Fred vs. Spring AI

Fred Knowledge Flow vs. Spring AI

Overview#

Fred is an open-source platform for deploying intelligent assistants that plan, use tools, cite sources, and explain their reasoning — powered by modular knowledge flows and agentic orchestration.

In contrast, Spring AI is a developer-focused Java library designed to simplify access to LLMs and AI tooling in Spring Boot applications.

Both are modern solutions for building LLM-enhanced applications, but they target different use cases and design philosophies.

What is Spring AI?#

Spring AI is a rapidly evolving library that provides:

• Easy integration with LLMs (OpenAI, Ollama, etc.)
• Embedding and vector store support
• Declarative chat templates, tool use, and memory
• Model Composition and Prompting (MCP): an orchestration framework for chaining models, prompts, tools, and routing logic

It is ideal for Java developers building AI-enhanced backend services.

Fred’s Value Proposition#

Fred provides a higher-level agentic architecture designed for both developers and end users. It’s composed of:

1. Multi-Agent Planning and Execution#

Fred uses:

• A planning → execution → validation loop
• LangGraph for structured, stateful agent flows
• Experts (“agentic flows”) dynamically selected per step
• Step-wise execution with metadata, supervision, and replanning

🟢 Spring AI MCP supports similar graph-based orchestration, but Fred integrates planning and validation logic with tool selection and reflection out-of-the-box.

2. Modular Knowledge Flow Layer#

Fred includes a separate knowledge_flow_app backend with:

• Document ingestion and metadata management
• Markdown previews and file attribution
• Vectorization, storage, and retrieval decoupled from agent logic

🟡 Spring AI supports EmbeddingRetriever and basic RAG flows, but lacks modular ingestion pipelines and preview-capable document stores.

3. End-User-Facing Platform#

Fred includes a full-featured frontend:

• Chat UI with session history, markdown rendering, and source previews. This UI is a powerful start point to design your own custom UI integrating a mix of interactive chat and custom pages leveraging backend services powered by generative AI.
• File upload support and inline citations
• WebSocket and REST streaming with structured message formats and subtypes

🔴 Spring AI is developer-facing only — no frontend, no session management, no UI support.

4. Built for LangGraph and Python#

Fred is Python-native and built around:

• LangGraph for stateful, graph-based flows
• FastAPI for async backend APIs
• Typed Pydantic schemas for structured agent communication

🟡 Spring AI is Java-native, leveraging Spring Boot conventions. Great for Java shops, but requires verbose configuration and has fewer agent-oriented abstractions.

Comparison Table#

Summary#

Fred is best suited for building full-featured, explainable AI assistants where planning, source attribution, tool reasoning, and traceability matter. It emphasizes structured flows, deep customization, and tight integration with frontend and backend components.

Spring AI, especially with MCP, is an excellent orchestration framework for Java developers. It is fast-evolving, convention-based, and well-suited for integrating LLMs into microservices — but lacks many higher-order agentic capabilities Fred provides today.