Tool calling — the ability for an AI model to invoke application-defined functions and act on the results — is the essential building block of agentic AI systems.
A model that can discover information, take action, and loop until a goal is reached is an agent. Spring AI 2.0 rearchitects tool calling from the ground up. In 1.x, each chat model implementation contained its own private tool execution loop — functional, but buried. There was no way to hook into it, observe intermediate steps, or compose it with other behaviors. You could call tools; you could not build on top of tool calling. 2.…
On behalf of the team and everyone who has contributed, I'm happy to announce that Spring AI 2.0.0 has been released and is now available from Maven Central! Release notes | Upgrade notes | Reference documentation | Javadoc Improved foundations Since the early days, Spring AI has grown very fast in terms of features, model support, contributions and users. That’s great and exciting, but we felt the need for setting a new baseline in terms of scope, quality and consistency in order to allow evolving the project in a sustainable way. So a few months ago, we took a step back and defined areas…
A New Session API for Spring AI — Structured, Compactable, Multi-Agent-Ready Part 7 of the Spring AI Agentic Patterns series completes the memory picture. After covering Agent Skills, AskUserQuestionTool, TodoWriteTool, Subagent Orchestration, A2A Integration, and AutoMemoryTools for long-term cross-session memory, we now add the complementary short-term layer: Spring AI Session. Storing conversation history as a flat message list works for short exchanges but breaks down as sessions grow — naive truncation silently discards tool-call sequences mid-exchange, leaving the model with orphaned…
File-Based Long-Term Memory for Spring AI Agents Agents are only as useful as what they remember. Spring AI's Chat Memory stores the full conversation and can persist it across restarts, but when the window fills, the oldest messages are evicted. The upcoming Session API will add recursive summarization to soften this, but precise facts are still lost when details get compressed away. AutoMemoryTools and AutoMemoryToolsAdvisor, part of the spring-ai-agent-utils toolkit, give your agents a durable, file-based long-term memory that persists across sessions. The design is inspired by Claude Code…
On behalf of the Spring AI engineering team and everyone who has contributed, I'm happy to announce that Spring AI 2.0.0-M3, 1.1.3, 1.0.4, have been released and are now available from Maven Central. These releases deliver important improvements, stability enhancements, and bug fixes across multiple release streams. ⚠️ Breaking Changes: 2.0.0-M3 includes several breaking changes, including MCP annotation package renames, MCP transport artifact relocations, Jackson 2 → Jackson 3 migration, and removal of conversation history from ToolContext. Review the Upgrade Notes before upgrading. 2.0.0-M…
Instead of one generalist agent doing everything, delegate to specialized agents. This keeps context windows focused—preventing the clutter that degrades performance. Task tool, part of the spring-ai-agent-utils toolkit, is a portable, model-agnostic Spring AI implementation inspired by Claude Code's subagents. It enables hierarchical agent architectures where specialized subagents handle focused tasks in dedicated context windows, returning only essential results to the parent. Beyond Claude's markdown-based format, the architecture is extensible—supporting A2A and other agentic protocols for…
Have you ever asked an AI agent to perform a complex multi-step task, only to find it skipped a critical step halfway through? You're not alone. Research shows that LLMs struggle with "lost in the middle" failures—forgetting tasks buried in long contexts.
When your agent juggles file edits, test execution, and documentation updates, important steps can silently disappear.
One solution, inspired by Claude Code, is to make planning explicit and observable with the help of a dedicated TodoWrite tool.
The result: agents that never skip steps and workflows you can observe in real-time. This is Part…
Traditional AI interactions follow a common pattern: you provide a prompt, the AI makes assumptions, and produces a response. When those assumptions don't match your needs, you're left iterating through corrections. Each assumption creates rework—wasting time and context. What if your AI agent could ask you clarifying questions before providing answers? The AskUserQuestionTool addresses this. It allows the AI agent to ask clarifying questions before answering, gathers requirements interactively, and creates a specification aligned with your actual needs from the start. Spring AI's…
subtitle: "LLM-Agnostic Skills That Run in Your Environment" Agent Skills are modular folders of instructions, scripts, and resources that AI agents can discover and load on demand. Instead of hardcoding knowledge into prompts or creating specialized tools for every task, skills provide a flexible way to extend agent capabilities. Spring AI's implementation brings Agent Skills to the Java ecosystem, ensuring LLM portability—define your skills once and use them with OpenAI, Anthropic, Google Gemini, or any other supported model. This is the first post in our Spring AI Agentic Patterns series…
When building AI agents with tool calling capabilities, developers often need insights into why an LLM chose a particular tool—not just which tool it selected. Understanding the model's reasoning process is important for debugging, observability, and building trustworthy AI systems. Spring AI now (2.0.0-SNAPSHOT/1.1.3-SNAPSHOT) includes the Tool Argument Augmenter feature that enables dynamic augmentation of tool input schemas with additional arguments before sending tool definitions to the LLM. This allows AI applications to capture extra information from the model—such as reasoning, inner…
