Local memory, a codebase index, and working discipline for AI coding agents. Zero dependencies, no network, MCP included. Measured, not asserted.

MCP server for AI-powered codebase indexing with semantic memory, smart bootstrap, and intelligent context compilation

The Private Context Layer & Super Brain for Your Codebase. Give AI agents persistent memory, deep dependency graphs, and instant code context

Codebase Context gives AI agents understanding of your codebase through semantic code search, team conventions, patterns, and memory, so they use fewer tokens, spend less time, and produce better, more familiar output.

AI Mind Map — MCP server that reduces AI coding agent token usage by 80-99% through persistent codebase memory, knowledge graphs, and intelligent context management

CodeMind is an AI agent memory system that maintains persistent, real-time context over complex codebases. By utilizing symbol graph storage, AST chunking, and intelligent file scanning, CodeMind enables LLMs to reason about relationships across large code structures without losing track of dependencies

AI-powered codebase intelligence platform — MCP Server, AST analysis, Knowledge Graph, and semantic memory with SQLite + sqlite-vec. Analyze any codebase in minutes.

Fast, fully-local code search & navigation for large codebases — cuts the tokens an AI coding agent spends finding code. Lexical + symbolic + semantic, memory-mapped, no GPU, no cloud.

Local codebase memory for AI assistants. Because re-explaining your project structure to Claude or Antigravity for the fifth time today gets old. Oh, it saves a LOT on token costs, too.

Point a cross-model AI engineering team at your own codebases. The Agent OS keeps named AI maintainers' identity, memory and review across labs alive between sessions — the Brain Neo.mjs runs on itself. Multi-tenant: onboarding a codebase is a config entry, not a fork.

Your codebase never forgets. Persistent, structured memory for AI coding agents.

A lightweight, powerful local memory server for AI agents supporting text, entities, and relations. Enables persistent codebase understanding and user preference management.

Persistent memory for AI coding assistants. Auto-captures decisions, patterns, and context from Claude Code sessions. Works with Cursor, Cline, Copilot via MCP. Never re-explain your codebase again.

🧠 MCP server giving AI assistants (Gemini, Claude, Cursor, Windsurf, Copilot) deep codebase understanding — 10 tools + persistent memory. Install: npx @giauphan/codeatlas-mcp

Local-first, code-aware engineering cognition for AI coding agents - persistent memory, contextual retrieval, and continuous cognition about your codebase.

Persistent local memory for AI coding agents — Claude Code, Codex CLI, Cursor, any MCP client. Temporal knowledge graph, procedural memory, AST codebase ingest, cross-project analogy. LongMemEval R@5 95.1%, LoCoMo 0.607, BEAM 1M 0.448. 74 MCP tools, 9 IDEs, 100% local.

MCP server that gives AI coding agents persistent memory of your codebase: notes pinned to files, spans, and symbols that survive refactors. Works with Claude Code, Cursor, and any MCP host.

Persistent memory and codebase knowledge graph for AI coding assistants — delivered as a single MCP server.

Local MCP server that gives AI coding agents persistent, searchable memory of your codebase

Indexed semantic & BM25 code search for AI agents. Give your AI instant memory of your codebase. Rust | MCP Server.

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

A high-performance Model Context Protocol (MCP) server written in Rust that extends AI models into autonomous coding agents with semantic memory, built-in Git tools, and codebase indexers

A code context MCP server backed by Oracle AI Database 26ai: indexes codebases, hybrid search, call graph, module wiki and agent memory for AI coding tools.

Decision memory layer for AI-assisted codebases — trả lời 'logic nghiệp vụ này có từ bao giờ, ai quyết, vì sao'. Markdown là nguồn sự thật, SQLite FTS5 để search, MCP server cho AI agent.

Memos builds a structural index (symbols, call edges, imports) of a codebase using tree-sitter and stores it in SQLite. It is the first layer of a larger Memory OS for AI coding agents — instead of grep-ing text, agents query structure (definitions, callers, callees).

A memory of the codebase your AI agent can't get from grep: a persistent call graph, semantic search, and per-symbol review learnings over any repo. Self-hosted MCP server — Go, tree-sitter, Apache AGE.

Go Architecture X-Ray is a Model Context Protocol server for inspecting Go codebases from an AI client. It runs over stdio and keeps a process-scoped in-memory analysis cache for the life of the MCP session.

Persistent codebase memory for AI coding agents — Claude Code, Codex, Cursor, Cline, Continue and any MCP client. Learns which files go together from how you work and serves compact, ranked context: the gold file on 40 of 40 public-benchmark queries to keyword ripgrep's 32 of 40, at 31-67× fewer tokens than ripgrep and 51-242× fewer than pasting every source file (a larger 150-question eval misses 10). Local-first, no telemetry by default.

## Contextual — Persistent Memory for AI Coding Agents AI coding agents forget everything the moment your session ends. **Contextual** fixes that by giving any MCP-capable AI client — for developers who use multiple AI coding tools at once — a real, persistent memory of your codebase: not just what the code says, but what changed and why. It's a **local-first MCP server**: your code, your index, and every query stay on your machine. The only network call is a lightweight, infrequent license check — no code, ever, leaves your device. Under the hood, Contextual builds a real understanding of your repository, not a text-similarity guess: - **Structural search** — tree-sitter AST parsing + call-graph resolution, so agents reason about how your code actually connects. - **Bitemporal history** — tracks not just when your code changed, but when the AI learned about it. Ask "did this bug fix happen before or after the agent's understanding of this function?" and get a real answer. - **Git-aware retrieval** — temporal, history-grounded context instead of a stale snapshot. - **Fast, self-contained** — index a 50k-line repository in about 5 minutes, then query it in ~94ms median, from a single ~0.8GB install. This extension connects Claude Desktop (or any MCP-capable client) to a Contextual installation already set up and activated on your machine — it does not provision Contextual from scratch.

Codebase knowledge graph for your project in DDEV — MCP server for AI coding agents, plus a browsable graph UI.

Local code intelligence for developers and AI agents. Connect code, docs and infrastructure in a persistent graph, with CBM Lens, MCP tools, agent integrations and change-aware test selection.

LeanKG: Stop Burning Tokens. Start Coding Lean.

Persistent decision memory for AI agents: rationale, rejected approaches, and entity history. Connect any compatible agent through local MCP or the CLI.

MCP server: any local git repo becomes codebase memory - commits, files, symbols and a call graph in SQLite, queried over stdio. 100% local, zero-setup via uvx.

Markdown wiki with full revision history, REST API and a built-in MCP server. Long-term codebase memory for coding agents — the companion layer to codebase-memory-mcp: the graph knows where code lives, go-wiki knows what it is for. Single Go binary.

the memory layer of your codebase. Knows the WHY, the WHAT, the WHERE-IT-BREAKS.

Local repository code search and codebase memory for Codex, Claude Code, and MCP coding agents

Persistent, verified memory for AI coding agents. CLI: dim.

AI-native version control in Rust — continuous capture, encrypted knowledge graph, agent governance

Persistent Intelligence Infrastructure for AI Agents

Local-first MCP server for CodeAtlas — AST analysis, codebase intelligence, semantic memory, and A2A orchestration.

Give your AI coding agent persistent memory — stop re-reading files every conversation. MCP server for Claude Code, Claude Desktop, Cursor, and other AI coding tools.

GUPPI (General-purpose Unifying Pluggable Intelligence) — Local agentic sidecar daemon, Lossless Semantic Tree memory engine, and telemetry deck.

Persistent organisational intelligence for AI agents. Turn codebases, documents, policies and decisions into a knowledge graph so Claude Code, Cursor and Copilot reason over architecture, dependencies and prior lessons — with confidence scores and evidence. 13 LLM backends + custom, fully offline capable.

Give AI coding agents a map before they read your code. Statically maps your codebase structure and dependencies, enriches components with LLM summaries, and links Git commits/Jira tickets to nodes. Optimizes agent context windows and token usage with compact, queryable JSON blueprints.

Span is a local MCP server that keeps the semantic understanding your coding agent worked out — the conclusions, the exact evidence behind them, and what needs another look when that evidence changes. Three tools: ask, record, reconsider.

Verified context for AI coding agents. A local context layer that intelligently compresses, filters, indexes, and verifies AI coding-agent tool output. 32 MCP tools across 10 layers: code intelligence (tree-sitter, 30+ languages), context selection, tool

Autonomous Repository Intelligence engine with web UI and MCP server. Unified semantic code understanding, local RAG, and agent working memory.

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Accurate project memory for coding agents, kept in sync with the codebase.

Local-first MCP server that gives AI coding agents repo and org memory from GitHub PR history, code, tests, regressions, architecture, and cross-repo impact.

The memory layer that gets better as you use it. What broke, why, what depended on what, even the "push this to Wednesday" kind of thing that never makes it into any doc. No one has to write it down. Anchor your context to your code. when the code moves, so does the memory. works on claude code, cursor, codex. Completely local.

🧠 MCP server for semantic code search, cross-chat memory & token-slim routing — cut LLM token usage 80-95%. 100% local, no API keys. Works with Claude Code, Cursor, Codex, Hermes.

ContextAtlas — context infrastructure for AI coding agents: hybrid retrieval, project memory and retrieval observability via CLI, MCP server or embeddable library. Tree-sitter indexing, LanceDB vector search, FTS5 and token-aware context packing.

Persistent ground truth for Claude Code. Stop AI agents from drifting on your codebase.

An exoskeleton for your AI coding agent - it remembers everything, sees your whole codebase, can undo any change and can't fake a result. Fewer wasted tokens, fewer broken builds. Claude Code, Cursor, Codex & more.

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns and delivers highly relevant context providing a deeper understanding of your codebase.

MCP server that loads a codebase into memory for faster access by Claude Code — live reload, Go single binary or npm.

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

A persistent, Git-versioned map of your whole codebase and database schema that coding agents read before they touch anything. Local-first MCP server + CLI in Go: a governed repository index of code knowledge that gives Claude Code, Codex, Cursor, and opencode long-term context, memory, and code intelligence.