{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-oraios--serena","slug":"oraios--serena","name":"serena","type":"mcp","url":"https://oraios.github.io/serena","page_url":"https://unfragile.ai/oraios--serena","categories":["mcp-servers","rag-knowledge","code-editors"],"tags":["agent","ai","ai-coding","claude","claude-code","codex","ide","jetbrains","language-server","mcp-server","programming","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-oraios--serena__cap_0","uri":"capability://search.retrieval.symbol.level.code.navigation.and.retrieval.via.language.server.abstraction","name":"symbol-level code navigation and retrieval via language server abstraction","description":"Serena abstracts 40+ language servers through a unified SolidLSP framework, enabling semantic symbol discovery (classes, functions, methods, variables) across codebases without regex or text-based matching. The system maintains a file buffer and symbol cache, translating LSP protocol responses into a unified symbol abstraction layer that agents can query for precise code locations, signatures, and relationships. This enables agents to navigate code at the semantic level rather than line-based text search.","intents":["Find all definitions of a specific function or class across the codebase","Locate all references to a symbol to understand its usage patterns","Retrieve the full signature and documentation of a method before modifying it","Navigate between related symbols (e.g., interface implementations, parent classes)"],"best_for":["AI agents performing multi-file code refactoring","LLM-based code analysis tools requiring semantic understanding","Teams building language-agnostic development agents"],"limitations":["Language server startup latency (typically 1-5 seconds per language) on first use","Some language servers (Go, Rust, C++) require manual PATH configuration; not auto-downloaded","Symbol resolution depends on language server quality — some languages have incomplete implementations","Caching system requires file system monitoring; may miss external file changes in real-time"],"requires":["Python 3.9+","uv package manager (astral.sh/uv)","Language-specific servers in PATH (gopls for Go, rust-analyzer for Rust, etc.) or auto-downloaded alternatives","Project must be initialized with 'serena init' or 'serena init -b JetBrains'"],"input_types":["symbol name (string)","file path (string)","line and column numbers (integers)","language identifier (string)"],"output_types":["symbol metadata (name, kind, location, signature)","reference locations (file, line, column)","symbol relationships (parent, children, implementations)"],"categories":["search-retrieval","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_1","uri":"capability://code.generation.editing.semantic.code.replacement.and.refactoring.via.symbol.aware.editing","name":"semantic code replacement and refactoring via symbol-aware editing","description":"Serena exposes ReplaceSymbolBodyTool and RenameSymbolTool that operate on the symbol abstraction layer rather than raw text. When an agent requests a symbol replacement, Serena uses the language server to locate the exact symbol boundaries, validate the replacement is syntactically sound, and apply the edit while preserving surrounding code structure. The system maintains a file buffer that tracks pending edits and can compose multiple symbol-level operations into a coherent transaction.","intents":["Replace the implementation of a function while keeping its signature intact","Rename a symbol (function, class, variable) and automatically update all references","Refactor method bodies without accidentally modifying similarly-named code elsewhere","Apply multi-file edits that preserve code structure and dependencies"],"best_for":["Agents performing automated code refactoring across large codebases","LLM-driven code generation tools that need to modify existing functions","Teams using AI for large-scale codebase migrations or pattern replacements"],"limitations":["Rename operations require re-indexing affected files; can add 100-500ms per rename in large codebases","Complex refactorings involving type changes may require manual validation — tool does not perform type checking","File buffer is in-memory only; requires explicit save operations to persist changes to disk","Some language servers have incomplete or buggy rename implementations (e.g., dynamic languages like Python)"],"requires":["Python 3.9+","Active language server for the target language","File must be open in the Serena file buffer (loaded via file operations)","Symbol must be resolvable by the language server (not in comments or strings)"],"input_types":["symbol name (string)","file path (string)","new symbol name or replacement body (string)","line and column range (integers)"],"output_types":["edit confirmation (boolean)","updated file content (string)","list of affected files (array of strings)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_10","uri":"capability://automation.workflow.cli.interface.for.project.initialization.configuration.and.server.management","name":"cli interface for project initialization, configuration, and server management","description":"Serena exposes a command-line interface (serena CLI) for project initialization, configuration management, and server lifecycle control. Key commands include 'serena init' (initialize project with language servers or JetBrains backend), 'serena-mcp-server' (start MCP server with optional transport mode and context), and configuration commands for managing project and global settings. The CLI supports flags for context selection (--context), transport mode (--transport), port (--port), and other options. The architecture uses a hierarchical command structure with subcommands for different operations.","intents":["Initialize a new Serena project with language servers or JetBrains backend","Start the MCP server with specific transport mode and context","Manage project and global configuration","Query project status and available tools"],"best_for":["Developers setting up Serena for the first time","Teams deploying Serena across multiple projects","DevOps engineers automating Serena deployment"],"limitations":["CLI is synchronous; long-running operations (server startup) block the terminal","No interactive mode for configuration; all settings must be provided via flags or config files","Error messages may be terse; requires documentation for troubleshooting","No built-in progress reporting for long operations"],"requires":["Python 3.9+","uv package manager installed","Serena installed globally via 'uv pip install serena' or similar"],"input_types":["command name (string: 'init', 'start-mcp-server', etc.)","flags (strings: --context, --transport, --port, etc.)","project root directory (string, optional)"],"output_types":["status messages (string)","configuration output (YAML or JSON)","server startup confirmation (string)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_11","uri":"capability://planning.reasoning.agent.oriented.task.execution.system.with.memory.and.state.management","name":"agent-oriented task execution system with memory and state management","description":"Serena includes a SerenaAgent core that manages task execution, memory, and state for LLM agents. The system maintains conversation history, tool call history, and project state across multiple interactions. The agent can decompose complex tasks into subtasks, track progress, and maintain context across tool invocations. The architecture supports different execution modes (synchronous, asynchronous) and integrates with the tool registry for seamless tool invocation. The system also provides hooks for custom logic (e.g., pre/post-tool execution).","intents":["Execute complex multi-step coding tasks with state management","Maintain conversation history and tool call history across interactions","Decompose tasks into subtasks and track progress","Integrate custom logic via hooks (pre/post-tool execution)"],"best_for":["Teams building LLM agents that perform multi-step coding tasks","Systems requiring persistent state and memory across interactions","Agents needing task decomposition and progress tracking"],"limitations":["Memory is in-process only; no persistence across server restarts","State management is single-agent; no multi-agent coordination","Task decomposition is manual; no automatic task planning","Hooks are synchronous; asynchronous hooks not supported"],"requires":["Python 3.9+","SerenaAgent instantiated with project context","Tool registry populated with available tools"],"input_types":["task description (string)","project context (object)","tool registry (object)"],"output_types":["task result (object)","execution history (array of tool calls)","final state (object)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_12","uri":"capability://tool.use.integration.language.specific.server.implementations.with.fallback.and.auto.download.strategies","name":"language-specific server implementations with fallback and auto-download strategies","description":"Serena maintains language-specific server implementations for 40+ languages, with intelligent fallback and auto-download strategies. For each language, the system defines a preferred server (e.g., rust-analyzer for Rust, gopls for Go) and fallback options. Servers that can be auto-downloaded (e.g., via npm, pip, or direct download) are handled automatically; others require manual PATH configuration. The LanguageServerManager handles server lifecycle, including startup, shutdown, and restart. The system also provides configuration for server-specific options (e.g., LSP initialization parameters).","intents":["Automatically initialize language servers for detected languages","Fall back to alternative servers if the preferred server is unavailable","Auto-download servers where possible; require manual configuration only when necessary","Configure server-specific options (LSP parameters, environment variables)"],"best_for":["Teams working with diverse language stacks","Organizations wanting minimal setup overhead for language servers","Developers building language-agnostic tools"],"limitations":["Auto-download only works for servers with public download URLs; some servers require manual installation","Fallback servers may have different capabilities; agents may need to handle capability differences","Server configuration is static at initialization; runtime changes require restart","Some languages have no fallback server; project initialization fails if preferred server is unavailable"],"requires":["Python 3.9+","For auto-downloaded servers: internet connectivity","For manual servers: language-specific tools in PATH (e.g., gopls for Go, rust-analyzer for Rust)"],"input_types":["language identifier (string: 'python', 'javascript', 'rust', etc.)","server configuration (object, optional)"],"output_types":["server status (string: 'running', 'failed', 'not-found')","server version (string)","server capabilities (array of strings)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_13","uri":"capability://tool.use.integration.lsp.protocol.handler.and.normalization.for.cross.server.compatibility","name":"lsp protocol handler and normalization for cross-server compatibility","description":"Serena implements an LSP protocol handler that normalizes responses from different language servers into a unified format. Language servers vary in their LSP implementation (some are strict, others have extensions or quirks), and the handler abstracts these differences. The system translates LSP protocol messages (textDocument/definition, textDocument/references, etc.) into Serena's internal symbol model, handling edge cases and server-specific behaviors. This enables agents to work with any LSP-compliant server without knowledge of server-specific quirks.","intents":["Work with multiple language servers without handling server-specific LSP quirks","Normalize LSP responses into a consistent format","Handle server-specific extensions and non-standard behaviors","Provide a unified interface to LSP functionality across servers"],"best_for":["Teams using multiple language servers with different LSP implementations","Systems requiring consistent behavior across language servers","Developers building language-agnostic tools on top of LSP"],"limitations":["Normalization adds ~10-20ms overhead per LSP call","Some server-specific extensions are lost in normalization","Handling edge cases requires knowledge of server-specific behaviors; not all quirks are handled","Protocol handler must be updated when new LSP features are added"],"requires":["Python 3.9+","LSP-compliant language server","LSP protocol implementation (typically handled by language server library)"],"input_types":["LSP protocol message (JSON-RPC 2.0 format)","language server type (string, for server-specific handling)"],"output_types":["normalized response (object conforming to Serena's symbol model)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_2","uri":"capability://tool.use.integration.mcp.server.exposure.of.ide.like.tools.with.context.aware.configuration","name":"mcp server exposure of ide-like tools with context-aware configuration","description":"Serena implements a native Model Context Protocol (MCP) server that exposes all semantic code tools (FindSymbolTool, FindReferencingSymbolsTool, ReplaceSymbolBodyTool, RenameSymbolTool) as MCP resources and tools. The server supports multiple transport modes (stdio for Claude Desktop, streamable-http for shared access) and context-aware configuration via the --context flag, which selects predefined tool sets and system prompts optimized for different client types (claude-code, ide, codex, agent, etc.). This allows the same Serena backend to adapt its interface to different LLM clients.","intents":["Integrate Serena tools into Claude Desktop, Cursor, VSCode, or terminal-based LLM clients","Expose different tool subsets depending on the client type (e.g., fewer tools for constrained environments)","Run Serena as a shared HTTP service accessible to multiple concurrent LLM clients","Customize system prompts and tool availability per client context"],"best_for":["Teams integrating Serena with Claude Code, Cursor, or VSCode","Organizations deploying Serena as a shared service for multiple AI agents","Developers building custom LLM clients that need IDE-like code tools"],"limitations":["Stdio transport mode requires the client to manage server lifecycle; no built-in server persistence","Streamable HTTP mode adds network latency (~50-200ms per tool call) compared to local stdio","Context selection is static at server startup; cannot switch contexts mid-session without restarting","MCP protocol overhead adds ~10-20ms per tool invocation compared to direct function calls"],"requires":["Python 3.9+","MCP client that supports the Model Context Protocol (Claude Desktop, Cursor, VSCode with MCP extension, or custom client)","Serena initialized with 'serena init' or 'serena init -b JetBrains'","For streamable-http: accessible port and network connectivity between client and server"],"input_types":["MCP tool requests (JSON-RPC 2.0 format)","context identifier (string: 'claude-code', 'ide', 'codex', 'agent', etc.)","transport mode flag (string: 'stdio' or 'streamable-http')"],"output_types":["MCP tool responses (JSON-RPC 2.0 format)","tool metadata and schemas (JSON)","system prompt (string)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_3","uri":"capability://code.generation.editing.jetbrains.ide.backend.integration.for.semantic.code.analysis","name":"jetbrains ide backend integration for semantic code analysis","description":"Serena can use a JetBrains IDE (IntelliJ, PyCharm, etc.) as its semantic analysis backend instead of language servers. The system communicates with the IDE via LSP protocol handler and JetBrains plugin, leveraging the IDE's built-in symbol resolution, type inference, and refactoring capabilities. This approach provides superior semantic understanding for JVM languages (Java, Kotlin, Scala) and Python, at the cost of requiring a running IDE instance. The architecture abstracts this backend choice behind the same symbol and tool interfaces, allowing agents to work with either LSP or JetBrains transparently.","intents":["Use JetBrains IDE's superior type inference and symbol resolution for Java, Kotlin, or Python projects","Leverage IDE refactoring capabilities (safe rename, extract method, etc.) for complex transformations","Integrate with existing JetBrains workflows and plugins","Perform semantic analysis on projects with complex type systems or dynamic features"],"best_for":["Teams using JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.) for Java, Kotlin, or Python development","Projects with complex type systems or advanced language features requiring IDE-level understanding","Organizations that want to leverage existing IDE infrastructure and plugins"],"limitations":["Requires a running JetBrains IDE instance; adds overhead and memory usage compared to language servers","JetBrains plugin must be installed and configured; not all JetBrains products are supported equally","IDE communication latency can be 200-500ms per operation compared to local language servers","Limited to languages supported by the specific JetBrains product (e.g., PyCharm for Python only)"],"requires":["Python 3.9+","JetBrains IDE (IntelliJ IDEA, PyCharm, WebStorm, etc.) version 2023.1 or later","Serena JetBrains plugin installed in the IDE","IDE must be running and the project must be open","Serena initialized with 'serena init -b JetBrains'"],"input_types":["symbol name (string)","file path (string)","line and column numbers (integers)"],"output_types":["symbol metadata from IDE (name, kind, location, type information)","reference locations (file, line, column)","refactoring suggestions (array of objects)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_4","uri":"capability://search.retrieval.codebase.aware.semantic.search.and.reference.finding","name":"codebase-aware semantic search and reference finding","description":"Serena implements FindSymbolTool and FindReferencingSymbolsTool that use language server symbol tables to perform semantic search across the entire codebase. Unlike text-based grep, these tools understand code structure and return only true semantic references (excluding comments, strings, and unrelated identifiers with the same name). The system maintains a symbol cache and file buffer to optimize repeated queries and provide fast turnaround for large codebases. Results include precise location information (file, line, column) and symbol metadata (kind, signature).","intents":["Find all usages of a function or class across the codebase to understand impact of changes","Locate all implementations of an interface or abstract class","Discover dead code by finding symbols with no references","Understand dependency chains by tracing symbol references across files"],"best_for":["Agents performing impact analysis before code changes","Teams conducting codebase audits or refactoring campaigns","Developers building code understanding tools or documentation generators"],"limitations":["Initial indexing can take 5-30 seconds for large codebases (100k+ lines)","Symbol cache must be refreshed when files change externally; no real-time file watching in all modes","Some language servers have incomplete reference finding (e.g., dynamic languages, template code)","Results may include false positives in codebases with complex macros or code generation"],"requires":["Python 3.9+","Active language server for the target language","Project initialized with 'serena init'","Symbol must be resolvable by the language server"],"input_types":["symbol name (string)","file path (string, optional for scoped search)","symbol kind filter (string, optional: 'function', 'class', 'variable', etc.)"],"output_types":["array of reference locations (file, line, column, context snippet)","symbol metadata (name, kind, signature)","reference count (integer)"],"categories":["search-retrieval","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_5","uri":"capability://code.generation.editing.multi.language.project.support.with.automatic.language.server.selection","name":"multi-language project support with automatic language server selection","description":"Serena automatically detects project languages based on file extensions and project configuration, then selects and initializes the appropriate language servers (or JetBrains backend). The system maintains a LanguageServerManager that handles server lifecycle (startup, shutdown, restart), caching, and fallback strategies. Agents can work with polyglot projects (e.g., Python + JavaScript + SQL) without explicit language configuration; Serena routes each file to the correct language server transparently. The architecture supports 40+ languages including Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, Ruby, PHP, and more.","intents":["Work with polyglot projects containing multiple programming languages","Automatically initialize language servers for detected languages","Switch between language-specific tools as the agent navigates different files","Support projects with mixed language stacks (e.g., backend Python + frontend JavaScript)"],"best_for":["Agents working on polyglot or microservice projects","Teams with diverse tech stacks wanting unified code tooling","Organizations building language-agnostic development agents"],"limitations":["Language detection relies on file extensions; ambiguous cases (e.g., .js could be Node.js or browser) may require manual configuration","Each language server consumes memory; projects with 10+ languages may exceed resource limits","Some language servers have incomplete implementations or missing features compared to others","Server startup time multiplies with language count; initial project load can be slow for polyglot projects"],"requires":["Python 3.9+","Language-specific servers in PATH or auto-downloaded (varies by language)","Project structure that allows language detection (standard file extensions)","Serena initialized with 'serena init'"],"input_types":["project root directory (string)","file path (string, for language detection)"],"output_types":["list of detected languages (array of strings)","language server status per language (object with language -> status mapping)","language-specific tool availability (object)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_6","uri":"capability://automation.workflow.project.aware.context.management.with.ignore.patterns.and.file.filtering","name":"project-aware context management with ignore patterns and file filtering","description":"Serena implements a project system that maintains awareness of project structure, file organization, and ignore patterns (similar to .gitignore). The system uses a configuration hierarchy (global config, project config, context-specific config) to determine which files are indexed, which tools are available, and how the system prompt is composed. File operations respect ignore patterns, preventing agents from accidentally modifying build artifacts, dependencies, or other non-source files. The architecture supports multiple project contexts (desktop-app, claude-code, ide, codex, agent) that customize tool availability and prompts for different use cases.","intents":["Prevent agents from modifying non-source files (node_modules, .git, build artifacts, etc.)","Customize tool availability and system prompts for different project contexts","Manage configuration across global, project, and context-specific levels","Optimize performance by excluding large directories from indexing"],"best_for":["Teams deploying Serena across diverse project types and contexts","Organizations needing fine-grained control over agent behavior per project","Developers building multi-context LLM agents"],"limitations":["Configuration hierarchy can be confusing; precedence rules (global < project < context) require documentation","Ignore patterns are static at project initialization; runtime changes require restart","No built-in file watching; external file changes may not be reflected until buffer refresh","Context selection is fixed at server startup; cannot switch contexts mid-session"],"requires":["Python 3.9+","Project initialized with 'serena init'","serena.yaml or .serena/config.yaml in project root (optional; defaults are used if absent)"],"input_types":["context identifier (string: 'desktop-app', 'claude-code', 'ide', 'codex', 'agent')","ignore patterns (array of strings, glob format)","configuration YAML (string)"],"output_types":["effective configuration (object)","list of indexed files (array of strings)","system prompt (string)","available tools (array of tool names)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_7","uri":"capability://code.generation.editing.file.buffer.management.with.in.memory.editing.and.transaction.support","name":"file buffer management with in-memory editing and transaction support","description":"Serena maintains an in-memory file buffer that tracks open files, pending edits, and file state. The buffer supports atomic transactions (multiple edits composed into a single operation) and provides methods to read, write, and query file content without hitting disk on every operation. The system integrates with language servers to keep symbol information synchronized with buffer state, enabling agents to make multiple edits and see updated symbol information without explicit file saves. The buffer also tracks file metadata (modification time, encoding) and supports rollback of uncommitted changes.","intents":["Make multiple edits to a file and see updated symbol information without saving to disk","Compose multiple file edits into atomic transactions","Rollback uncommitted changes if an operation fails","Query file content and metadata without disk I/O"],"best_for":["Agents performing multi-step code transformations","Tools requiring transactional semantics for code edits","Systems needing to preview changes before committing to disk"],"limitations":["Buffer is in-memory only; requires explicit save operations to persist to disk","Large files (>10MB) may cause memory pressure; no streaming or chunked reading","Rollback only works for uncommitted changes; no persistent undo history","Concurrent edits from multiple agents are not supported; single-writer model only"],"requires":["Python 3.9+","File must be loaded into buffer via file operations before editing","Sufficient memory for all open files (typically <1MB per file)"],"input_types":["file path (string)","file content (string)","edit range (line, column, length)","replacement text (string)"],"output_types":["updated file content (string)","buffer state (object with file metadata)","transaction status (success/failure)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_8","uri":"capability://tool.use.integration.tool.registry.and.execution.pipeline.with.schema.based.validation","name":"tool registry and execution pipeline with schema-based validation","description":"Serena implements a tool architecture with a central tool registry that defines all available tools (FindSymbolTool, FindReferencingSymbolsTool, ReplaceSymbolBodyTool, RenameSymbolTool, file operations, etc.) with JSON schemas for input validation and output typing. The execution pipeline validates tool inputs against schemas, executes the tool, and returns typed outputs. The system supports tool composition (chaining tools together) and error handling with detailed error messages. Tools are context-aware; the active context determines which tools are exposed to the agent.","intents":["Define and validate tool inputs before execution","Chain multiple tools together for complex operations","Provide type-safe tool interfaces to LLM clients","Handle tool errors gracefully with detailed error messages"],"best_for":["Teams building custom tools on top of Serena","LLM clients requiring schema-based tool validation","Systems needing tool composition and error handling"],"limitations":["Schema validation adds ~5-10ms overhead per tool call","Tool composition requires explicit chaining; no automatic dependency resolution","Error messages depend on tool implementation quality; some tools may provide vague errors","Tool registry is static at server startup; cannot add tools dynamically"],"requires":["Python 3.9+","Tool must be registered in the tool registry","Input must conform to tool schema (JSON)"],"input_types":["tool name (string)","tool input (JSON conforming to schema)"],"output_types":["tool output (JSON conforming to schema)","error message (string, if tool fails)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-oraios--serena__cap_9","uri":"capability://data.processing.analysis.caching.system.for.language.server.responses.and.symbol.metadata","name":"caching system for language server responses and symbol metadata","description":"Serena implements a caching layer that stores language server responses (symbol definitions, references, type information) and symbol metadata to avoid redundant queries. The cache is keyed by file path, symbol name, and query type, and is invalidated when files change. The system uses file modification time tracking to detect external changes and refresh cache entries as needed. For large codebases, the cache can reduce symbol lookup latency from 500ms to <50ms on cache hits.","intents":["Reduce latency for repeated symbol queries in the same session","Avoid redundant language server calls for the same symbol","Optimize performance for large codebases with frequent symbol lookups","Maintain cache consistency when files change"],"best_for":["Agents performing repeated operations on the same symbols","Large codebases where symbol lookup latency is a bottleneck","Systems requiring fast turnaround for interactive use"],"limitations":["Cache invalidation relies on file modification time; may miss changes in some scenarios","Cache is in-memory only; not persisted across sessions","Large codebases may exhaust memory with full symbol cache","Cache coherency issues if files are modified externally without notifying Serena"],"requires":["Python 3.9+","Language server must be active","File system must support modification time tracking"],"input_types":["file path (string)","symbol name (string)","query type (string: 'definition', 'references', 'type', etc.)"],"output_types":["cached result (object, if cache hit)","cache hit/miss status (boolean)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","uv package manager (astral.sh/uv)","Language-specific servers in PATH (gopls for Go, rust-analyzer for Rust, etc.) or auto-downloaded alternatives","Project must be initialized with 'serena init' or 'serena init -b JetBrains'","Active language server for the target language","File must be open in the Serena file buffer (loaded via file operations)","Symbol must be resolvable by the language server (not in comments or strings)","uv package manager installed","Serena installed globally via 'uv pip install serena' or similar","SerenaAgent instantiated with project context"],"failure_modes":["Language server startup latency (typically 1-5 seconds per language) on first use","Some language servers (Go, Rust, C++) require manual PATH configuration; not auto-downloaded","Symbol resolution depends on language server quality — some languages have incomplete implementations","Caching system requires file system monitoring; may miss external file changes in real-time","Rename operations require re-indexing affected files; can add 100-500ms per rename in large codebases","Complex refactorings involving type changes may require manual validation — tool does not perform type checking","File buffer is in-memory only; requires explicit save operations to persist changes to disk","Some language servers have incomplete or buggy rename implementations (e.g., dynamic languages like Python)","CLI is synchronous; long-running operations (server startup) block the terminal","No interactive mode for configuration; all settings must be provided via flags or config files","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.737241405313887,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-02T18:56:45Z"},"community":{"stars":23764,"forks":1596,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=oraios--serena","compare_url":"https://unfragile.ai/compare?artifact=oraios--serena"}},"signature":"kzx6LbrWK51tG+XJ6Yp46OrR7VFTeS3RzIfCoCOkkVIfc1K0ocBT0zDPGv9EEFjB+pIa93GiVl4MoYB2U8OtDg==","signedAt":"2026-06-20T09:28:41.394Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/oraios--serena","artifact":"https://unfragile.ai/oraios--serena","verify":"https://unfragile.ai/api/v1/verify?slug=oraios--serena","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}