user identities across channels for consistent context and permissions","Handle channel-specific features (threads, reactions, file uploads) transparently"],"best_for":["Teams building multi-channel chatbots or assistants","Organizations with users across Discord, Slack, Telegram, and other platforms","Developers wanting to avoid channel-specific SDK complexity"],"limitations":["Channel-specific features (Discord threads, Slack blocks, Telegram inline keyboards) require custom handling; not all features are abstracted","Message rate limits vary by channel; no built-in rate limiting coordination across channels","User identity mapping across channels is manual; no automatic cross-channel user linking","File upload/download support varies by channel; some channels have size or format restrictions"],"requires":["Python 3.12+","API credentials/tokens for each enabled channel (Discord bot token, Telegram API key, Slack app token, etc.)","Network connectivity to channel APIs","Channel-specific permissions (e.g., Discord server admin, Telegram bot owner)"],"input_types":["channel-native messages (Discord messages, Telegram updates, Slack events)","normalized internal message objects","user commands and interactions"],"output_types":["channel-native responses","formatted text messages","file attachments","structured interactions (buttons, menus)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_4","uri":"capability://automation.workflow.agent.packs.marketplace.for.pre.built.agent.templates","name":"agent packs marketplace for pre-built agent templates","description":"Cognithor provides an Agent Packs marketplace where developers can publish, discover, and install pre-configured agent templates that bundle LLM provider selection, memory configuration, tool sets, and channel integrations. Packs are versioned, dependency-managed, and installable via a package manager pattern, allowing rapid agent deployment without manual configuration.","intents":["Quickly deploy a specialized agent (e.g., customer support, research assistant) without building from scratch","Share agent configurations across teams or with the community","Discover best-practice agent architectures for specific use cases","Manage agent dependencies and versions across multiple deployments"],"best_for":["Teams wanting to rapidly prototype agents for common use cases","Organizations standardizing on agent templates across departments","Individual developers building agents for specific domains"],"limitations":["Agent Packs are templates; customization still requires code changes or configuration overrides","Marketplace governance and quality control are not detailed; no built-in security scanning for malicious packs","Pack dependencies may conflict with local environment or other installed packs","No automatic pack updates; manual version management required"],"requires":["Python 3.12+","Access to Agent Packs marketplace (online or self-hosted)","Package manager CLI or Python API for installing packs"],"input_types":["pack identifiers (name, version)","configuration overrides (YAML, JSON)","custom tool definitions"],"output_types":["installed agent template","configuration files","dependency manifests"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_5","uri":"capability://safety.moderation.local.first.inference.with.zero.telemetry.and.gdpr.compliance","name":"local-first inference with zero telemetry and gdpr compliance","description":"Cognithor is architected as a local-first system where agents run entirely on-premises with no data transmission to external telemetry services or cloud logging. Supports local LLM inference via Ollama integration, local vector databases, and local knowledge storage, enabling GDPR-compliant deployments where sensitive data never leaves the organization's infrastructure.","intents":["Deploy agents in regulated environments (healthcare, finance, government) without cloud data transfer","Ensure user data privacy by running inference locally without third-party telemetry","Reduce latency and bandwidth costs by avoiding cloud API calls for inference","Maintain full data ownership and audit trails for compliance requirements"],"best_for":["Organizations in GDPR, HIPAA, or other regulated jurisdictions","Teams with strict data residency requirements","Developers building privacy-first applications","Enterprises wanting to avoid vendor lock-in with cloud LLM providers"],"limitations":["Local LLM inference (Ollama) is slower than cloud APIs; typical latency 2-10x higher depending on model size and hardware","Requires significant local compute resources (GPU recommended); not suitable for resource-constrained environments","No built-in model optimization (quantization, pruning); users must manage model sizes and performance trade-offs","Local-only deployment limits access to latest proprietary models (GPT-4, Claude 3); open-source models may lag in capability"],"requires":["Python 3.12+","Local compute infrastructure (CPU/GPU) for running LLMs","Ollama or compatible local LLM runtime","Local vector database (Chroma, FAISS, Weaviate)","Persistent storage for knowledge vaults and agent state"],"input_types":["prompts and queries","documents for local indexing","agent configurations"],"output_types":["local inference results","audit logs","compliance reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_6","uri":"capability://planning.reasoning.autonomous.agent.orchestration.with.planning.and.reasoning","name":"autonomous agent orchestration with planning and reasoning","description":"Cognithor provides an agent orchestration layer that enables autonomous agents to decompose complex tasks into sub-tasks, plan execution sequences, and reason about tool choices using chain-of-thought patterns. Agents can dynamically select from available tools, evaluate outcomes, and adjust strategies based on feedback without explicit human instruction for each step.","intents":["Build agents that autonomously break down complex requests into executable steps","Enable agents to reason about which tools to use and in what order","Allow agents to recover from tool failures by trying alternative approaches","Create multi-step workflows that adapt based on intermediate results"],"best_for":["Developers building autonomous research or analysis agents","Teams deploying agents for complex, multi-step business processes","Organizations wanting agents that can handle ambiguous or open-ended tasks"],"limitations":["Reasoning and planning add significant latency; complex task decomposition may require 5-10 LLM calls, adding 5-30 seconds per task","Agent behavior is non-deterministic; same input may produce different execution paths depending on LLM sampling","No built-in cost control; agents may make excessive tool calls or LLM queries without explicit budgets","Debugging agent reasoning is difficult; no built-in tracing or visualization of decision trees"],"requires":["Python 3.12+","LLM provider with function calling support (OpenAI, Anthropic, etc.)","Tool registry with clear schemas for agent decision-making","Sufficient LLM context window for task decomposition and reasoning"],"input_types":["natural language task descriptions","structured task specifications","feedback from previous execution attempts"],"output_types":["task execution plans","tool invocation sequences","reasoning traces","final task results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_7","uri":"capability://data.processing.analysis.document.analysis.and.knowledge.extraction.with.ocr.and.semantic.parsing","name":"document analysis and knowledge extraction with ocr and semantic parsing","description":"Cognithor integrates document analysis capabilities that extract structured knowledge from unstructured documents (PDFs, images, text files) using OCR for scanned documents and semantic parsing for text extraction. Extracted content is automatically indexed into the knowledge vault and made queryable through semantic search, enabling agents to reason over document collections without manual preprocessing.","intents":["Extract text and structure from scanned PDFs or images without manual transcription","Automatically index document collections for semantic search and retrieval","Parse documents into structured data (tables, entities, relationships) for knowledge graphs","Enable agents to cite specific document sections when answering questions"],"best_for":["Organizations with large document archives (contracts, research papers, technical docs)","Teams building document-aware agents or research assistants","Developers needing to extract structured data from unstructured documents"],"limitations":["OCR accuracy varies by document quality; handwritten or low-resolution documents may have high error rates","Semantic parsing may miss domain-specific terminology or context; requires custom extraction rules for specialized documents","Large document collections require significant indexing time and storage; no built-in incremental indexing","Document layout and formatting information is lost during text extraction; table structures may not be preserved"],"requires":["Python 3.12+","OCR library (Tesseract, EasyOCR, or cloud OCR service)","Vector database for semantic indexing","Document file formats: PDF, PNG, JPG, TIFF, TXT, DOCX"],"input_types":["PDF files","scanned images (PNG, JPG, TIFF)","text documents (TXT, DOCX)","document URLs"],"output_types":["extracted text","structured data (JSON, tables)","semantic embeddings","document metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_8","uri":"capability://memory.knowledge.obsidian.vault.integration.for.knowledge.management","name":"obsidian vault integration for knowledge management","description":"Cognithor integrates with Obsidian vaults, allowing agents to read from and write to Obsidian notes, leverage Obsidian's graph structure for knowledge relationships, and maintain bidirectional sync between agent memory and Obsidian. This enables users to manage agent knowledge using Obsidian's familiar interface while agents benefit from structured, interconnected knowledge.","intents":["Use Obsidian as a knowledge management interface for agent-backed systems","Maintain agent knowledge in Obsidian format for human readability and editing","Leverage Obsidian's graph visualization to understand agent knowledge relationships","Sync agent-generated insights back to Obsidian for human review and curation"],"best_for":["Knowledge workers using Obsidian who want AI-powered agents","Teams managing knowledge bases in Obsidian format","Developers building hybrid human-AI knowledge systems"],"limitations":["Obsidian vault sync requires file system access; not suitable for cloud-only deployments","Bidirectional sync can create conflicts if both agent and human edit notes simultaneously; no built-in conflict resolution","Obsidian graph structure is implicit (via links); converting to formal knowledge graphs requires additional processing","Large Obsidian vaults (10,000+ notes) may have performance issues with full-vault indexing"],"requires":["Python 3.12+","Obsidian vault on local file system","Obsidian API or file system access to vault directory","File system permissions to read/write vault notes"],"input_types":["Obsidian note files (Markdown)","vault graph structure","note metadata and tags"],"output_types":["new Obsidian notes","updated note content","vault graph updates","backlinks and relationships"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-alex8791-cyber--cognithor__cap_9","uri":"capability://tool.use.integration.apache.2.0.open.source.framework.with.extensible.architecture","name":"apache 2.0 open-source framework with extensible architecture","description":"Cognithor is released under Apache 2.0 license with a modular, extensible architecture that allows developers to add custom LLM providers, tools, memory backends, and channels through well-defined plugin interfaces. The codebase is designed for community contribution with clear separation of concerns between core orchestration, provider adapters, and integrations.","intents":["Extend Cognithor with custom LLM providers or proprietary models","Add domain-specific tools or integrations without modifying core code","Contribute improvements back to the open-source project","Fork and customize Cognithor for specific organizational needs"],"best_for":["Organizations wanting to customize agent behavior for proprietary use cases","Open-source contributors wanting to extend the agent framework","Teams with specific LLM providers or tools not in the standard set","Developers building commercial products on top of Cognithor"],"limitations":["Open-source means no guaranteed support or SLA; community support is best-effort","Extensibility requires understanding Cognithor's plugin architecture; poorly written plugins can destabilize agents","No built-in governance for community contributions; quality and security of third-party plugins vary","Apache 2.0 license allows commercial use but requires attribution and license compliance"],"requires":["Python 3.12+","Git for cloning and contributing to the repository","Understanding of Cognithor's plugin interfaces and architecture","Development environment for testing custom extensions"],"input_types":["plugin code (Python modules)","configuration files for registering plugins","provider adapters and tool definitions"],"output_types":["extended Cognithor installation","custom provider/tool implementations","pull requests for upstream contribution"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.12+","API keys for cloud providers (OpenAI, Anthropic, Google) or local Ollama instance","Network access to provider endpoints or local model files","MCP server running (local or remote)","API credentials for external services that tools depend on","JSON schema knowledge for defining custom tools","Knowledge graph database (Neo4j, ArangoDB, or similar)","Entity extraction and relationship extraction models (LLM-based or ML models)","Schema definition for domain entities and relationships","Vector database (e.g., Chroma, Weaviate, or local FAISS)"],"failure_modes":["Provider-specific features (vision, function calling) may not be fully normalized across all 19 providers","Token counting accuracy varies by provider; local estimation may differ from actual usage","Streaming response handling adds ~50-100ms latency due to normalization layer","Tool execution is synchronous; no built-in parallelization of independent tool calls","Error handling and retry logic must be implemented at the agent level, not in the tool registry","Tool schemas must be manually maintained if underlying APIs change","145 tools cover common use cases but may require custom tool development for domain-specific integrations","Knowledge graph construction requires manual schema definition or expensive LLM-based extraction; no automatic schema inference","Graph traversal for reasoning adds latency; multi-hop queries can take 500ms-2s depending on graph size","Entity linking and disambiguation are error-prone; incorrect entity merging can corrupt graph structure","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2179478615241617,"quality":0.47,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2026-05-03T13:52:56Z"},"community":{"stars":121,"forks":18,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alex8791-cyber--cognithor","compare_url":"https://unfragile.ai/compare?artifact=alex8791-cyber--cognithor"}},"signature":"cUoAVGHXAP8MUETwNbdzkXmTW7NDqHjASqO36VxGRsqm7AB3LxM8jkmfXIo4CmTw0P2AEL7CGU9hI6j09M1WAQ==","signedAt":"2026-06-20T06:39:33.409Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alex8791-cyber--cognithor","artifact":"https://unfragile.ai/alex8791-cyber--cognithor","verify":"https://unfragile.ai/api/v1/verify?slug=alex8791-cyber--cognithor","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"}}