Agentic Tree Search narrows RAG to three tools

// 45d agoOPENSOURCE RELEASE

Agentic Tree Search narrows RAG to three tools

Agentic Tree Search is a reference pattern and open-source implementation for agentic knowledge retrieval that keeps everything local and database-native. It models a knowledge base as a relational tree in SQL Server, then exposes just three Semantic Kernel tools to the agent: browse the map, read a node, and search nodes. The repo emphasizes zero filesystem dependence, no initial vector store, and a clean upgrade path from `LIKE` search to Full-Text or vectors later without changing the agent interface. It is demonstrated with Qwen3:8b via Ollama and positioned as a practical on-premise alternative to file-based RAG setups.

// ANALYSIS

Hot take: this is less a flashy app and more a solid infra pattern for teams that already live in SQL Server and want agentic retrieval without adding a vector stack on day one.

–The main strength is the constrained tool surface: three tools is small enough for weaker models to use reliably, which matters more than theoretical elegance.
–The self-join tree schema preserves the folder-like mental model while keeping the storage/query layer relational and governable.
–Starting with `LIKE` is intentionally unglamorous, but it makes the system immediately usable and easy to evolve.
–The strongest selling point is interface stability: search and chunking can change underneath the agent without prompt or tool churn.
–This is a good fit for enterprise/on-prem environments; it is a weaker fit if your source of truth is already a Git repo and human editorial workflow.

// TAGS

local-ragsemantic-kernelsql-serverollamaqwen3on-premknowledge-retrievalagentic-aifull-text-search

DISCOVERED

45d ago

2026-04-20

PUBLISHED

45d ago

2026-04-20

RELEVANCE

8/ 10

AUTHOR

Square-Drink3290

// KEEP READING

More AI developer news from the feed

EXPLORE FULL FEED

NEWS32m ago

Anthropic's Claude models demonstrate rapid acceleration in recursive self-improvement, with the Mythos Preview model achieving a 52x speedup on optimization tasks.

Anthropic's latest models have shown dramatic progress in recursive self-improvement (RSI) capabilities. According to internal reports, Anthropic tasks newly released models with optimizing the training code for smaller AI models. While Claude Opus 4 averaged a 3x speedup in May 2024, the newly developed Mythos Preview model achieved a 52x speedup in April 2026, demonstrating that AI-driven self-optimization is accelerating at an exponential rate.

UPDATE49m ago

ChatGPT rolls out background memory system

OpenAI is rolling out a new background memory system for ChatGPT Plus and Pro users in the US that doubles capacity and automatically curates memories using broader chat history via a process called "dreaming." Users retain full control with the ability to manage saved memories through a new dashboard or revert to the legacy memory experience in settings.

LAUNCH1h ago

ElevenLabs has introduced Flows Agent in ElevenCreative, a conversational assistant that automatically builds and iterates node-based, multi-modal creative workflows.

ElevenLabs has introduced the Flows Agent within its ElevenCreative platform, a tool that allows creators to build and modify complete creative workflows using natural language. The agent handles tasks such as selecting models, creating nodes, wiring connections, and running generations across over 50 image, video, voice, music, and sound effects models. With an active assist mode, users maintain cost control by approving expensive operations, while the system supports background processing so workflows can complete even after closing the tab. Users can iterate on their pipelines dynamically through conversation—such as swapping voices, backgrounds, or languages—without rebuilding the entire flow from scratch.