Enterprise-grade (40m+ LOC) codebase intelligence, zero-setup, local & private Plugin/Skill/Extension or MCP: hybrid semantic search, polyglot dependency graphs, symbol-level impact analysis & call-flow, interactive HTML viewer, cross-project & branch-aware search, DB/API/infra knowledge. 61% less tokens, 84% fewer calls, 37x faster. Cloud in beta.

AI Agent Orchestrator with Skills System - Give AI Agents superpowers: memory search, code graph queries, agent-to-agent messaging. Manage Claude, Codex or any AI Agent from one dashboard. Move Agents between computers and locations

Local codebase intelligence CLI + MCP server for AI coding agents: SQLite code graph, 28 languages, 238 commands, 224 MCP tools, change-safety gates, audit evidence, zero API keys.

[FORGE 2025] Incorporating Agile methodology into agents to create complex real-world softwares

Symbol Delta Ledger (SDL-MCP) is a policy-centered context budget layer for coding agents: Symbol-graph intelligence combined with precision tools. It turns sprawling codebases into compact, high-signal context that saves tokens, speeds up workflows, and improves agent output.

A code-graph demo using GraphRAG-SDK and FalkorDB

CLI & MCP for GitHits - The Code Context Layer for AI Coding Agents

Source code graph RAG (GraphRAG) for C/C++ development based on clang/clangd

AI coding tool skill (e.g., Claude Code) centered on Karpathy's LLM Wiki pattern turns codebases into wikis, auto-analyzes, and generates docs similar to DeepWiki and ZRead with diagrams.

Whole-codebase knowledge for AI coding agents. A field-aware code graph (functions, classes, methods, fields, references) plus persistent memory. Rust, Postgres + pgvector, MCP.

CodeStory is a codebase grounding engine that preindexes code into a knowledge graph and enriches it with semantic context. Paired with coding agents, it results in fewer tokens, fewer tool calls, and remains 100% local.

Persistent, verified memory for coding agents — so they stop re-explaining your codebase and never act on stale knowledge. Every memory is checked against your actual code; lives in your repo as plain files, shared via git. No account, no DB. Install: npx -y @kage-core/kage-graph-mcp install

Local pre-flight linter and architecture gate for AI agents. Uses tree-sitter, Stack Graphs, and Datalog to mechanically block structural drift, layering bypasses, and scope creep on virtual ASTs before code changes land.

Your agent says it's done. Vinv says prove it. Real traces + live code graph + closed-loop verify, served to your agent over MCP.

The shell around your coding agent: a neuro-symbolic code graph with calibrated trust, via MCP.

Local code search for AI agents: six fast, purpose-built tools that return ranked answers, not raw grep. Because maybe grep isn't all you need... 🍬

Auditable code intelligence for AI agents - map dependencies, predict change impact, select the right tests, and verify refactors before they ship.

AI pull-request reviewer: hybrid RAG + a code graph + Claude Code. Whole-repo context, inline GitHub comments grounded on exact code. MCP server + Claude Code plugin.

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