{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-voltagent-core","slug":"voltagent-core","name":"@voltagent/core","type":"repo","url":"https://github.com/VoltAgent/voltagent#readme","page_url":"https://unfragile.ai/voltagent-core","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-voltagent-core__cap_0","uri":"capability://planning.reasoning.agentic.task.decomposition.and.execution.planning","name":"agentic task decomposition and execution planning","description":"VoltAgent decomposes complex user intents into executable subtasks through a planning layer that sequences operations and manages dependencies between steps. The framework uses a state machine approach to track task progression, allowing agents to break down multi-step workflows (e.g., 'research a topic and write a report') into discrete, chainable operations with explicit state transitions and rollback capabilities.","intents":["I need my agent to break down a complex user request into smaller, manageable steps automatically","I want to define workflows where later steps depend on outputs from earlier steps","I need to handle task failures gracefully with retry logic and state recovery"],"best_for":["teams building multi-step AI workflows in JavaScript/TypeScript","developers creating autonomous agents that need structured task planning","builders prototyping complex agent behaviors without managing orchestration manually"],"limitations":["planning layer adds latency per decomposition step — no benchmarks provided for complex 10+ step workflows","state machine approach requires explicit task definition upfront — dynamic runtime task generation not documented","no built-in distributed execution — all tasks execute in single Node.js process"],"requires":["Node.js 14+ (typical for npm packages)","JavaScript/TypeScript runtime environment","LLM provider integration (OpenAI, Anthropic, or compatible API)"],"input_types":["natural language task descriptions","structured task definitions (JSON/objects)","agent context and memory state"],"output_types":["task execution plan (structured)","step-by-step execution results","final aggregated output from all subtasks"],"categories":["planning-reasoning","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"VoltAgent provides a provider-agnostic LLM interface that abstracts away differences between OpenAI, Anthropic, and other compatible APIs, allowing developers to swap providers without changing agent code. The abstraction layer handles request/response normalization, token counting, cost tracking, and provider-specific parameter mapping (e.g., temperature, max_tokens) through a unified schema.","intents":["I want to switch between OpenAI and Anthropic without rewriting my agent logic","I need to track token usage and costs across different LLM providers","I want to use local LLMs (Ollama, LM Studio) alongside cloud providers in the same agent"],"best_for":["teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","cost-conscious builders who want to route requests to cheaper models dynamically","developers building hybrid systems mixing cloud and local LLMs"],"limitations":["abstraction may not expose provider-specific advanced features (e.g., OpenAI's vision_detail parameter)","token counting accuracy depends on provider's tokenizer — approximations used for non-OpenAI models","streaming response handling may have latency differences across providers not normalized by abstraction"],"requires":["Node.js 14+","API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","TypeScript or JavaScript runtime"],"input_types":["prompt text","message arrays with role/content structure","system instructions","provider configuration objects"],"output_types":["normalized LLM responses (text)","token usage metrics (input_tokens, output_tokens)","cost estimates","structured data from tool calls"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_10","uri":"capability://automation.workflow.agent.testing.and.simulation.with.mock.llm.responses","name":"agent testing and simulation with mock llm responses","description":"VoltAgent includes testing utilities that allow developers to mock LLM responses and tool execution for unit testing agents without making real API calls. The framework can simulate different LLM behaviors (success, failure, timeout) and tool responses to test agent error handling and decision-making logic in isolation.","intents":["I want to test my agent logic without calling real LLMs or external APIs","I need to simulate different LLM responses (success, failure, timeout) to test error handling","I want to verify that my agent makes correct decisions based on specific tool outputs"],"best_for":["developers writing unit tests for agent logic","teams implementing CI/CD pipelines for agent code","builders validating agent behavior before production deployment"],"limitations":["mock responses may not capture real LLM behavior nuances — tests may pass but fail in production","setting up comprehensive mocks for complex agents is time-consuming","no built-in test data generation — developers must manually define mock responses","mocking doesn't test actual LLM performance or token usage"],"requires":["Node.js 14+","TypeScript or JavaScript","testing framework (Jest, Mocha, or compatible)","understanding of agent behavior to write meaningful mocks"],"input_types":["agent code to test","mock LLM response definitions","mock tool response definitions","test input data"],"output_types":["test results (pass/fail)","agent execution traces from tests","coverage metrics for agent code paths"],"categories":["automation-workflow","testing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_2","uri":"capability://tool.use.integration.tool.function.calling.with.schema.based.validation.and.execution","name":"tool/function calling with schema-based validation and execution","description":"VoltAgent enables agents to call external tools and APIs through a schema-based function registry where developers define tool signatures (parameters, types, descriptions) and VoltAgent automatically handles LLM function-calling protocol negotiation, parameter validation, and execution. The framework maps LLM-generated function calls to actual JavaScript functions with type checking and error handling.","intents":["I want my agent to call APIs and execute functions based on LLM decisions","I need to ensure function parameters are validated before execution to prevent errors","I want to define tools once and have them work across different LLM providers that support function calling"],"best_for":["developers building agents that interact with external APIs and databases","teams creating autonomous workflows that need deterministic tool execution","builders prototyping agent-driven automation without manual API integration code"],"limitations":["schema validation adds overhead — no async validation support documented","tool execution is synchronous by default — long-running operations may block agent loop","no built-in retry logic for failed tool calls — requires manual error handling in tool definitions","function registry is in-memory only — no persistence across agent restarts"],"requires":["Node.js 14+","TypeScript or JavaScript","LLM provider with function-calling support (OpenAI, Anthropic, etc.)","tool functions must be JavaScript/TypeScript callables"],"input_types":["tool schema definitions (JSON Schema format)","tool function implementations (JavaScript functions)","LLM-generated function call requests"],"output_types":["function execution results (any type)","validation errors (structured)","tool call logs and traces"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_3","uri":"capability://memory.knowledge.agent.memory.and.context.management.with.configurable.storage.backends","name":"agent memory and context management with configurable storage backends","description":"VoltAgent provides a memory abstraction layer that stores agent state, conversation history, and intermediate results with pluggable storage backends (in-memory, Redis, database). The framework manages context window optimization by summarizing or pruning old messages to fit within LLM token limits while preserving semantic relevance through configurable retention policies.","intents":["I want my agent to remember previous interactions and use that context in future requests","I need to persist agent state across restarts without losing conversation history","I want to optimize token usage by automatically summarizing old messages when context gets too large"],"best_for":["teams building long-running agents that need persistent memory","developers creating multi-turn conversational agents with context awareness","builders optimizing token costs by managing context window efficiently"],"limitations":["in-memory storage is not persistent — agent restarts lose all history unless explicitly saved","context summarization uses LLM calls which adds latency and cost","no built-in semantic deduplication — similar memories may be stored redundantly","storage backend integration requires manual setup — no automatic database connection pooling"],"requires":["Node.js 14+","TypeScript or JavaScript","optional: Redis or database for persistent storage backends","LLM provider for context summarization features"],"input_types":["conversation messages (role/content pairs)","agent state objects","metadata tags for memory organization"],"output_types":["retrieved memory context (text)","memory summaries (text)","memory metadata and timestamps"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_4","uri":"capability://automation.workflow.agent.lifecycle.management.with.initialization.execution.and.cleanup.hooks","name":"agent lifecycle management with initialization, execution, and cleanup hooks","description":"VoltAgent provides lifecycle hooks (onInit, onExecute, onCleanup) that allow developers to inject custom logic at key agent stages — initialization for setup, execution for request processing, and cleanup for resource teardown. This pattern enables agents to manage external resources (database connections, API clients, file handles) safely across multiple invocations.","intents":["I want to initialize database connections or API clients once when the agent starts, not on every request","I need to clean up resources (close connections, flush logs) when the agent shuts down","I want to inject custom middleware or logging at specific points in the agent execution flow"],"best_for":["teams building production agents that manage external resources","developers creating agents with complex initialization requirements","builders implementing observability and logging across agent lifecycle"],"limitations":["lifecycle hooks are sequential — no parallel initialization support documented","error handling in hooks may not prevent agent startup — unclear failure semantics","no built-in timeout for lifecycle operations — long-running init could block agent availability","hooks are agent-level only — no per-request lifecycle hooks documented"],"requires":["Node.js 14+","TypeScript or JavaScript","understanding of async/await for lifecycle hook implementation"],"input_types":["agent configuration objects","request context for execution hooks","shutdown signals for cleanup hooks"],"output_types":["initialization status (success/failure)","execution results from request processing","cleanup completion status"],"categories":["automation-workflow","resource-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_5","uri":"capability://text.generation.language.agent.response.formatting.and.output.templating","name":"agent response formatting and output templating","description":"VoltAgent includes a response formatting layer that allows developers to define output templates and schemas for agent responses, ensuring consistent structure across different agent behaviors. The framework can format agent outputs as JSON, markdown, plain text, or custom formats, with optional validation against defined schemas before returning to users.","intents":["I want my agent to always return responses in a specific JSON structure, not raw LLM text","I need to format agent outputs as markdown for display in a web UI","I want to validate agent responses against a schema before returning them to users"],"best_for":["teams building agent APIs that need consistent response formats","developers creating multi-agent systems with standardized output contracts","builders integrating agents into applications requiring structured data"],"limitations":["formatting adds latency — LLM must generate text that conforms to template format","schema validation may reject valid but unexpected agent outputs","custom format templates require manual definition — no auto-generation from examples","no built-in format negotiation — client must know expected response format upfront"],"requires":["Node.js 14+","TypeScript or JavaScript","JSON Schema definitions for response validation (optional)"],"input_types":["raw agent output (text)","response template definitions","schema definitions for validation"],"output_types":["formatted responses (JSON, markdown, text)","validation errors if schema validation fails","structured data matching defined schema"],"categories":["text-generation-language","output-formatting"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_6","uri":"capability://automation.workflow.agent.error.handling.and.recovery.with.fallback.strategies","name":"agent error handling and recovery with fallback strategies","description":"VoltAgent implements error handling at multiple levels — LLM call failures, tool execution errors, and task decomposition failures — with configurable fallback strategies (retry with backoff, fallback to simpler model, graceful degradation). The framework tracks error context and allows agents to recover from transient failures without losing state.","intents":["I want my agent to retry failed LLM calls with exponential backoff instead of failing immediately","I need to fall back to a simpler, faster model if the primary model fails or times out","I want detailed error logs showing exactly where and why an agent request failed"],"best_for":["teams building production agents that need high reliability","developers creating agents that interact with unreliable external services","builders implementing observability and debugging for agent failures"],"limitations":["retry logic adds latency — exponential backoff may delay responses significantly","fallback strategies require manual configuration — no automatic strategy selection","error context is local to agent instance — no distributed error tracking","recovery from tool execution errors may require manual intervention in complex workflows"],"requires":["Node.js 14+","TypeScript or JavaScript","configuration for retry policies and fallback models"],"input_types":["error objects from LLM calls or tool execution","retry configuration (max attempts, backoff strategy)","fallback model specifications"],"output_types":["retry results (success or final failure)","error logs with context and stack traces","fallback execution results"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_7","uri":"capability://automation.workflow.agent.observability.and.tracing.with.structured.logging","name":"agent observability and tracing with structured logging","description":"VoltAgent provides structured logging and tracing capabilities that capture agent execution flow, LLM calls, tool invocations, and decision points with timestamps and metadata. The framework integrates with standard logging libraries and can export traces to observability platforms (e.g., OpenTelemetry) for monitoring and debugging agent behavior in production.","intents":["I want to see exactly what my agent is doing at each step — which tools it called, what LLM prompts it used, what decisions it made","I need to debug why an agent produced an unexpected output by reviewing its execution trace","I want to monitor agent performance in production with metrics like latency, token usage, and error rates"],"best_for":["teams operating agents in production and needing visibility into behavior","developers debugging complex agent workflows with multiple steps","builders implementing monitoring and alerting for agent systems"],"limitations":["structured logging adds overhead — trace collection may impact agent latency","trace storage requires external system — no built-in persistence","sensitive data (API keys, user inputs) may be logged — requires careful filtering configuration","OpenTelemetry integration may require additional setup and dependencies"],"requires":["Node.js 14+","TypeScript or JavaScript","logging library (Winston, Pino, or compatible)","optional: OpenTelemetry collector for distributed tracing"],"input_types":["agent execution events (LLM calls, tool invocations, decisions)","logging configuration (level, format, filters)","trace context from parent requests"],"output_types":["structured log entries (JSON format)","execution traces with timing information","metrics (latency, token usage, error counts)"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_8","uri":"capability://automation.workflow.agent.configuration.and.customization.through.declarative.schemas","name":"agent configuration and customization through declarative schemas","description":"VoltAgent allows developers to configure agent behavior through declarative configuration objects (JSON/YAML) that define model selection, tool availability, memory settings, response formats, and error handling policies. The framework validates configurations against schemas and applies them at agent initialization, enabling environment-specific customization without code changes.","intents":["I want to change which LLM model my agent uses without modifying code — just update a config file","I need different agent configurations for development, staging, and production environments","I want to enable/disable specific tools for different user groups through configuration"],"best_for":["teams managing multiple agent deployments with different configurations","developers wanting to separate agent logic from configuration","builders implementing multi-tenant systems where agents need per-tenant customization"],"limitations":["configuration validation happens at startup — invalid configs may not be caught until runtime","no hot-reload of configuration — agent restarts required for config changes","complex conditional logic in configs is difficult to express — may require code for sophisticated customization","configuration schema must be defined upfront — no dynamic schema generation"],"requires":["Node.js 14+","TypeScript or JavaScript","configuration files (JSON or YAML format)"],"input_types":["configuration objects (JSON/YAML)","environment variables for secrets","schema definitions for validation"],"output_types":["validated configuration objects","configuration validation errors","applied agent settings"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-voltagent-core__cap_9","uri":"capability://planning.reasoning.agent.composition.and.chaining.with.explicit.data.flow","name":"agent composition and chaining with explicit data flow","description":"VoltAgent enables developers to compose multiple agents into workflows where outputs from one agent feed into inputs of another, with explicit data transformation between steps. The framework manages data flow, type validation, and error propagation across agent chains, allowing complex multi-agent systems to be defined declaratively.","intents":["I want to chain multiple agents together where one agent's output becomes another agent's input","I need to transform data between agent steps (e.g., extract JSON from one agent's output before passing to next)","I want to define complex workflows with conditional branching based on agent outputs"],"best_for":["teams building complex multi-agent systems with clear data dependencies","developers creating workflows that require sequential agent processing","builders implementing agent pipelines for document processing, research, or analysis"],"limitations":["agent chaining adds latency — each agent call is sequential, no parallelization","data transformation between agents requires manual definition — no automatic schema mapping","error in one agent may cascade through chain — requires explicit error handling at each step","no built-in visualization of agent chains — complex workflows are hard to understand"],"requires":["Node.js 14+","TypeScript or JavaScript","multiple agent instances configured and initialized"],"input_types":["initial input data for first agent","agent chain definitions (sequence of agents)","data transformation specifications between steps"],"output_types":["final output from last agent in chain","intermediate outputs from each step (if captured)","execution trace showing data flow through chain"],"categories":["planning-reasoning","agent-composition"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ (typical for npm packages)","JavaScript/TypeScript runtime environment","LLM provider integration (OpenAI, Anthropic, or compatible API)","Node.js 14+","API keys for at least one supported LLM provider (OpenAI, Anthropic, etc.)","TypeScript or JavaScript runtime","TypeScript or JavaScript","testing framework (Jest, Mocha, or compatible)","understanding of agent behavior to write meaningful mocks","LLM provider with function-calling support (OpenAI, Anthropic, etc.)"],"failure_modes":["planning layer adds latency per decomposition step — no benchmarks provided for complex 10+ step workflows","state machine approach requires explicit task definition upfront — dynamic runtime task generation not documented","no built-in distributed execution — all tasks execute in single Node.js process","abstraction may not expose provider-specific advanced features (e.g., OpenAI's vision_detail parameter)","token counting accuracy depends on provider's tokenizer — approximations used for non-OpenAI models","streaming response handling may have latency differences across providers not normalized by abstraction","mock responses may not capture real LLM behavior nuances — tests may pass but fail in production","setting up comprehensive mocks for complex agents is time-consuming","no built-in test data generation — developers must manually define mock responses","mocking doesn't test actual LLM performance or token usage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2343369200227532,"quality":0.32,"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":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.473Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":11024,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=voltagent-core","compare_url":"https://unfragile.ai/compare?artifact=voltagent-core"}},"signature":"8mFiGEs2KNyDAZGa4Hzn+bMmLryX7fo4OnLt0vSqrHIscrd+5JplKLhqkEQqBpvmYNqW8gaBiUTjKJb/Q/6wBQ==","signedAt":"2026-06-20T18:35:18.852Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/voltagent-core","artifact":"https://unfragile.ai/voltagent-core","verify":"https://unfragile.ai/api/v1/verify?slug=voltagent-core","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"}}