- Serena provides essential semantic code retrieval, editing, refactoring and debugging tools that are akin to an IDE's capabilities,
operating at the symbol level and exploiting relational structure.
- It integrates with any client/LLM via the model context protocol (MCP).
Serena's agent-first tool design involves robust high-level abstractions, distinguishing it from
approaches that rely on low-level concepts like line numbers or primitive search patterns.
Practically, this means that your agent operates faster, more efficiently and more reliably, especially in larger and
more complex codebases.
[!TIP]
Serena v2 with the new REPL-based agent interface has been released.
Learn more
about the new interface and how to configure it.
Video
What Our "End Users" Say
While it is humans who download and set up Serena, our end users are essentially AI agents.
As the ones actually applying Serena's tools, they are in the best position to evaluate Serena.
We crafted an unbiased evaluation prompt that leads the agent to perform ~20 routine coding tasks,
representative of everyday development work,
in order to estimate the value added by Serena's tools when used alongside its own built-ins.
Here's a one-sentence summary of what the agents had to say:
Opus 4.6 (high) in Claude Code on a large Python codebase:
"Serena's IDE-backed semantic tools are the single most impactful addition to my toolkit – cross-file renames, moves, and reference lookups that
would cost me 8–12 careful, error-prone steps collapse into one atomic call, and I would absolutely ask any developer I work with to set them up."
GPT 5.4 (high) in Codex CLI on a Java codebase:
"As a coding AI agent, I would ask my owner to add Serena because it gives me the missing IDE-level understanding of symbols, references, and
refactorings, turning fragile text surgery into calmer, faster, more confident code changes where semantics matter."
GPT 5.4 (medium) in Copilot CLI on a large, multi-language monorepo:
"As a coding agent, I’d absolutely ask my owner to add Serena because it makes me noticeably sharper and calmer on
real code – especially symbol-aware navigation, cross-file refactors, and monorepo dependency jumps – while I still lean
on built-ins for tiny text edits and non-code work."
Different agents in different settings independently converge on the same verdict.
Give your agent the tools it has been asking for and add Serena MCP to your client!
See our documentation for the full methodology and much more detailed evaluation results, or run your own evaluation on a project of your choice.
How Serena Works
Serena provides the necessary tools for coding workflows,
but an LLM is required to do the actual work, orchestrating tool use.
Serena can extend the functionality of your existing AI client via the model context protocol (MCP).
Most modern AI chat clients directly support MCP, including
- terminal-based clients like Claude Code, Codex, OpenCode, or Gemini-CLI,
- IDEs and IDE assistant plugins for VSCode, Cursor and JetBrains IDEs (Copilot, Junie, JetBrains AI Assistant, etc.),
- desktop and web clients like Claude Desktop, Codex App, or OpenWebUI.
:tv: See also: Introduction to Serena in 5 Minutes (YouTube)
To connect the Serena MCP server to your client, you either
- provide the client with a launch command that allows it to start the MCP server, or
- start the Serena MCP server yourself in HTTP mode and provide the client with the URL.
See the Quick Start section below for information on how to get started.
Programming Language Support & Semantic Analysis Capabilities
Serena provides a set of versatile code querying and editing functionalities
based on symbolic understanding of the code.
Equipped with these capabilities, your agent discovers and edits code just like a seasoned developer
making use of an IDE's capabilities would.
Serena can efficiently find the right context and do the right thing even in very large and
complex projects!
There are two alternative technologies powering these capabilities:
- Language servers implementing the language server protocol (LSP) — the free/open-source alternative
which is used by default.
- The Serena JetBrains Plugin, which leverages the powerful code analysis and editing
capabilities of your JetBrains IDE (paid plugin; free trial available).
You can choose either of these backends depending on your preferences and requirements.
Language Servers
Serena incorporates a powerful abstraction layer for the integration of language servers that implement the language server protocol (LSP).
The underlying language servers are typically open-source projects or at least freely available for use.
When using Serena's language server backend, we provide support for over 40 programming languages, including
Ada / SPARK, AL, Angular, Ansible, Astro, Bash, BSL, C#, C/C++, Clojure, Crystal, CUE, Dart, Deno, Elixir, Elm, Erlang, Fortran, F#, GDScript, Gleam, GLSL, Go, GraphQL, Groovy, Haskell, Haxe, HLSL, HTML, Java, JavaScript, JSON, Julia, Kotlin, LaTeX, Lean 4, Lua, Luau, Markdown, MATLAB, mSL, Nextflow, Nix, OCaml, Pascal, Perl, PHP, PowerShell, Python, QML, R, Rego, Ruby, Rust, Scala, SCSS / Sass / CSS, Solidity, Svelte, Swift, SystemVerilog, Terraform, TOML, TypeScript, VB.NET, Vue, WGSL, Wolfram Language, YAML, and Zig.
The Serena JetBrains Plugin
The paid Serena JetBrains Plugin (free trial available)
leverages the powerful code analysis capabilities of your JetBrains IDE.
The plugin naturally supports all programming languages and frameworks that are supported by JetBrains IDEs,
including IntelliJ IDEA, PyCharm, Android Studio, WebStorm, PhpStorm, RubyMine, GoLand, and potentially others (Rider and CLion are unsupported though).
See our documentation page for further details and instructions on how to apply the plugin.
Features
Serena provides a wide range of tools for efficient code retrieval, editing and refactoring, as well as
a memory system for long-lived agent workflows.
Given its large scope, Serena adapts to your needs by offering a multi-layered configuration system.
Retrieval
Serena's retrieval tools allow agents to explore codebases at the symbol level, understanding structure and relationships
without reading entire files.
| Capability | Language Servers | JetBrains Plugin |
|---|
| find symbol | yes | yes |
| symbol overview (file outline) | yes | yes |
| find referencing symbols | yes | yes |
| search in project dependencies | -- | yes |
| type hierarchy | -- | yes |
| find declaration | yes* | yes |
| find implementations | yes** | yes |
| query external projects | yes | yes |
| diagnostics/inspections | yes | yes |
*: Will generally not work for declarations in external dependencies.
**: Only available for some languages, limited by the language server functionality.
Refactoring
Without precise refactoring tools, agents are forced to resort to unreliable and expensive search and replace operations.
| Capability | Language Servers | JetBrains Plugin |
|---|
| rename | yes (only symbols) | yes (symbols, files, directories) |
| move (symbol, file, directory) | -- | yes |
| inline | -- | yes |
| propagate deletions (remove unused code) | -- | yes |
Symbolic Editing
Serena's symbolic editing tools are less error-prone and much more token-efficient than typical alternatives.
| Capability | Language Servers | JetBrains Plugin |
|---|
| replace symbol body | yes | yes |
| insert after symbol | yes | yes |
| insert before symbol | yes | yes |
| safe delete | yes | yes |
Interactive Debugging
Exclusive to the JetBrains plugin, Serena supports a highly general debugging tool,
which allows an agent to set breakpoints, inspect variables, evaluate expressions and control execution flow
via a persistent REPL-style interface.
Basic Features
Beyond its semantic capabilities, Serena includes a set of basic utilities for completeness.
When Serena is used inside an agentic harness such as Claude Code or Codex, these tools are typically disabled by default,
since the surrounding harness already provides overlapping file, search, and shell capabilities.
search_for_pattern – flexible regex search across the codebasereplace_content – agent-optimised regex-based and literal text replacementlist_dir / find_file – directory listing and file searchread_file – read files or file chunksexecute_shell_command – run shell commands (e.g. builds, tests, linters)
Memory Management
A memory system is elemental to long-lived agent workflows, especially when knowledge is to be shared across
sessions, users and projects.
Despite its simplicity, we received positive feedback from many users who tend to combine Serena's memory management system with their
agent's internal system (e.g., AGENTS.md files).
It can easily be disabled if you prefer to use something else.
Configurability
Active tools, tool descriptions, prompts, language backend details and many other aspects of Serena
can be flexibly configured on a per-case basis by simply adjusting a few lines of YAML.
To achieve this, Serena offers multiple levels of (composable) configuration:
- global configuration
- MCP launch command (CLI) configuration
- per-project configuration (with local overrides)
- execution context-specific configuration (e.g. for particular clients)
- dynamically composable configuration fragments (modes)
User Guide
Please refer to the user guide for detailed instructions on how to use Serena effectively.
Acknowledgements
A significant part of Serena, especially support for various languages, was contributed by the open source community.
We are very grateful for the many contributors who made this possible and who played an important role in making Serena
what it is today.
License
Serena is licensed per component: SolidLSP (src/solidlsp) under the MIT License,
the Serena application (everything else) under GPL-3.0-or-later.
Distributions combining both are as a whole subject to the GPL.
See LICENSE for the authoritative overview and the
license documentation for the background.
Contributions require acceptance of our CLA; see CONTRIBUTING.md.