{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"eliza","slug":"eliza","name":"Eliza","type":"framework","url":"https://github.com/elizaOS/eliza","page_url":"https://unfragile.ai/eliza","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"eliza__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.shared.runtime.context","name":"multi-agent orchestration with shared runtime context","description":"Manages multiple AgentRuntime instances within a single server process, enabling inter-agent communication and state sharing through a unified event system and message service. Each agent maintains isolated character definitions and memory while accessing shared model providers and platform connectors, coordinated via the elizaOS server's message routing layer that dispatches events across agent boundaries.","intents":["Run multiple AI agents in parallel within one application without separate processes","Enable agents to communicate and coordinate on complex tasks","Share expensive resources like model API connections across agents","Manage agent lifecycle and state transitions centrally"],"best_for":["teams building multi-agent systems for social platforms or autonomous workflows","developers needing agent collaboration without distributed system complexity","applications requiring coordinated AI behavior across multiple personas"],"limitations":["All agents run in same Node.js/Bun process — no true parallelism, single-threaded event loop bottleneck","Shared memory backend (PostgreSQL/PGLite) becomes contention point under high agent concurrency","No built-in distributed tracing across agent boundaries — observability requires custom instrumentation","Agent isolation is logical only; memory leaks in one agent affect all agents in process"],"requires":["Node.js 18+ or Bun 1.0+","PostgreSQL 12+ or PGLite for shared state","Model provider API keys (OpenAI, Anthropic, etc.)","@elizaos/core and @elizaos/server packages"],"input_types":["character definitions (JSON)","event objects (typed via protobuf schema)","message payloads (text, structured data)"],"output_types":["event emissions","inter-agent messages","state mutations in shared memory store"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_1","uri":"capability://tool.use.integration.plugin.based.model.provider.abstraction.with.multi.provider.support","name":"plugin-based model provider abstraction with multi-provider support","description":"Abstracts LLM interactions through a plugin architecture that supports OpenAI, Anthropic, Google Gemini, Ollama, AWS Bedrock, OpenRouter, and custom providers. Each provider is loaded at runtime as a plugin implementing a standardized interface, allowing agents to switch models or use multiple providers simultaneously without code changes. Settings and configuration are injected via environment variables and character definitions.","intents":["Switch between different LLM providers without rewriting agent logic","Use multiple models in parallel for ensemble or fallback patterns","Add custom model providers (local, proprietary, or specialized)","Manage API keys and provider-specific settings centrally"],"best_for":["developers building model-agnostic agents","teams evaluating multiple LLM providers","enterprises with custom or on-premise model infrastructure"],"limitations":["Provider plugins must implement full interface — partial implementations not supported","No automatic fallback or retry logic across providers; must be implemented in action layer","Provider-specific features (vision, function calling) require custom handling per provider","Configuration via environment variables becomes unwieldy with many providers"],"requires":["Node.js 18+ or Bun 1.0+","API keys for chosen providers (OpenAI, Anthropic, etc.)","@elizaos/core package","Plugin packages: @elizaos/plugin-openai, @elizaos/plugin-anthropic, etc."],"input_types":["prompt text","system messages","structured function calling schemas"],"output_types":["model completions (text)","structured responses (JSON)","function call specifications"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_10","uri":"capability://automation.workflow.cli.and.project.scaffolding.with.environment.configuration","name":"cli and project scaffolding with environment configuration","description":"Provides elizaos CLI binary for project creation, agent management, and development workflows. CLI scaffolds new agent projects with boilerplate configuration, plugin setup, and example agents. Environment configuration is managed via .env files with validation and type checking. CLI commands enable local development (agent startup, hot reload), testing, and deployment preparation.","intents":["Quickly scaffold new agent projects without manual setup","Manage environment variables and secrets for different deployment environments","Start and manage agents during development","Validate configuration before deployment"],"best_for":["developers new to elizaOS wanting quick project setup","teams managing multiple agent projects","DevOps engineers automating agent deployment"],"limitations":["CLI scaffolding generates opinionated project structure; customization requires manual changes","Environment validation is basic; complex validation logic requires custom scripts","No built-in secret management; secrets must be managed externally","CLI is synchronous; long-running operations (model downloads) block terminal"],"requires":["Node.js 18+ or Bun 1.0+","npm or Bun package manager","@elizaos/elizaos package (CLI binary)",".env file with configuration"],"input_types":["CLI arguments","environment variables","configuration files (JSON, YAML)"],"output_types":["scaffolded project files","validation reports","agent startup logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_11","uri":"capability://automation.workflow.web.dashboard.and.desktop.ui.for.agent.management.and.monitoring","name":"web dashboard and desktop ui for agent management and monitoring","description":"Provides web-based dashboard and Tauri desktop application for managing agents, viewing logs, and monitoring performance. Dashboard displays agent status, message history, memory contents, and action execution logs. Desktop app packages dashboard as standalone application with native OS integration. Both UIs communicate with elizaOS server via REST/WebSocket APIs.","intents":["Monitor agent behavior and performance in real-time","Debug agent decisions by inspecting memory and action execution","Manage multiple agents from single interface","View agent logs and error messages"],"best_for":["developers debugging agent behavior","teams monitoring production agents","non-technical users managing agents without CLI"],"limitations":["Dashboard is read-only for most operations; agent control requires API calls","Real-time updates depend on WebSocket connection; network issues cause stale data","Desktop app requires Tauri runtime; not available on all platforms","Memory inspection shows raw embeddings; no visualization of semantic relationships"],"requires":["elizaOS server running with REST/WebSocket APIs","Web browser for dashboard (Chrome, Firefox, Safari)","Optional: Tauri runtime for desktop app","Network connectivity to elizaOS server"],"input_types":["agent state (via REST/WebSocket)","logs and events","memory objects"],"output_types":["rendered UI","real-time updates","exported logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_12","uri":"capability://data.processing.analysis.protocol.buffer.schema.system.for.typed.message.serialization.and.cross.language.communication","name":"protocol buffer schema system for typed message serialization and cross-language communication","description":"Uses Protocol Buffers (protobuf) to define typed schemas for messages, events, and data structures, enabling type-safe serialization and cross-language communication. Schemas are defined in .proto files and compiled to TypeScript, Python, and Rust code. All inter-process communication (agent-to-agent, server-to-client) uses protobuf-serialized messages, ensuring type safety and backward compatibility.","intents":["Ensure type safety across language boundaries","Enable backward-compatible message evolution","Reduce message serialization overhead vs JSON","Generate type definitions automatically from schemas"],"best_for":["teams using multiple languages in agent system","applications requiring strict type safety","systems needing backward-compatible message evolution"],"limitations":["Protobuf learning curve for developers unfamiliar with schema definition","Schema changes require recompilation and redeployment","Debugging protobuf messages is harder than JSON (binary format)","Protobuf overhead is minimal but adds complexity vs simple JSON"],"requires":["Protobuf compiler (protoc)","Language-specific protobuf libraries (protobufjs for TypeScript, etc.)",".proto schema files","Build system integration for schema compilation"],"input_types":[".proto schema definitions","message data (structured)"],"output_types":["compiled type definitions","serialized binary messages","generated code (TypeScript, Python, Rust)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_13","uri":"capability://planning.reasoning.event.system.with.typed.event.emission.and.subscription","name":"event system with typed event emission and subscription","description":"Implements a typed event system where agents and components emit and subscribe to events using TypeScript interfaces. Events are defined as types with payload schemas; subscribers register handlers for specific event types. Event emission is synchronous with optional async handlers. The event system enables loose coupling between agents and components while maintaining type safety.","intents":["Enable agents to react to events from other agents or external systems","Implement event-driven workflows without tight coupling","Broadcast state changes across agent boundaries","Build event-driven debugging and monitoring"],"best_for":["developers building event-driven agent systems","teams implementing complex agent interactions","applications requiring loose coupling between components"],"limitations":["Event handlers are synchronous; async handlers require custom wrapper","No built-in event persistence; events are lost if not handled immediately","Event ordering is not guaranteed across multiple subscribers","No built-in event filtering or routing; all subscribers receive all events"],"requires":["Node.js 18+ or Bun 1.0+","@elizaos/core package","TypeScript for type-safe event definitions"],"input_types":["event type definitions","event payloads","subscriber handlers"],"output_types":["event emissions","handler invocations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_14","uri":"capability://automation.workflow.logging.and.observability.with.structured.logging.and.performance.metrics","name":"logging and observability with structured logging and performance metrics","description":"Provides structured logging system that captures agent actions, decisions, and errors with context (agent ID, timestamp, action name). Logs are written to files and optionally to external services (Datadog, CloudWatch). Performance metrics track action execution time, memory usage, and API call counts. Logging is configurable per component with different verbosity levels.","intents":["Debug agent behavior by inspecting detailed logs","Monitor agent performance and identify bottlenecks","Track agent actions for auditing and compliance","Integrate with external observability platforms"],"best_for":["developers debugging agent behavior","teams monitoring production agents","enterprises requiring audit trails"],"limitations":["Logging overhead can impact performance under high load","External observability integration requires custom configuration","Log retention is not managed; disk space can grow unbounded","Performance metrics are basic; no built-in profiling or flame graphs"],"requires":["Node.js 18+ or Bun 1.0+","@elizaos/core package","File system for log storage","Optional: external observability service (Datadog, CloudWatch, etc.)"],"input_types":["log messages","performance metrics","error objects"],"output_types":["log files","metrics data","observability service events"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_15","uri":"capability://data.processing.analysis.database.integration.with.postgresql.and.pglite.for.persistent.state","name":"database integration with postgresql and pglite for persistent state","description":"Provides database abstraction layer supporting PostgreSQL for production and PGLite (SQLite in WASM) for local development. All persistent state (memories, entities, relationships, messages) is stored in database with schema migrations. Database connection is managed centrally; agents access data through typed query interfaces. PGLite enables zero-setup local development without external database.","intents":["Persist agent state across restarts","Query agent memories and knowledge programmatically","Implement multi-agent state sharing via database","Develop locally without PostgreSQL setup"],"best_for":["developers building production agents","teams requiring persistent agent state","developers wanting zero-setup local development"],"limitations":["Database schema is fixed; custom tables require manual migration","Query performance depends on database indexing; no automatic optimization","PGLite is SQLite-based; not suitable for high-concurrency production use","Database connection pooling is basic; high-concurrency scenarios require tuning"],"requires":["PostgreSQL 12+ for production or PGLite for development","Database credentials (for PostgreSQL)","@elizaos/core package","Database migration tools (optional)"],"input_types":["agent state objects","memories and embeddings","entity and relationship data"],"output_types":["persisted state","query results","database migrations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_2","uri":"capability://memory.knowledge.character.driven.agent.personality.and.memory.system","name":"character-driven agent personality and memory system","description":"Defines agent behavior through a character system that specifies personality traits, knowledge base, example interactions, and behavioral rules as structured data (JSON). The character definition drives prompt engineering, memory retrieval, and action selection; agents use character context to maintain consistent personas across conversations. Memory is stored in PostgreSQL/PGLite with vector embeddings for semantic retrieval, enabling agents to learn from past interactions.","intents":["Define agent personality and behavior without writing custom code","Maintain consistent agent personas across multiple conversations","Build knowledge bases that agents can reference and learn from","Implement different agent archetypes (trader, social media manager, analyst) from same codebase"],"best_for":["non-technical users defining agent behavior via configuration","teams managing multiple agent personas","applications requiring consistent agent personality across platforms"],"limitations":["Character definitions are static at runtime — changing personality requires agent restart","No built-in versioning or A/B testing of character definitions","Memory retrieval quality depends on embedding model choice; no automatic tuning","Large character definitions (extensive knowledge bases) increase prompt token usage"],"requires":["PostgreSQL 12+ or PGLite for vector storage","Embedding model (via model provider plugin)","Character definition JSON file","@elizaos/core package"],"input_types":["character definition (JSON with personality, knowledge, examples)","conversation history","user messages"],"output_types":["agent responses","memory embeddings","action selections"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_3","uri":"capability://planning.reasoning.action.execution.pipeline.with.evaluators.and.state.composition","name":"action execution pipeline with evaluators and state composition","description":"Implements a typed action execution system where agents select and execute actions based on current state, character context, and memory. Actions are plugins implementing a standardized interface; evaluators assess whether actions should run; providers supply dynamic state (market data, social feeds, etc.). The pipeline uses state composition to merge character context, memory, and provider data into a unified state object passed to the model for decision-making.","intents":["Execute external actions (API calls, database writes) based on agent reasoning","Compose dynamic state from multiple sources for informed decision-making","Evaluate action preconditions and safety constraints before execution","Implement custom actions without modifying core framework"],"best_for":["developers building agents that interact with external systems","teams implementing complex decision logic with multiple constraints","applications requiring action safety validation and auditing"],"limitations":["Action execution is sequential — no parallel action execution within single agent step","Evaluators are synchronous; async precondition checks require custom wrapper actions","State composition happens at runtime; large state objects increase model context usage","No built-in action rollback or transaction semantics for failed executions"],"requires":["Node.js 18+ or Bun 1.0+","@elizaos/core package","Action plugins implementing Action interface","Model provider for action selection"],"input_types":["agent state (character, memory, providers)","user message","action definitions (name, description, parameters)"],"output_types":["selected action","action parameters","action result (success/failure with data)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_4","uri":"capability://tool.use.integration.platform.connector.system.for.multi.channel.deployment","name":"platform connector system for multi-channel deployment","description":"Abstracts platform-specific communication through a plugin architecture supporting Discord, Telegram, Twitter/X, Farcaster, and custom platforms. Each platform connector handles authentication, message routing, and protocol-specific formatting. Agents receive platform-agnostic message objects and respond with text/structured data; connectors translate to platform-specific formats. The message service routes incoming messages to appropriate agents and broadcasts responses.","intents":["Deploy same agent to multiple social platforms without code duplication","Handle platform-specific authentication and rate limiting","Receive messages from multiple platforms and route to agents","Add custom platform connectors for proprietary or internal systems"],"best_for":["teams running agents across multiple social platforms","developers building platform-agnostic agent applications","enterprises integrating agents with internal communication systems"],"limitations":["Platform connectors are synchronous; async message handling requires custom wrapper","Rate limiting is per-connector; no global rate limiting across platforms","Rich media (images, videos) support varies by platform — requires custom handling","Authentication credentials must be managed externally; no built-in secret management"],"requires":["Node.js 18+ or Bun 1.0+","@elizaos/core and @elizaos/server packages","Platform API credentials (Discord token, Twitter API key, etc.)","Platform connector plugins (@elizaos/plugin-discord, @elizaos/plugin-twitter, etc.)"],"input_types":["platform-specific messages (Discord messages, tweets, Telegram updates)","user IDs and authentication tokens"],"output_types":["platform-agnostic message objects","agent responses formatted for each platform"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_5","uri":"capability://memory.knowledge.vector.backed.memory.and.rag.with.semantic.retrieval","name":"vector-backed memory and rag with semantic retrieval","description":"Stores agent memories and knowledge as vector embeddings in PostgreSQL or PGLite, enabling semantic similarity search without explicit indexing. Memories are created from conversations, documents, and knowledge bases; embeddings are generated via model provider plugins. The memory system retrieves relevant past interactions and knowledge based on semantic similarity to current context, injecting retrieved memories into prompts for grounded reasoning.","intents":["Enable agents to learn from past interactions without explicit memory management","Build knowledge bases that agents can query semantically","Implement document ingestion and RAG without external vector databases","Retrieve relevant context for informed decision-making"],"best_for":["developers building agents that learn over time","teams implementing RAG without external vector databases","applications requiring semantic search over agent knowledge"],"limitations":["Embedding quality depends on model choice; no automatic tuning or re-embedding","Vector search performance degrades with large memory stores (millions of embeddings)","No built-in memory pruning or archival; memory grows unbounded","Semantic retrieval can return irrelevant results if embedding model is poor fit for domain"],"requires":["PostgreSQL 12+ with pgvector extension or PGLite","Embedding model (via model provider plugin)","@elizaos/core package","Document ingestion pipeline (custom or via plugins)"],"input_types":["conversation messages","documents (text, markdown)","knowledge base entries","query text"],"output_types":["vector embeddings","retrieved memory objects","similarity scores"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_6","uri":"capability://tool.use.integration.rest.websocket.server.with.real.time.agent.communication","name":"rest/websocket server with real-time agent communication","description":"Provides HTTP REST API and WebSocket server for agent interaction and management. REST endpoints expose agent lifecycle management, message sending, and state queries; WebSocket connections enable real-time bidirectional communication with agents. The server handles authentication, message routing to appropriate agents, and event broadcasting. Built on Node.js/Bun with typed request/response schemas.","intents":["Expose agents via HTTP API for external integrations","Enable real-time chat with agents via WebSocket","Manage agent lifecycle (start, stop, reload) remotely","Query agent state and memory programmatically"],"best_for":["developers embedding agents in web applications","teams building agent management dashboards","applications requiring programmatic agent control"],"limitations":["WebSocket connections are per-client; no built-in connection pooling or load balancing","Authentication is basic (API keys); no built-in OAuth or advanced auth","Message ordering is not guaranteed across multiple WebSocket connections","Server is single-process; horizontal scaling requires external load balancer"],"requires":["Node.js 18+ or Bun 1.0+","@elizaos/server package","Agent runtime (@elizaos/core)","Optional: reverse proxy for HTTPS and load balancing"],"input_types":["HTTP requests (JSON)","WebSocket messages (JSON)","query parameters"],"output_types":["HTTP responses (JSON)","WebSocket events (JSON)","server-sent events"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_7","uri":"capability://memory.knowledge.entity.and.relationship.system.for.knowledge.graph.construction","name":"entity and relationship system for knowledge graph construction","description":"Tracks entities (people, organizations, concepts) and relationships between them, building a knowledge graph from agent interactions and ingested data. Entities and relationships are stored in PostgreSQL with semantic embeddings, enabling agents to understand context and connections. The system supports entity resolution (linking mentions to canonical entities) and relationship inference.","intents":["Build knowledge graphs from agent interactions automatically","Track relationships between entities for context-aware reasoning","Implement entity resolution to avoid duplicate tracking","Enable agents to understand social networks and organizational structures"],"best_for":["developers building knowledge-intensive agents","teams tracking relationships in social or organizational contexts","applications requiring entity-aware reasoning"],"limitations":["Entity resolution is heuristic-based; no built-in disambiguation for ambiguous names","Relationship inference requires custom logic; no automatic inference engine","Knowledge graph grows unbounded; no built-in pruning or archival","Query performance degrades with large graphs; no built-in optimization"],"requires":["PostgreSQL 12+ for entity/relationship storage","@elizaos/core package","Embedding model for entity embeddings","Custom entity extraction logic (via actions or providers)"],"input_types":["conversation text","entity mentions","relationship descriptions"],"output_types":["entity objects","relationship objects","knowledge graph queries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_8","uri":"capability://tool.use.integration.plugin.architecture.for.extensible.actions.evaluators.and.providers","name":"plugin architecture for extensible actions, evaluators, and providers","description":"Provides a plugin system for extending agent capabilities through custom actions (executable behaviors), evaluators (precondition checkers), and providers (state suppliers). Plugins are loaded at runtime from npm packages or local directories, implementing standardized TypeScript interfaces. The bootstrap plugin provides core functionality; official plugins extend with platform connectors, model providers, and common actions.","intents":["Add custom actions without modifying core framework","Implement domain-specific evaluators and constraints","Create custom state providers for specialized data sources","Distribute agent extensions as reusable npm packages"],"best_for":["developers building extensible agent systems","teams creating reusable agent components","enterprises with custom business logic requirements"],"limitations":["Plugin interface is TypeScript-only; no Python or Rust plugin support","Plugin loading is synchronous; large plugins can block agent startup","No plugin versioning or dependency resolution; conflicts require manual management","Plugin isolation is logical only; plugins can access and modify shared state"],"requires":["Node.js 18+ or Bun 1.0+","TypeScript knowledge for plugin development","@elizaos/core package","npm or local file system for plugin distribution"],"input_types":["plugin configuration (JSON)","action parameters","state objects"],"output_types":["action results","evaluation results","provider state"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__cap_9","uri":"capability://tool.use.integration.multi.language.runtime.support.with.typescript.rust.wasm.and.python.bindings","name":"multi-language runtime support with typescript, rust, wasm, and python bindings","description":"Provides TypeScript as primary runtime with Rust implementation compiled to WASM for performance-critical paths, and Python bindings for integration with Python ecosystems. Core logic is implemented in TypeScript (@elizaos/core); Rust components handle computationally expensive operations (embeddings, vector operations). Python bindings expose core APIs for Python developers. Cross-language interoperability is achieved through protobuf schemas and language-specific wrappers.","intents":["Use elizaOS agents in TypeScript/Node.js applications","Integrate agents with Python data science and ML workflows","Optimize performance-critical paths with Rust/WASM","Build agents in preferred language without rewriting core"],"best_for":["polyglot teams using TypeScript, Python, and Rust","applications requiring high-performance agent operations","teams integrating agents with existing Python ML pipelines"],"limitations":["Python bindings are incomplete; not all TypeScript APIs are exposed","Rust/WASM compilation adds build complexity and deployment size","Cross-language debugging is difficult; errors may not propagate clearly","Performance gains from Rust are marginal for most agent operations (I/O bound)"],"requires":["Node.js 18+ or Bun 1.0+ for TypeScript runtime","Python 3.9+ for Python bindings","Rust toolchain for WASM compilation (optional)","Protobuf compiler for schema generation"],"input_types":["TypeScript code","Python code","Protobuf schemas"],"output_types":["compiled WASM modules","Python packages","TypeScript type definitions"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"eliza__headline","uri":"capability://tool.use.integration.ai.agent.framework.for.building.autonomous.agents","name":"ai agent framework for building autonomous agents","description":"Eliza is a TypeScript framework designed for creating and managing autonomous AI agents, featuring multi-agent communication, a plugin system, and integration with platforms like Discord and Twitter.","intents":["best AI agent framework","AI framework for autonomous agents","how to build AI agents with TypeScript","top frameworks for AI agent development","AI agents for social media integration"],"best_for":["developers looking to create AI agents"],"limitations":["requires knowledge of TypeScript"],"requires":["TypeScript environment"],"input_types":["code","plugins"],"output_types":["autonomous agents"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ or Bun 1.0+","PostgreSQL 12+ or PGLite for shared state","Model provider API keys (OpenAI, Anthropic, etc.)","@elizaos/core and @elizaos/server packages","API keys for chosen providers (OpenAI, Anthropic, etc.)","@elizaos/core package","Plugin packages: @elizaos/plugin-openai, @elizaos/plugin-anthropic, etc.","npm or Bun package manager","@elizaos/elizaos package (CLI binary)",".env file with configuration"],"failure_modes":["All agents run in same Node.js/Bun process — no true parallelism, single-threaded event loop bottleneck","Shared memory backend (PostgreSQL/PGLite) becomes contention point under high agent concurrency","No built-in distributed tracing across agent boundaries — observability requires custom instrumentation","Agent isolation is logical only; memory leaks in one agent affect all agents in process","Provider plugins must implement full interface — partial implementations not supported","No automatic fallback or retry logic across providers; must be implemented in action layer","Provider-specific features (vision, function calling) require custom handling per provider","Configuration via environment variables becomes unwieldy with many providers","CLI scaffolding generates opinionated project structure; customization requires manual changes","Environment validation is basic; complex validation logic requires custom scripts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=eliza","compare_url":"https://unfragile.ai/compare?artifact=eliza"}},"signature":"64fh4XVCvW5qQakITULuRPtieHC45UokCtDPUKufOyhzWOH/nQudNwZEnUEnhDzxTPwTz/ct08fYp93pyRsEDw==","signedAt":"2026-06-20T10:43:08.323Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/eliza","artifact":"https://unfragile.ai/eliza","verify":"https://unfragile.ai/api/v1/verify?slug=eliza","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"}}