Agentic Chat¶
Agentic Chat is the unified runtime where Spring AI Playground combines documents, tools, models, and conversation state.
This unified interface lets you:
- run RAG workflows grounded in indexed documents
- execute tool-enabled agent flows through MCP
- test complete agent strategies by combining documents and tools in a single chat session
Key Features¶
- document selection for RAG grounding
- MCP connection selection for tool-enabled execution
- real-time visibility into retrieved context and tool usage
- one conversational surface for both chain-style and agentic patterns
This area is closely aligned with Spring AI's workflow and agentic guidance. If you want the conceptual background behind these two modes, see Building Effective Agents.
Two Integrated Paradigms¶
1. RAG: Knowledge via Chain Workflow¶
When documents are selected, Agentic Chat follows a deterministic retrieval pattern:
- retrieval from the vector store
- prompt augmentation with grounded context
- response generation based on that context
2. MCP: Actions via Agentic Reasoning¶
When MCP connections are enabled, Agentic Chat can behave like an agent:
- reasoning about which tools are needed
- invoking tools through MCP
- observing the result
- continuing or answering directly
Workflow Integration¶
The intended end-to-end flow is:
- prepare tools in Tool Studio or connect them in MCP Server
- prepare knowledge in Vector Database
- enable the relevant documents and MCP connections in Agentic Chat
- send a request and observe how retrieval and tool use combine
This is the place where the rest of the product becomes visible as one coherent system rather than separate screens. The outputs of Tool Studio, MCP Server, and Vector Database all converge here.
Requirements for Agentic Reasoning¶
Basic chat can work with any supported provider. Tool-enabled agentic behavior works best with models that support function calling and stronger reasoning.
For Ollama-based flows:
- use tool-capable models from Ollama's Tool Category
- use reasoning-capable models from Ollama's Thinking Category
- validate tools in MCP Inspector before relying on them in Agentic Chat
The default playground.chat.models list features qwen3.5 and gemma4 for stronger tool-oriented reasoning, with gpt-oss and deepseek-r1 as alternatives. See Picking a Model in the Tutorials for the tradeoffs.
Agentic Chat Architecture Overview¶
The diagram below is included as a conceptual reference to the related agentic systems material in the Spring AI docs.
It is included here to explain how the Playground's Agentic Chat maps onto the broader Spring AI mental model. In this project, the diagram is not describing a separate product feature hidden behind the UI. It is a conceptual reference for understanding how the Playground combines model reasoning, retrieval, tool execution, and memory in one chat runtime.
If you want the fuller conceptual background, start with Building Effective Agents. That reference explains the workflow-versus-agent distinction that this Playground makes concrete through Tool Studio, MCP Server, Vector Database, and Agentic Chat.
This Chat experience facilitates exploration of Spring AI's workflow and agentic paradigms, empowering developers to build AI systems that combine chain-based RAG workflows with agentic, tool-augmented reasoning. In practice, it follows Spring AI's Agentic Systems architecture, where grounded retrieval and dynamic tool execution coexist in one context-aware chat runtime.
| Component | Type | Description | Configuration Location | Key Benefits | Model Requirements |
|---|---|---|---|---|---|
| LLM | Core Model | Executes chain-based workflows and performs agentic reasoning for tool usage within a unified chat runtime. | Agentic Chat | Central reasoning and response generation; supports both deterministic workflows and agentic patterns. | Chat models; tool-aware and reasoning-capable models recommended. |
| Retrieval (RAG) | Chain Workflow | Deterministic retrieval and prompt augmentation using vector search over selected documents. | Vector Database | Predictable, controllable knowledge grounding; tunable retrieval parameters such as Top-K and thresholds. | Standard chat plus embedding models. |
| Tools (MCP) | Agentic Execution | Dynamic tool selection and invocation via MCP, driven by LLM reasoning and tool schemas. | Tool Studio, MCP Server | Enables external actions, multi-step reasoning, and adaptive behavior. | Tool-enabled models with function calling and reasoning support. |
| Memory | Shared Agentic State | Sliding window conversation memory shared across workflows and agents through ChatMemoryAdvisor and the underlying Spring AI chat memory support. |
Spring AI chat runtime (InMemoryChatMemory) |
Coherent multi-turn dialogue with a sliding window improves coherence, planning, and tool usage quality. | Models benefit from longer context and structured reasoning. |
By leveraging these elements, Agentic Chat goes beyond basic Q&A and becomes a practical environment for building effective, modular AI applications that combine workflow predictability with agentic autonomy.
→ Try it: Tutorials — end-to-end flows that combine Tool Studio, MCP Inspector, Vector Database, and Agentic Chat.