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06:01

Turn any codebase into a queryable knowledge graph, built for AI coding assistants

将任意代码库转化为可查询知识图谱，专为AI编码助手设计

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, etc. into a queryable knowledge graph. It uses tree-sitter for local deterministic AST parsing to extract code relationships (calls, imports, inheritance) without any LLM calls. Non-code files are semantically extracted via your AI assistant's model. The output includes an interactive HTML visualization, CLI queries (query/path/explain), and an MCP server for team use. Every edge is tagged EXTRACTED or INFERRED, providing transparency. Ideal for engineers navigating large monorepos, tracing dependencies, or understanding architecture.

github.com · 59 min · AI Agents · Developer Tools · Knowledge Graph · LLM · Mcp
06:01

From Loop to Graph: The Real Shift in AI Agent Improvement

从单循环到图网络：AI Agent 改进架构的演进与陷阱

This essay explores the shift from single-loop optimization to graph-structured improvement in AI agent engineering. It dissects four failure modes of single loops: Goodhart's law, blindness to target correctness, conflict among loops, and measurement decay. Mature systems employ graphs of loops—champion-challenger, drift monitoring, rollback, and audit loops—each watching and constraining others. But graphs alone fail without anchors: measures that touch reality, frozen rules, and human judgment on what 'better' means. The key is groundedness, not topology. For AI engineers, MLOps practitioners, and agent designers.

x.com · 13 min · Agent Architecture · AI Engineering · Loop Engineering · Orchestration
06:00

Graph Engineering with Claude: 14-Step roadmap from 0 to graph architect (Full Course)

Agent图工程14步：从线性脚本到并行图谱

Most multi-step agents are built as linear chains, each step waiting for the previous one, wasting parallelism and creating bottlenecks. This article introduces graph engineering for agents using Claude Code's dynamic workflows. It presents 14 steps to transform linear scripts into directed acyclic graphs (DAGs) where nodes are bounded work units with contracts (validated input/output schemas) and edges are data dependencies. Key patterns include parallel fan-out (parallel()), fan-in barriers, runtime routing, adversarial verifiers, and convergent cycles. The author provides JavaScript/TypeScript code examples and topology diagrams, showing how to scale agents from a single-threaded line to a coordinated fleet. Targeted at engineers building complex agent systems who want to improve throughput, reliability, and cost efficiency.

x.com · 21 min · Agent Architecture · Agents · Claude Code · Orchestration · Parallel Computing