Amazon Q Developer
Generative AI coding assistant for building, operating, and transforming software
Serena
Turn any LLM into a code‑aware development agent
Serena equips LLMs with IDE‑like semantic code retrieval and editing, supporting dozens of languages and integrating via MCP with many clients, all free and open‑source.
Overview
Who Benefits
Serena is designed for developers and teams that rely on LLM‑powered coding assistants to work on medium‑to‑large codebases. It addresses the inefficiency of having agents read whole files or perform brittle grep‑style searches, delivering precise, symbol‑level operations.
What It Does
By leveraging language‑server‑protocol (LSP) back‑ends, Serena provides tools such as find_symbol, find_referencing_symbols, and insert_after_symbol. These enable IDE‑like navigation, retrieval, and editing across more than twenty programming languages. The toolkit is LLM‑agnostic and integrates through a Model Context Protocol (MCP) server, making it compatible with Claude Code, Claude Desktop, VSCode, Cursor, terminal‑based clients, and custom agent frameworks.
How to Deploy
Run the MCP server locally, via Docker, Nix, or as a command‑line utility, then connect your preferred LLM client. Because the toolset is decoupled from any specific framework, you can embed it in existing pipelines or extend it with new language adapters without altering core logic.
Highlights
Symbol‑level code search and edit (e.g., find_symbol, insert_after_symbol)
Model Context Protocol server for seamless LLM integration
LSP‑based support for 20+ programming languages
Framework‑agnostic design usable from CLI, IDEs, or custom agents
Pros
- Reduces token usage dramatically for large repositories
- Works with any LLM or agent framework
- Free and open‑source
- Easily extensible to new languages via LSP adapters
Considerations
- Limited benefit for very small projects or initial code creation
- Java language server startup can be slow
- Some language servers have platform‑specific quirks (e.g., C/C++)
- Requires installation of external language servers for certain languages
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Fit guide
Great for
- Teams using LLM‑powered coding assistants on medium‑to‑large codebases
- Developers needing precise, symbol‑aware code modifications
- Projects spanning multiple programming languages
- Organizations seeking a cost‑effective way to boost LLM efficiency
Not ideal when
- One‑off scripts or very small codebases
- Purely new‑from‑scratch code generation without existing symbols
- Environments where installing language servers is prohibited
- Users requiring an out‑of‑the‑box GUI IDE without additional setup
How teams use it
Token‑efficient refactoring in Claude Code
Agent locates and updates target symbols across a 200k‑line repo, cutting token consumption by ~40%.
Cross‑language bug fix via VSCode extension
Developer invokes Serena tools to find referencing symbols in Python and TypeScript, applying fix in seconds.
Automated feature addition in CI pipeline
CI job runs Serena MCP server to insert new logging after a specific function, ensuring consistent code changes without manual edits.
Custom agent integration for internal tooling
Team embeds Serena's API into their proprietary agent framework, gaining IDE‑like capabilities without rewriting existing logic.
Tech snapshot
Python87%
JavaScript4%
AL1%
Elixir1%
CSS1%
Erlang1%
Frequently asked questions
Do I need a specific LLM to use Serena?
No, Serena is LLM‑agnostic; it provides tools that any model can call via MCP or OpenAPI.
Which programming languages are supported out of the box?
Serena leverages LSP and includes ready support for Python, TypeScript/JavaScript, PHP, Go, R, Rust, C/C++, Zig, C#, Ruby, Swift, Kotlin, Java, Clojure, Dart, Bash, Lua, Nix, Elixir, Erlang, and more.
How is Serena deployed?
Run the provided MCP server locally, via Docker, Nix, or as a command‑line tool; then connect your LLM client.
Is Serena free for commercial use?
Yes, it is released under an open‑source license and can be used commercially at no cost.
What are the limitations with large Java projects?
Java language server startup is slower and may have platform‑specific issues, especially on macOS and Linux, which are being addressed.
Project at a glance
Active- Stars
- 18,940
- Watchers
- 18,940
- Forks
- 1,294
LicenseMIT
Repo age10 months old
Last commit13 hours ago
Primary languagePython
Last synced 12 hours ago