by oraios
A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent
# Add to your Claude Code skills
git clone https://github.com/oraios/serenaLast scanned: 4/16/2026
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serena is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by oraios. A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent. It has 30,120 GitHub stars.
Yes. serena passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/oraios/serena" and add it to your Claude Code skills directory (see the Installation section above).
serena is primarily written in Python. It is open-source under oraios on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh serena against similar tools.
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This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
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.
[!IMPORTANT] Do not install Serena via an MCP or plugin marketplace! They contain outdated and suboptimal installation commands. Instead, follow our Quick Start instructions.
https://github.com/user-attachments/assets/8d11646e-b80e-4723-b9d7-32d6101b5f58
:tv: Longer video: Introduction to Serena in 5 Minutes (YouTube)
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.
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
:tv: See also: Introduction to Serena in 5 Minutes (YouTube)
To connect the Serena MCP server to your client, you either
See the Quick Start section below for information on how to get started.
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:
You can choose either of these backends depending on your preferences and requirements.
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, 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, Vue, WGSL, Wolfram Language, YAML, and Zig.
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.
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.
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.
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 |
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 |