25 lines
2.2 KiB
Markdown
25 lines
2.2 KiB
Markdown
# Agent Engine in Vertex AI
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[Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview)
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is a managed service that helps you to build and deploy agent reasoning
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frameworks. It gives you the flexibility to choose how much reasoning you want
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to delegate to the LLM and how much you want to handle with customized code. You
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can define Python functions that get used as tools via Gemini Function Calling.
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Agent Engine integrates closely with the Python SDK for the Gemini model in
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Vertex AI, and it can manage prompts, agents, and examples in a modular way.
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Agent Engine is compatible with LangChain, LlamaIndex, or other Python
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frameworks.
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## Sample notebooks
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| Description | Sample Name |
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| ------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------- |
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| Intro to Building and Deploying an Agent with Agent Engine in Vertex AI | [intro_agent_engine.ipynb](intro_agent_engine.ipynb) |
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| Debugging and Optimizing Agents: A Guide to Tracing in Agent Engine | [tracing_agents_in_agent_engine.ipynb](tracing_agents_in_agent_engine.ipynb) |
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| Building a Conversational Search Agent with Agent Engine and RAG on Vertex AI Search | [tutorial_vertex_ai_search_rag_agent.ipynb](tutorial_vertex_ai_search_rag_agent.ipynb) |
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| Building and Deploying a Google Maps API Agent with Agent Engine | [tutorial_google_maps_agent.ipynb](tutorial_google_maps_agent.ipynb) |
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| Building and Deploying a LangGraph Application with Agent Engine in Vertex AI | [tutorial_langgraph.ipynb](tutorial_langgraph.ipynb) |
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| Deploying a RAG Application with AlloyDB with Agent Engine | [tutorial_alloydb_rag_agent.ipynb](tutorial_alloydb_rag_agent.ipynb) |
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| Deploying a RAG Application with Cloud SQL for PostgreSQL with Agent Engine | [tutorial_cloud_sql_pg_rag_agent.ipynb](tutorial_cloud_sql_pg_rag_agent.ipynb) |
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