{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-taskingai--taskingai","slug":"taskingai--taskingai","name":"TaskingAI","type":"repo","url":"https://www.tasking.ai","page_url":"https://unfragile.ai/taskingai--taskingai","categories":["app-builders"],"tags":["agent","ai","ai-native","function-call","generative-ai","gpt","langchain","llm","rag","retrieval-augmented-generation","vector"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-taskingai--taskingai__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.model.abstraction.and.routing","name":"multi-provider llm model abstraction and routing","description":"Unifies integration with hundreds of LLM providers (OpenAI, Anthropic, Google Gemini, etc.) through a standardized inference API gateway that abstracts provider-specific APIs into a common interface. The Inference Service handles provider registration, credential management, and request routing via a FastAPI application that translates unified chat completion requests into provider-specific API calls, enabling seamless model switching without application code changes.","intents":["I want to build an agent that can switch between different LLM providers without rewriting code","I need to manage API credentials for multiple LLM providers in a centralized location","I want to test my application against different models from different vendors to compare performance"],"best_for":["Teams building multi-model AI applications","Developers evaluating LLM providers before committing to one","Organizations with vendor lock-in concerns"],"limitations":["Provider-specific features (vision, function calling nuances) may require custom handling despite abstraction","Latency overhead from abstraction layer adds ~50-100ms per inference request","Rate limiting and quota management must be configured per provider separately"],"requires":["Python 3.9+","API keys for desired LLM providers","PostgreSQL database for model configuration storage","FastAPI 0.95+"],"input_types":["chat messages (text)","model identifiers","provider credentials"],"output_types":["chat completions (text)","token usage metadata","provider response objects"],"categories":["tool-use-integration","model-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.system.with.vector.search","name":"retrieval-augmented generation (rag) system with vector search","description":"Implements a complete RAG pipeline with document ingestion, vector embedding, and semantic search capabilities. The Retrieval System API manages document storage in object storage, maintains vector embeddings in a vector database, and executes semantic search queries to retrieve contextually relevant documents. This enables LLM applications to augment prompts with external knowledge without fine-tuning, using a retrieval-first architecture that separates document indexing from inference.","intents":["I want to build a chatbot that answers questions based on my company's internal documents","I need to retrieve relevant context from a large document corpus to feed into an LLM","I want to implement semantic search over my knowledge base without building a custom search engine"],"best_for":["Teams building knowledge-base-driven chatbots","Organizations with large document repositories needing semantic search","Developers implementing question-answering systems over proprietary data"],"limitations":["Vector embedding quality depends on embedding model choice; no built-in fine-tuning for domain-specific embeddings","Retrieval latency scales with corpus size; no automatic indexing optimization strategies","Requires separate object storage configuration; no built-in document preprocessing for PDFs, images, or complex formats"],"requires":["PostgreSQL for metadata storage","Vector database (e.g., Milvus, Pinecone, or compatible)","Object storage (S3-compatible or local filesystem)","Embedding model API key (OpenAI, Hugging Face, etc.)"],"input_types":["documents (text, PDF)","search queries (text)","document metadata"],"output_types":["retrieved document chunks (text)","relevance scores","document metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_10","uri":"capability://tool.use.integration.inference.service.with.provider.specific.api.integration","name":"inference service with provider-specific api integration","description":"Implements a dedicated Inference Service that handles communication with various LLM providers through provider-specific API clients. The service translates unified chat completion requests from the Backend into provider-specific formats (OpenAI, Anthropic, Google Gemini, etc.), manages provider credentials, handles streaming responses, and returns standardized results. This service is decoupled from the Backend, enabling independent scaling and updates without affecting other components.","intents":["I want to send inference requests to different LLM providers without knowing their specific APIs","I need to handle streaming responses from LLMs efficiently","I want to manage provider-specific features (vision, function calling) transparently"],"best_for":["Teams building multi-provider LLM applications","Developers needing provider abstraction without vendor lock-in","Organizations requiring high-throughput inference with provider diversity"],"limitations":["Provider-specific features (vision models, advanced function calling) may not be fully abstracted","Streaming response handling adds complexity; some providers have different streaming formats","Error handling varies by provider; no unified error taxonomy across all providers"],"requires":["Python 3.9+","Provider API keys for each supported provider","Network connectivity to provider APIs","Backend API for request routing"],"input_types":["unified chat completion requests (JSON)","provider credentials","model identifiers"],"output_types":["chat completions (text or streaming)","token usage metadata","provider-specific response metadata"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_11","uri":"capability://memory.knowledge.conversation.history.persistence.and.context.management","name":"conversation history persistence and context management","description":"Manages persistent storage of conversation history in PostgreSQL with full message tracking, metadata, and context preservation. Each conversation maintains a complete message history with timestamps, token usage, and provider information. The system enables retrieving conversation history for context injection into subsequent requests, supporting multi-turn interactions where the LLM can reference previous messages. Context is managed at the database level, allowing applications to retrieve and manipulate conversation state independently of the inference service.","intents":["I want my chatbot to remember previous messages in a conversation","I need to retrieve conversation history for analytics or audit purposes","I want to implement conversation branching or alternative response exploration"],"best_for":["Teams building multi-turn chatbot applications","Organizations requiring conversation audit trails","Developers implementing conversation analytics"],"limitations":["Context window limitations of underlying LLMs still apply; no automatic context compression or summarization","Conversation history grows unbounded; no built-in pruning or archival strategies","No built-in conversation branching; implementing alternative responses requires custom logic"],"requires":["PostgreSQL database","Backend API running","Conversation IDs for tracking"],"input_types":["user messages (text)","assistant responses (text)","metadata (timestamps, token usage)"],"output_types":["conversation history (structured)","message metadata","context for inference"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_12","uri":"capability://tool.use.integration.built.in.plugin.library.with.common.integrations","name":"built-in plugin library with common integrations","description":"Provides a set of pre-built plugins that implement common tool integrations such as web search, calculations, and API calls. These built-in plugins are registered in the Plugin Service with JSON schemas and can be immediately used by assistants without custom development. The plugin architecture allows extending this library with custom plugins, enabling organizations to build domain-specific tools while leveraging common integrations out of the box.","intents":["I want my assistant to search the web for current information","I need my agent to perform calculations or data transformations","I want to use common integrations without building custom plugins"],"best_for":["Teams building assistants that need common capabilities","Developers prototyping agents quickly without custom tool development","Organizations standardizing on a set of common integrations"],"limitations":["Built-in plugins are limited to common use cases; domain-specific integrations require custom plugins","Plugin capabilities are fixed; no configuration options for customizing plugin behavior","Web search plugin depends on external search APIs; quality and availability depend on provider"],"requires":["Plugin Service running","API keys for external services (web search, etc.)","Assistants configured to use plugins"],"input_types":["tool invocation requests (JSON)","tool parameters"],"output_types":["tool results (JSON)","error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_13","uri":"capability://automation.workflow.redis.caching.layer.for.performance.optimization","name":"redis caching layer for performance optimization","description":"Implements a Redis caching layer that improves performance by caching frequently accessed data such as model configurations, assistant definitions, and retrieval results. The Backend Layer uses Redis to reduce database queries and improve response latency for common operations. Cache invalidation is handled through application logic, ensuring consistency between cached and persistent data.","intents":["I want to improve API response latency for frequently accessed data","I need to reduce database load from repeated queries","I want to cache retrieval results to speed up RAG operations"],"best_for":["High-traffic deployments requiring performance optimization","Teams with strict latency requirements","Organizations running multi-user systems with shared data"],"limitations":["Cache invalidation must be managed at the application level; no automatic invalidation on data changes","Redis is single-node in Docker Compose; no built-in clustering or replication for high availability","Cache memory is limited; large datasets may not fit in cache, requiring eviction policies"],"requires":["Redis 6+","Backend API configured to use Redis","Sufficient memory for cache data"],"input_types":["cache keys (strings)","cached data (JSON, serialized objects)"],"output_types":["cached values","cache hits/misses"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_14","uri":"capability://data.processing.analysis.object.storage.integration.for.document.and.binary.data.management","name":"object storage integration for document and binary data management","description":"Integrates with object storage (S3-compatible or local filesystem) to store documents, embeddings, and other binary data used by the RAG system. The Retrieval System API manages document uploads, storage, and retrieval through a standardized object storage interface. This separation of document storage from the database enables efficient handling of large files and reduces database size, while the abstraction allows switching between different storage backends.","intents":["I want to store large documents for my RAG system without bloating the database","I need to manage document lifecycle (upload, delete, archive)","I want to use cloud storage (S3) for scalability and durability"],"best_for":["Teams managing large document repositories","Organizations using cloud storage for cost and scalability","Developers building RAG systems with large document sets"],"limitations":["Object storage abstraction is basic; no built-in versioning, lifecycle policies, or access control","Document retrieval requires separate API calls; no streaming or range requests","No built-in backup or disaster recovery; relies on storage provider's durability guarantees"],"requires":["Object storage backend (S3-compatible or local filesystem)","Storage credentials (for cloud storage)","Sufficient storage capacity for documents"],"input_types":["documents (binary files)","document metadata"],"output_types":["document URLs/paths","storage metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_2","uri":"capability://tool.use.integration.plugin.system.with.function.calling.and.tool.execution","name":"plugin system with function calling and tool execution","description":"Manages a plugin architecture that enables LLMs to call external tools and functions through a standardized interface. The Plugin Service exposes a registry of available tools with JSON schemas, handles function invocation requests from LLMs, executes tool logic, and returns results back to the inference pipeline. Built-in plugins provide common capabilities (web search, calculations, etc.), while custom plugins can be registered via the Plugin API Gateway for domain-specific integrations.","intents":["I want my AI assistant to call external APIs and tools when needed","I need to define a set of functions that my LLM can invoke dynamically","I want to extend my agent with custom tools without modifying the core inference logic"],"best_for":["Developers building agentic systems with tool-calling capabilities","Teams integrating LLMs with existing APIs and microservices","Organizations building domain-specific AI assistants with custom tooling"],"limitations":["Plugin execution is synchronous; no built-in support for long-running async operations or background jobs","Tool schema validation relies on JSON Schema; complex type systems may require custom serialization","No built-in retry logic or error recovery for failed tool calls; applications must implement their own resilience"],"requires":["Python 3.9+","Plugin Service running (separate from Backend)","JSON schema definitions for each tool","LLM provider supporting function calling (OpenAI, Anthropic, etc.)"],"input_types":["tool schemas (JSON Schema)","function call requests (structured)","tool parameters (JSON)"],"output_types":["tool execution results (JSON)","error messages","execution metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_3","uri":"capability://text.generation.language.assistant.creation.and.conversation.management","name":"assistant creation and conversation management","description":"Provides APIs for creating and configuring AI assistants with persistent conversation history and state management. The Assistant Operations API enables defining assistants with specific system prompts, model selections, tool bindings, and RAG configurations. Conversations are stored in PostgreSQL with full history tracking, enabling multi-turn interactions where context is maintained across requests. The architecture separates assistant definitions from conversation instances, allowing multiple conversations per assistant.","intents":["I want to create a reusable AI assistant with a specific personality and capabilities","I need to maintain conversation history across multiple user interactions","I want to configure different assistants with different tools and knowledge bases"],"best_for":["Teams building chatbot applications with persistent state","Developers creating multiple specialized AI assistants for different use cases","Organizations needing conversation analytics and audit trails"],"limitations":["Conversation history grows unbounded; no built-in pruning or summarization for long conversations","Context window limitations of underlying LLMs still apply; no automatic context compression","Assistant configuration changes don't retroactively affect existing conversations"],"requires":["PostgreSQL database for conversation storage","Backend API running","At least one configured LLM model","Optional: tools and RAG collections to bind to assistant"],"input_types":["assistant configuration (JSON)","user messages (text)","conversation IDs"],"output_types":["assistant responses (text)","conversation history (structured)","metadata (timestamps, token usage)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_4","uri":"capability://text.generation.language.interactive.playground.ui.for.model.and.assistant.testing","name":"interactive playground ui for model and assistant testing","description":"Provides a web-based Playground UI that enables interactive testing and experimentation with LLM models and configured assistants. The frontend communicates with the Backend API to execute inference requests, display responses in real-time, and visualize token usage and provider information. The playground supports switching between models, adjusting parameters, and testing tool calling without requiring code changes, serving as both a development tool and a way to validate assistant behavior before deployment.","intents":["I want to test different LLM models and compare their responses interactively","I need to debug my assistant's behavior and see what tools it's calling","I want to experiment with different prompts and parameters without writing code"],"best_for":["Non-technical stakeholders validating AI assistant behavior","Developers debugging agent behavior and tool calling","Teams iterating on assistant prompts and configurations"],"limitations":["Playground is single-user; no multi-user collaboration or session sharing","No built-in conversation export or logging beyond what the database stores","Limited to text-based interactions; no support for multimodal inputs in UI"],"requires":["Frontend service running (Node.js/React)","Backend API accessible","Web browser with modern JavaScript support","Configured models and assistants in the system"],"input_types":["text prompts","model selections","parameter adjustments"],"output_types":["text responses","token usage metrics","provider information","tool call traces"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_5","uri":"capability://automation.workflow.model.and.provider.management.ui","name":"model and provider management ui","description":"Provides a web-based interface for registering, configuring, and managing LLM models from various providers. The Models Management UI allows users to add provider credentials, select which models to enable, configure model-specific parameters, and view available models from each provider. This configuration is stored in PostgreSQL and used by the Inference Service to route requests appropriately, centralizing provider credential management and model availability configuration.","intents":["I want to register my API keys for multiple LLM providers in one place","I need to enable/disable specific models without restarting the system","I want to see which models are available from each provider and their capabilities"],"best_for":["Platform administrators managing multi-tenant or multi-model deployments","Teams rotating between different LLM providers","Organizations managing provider credentials securely"],"limitations":["Credentials are stored in PostgreSQL; no built-in encryption at rest (requires database-level encryption)","No audit logging of credential changes or model configuration modifications","Model availability is static; no automatic sync with provider's current model list"],"requires":["Frontend service running","Backend API accessible","PostgreSQL database","Valid API keys for desired providers"],"input_types":["provider credentials (API keys)","model selections","configuration parameters"],"output_types":["model registry (structured)","provider status","configuration confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_6","uri":"capability://memory.knowledge.retrieval.system.ui.for.document.and.knowledge.base.management","name":"retrieval system ui for document and knowledge base management","description":"Provides a web-based Retrieval Management UI for uploading documents, managing knowledge bases, configuring vector embeddings, and testing semantic search. Users can upload documents (text, PDFs), view indexed documents and their embeddings, configure embedding models, and test retrieval queries to validate that relevant documents are being retrieved. The UI communicates with the Retrieval System API to manage documents in object storage and embeddings in the vector database.","intents":["I want to upload documents to build a knowledge base for my RAG system","I need to test that my retrieval system is finding relevant documents","I want to see what documents are indexed and manage their lifecycle"],"best_for":["Knowledge managers building and maintaining knowledge bases","Teams validating RAG system retrieval quality","Organizations managing document lifecycle for AI applications"],"limitations":["No built-in document preprocessing; PDFs and complex formats require external conversion","Bulk upload operations are not optimized; large document batches may timeout","No built-in deduplication or document versioning; managing document updates requires manual deletion and re-upload"],"requires":["Frontend service running","Backend API accessible","Object storage configured","Vector database configured","Embedding model API key"],"input_types":["documents (text, PDF)","search queries (text)","document metadata"],"output_types":["document index (structured)","search results with relevance scores","embedding metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_7","uri":"capability://tool.use.integration.plugin.and.tool.management.ui","name":"plugin and tool management ui","description":"Provides a web-based Plugin Management UI for viewing available plugins, configuring tool parameters, and testing tool execution. Users can see built-in plugins and custom plugins registered in the system, view their JSON schemas, configure tool-specific settings, and test tool calls to validate behavior. The UI communicates with the Plugin API Gateway to manage plugin configurations and execute test invocations.","intents":["I want to see what tools are available for my assistants to use","I need to configure tool-specific parameters and API keys","I want to test that a tool is working correctly before using it in production"],"best_for":["Developers managing tool integrations for agents","Teams validating tool behavior before deployment","Organizations managing custom tool configurations"],"limitations":["Tool schema validation is limited to JSON Schema; complex type systems may not be fully represented","No built-in tool versioning; updating a tool schema affects all assistants using it","Test execution is synchronous; no support for testing long-running or async tools"],"requires":["Frontend service running","Backend API accessible","Plugin Service running","Registered plugins with JSON schemas"],"input_types":["tool schemas (JSON Schema)","tool parameters (JSON)","test invocation requests"],"output_types":["tool registry (structured)","test execution results","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_8","uri":"capability://tool.use.integration.fastapi.based.restful.backend.api.with.layered.architecture","name":"fastapi-based restful backend api with layered architecture","description":"Implements the Backend Layer as a FastAPI application that exposes RESTful APIs for all TaskingAI operations. The architecture uses clear separation of concerns with distinct API endpoints for Model Operations, Assistant Operations, Retrieval System, and Plugin management. Each API communicates with corresponding services (Inference Service, Plugin Service) through defined interfaces, with PostgreSQL as the primary data store and Redis for caching. The layered design enables independent scaling and testing of each component.","intents":["I want to build applications that integrate with TaskingAI through a REST API","I need to programmatically manage models, assistants, documents, and tools","I want to integrate TaskingAI into my existing application architecture"],"best_for":["Developers building applications on top of TaskingAI","Teams integrating TaskingAI into existing microservice architectures","Organizations deploying TaskingAI as a backend service"],"limitations":["API is synchronous; long-running operations (document indexing, large inference requests) may timeout","No built-in API versioning; breaking changes require careful migration planning","Rate limiting and quota management must be implemented at the application level or via reverse proxy"],"requires":["Python 3.9+","FastAPI 0.95+","PostgreSQL 12+","Redis 6+","HTTP client library (requests, httpx, etc.)"],"input_types":["JSON request bodies","URL parameters","HTTP headers"],"output_types":["JSON responses","HTTP status codes","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-taskingai--taskingai__cap_9","uri":"capability://automation.workflow.docker.compose.based.deployment.orchestration","name":"docker compose-based deployment orchestration","description":"Provides a complete Docker Compose configuration that orchestrates all TaskingAI components (Frontend, Backend, Inference Service, Plugin Service, PostgreSQL, Redis, Object Storage) into a single deployable unit. The docker-compose.yml file defines service dependencies, environment variables, volume mounts, and networking, enabling single-command deployment of the entire system. This approach abstracts infrastructure complexity and ensures consistent environments across development, testing, and production.","intents":["I want to deploy TaskingAI locally for development and testing","I need to set up a complete TaskingAI environment without manually configuring each service","I want to ensure all components are properly networked and configured together"],"best_for":["Developers setting up local development environments","Teams deploying TaskingAI to Docker-based infrastructure","Organizations standardizing on containerized deployments"],"limitations":["Docker Compose is single-host; no built-in support for multi-node clustering or high availability","Persistent data (PostgreSQL, object storage) requires external volume management for production","No built-in monitoring, logging aggregation, or service health checks beyond Docker's basic restart policies"],"requires":["Docker 20.10+","Docker Compose 2.0+","Sufficient disk space for all services and data",".env file with configuration (API keys, database credentials, etc.)"],"input_types":["docker-compose.yml configuration",".env environment variables","Docker images"],"output_types":["running containers","service logs","exposed ports"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for desired LLM providers","PostgreSQL database for model configuration storage","FastAPI 0.95+","PostgreSQL for metadata storage","Vector database (e.g., Milvus, Pinecone, or compatible)","Object storage (S3-compatible or local filesystem)","Embedding model API key (OpenAI, Hugging Face, etc.)","Provider API keys for each supported provider","Network connectivity to provider APIs"],"failure_modes":["Provider-specific features (vision, function calling nuances) may require custom handling despite abstraction","Latency overhead from abstraction layer adds ~50-100ms per inference request","Rate limiting and quota management must be configured per provider separately","Vector embedding quality depends on embedding model choice; no built-in fine-tuning for domain-specific embeddings","Retrieval latency scales with corpus size; no automatic indexing optimization strategies","Requires separate object storage configuration; no built-in document preprocessing for PDFs, images, or complex formats","Provider-specific features (vision models, advanced function calling) may not be fully abstracted","Streaming response handling adds complexity; some providers have different streaming formats","Error handling varies by provider; no unified error taxonomy across all providers","Context window limitations of underlying LLMs still apply; no automatic context compression or summarization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5835448310403762,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2026-05-03T13:58:29.527Z","last_commit":"2024-12-02T22:18:38Z"},"community":{"stars":5379,"forks":358,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=taskingai--taskingai","compare_url":"https://unfragile.ai/compare?artifact=taskingai--taskingai"}},"signature":"7BBzgj5kru1Ss7P7hRdF0vZM1v9PomEzZetVdJ2gzQzWD6gTTWegCj4kpgytSAWwvC7QcbsxiSWbNc6uz3QcAw==","signedAt":"2026-06-20T17:30:25.245Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/taskingai--taskingai","artifact":"https://unfragile.ai/taskingai--taskingai","verify":"https://unfragile.ai/api/v1/verify?slug=taskingai--taskingai","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"}}