Sourcegraph

Agentic Batch Changes is a Sourcegraph product for coordinating code changes across hundreds or thousands of repositories, or across large monorepos. It is built on Sourcegraph's Batch Changes and Deep Search technologies: an engineer describes a change in plain language, the system scopes it across an indexed codebase, builds a plan, tests the change in one repository, and rolls it out while adapting to repository differences. For each part of the plan, it chooses between generating a deterministic script and assigning a coding agent with repository context. It opens pull requests, responds to CI failures, tracks their status through merge, displays generated diffs, and pauses for human decisions when needed. Sourcegraph states that the product is available to Sourcegraph Cloud customers and uses outcome-based pricing based on changesets merged rather than tokens, seats, or attempts.

CodeGraph is a local code-intelligence tool for AI coding agents, distributed as a CLI, MCP server, and embeddable npm library by Colby McHenry. It parses source code into a SQLite knowledge graph of symbols, files, calls, imports, inheritance, dependencies, framework routes, and cross-language bridges, then resolves references and provides full-text search, source context, call paths, callers, callees, impact analysis, and affected-test discovery. Its primary MCP operation, codegraph_explore, returns relevant verbatim source together with relationships and blast-radius information so agents can query structure without file-by-file exploration. A native Rust parsing kernel supports more than 20 languages, with a portable fallback for unsupported platforms or files that fail native parsing. A native file watcher incrementally updates the local graph after changes, while connect-time reconciliation catches edits made between agent sessions. The tool installs and configures MCP integrations for agents including Claude Code, Cursor, Codex CLI, OpenCode, Gemini CLI, and GitHub Copilot; projects are initialized with codegraph init and stored locally under .codegraph. The repository states that the software is MIT-licensed and that telemetry can be disabled.

Code-Graph-RAG is an open-source retrieval-augmented generation system for querying, understanding, and editing multilingual codebases. Its Tree-sitter-based parser extracts functions, classes, methods, modules, and relationships, then stores them in Memgraph under a unified graph schema. An interactive CLI converts natural-language questions into Cypher queries, retrieves source code by name or intent, and supports AI-assisted editing and optimization. It provides AST-based surgical patches with diff previews, structural search and rewriting through ast-grep, dead-code discovery by traversing call and reference edges, and runtime-call overlays from test traces or production eBPF profiles. The project also supports MCP integration and publishes the `cgr` command-line tool to PyPI.

GitNexus, developed by Akon Labs, is a code-intelligence engine that indexes repositories into a knowledge graph for code exploration and AI-agent context. Its indexing pipeline walks the file tree, parses source with Tree-sitter, resolves imports, calls, inheritance, constructor-inferred receiver types and other relationships, groups symbols into functional communities, traces execution processes, and builds BM25-plus-semantic hybrid search indexes backed by LadybugDB. The resulting graph supports MCP tools and CLI commands for process-grouped search, symbol context, call-path tracing, blast-radius and Git-diff impact analysis, structural checks, coordinated renaming, API and route mapping, taint and dependence queries, and Cypher access; repository groups can link contracts and impacts across multiple repositories. The CLI runs locally and can connect editors such as Claude Code, Cursor, Codex and others through MCP, skills and selected hooks. GitNexus also provides a browser-based WebAssembly UI with an interactive graph explorer and AI chat, plus a local HTTP server and Docker deployment mode for accessing indexed repositories through a backend. The web-only mode keeps repository processing in the browser and is constrained by browser memory, while the native CLI stores indexes locally in each repository's .gitnexus directory and uses a global registry for multi-repository access.

Repowise is an open-source, self-hosted codebase-intelligence tool for developers and coding agents. It builds a local index of source code, dependency and symbol relationships, Git history, tests, documentation, architectural decisions, dead code, and code-health signals, then exposes the indexed evidence through an MCP server, local dashboard, CLI, editor integrations, and pull-request analysis. Its graph is built from AST-parsed code and Git data, with confidence-stamped call resolution, change-impact and co-change analysis, hotspot and ownership history, documentation-drift checks, decision evidence, and deterministic code-health detectors. The MCP interface provides task-oriented tools for repository overviews, cited answers, file and symbol context, code search, risk analysis, change risk, architectural rationale, dead-code detection, and health and refactoring plans. Optional model-written documentation and prose can be generated through a configured provider, while the core graph, risk, health, test, and dead-code analysis does not require LLM calls or an API key. Repowise also includes reversible command-output distillation for agents, proactive context hooks, generated agent instruction files, a GitHub PR bot, multi-repository workspace analysis, contract and cross-repository blast-radius mapping, and a VS Code extension. It can be installed with pip and run locally; the repository states that raw source is processed transiently and not persisted, while indexed graph data, embeddings, wiki pages, and Git metadata are stored. The core project is licensed under AGPL-3.0, with commercial licensing available for specified enterprise capabilities and embedding scenarios.