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voltagent

AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework

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<a href="https://voltagent.dev">Home Page</a> |
<a href="https://voltagent.dev/docs/">Documentation</a> |
<a href="https://github.com/voltagent/voltagent/tree/main/examples">Examples</a> 

VoltAgent is an end-to-end AI Agent Engineering Platform that consists of two main parts:

Build agents with full code control and ship them with production-ready visibility and operations.

With the open-source framework, you can build intelligent agents with memory, tools, and multi-step workflows while connecting to any AI provider. Create sophisticated multi-agent systems where specialized agents work together under supervisor coordination.

  • Core Runtime (@voltagent/core): Define agents with typed roles, tools, memory, and model providers in one place so everything stays organized.
  • Workflow Engine: Describe multi-step automations declaratively rather than stitching together custom control flow.
  • Supervisors & Sub-Agents: Run teams of specialized agents under a supervisor runtime that routes tasks and keeps them in sync.
  • Tool Registry & MCP: Ship Zod-typed tools with lifecycle hooks and cancellation, and connect to Model Context Protocol servers without extra glue code.
  • LLM Compatibility: Swap between OpenAI, Anthropic, Google, or other providers by changing config, not rewriting agent logic.
  • Memory: Attach durable memory adapters so agents remember important context across runs.
  • Resumable Streaming: Let clients reconnect to in-flight streams after refresh and continue receiving the same response.
  • Retrieval & RAG: Plug in retriever agents to pull facts from your data sources and ground responses (RAG) before the model answers.
  • VoltAgent Knowledge Base: Use the managed RAG service for document ingestion, chunking, embeddings, and search.
  • Voice: Add text-to-speech and speech-to-text capabilities with OpenAI, ElevenLabs, or custom voice providers.
  • Guardrails: Intercept and validate agent input or output at runtime to enforce content policies and safety rules.
  • Evals: Run agent eval suites alongside your workflows to measure and improve agent behavior.

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