act101

act101 is an AI-powered developer tool built for semantic code refactoring, structural analysis, and cross-language migrations. Instead of relying on fragile whole-file rewrites, it gives AI coding agents AST-aware operations that understand programming languages at a structural level. The platform supports 163 programming grammars and provides more than 180 refactoring operations, including extracting functions, renaming symbols, moving modules, generating code, and reorganizing imports across entire repositories with consistency and undo support.

The platform works as an MCP-native server designed for AI coding environments like Claude Code, Cursor, Codex, and OpenCode. Developers can connect act101 directly to their AI agents so those agents can perform real semantic code transformations instead of generating approximate edits from raw text.

One of act101’s biggest advantages is language-aware refactoring. The tool can help AI agents refactor projects written in Python, Rust, TypeScript, Go, Ruby, Java, C, COBOL, Elixir, and many other languages. Because operations are AST-aware, formatting, comments, imports, and project structure stay intact during changes. Every operation also includes automatic checkpointing and instant undo functionality.

The platform also specializes in large-scale language migration workflows. Developers can use act101 to port projects such as C to Rust, Ruby to Elixir, COBOL to Java, PHP to TypeScript, or Python 2 to Python 3. Instead of treating migration as a simple conversion task, act101 introduces structured migration workflows using contracts, inventories, dependency ordering, manifests, and migration state tracking. This approach helps teams reduce behavioral drift, dependency issues, and migration confusion in large enterprise codebases.

act101 includes deep repository analysis capabilities as well. The platform provides over 30 analysis tools that inspect coupling, dead code, hotspots, architectural boundaries, dependency cycles, migration readiness, and type completeness. AI agents can use these analyses to understand the structure of a codebase before making modifications. This helps reduce risky edits and improves automated engineering workflows.

The platform is highly optimized for token efficiency when used with AI coding systems. Instead of loading entire files into an AI context window, act101 returns only the relevant AST-derived slices of code. According to its benchmarks, some operations reduce token usage by more than 95%, helping AI agents stay within context limits while working on large repositories.

Another major focus is security and local execution. act101 runs as a single native Rust binary with no plugin runtime, no dependency graph, and no cloud-based code uploads. Code stays on the developer’s machine, minimizing supply-chain risks and protecting private repositories. The tool parses projects on demand without indexing or caching, ensuring results stay fresh and synchronized with the codebase.

The platform offers multiple pricing tiers. A free plan supports personal and open-source use with query tools and basic refactoring operations. Paid plans unlock commercial licenses, advanced analysis features, structural operations, and premium language support. Enterprise offerings focus on large-scale migration projects and autonomous AI agent fleets operating across production systems.

Overall, act101 positions itself as infrastructure for AI-native software engineering. Instead of AI merely suggesting code changes, the platform enables agents to perform reliable, semantic, and reversible engineering operations directly on real-world codebases.