{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ix","slug":"ix","name":"IX","type":"repo","url":"https://github.com/kreneskyp/ix","page_url":"https://unfragile.ai/ix","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ix__cap_0","uri":"capability://planning.reasoning.visual.chain.graph.editor.with.langchain.component.mapping","name":"visual chain graph editor with langchain component mapping","description":"Provides a React-based drag-and-drop interface for constructing AI agent workflows as directed acyclic graphs. Components (LLMs, tools, memory systems, retrievers) are visually connected as nodes with configurable parameters, then compiled into executable LangChain runnables. The editor maintains a relational data model of chain definitions that map to LangChain's component registry, enabling non-technical users to compose complex agent logic without writing code.","intents":["I want to visually design an agent workflow without writing Python or JavaScript","I need to connect multiple LLM calls, tools, and memory systems in a specific sequence","I want to reuse and version-control agent chain definitions across my team"],"best_for":["non-technical product managers prototyping agent behaviors","teams building multiple similar agents and needing visual consistency","organizations wanting to audit agent logic through visual inspection"],"limitations":["Complex conditional logic beyond basic if/else branching requires custom component development","Graph cycles are not supported — chains must be acyclic","Real-time collaboration on the same chain is not built-in; requires external locking mechanism"],"requires":["React 16.8+ (frontend)","Django/FastAPI backend running (Python 3.8+)","LangChain 0.0.200+ installed in backend","Modern browser with WebSocket support"],"input_types":["component configuration objects (JSON)","LangChain component class references","chain graph topology (node/edge definitions)"],"output_types":["executable LangChain Runnable objects","chain definition JSON (persisted to database)","serialized agent execution plans"],"categories":["planning-reasoning","visual-workflow-builder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_1","uri":"capability://planning.reasoning.multi.agent.orchestration.with.shared.conversation.context","name":"multi-agent orchestration with shared conversation context","description":"Enables multiple autonomous agents to collaborate within a single chat session by maintaining a shared task context and conversation history. Each agent can execute its assigned chain, access previous messages and artifacts from other agents, and contribute results back to the conversation. The system uses a task-based execution model where each user interaction spawns a task that routes to the appropriate agent(s), with all outputs logged and accessible to subsequent agents.","intents":["I want one agent to research a topic and another agent to summarize findings in the same conversation","I need agents to hand off work to each other based on task complexity or specialization","I want to see the full conversation history including all agent interactions and reasoning"],"best_for":["teams building complex workflows requiring specialized sub-agents (research, analysis, writing)","applications where agent collaboration improves output quality through division of labor","scenarios requiring audit trails of multi-agent decision-making"],"limitations":["No built-in deadlock detection if agents create circular dependencies","Agent routing logic must be explicitly defined in chain configuration; no automatic agent selection","Conversation context grows unbounded — requires manual pruning or summarization for long sessions"],"requires":["Task execution backend (Celery or similar async task queue)","Shared message storage (database with transaction support)","Agent definitions configured in chain editor","WebSocket connection for real-time chat updates"],"input_types":["user chat messages (text)","task metadata (agent assignment, priority)","previous conversation messages (for context)"],"output_types":["agent responses (text, structured data, artifacts)","task execution logs (with timing and errors)","conversation history (chronologically ordered messages)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_10","uri":"capability://text.generation.language.chat.interface.with.real.time.agent.interaction.and.artifact.preview","name":"chat interface with real-time agent interaction and artifact preview","description":"Provides a web-based chat interface for interacting with agents in real-time. Users send messages, which are routed to the appropriate agent(s) based on chain configuration. Agent responses stream back in real-time through WebSocket connections, with intermediate steps (tool calls, reasoning) displayed as they occur. The interface includes a sidebar for viewing generated artifacts (code, documents, images) with preview capabilities. Users can manage conversation history, create new tasks, and switch between agents within the same session.","intents":["I want to interact with my agent through a natural chat interface","I need to see artifacts generated by the agent (code, documents) alongside the conversation","I want to manage multiple conversations and switch between agents seamlessly"],"best_for":["end-users interacting with agents for the first time (low technical barrier)","teams wanting a unified interface for all agent interactions","applications requiring artifact preview and download capabilities"],"limitations":["Chat interface is web-only; no mobile app or desktop client","Real-time streaming requires WebSocket support; not suitable for environments with strict firewall rules","Artifact preview is limited to text, images, and code; binary formats require download","No built-in conversation search or filtering; requires manual scrolling through history"],"requires":["React frontend running in a modern browser","WebSocket connection to backend","Backend API for message routing and artifact retrieval","User authentication system"],"input_types":["user chat messages (text, with optional file attachments)","agent selection (which agent to route to)","conversation context (previous messages)"],"output_types":["agent responses (text, streamed in real-time)","intermediate steps (tool calls, reasoning, displayed as they occur)","artifacts (code, documents, images with preview)","execution status (running, completed, error)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_11","uri":"capability://tool.use.integration.component.registry.and.dynamic.plugin.system.for.extending.capabilities","name":"component registry and dynamic plugin system for extending capabilities","description":"Provides a component registry that maps LangChain classes to visual node types in the chain editor. New components can be registered by defining a configuration object with metadata (name, description, input/output schemas). The system dynamically generates UI forms for component configuration based on the schema. Custom components can be added by extending the registry without modifying the core platform. The registry supports versioning of components, enabling backward compatibility as components evolve.","intents":["I want to add custom LLM providers or tools to the platform without modifying core code","I need to create domain-specific components (e.g., medical diagnosis tool) and make them available to all agents","I want to version components and ensure agents use compatible versions"],"best_for":["teams building custom extensions to IX","organizations with proprietary tools or integrations","platforms built on top of IX requiring customization"],"limitations":["Component configuration is declarative; complex initialization logic requires custom Python code","UI form generation is limited to standard input types; custom UI requires frontend modification","Component versioning is manual; no automatic compatibility checking between versions","No built-in component marketplace or sharing mechanism"],"requires":["LangChain component class (or compatible interface)","Component configuration object (JSON schema)","Python code for component initialization (if needed)","Access to the component registry (backend)"],"input_types":["component configuration (name, description, schemas)","component implementation (Python class or function)","version metadata (version number, compatibility info)"],"output_types":["registered component (available in chain editor)","UI form for component configuration","component instance (ready for use in chains)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_2","uri":"capability://automation.workflow.task.execution.and.logging.with.artifact.management","name":"task execution and logging with artifact management","description":"Tracks individual agent execution instances as tasks, capturing full execution logs, generated artifacts, and conversation history. Each task maintains a relational link to the chain definition, agent, user, and all outputs produced during execution. Artifacts (generated code, documents, images, etc.) are stored separately with metadata and can be grouped, versioned, and retrieved through REST/GraphQL APIs. The system provides structured logging at each step of chain execution, enabling debugging and performance analysis.","intents":["I want to see exactly what my agent did, including all intermediate steps and tool calls","I need to retrieve artifacts generated by agents (code, documents, images) from past conversations","I want to analyze agent performance and identify bottlenecks in execution"],"best_for":["teams requiring audit trails and compliance logging for AI-generated outputs","applications where artifacts are primary deliverables (code generation, document creation)","debugging and optimization of agent behavior across multiple executions"],"limitations":["Artifact storage is database-backed; large binary files (videos, high-res images) require external object storage integration","Task logs grow indefinitely — requires periodic archival or cleanup policies","No built-in cost tracking per task; requires custom instrumentation to measure LLM API spend"],"requires":["PostgreSQL or compatible database (for task and artifact records)","File storage backend (local filesystem or S3-compatible)","Task queue for async execution (Celery, RQ, or similar)","User authentication system (Django auth or custom)"],"input_types":["chain execution events (step start/end, tool calls)","generated artifacts (files, text, structured data)","execution metadata (timing, errors, token counts)"],"output_types":["task records (with full execution history)","artifact metadata and content (via REST/GraphQL)","execution logs (structured, queryable)","performance metrics (latency, token usage)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_3","uri":"capability://tool.use.integration.rest.and.graphql.api.for.chain.and.agent.management","name":"rest and graphql api for chain and agent management","description":"Exposes chain definitions, agent configurations, task execution, and artifact retrieval through both REST and GraphQL endpoints. The REST API provides CRUD operations on chains, agents, and tasks with standard HTTP semantics. The GraphQL API enables complex queries combining chains, agents, tasks, and artifacts with flexible filtering and pagination. Both APIs support authentication, authorization, and rate limiting. The API layer abstracts the underlying LangChain execution, allowing external systems to trigger agent execution and retrieve results.","intents":["I want to programmatically create and update agent chains from my application","I need to trigger agent execution from external systems and poll for results","I want to query execution history and artifacts with complex filters (by agent, date, status)"],"best_for":["teams integrating IX into larger application ecosystems","building custom frontends or mobile apps on top of IX","automating agent deployment and configuration through CI/CD pipelines"],"limitations":["GraphQL schema is auto-generated from Django models; complex custom queries may require schema extensions","No built-in API versioning — breaking changes require coordination across clients","Rate limiting is configurable but not enforced by default; requires middleware setup"],"requires":["FastAPI or Django REST Framework (backend)","Graphene or similar GraphQL library (for GraphQL endpoint)","API authentication (token-based, OAuth2, or custom)","HTTP client library (requests, fetch, axios, etc.)"],"input_types":["JSON request bodies (chain/agent definitions)","GraphQL queries and mutations","URL parameters (filtering, pagination)","HTTP headers (authentication tokens)"],"output_types":["JSON responses (chain/agent/task records)","GraphQL query results (nested, filtered data)","HTTP status codes and error messages","artifact content (files, text, binary data)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_4","uri":"capability://tool.use.integration.llm.provider.abstraction.and.configuration.management","name":"llm provider abstraction and configuration management","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, Google, local Ollama, etc.) behind a unified component interface. Users configure LLM credentials and model selection in the platform settings, then reference LLM components in chains by name without embedding API keys. The system supports dynamic provider switching, model parameter tuning (temperature, max_tokens, etc.), and fallback chains if a provider fails. Configuration is stored securely in the database with environment variable substitution for sensitive credentials.","intents":["I want to switch between OpenAI and Anthropic models without rebuilding my agent chains","I need to configure different LLM parameters (temperature, max_tokens) for different agents","I want to use local Ollama models for privacy-sensitive tasks while using cloud APIs for others"],"best_for":["teams evaluating multiple LLM providers and wanting to A/B test","organizations with compliance requirements to use local or private LLMs","applications requiring cost optimization through provider selection"],"limitations":["Provider-specific features (vision, function calling) are not abstracted — chains may break if switching providers","No built-in cost tracking per provider; requires custom instrumentation","Credential rotation requires manual updates in the platform; no automatic secret refresh"],"requires":["LangChain 0.0.200+ with provider integrations installed","API keys or credentials for each provider (stored in environment or database)","Secure credential storage (encrypted database fields or secrets manager)","Network access to provider APIs or local Ollama instance"],"input_types":["provider configuration (API endpoint, model name, credentials)","model parameters (temperature, max_tokens, system prompt)","fallback chain definitions"],"output_types":["LLM component instances (ready for use in chains)","provider health status (for monitoring)","token usage and cost estimates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_5","uri":"capability://tool.use.integration.tool.integration.and.function.calling.with.schema.based.routing","name":"tool integration and function calling with schema-based routing","description":"Enables agents to call external tools and APIs through a schema-based function registry. Tools are defined as LangChain Tool objects with JSON schemas describing inputs/outputs, then registered in the platform. When an agent needs to use a tool, the LLM generates a function call matching the schema, which is routed to the appropriate tool implementation. The system supports native function calling APIs (OpenAI, Anthropic) when available, and falls back to prompt-based tool use for other providers. Tool results are automatically parsed and returned to the agent.","intents":["I want my agent to call external APIs (weather, search, database queries) during execution","I need to define custom tools specific to my domain and make them available to all agents","I want to control which tools each agent can access for security and cost reasons"],"best_for":["agents that need to interact with external systems (APIs, databases, file systems)","teams building domain-specific tools and wanting to share them across agents","applications requiring tool access control and audit logging"],"limitations":["Tool schemas must be manually defined; no automatic schema generation from function signatures","Parallel tool calling is not supported — tools execute sequentially","Tool error handling is basic; complex retry logic requires custom tool wrappers"],"requires":["LangChain Tool class or compatible interface","JSON schema definitions for tool inputs/outputs","Tool implementation (Python functions, API endpoints, etc.)","LLM provider supporting function calling (OpenAI, Anthropic, etc.)"],"input_types":["tool definitions (name, description, JSON schema)","tool implementations (Python functions or API endpoints)","tool access control rules (agent-to-tool mappings)"],"output_types":["function call results (structured data, text, or errors)","tool execution logs (with timing and error details)","tool usage analytics (which tools are called, how often)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_6","uri":"capability://memory.knowledge.memory.and.context.management.with.configurable.storage.backends","name":"memory and context management with configurable storage backends","description":"Provides multiple memory types (conversation history, entity memory, summary memory, vector-based retrieval) that agents can use to maintain context across interactions. Memory components are configured in the chain editor and integrated with the task execution system. The platform supports different storage backends (in-memory, database, Redis) for different memory types, with automatic context window management to prevent token overflow. Memory can be scoped to individual conversations, users, or agents, with configurable retention policies.","intents":["I want my agent to remember previous conversations with the same user","I need to summarize long conversations to fit within the LLM's context window","I want to store and retrieve relevant facts about entities (users, products, etc.) across conversations"],"best_for":["multi-turn conversational agents that need persistent context","applications with long conversation histories that exceed LLM context limits","teams building personalized agents that adapt based on user history"],"limitations":["Memory is not automatically deduplicated; requires custom logic to prevent redundant storage","Context window management is heuristic-based; may not optimize for all use cases","Cross-conversation memory retrieval requires manual query logic; no built-in semantic search"],"requires":["Memory component definitions (conversation, entity, summary, vector)","Storage backend (database, Redis, or in-memory)","Vector database for semantic memory (optional, e.g., Pinecone, Weaviate)","Embedding model for vector-based memory"],"input_types":["conversation messages (text)","entity data (structured facts)","memory configuration (type, backend, retention policy)"],"output_types":["retrieved context (previous messages, facts, summaries)","memory statistics (size, age, relevance scores)","pruned context (for token limit compliance)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_7","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.document.ingestion.and.semantic.search","name":"retrieval-augmented generation (rag) with document ingestion and semantic search","description":"Enables agents to ingest documents (PDFs, web pages, text files) and retrieve relevant context using semantic search. Documents are chunked, embedded using a configurable embedding model, and stored in a vector database. When an agent needs context, it performs semantic search to retrieve the most relevant chunks, which are injected into the LLM prompt. The system supports multiple document sources (file upload, web crawling, API integration) and vector backends (Pinecone, Weaviate, Chroma, etc.). Retrieval can be configured with different search strategies (similarity, MMR, fusion) and result filtering.","intents":["I want my agent to answer questions based on my company's documentation or knowledge base","I need to ingest web pages or PDFs and have the agent cite specific sources","I want to retrieve the most relevant context from a large document corpus without exceeding token limits"],"best_for":["customer support agents answering questions from documentation","research agents synthesizing information from multiple sources","applications requiring source attribution and fact-checking"],"limitations":["Chunking strategy is fixed; complex documents may require custom preprocessing","Semantic search quality depends on embedding model; may miss relevant context if queries are semantically distant","No built-in deduplication of similar chunks; can lead to redundant context injection","Vector database scaling requires manual sharding; not suitable for extremely large corpora (>10M documents)"],"requires":["Document sources (files, URLs, or API endpoints)","Embedding model (OpenAI, Hugging Face, or local)","Vector database (Pinecone, Weaviate, Chroma, etc.)","Document chunking and preprocessing logic"],"input_types":["documents (PDF, text, HTML, markdown)","document metadata (source, date, author)","retrieval queries (user questions or agent prompts)","search configuration (strategy, filters, top-k)"],"output_types":["retrieved document chunks (with metadata and similarity scores)","augmented prompts (with context injected)","source citations (document references for attribution)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_8","uri":"capability://planning.reasoning.agent.debugging.and.execution.tracing.with.step.by.step.visualization","name":"agent debugging and execution tracing with step-by-step visualization","description":"Provides detailed execution traces for agent chains, showing each step of execution with inputs, outputs, and intermediate results. The chat interface displays agent reasoning in real-time, including tool calls, LLM responses, and decision points. Users can inspect the execution trace to understand why an agent made a particular decision or where it failed. The system captures timing information for each step, enabling performance profiling. Traces are persisted in the task log and can be replayed or analyzed post-execution.","intents":["I want to see exactly what my agent is thinking and why it made a particular decision","I need to debug agent failures by inspecting the execution trace","I want to optimize agent performance by identifying slow steps in the chain"],"best_for":["developers building and iterating on agent chains","teams troubleshooting agent failures in production","performance optimization and cost analysis"],"limitations":["Trace storage grows with execution complexity; long chains with many tool calls generate large logs","Real-time trace streaming requires WebSocket connection; not suitable for offline analysis","Trace visualization is limited to the web UI; no programmatic trace export format"],"requires":["Task execution backend with step-level logging","WebSocket connection for real-time updates","React frontend with trace visualization components","Structured logging at each chain step"],"input_types":["chain execution events (step start/end, tool calls, LLM responses)","execution metadata (timing, tokens, errors)","agent reasoning (intermediate thoughts, decisions)"],"output_types":["execution trace (step-by-step visualization)","performance metrics (latency per step, total execution time)","error details (with stack traces and context)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ix__cap_9","uri":"capability://automation.workflow.docker.based.deployment.with.containerized.execution.environment","name":"docker-based deployment with containerized execution environment","description":"Packages the entire IX platform (React frontend, Django/FastAPI backend, task queue, database) as Docker containers orchestrated with Docker Compose. The deployment includes pre-configured services for PostgreSQL, Redis, and optional services for vector databases. Users can deploy IX on any Docker-compatible infrastructure (local machine, cloud VMs, Kubernetes). The system includes GitHub Actions workflows for automated testing, building, and publishing Docker images. Configuration is managed through environment variables and Docker Compose overrides, enabling easy customization for different deployment environments.","intents":["I want to deploy IX on my own infrastructure without vendor lock-in","I need to run IX in an isolated environment with specific security and compliance requirements","I want to scale IX horizontally by running multiple backend instances"],"best_for":["teams with on-premises or private cloud infrastructure","organizations with strict data residency or compliance requirements","applications requiring custom integrations or modifications to IX"],"limitations":["Horizontal scaling requires external load balancing and shared database; not built-in","Task queue (Celery) requires separate configuration for distributed execution; single-instance deployments are limited","Database migrations must be run manually during upgrades; no automatic schema management","Monitoring and logging require external tools (Prometheus, ELK, etc.); not included in base deployment"],"requires":["Docker 20.10+ and Docker Compose 2.0+","PostgreSQL 12+ (or compatible database)","Redis 6.0+ (for task queue and caching)","Minimum 4GB RAM and 20GB disk space","Network access to LLM provider APIs"],"input_types":["Docker Compose configuration (docker-compose.yml)","environment variables (API keys, database credentials)","custom Dockerfiles (for extensions or modifications)"],"output_types":["running Docker containers (frontend, backend, database, queue)","application logs (from all services)","health check status (for monitoring)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["React 16.8+ (frontend)","Django/FastAPI backend running (Python 3.8+)","LangChain 0.0.200+ installed in backend","Modern browser with WebSocket support","Task execution backend (Celery or similar async task queue)","Shared message storage (database with transaction support)","Agent definitions configured in chain editor","WebSocket connection for real-time chat updates","React frontend running in a modern browser","WebSocket connection to backend"],"failure_modes":["Complex conditional logic beyond basic if/else branching requires custom component development","Graph cycles are not supported — chains must be acyclic","Real-time collaboration on the same chain is not built-in; requires external locking mechanism","No built-in deadlock detection if agents create circular dependencies","Agent routing logic must be explicitly defined in chain configuration; no automatic agent selection","Conversation context grows unbounded — requires manual pruning or summarization for long sessions","Chat interface is web-only; no mobile app or desktop client","Real-time streaming requires WebSocket support; not suitable for environments with strict firewall rules","Artifact preview is limited to text, images, and code; binary formats require download","No built-in conversation search or filtering; requires manual scrolling through history","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ix","compare_url":"https://unfragile.ai/compare?artifact=ix"}},"signature":"oUou2UnfDDsbagoz4o0NGrzK5XHgsuOkf7AZ28FbJ25ocsFYfwXvXiyk2xNXelfXpXn93YeG14V58J45BzkfBA==","signedAt":"2026-06-21T10:38:44.552Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ix","artifact":"https://unfragile.ai/ix","verify":"https://unfragile.ai/api/v1/verify?slug=ix","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"}}