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serena

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Basic Information

Serena is an open-source toolkit that turns a language model into a coding agent that works directly on a codebase. It provides an MCP server and libraries so clients and IDEs can call symbolic code-retrieval and editing tools rather than scanning raw files. Serena is framework- and model-agnostic and is intended to be integrated with MCP-capable clients such as Claude Code, Claude Desktop, Codex, terminal clients, IDEs and local GUIs. The project uses language servers (LSP) for semantic understanding, supports many languages out of the box (including Python, TypeScript/JavaScript, Go, Rust, Java, C#, PHP and others), and can be run locally via uv/uvx, as a subprocess over stdio, or in SSE/http mode. It is designed for developers and teams who want an IDE-like symbolic interface for LLM-based code analysis and automated edits without requiring paid API access.

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App URL
https://github.com/oraios/serena
Github Repository
https://github.com/oraios/serena/blob/main/README.md

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App Details

Features
Serena exposes a rich set of symbolic tools for coding workflows including find_symbol, find_referencing_symbols, get_symbols_overview, insert_after_symbol, insert_before_symbol, replace_symbol_body and search_for_pattern. It integrates language servers through a Solid-LSP layer for semantic analysis and offers project activation, indexing, onboarding and a project-specific memory store to persist summaries and state. Serena runs as an MCP server with stdio or SSE transport, provides a web dashboard and an optional GUI for logs and shutdown, and supports contexts and modes to tailor system prompts and available tools. The toolset also includes shell execution, file operations, summarization and various optional safety/config toggles, plus extensibility by adding new Tool subclasses or language adapters.
Use Cases
Serena improves the effectiveness and efficiency of coding agents by enabling symbolic, IDE-like lookups and edits that reduce token usage and help models find the precise context to change. It is particularly useful in medium-to-large codebases where reading whole files is inefficient; features such as indexing, onboarding, memories and language-server-powered symbol discovery make navigation and editing more accurate. Integration options let teams use Serena with existing clients or embed it as a library. It supports read-only operation to prevent edits, optional disabling of risky tools, and automation through shell execution and tests so agents can iterate and validate changes. The README notes smaller single-file projects gain less from Serena and some Docker and language-specific caveats exist.

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