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**Resources:** |
- [Tutorial](https://voltagent.dev/examples/agents/recipe-generator) |
- [Video](https://youtu.be/KjV1c6AhlfY) |
- [Source Code](https://github.com/VoltAgent/voltagent/tree/main/examples/with-recipe-generator) |
### AI Research Assistant Agent |
A research workflow implementation where multiple agents work in parallel for data collection and analysis. |
 |
**Technical Details:** |
- 4 different research agents running in parallel |
- Type-safe workflow chaining |
- OpenTelemetry traces for inter-agent dependency visualization |
- Markdown-formatted research report output |
**Resources:** |
- [Tutorial](https://voltagent.dev/examples/agents/research-assistant) |
- [Video](https://youtu.be/j6KAUaoZMy4) |
- [Source Code](https://github.com/VoltAgent/voltagent/tree/main/examples/with-research-assistant) |
## More Examples |
### Integration Examples |
#### → [GitHub Repository Analyzer](https://github.com/VoltAgent/voltagent/tree/main/examples/github-repo-analyzer) |
This example demonstrates how agents can analyze repository code and automatically generate summaries about project structure, dependencies, and potential issues. |
#### → [RAG Chatbot](https://github.com/VoltAgent/voltagent/tree/main/examples/with-rag-chatbot) |
A document-grounded conversational bot that retrieves relevant information from your knowledge base and provides responses with proper citations. |
#### → [Tavily Web Search](https://github.com/VoltAgent/voltagent/tree/main/examples/with-tavily-search) |
Integrate real-time web search capabilities into your agents, allowing them to augment responses with up-to-date information from the internet. |
### Vector Database & RAG |
#### → [Chroma Vector Database](https://github.com/VoltAgent/voltagent/tree/main/examples/with-chroma) |
This example shows how to implement RAG (Retrieval-Augmented Generation) using Chroma, demonstrating both automatic retrieval and tool-driven retrieval patterns for enhanced context. |
#### → [Pinecone Vector Search](https://github.com/VoltAgent/voltagent/tree/main/examples/with-pinecone) |
Build semantic search capabilities using Pinecone's vector database, enabling your agents to find contextually similar information through embeddings. |
#### → [Qdrant Vector Database](https://github.com/VoltAgent/voltagent/tree/main/examples/with-qdrant) |
Compare two different retrieval strategies: retriever-on-every-turn where documents are fetched automatically, versus LLM-decides where the model determines when to search. |
#### → [Postgres with pgvector](https://github.com/VoltAgent/voltagent/tree/main/examples/with-postgres) |
Use PostgreSQL with the pgvector extension for both structured data storage and semantic similarity search in a single database. |
### LLM Providers |
#### → [Anthropic Claude](https://github.com/VoltAgent/voltagent/tree/main/examples/with-anthropic) |
Connect your agents to Anthropic's Claude models through the AI SDK, giving you access to advanced reasoning and long-context capabilities. |
#### → [Google Gemini AI](https://github.com/VoltAgent/voltagent/tree/main/examples/with-google-ai) |
Integrate Google's Gemini models into your VoltAgent applications using the AI SDK provider for multimodal AI capabilities. |
#### → [Google Vertex AI](https://github.com/VoltAgent/voltagent/tree/main/examples/with-google-vertex-ai) |
Deploy agents using Google Cloud's Vertex AI platform, leveraging enterprise-grade infrastructure and model management. |
#### → [Groq LPU Inference](https://github.com/VoltAgent/voltagent/tree/main/examples/with-groq-ai) |
Achieve ultra-low latency responses by running your agents on Groq's specialized LPU (Language Processing Unit) hardware. |
#### → [Amazon Bedrock](https://github.com/VoltAgent/voltagent/tree/main/examples/with-amazon-bedrock) |
Configure your agents to use AWS Bedrock's foundation models, accessing a variety of AI models through Amazon's managed service. |
#### → [xAI Grok](https://github.com/VoltAgent/voltagent/tree/main/examples/with-xsai) |
Power your agents with xAI's Grok models for real-time understanding and generation capabilities. |
### MCP (Model Context Protocol) |
#### → [MCP Client Basics](https://github.com/VoltAgent/voltagent/tree/main/examples/with-mcp) |
Learn how to connect your agents to Model Context Protocol servers and invoke their tools, enabling standardized integration with external services. |
#### → [Custom MCP Server](https://github.com/VoltAgent/voltagent/tree/main/examples/with-mcp-server) |
Build your own MCP server that exposes custom tools to agents, allowing you to create reusable tool ecosystems across different agent applications. |
#### → [Composio MCP Integration](https://github.com/VoltAgent/voltagent/tree/main/examples/with-composio-mcp) |
Integrate Composio's suite of third-party application actions into your agents through the Model Context Protocol interface. |
#### → [Google Drive MCP](https://github.com/VoltAgent/voltagent/tree/main/examples/with-google-drive-mcp) |
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