{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-plandex-ai--plandex","slug":"plandex-ai--plandex","name":"plandex","type":"agent","url":"https://plandex.ai","page_url":"https://unfragile.ai/plandex-ai--plandex","categories":["ai-agents"],"tags":["ai","ai-agents","ai-developer-tools","ai-tools","cli","command-line","developer-tools","git","golang","gpt-4","llm","openai","polyglot-programming","terminal","terminal-based","terminal-ui"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-plandex-ai--plandex__cap_0","uri":"capability://planning.reasoning.multi.step.plan.decomposition.and.execution.with.chat.driven.refinement","name":"multi-step plan decomposition and execution with chat-driven refinement","description":"Plandex breaks down large coding tasks into sequential plans that progress through distinct lifecycle phases (chat, tell, continue, build, apply). Each phase uses specialized AI models to discuss requirements, describe implementation tasks, execute code generation, and apply changes to the repository. The system maintains plan state in a persistent database and streams responses through a terminal UI, allowing developers to iteratively refine plans before committing changes.","intents":["I need to break down a large feature into manageable steps and execute them incrementally","I want to discuss implementation details with an AI before it generates code","I need to review and refine AI-generated plans before applying them to my codebase","I want to continue a partially-completed plan and adjust direction mid-execution"],"best_for":["teams building large features across multiple files","developers who want human-in-the-loop control over AI code generation","projects requiring iterative refinement before code application"],"limitations":["Plan state is tied to a single repository context — cannot easily transfer plans between projects","Chat phase requires manual prompting; no automatic task decomposition from natural language","Build and apply phases are sequential — no parallel execution of independent tasks"],"requires":["Go 1.18+ (for CLI)","API key for OpenAI, Anthropic, or other supported LLM provider","Git repository initialized in project directory","Plandex server running (local or cloud-hosted)"],"input_types":["natural language instructions","code files","directory structures","images and mockups","URLs and documentation"],"output_types":["structured plan objects with phases","AI-generated code modifications","file diffs ready for application","streaming terminal output"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_1","uri":"capability://memory.knowledge.context.aware.codebase.indexing.with.tree.sitter.project.maps","name":"context-aware codebase indexing with tree-sitter project maps","description":"Plandex indexes project directories using tree-sitter AST parsing to generate semantic project maps that represent file structure, function signatures, and type definitions without loading full file contents. This enables projects with 20M+ tokens of indexable content to fit within a 2M token effective context window. The system uses context caching to reduce API costs and latency, and developers can selectively load files, directories, or tree-only views to control token usage.","intents":["I want to give the AI context about my entire project structure without exceeding token limits","I need to load only function signatures and type definitions, not full file contents","I want to reduce API costs by caching context across multiple plan executions","I need to understand which files are relevant to a task before loading them"],"best_for":["large codebases (100+ files) where full context loading is prohibitively expensive","teams using context caching-enabled models (GPT-4, Claude 3.5+)","polyglot projects with mixed language support"],"limitations":["Tree-sitter maps provide signatures only — semantic understanding requires full file loading","Context caching reduces costs but adds latency on first request (cache population)","Supported languages limited to tree-sitter's language bindings (40+ languages, but not all)","Map generation requires parsing entire codebase on each context refresh"],"requires":["Project files in supported tree-sitter languages (Go, Python, JavaScript, TypeScript, Rust, C++, etc.)","Plandex server with tree-sitter bindings compiled","LLM provider supporting context caching (OpenAI, Anthropic)"],"input_types":["directory paths","file globs","git repository state"],"output_types":["tree-sitter AST project maps","file signature summaries","token count estimates","context cache metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_10","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.function.calling.interface","name":"multi-provider llm abstraction with unified function calling interface","description":"Plandex abstracts multiple LLM providers (OpenAI, Anthropic, Ollama) behind a unified interface, enabling developers to switch providers without changing plan logic. The system implements provider-specific adapters that handle API differences (function calling syntax, streaming, context windows) and normalize responses into a common format. Function calling is supported across all providers through a schema-based registry that maps tool definitions to provider-specific formats.","intents":["I want to switch from OpenAI to Anthropic without rewriting my plans","I need to use a local Ollama model for privacy-sensitive tasks","I want to compare results across different providers","I need to handle provider API changes without modifying plan execution logic"],"best_for":["organizations with multi-provider LLM strategies","teams requiring local/on-premise model execution","projects needing provider flexibility for cost or compliance reasons"],"limitations":["Provider abstraction adds latency — each request goes through adapter layer","Function calling support varies by provider — some providers have limited tool support","Context window sizes differ across providers — may require different context strategies","Streaming implementation differs by provider — may cause UI inconsistencies"],"requires":["API keys for at least one supported LLM provider","Plandex server with provider adapters compiled","Network access to provider endpoints (or local Ollama instance)"],"input_types":["provider configuration","function calling schemas","model parameters"],"output_types":["normalized LLM responses","function calling results","provider-agnostic metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_11","uri":"capability://data.processing.analysis.database.backed.plan.persistence.with.migration.based.schema.management","name":"database-backed plan persistence with migration-based schema management","description":"Plandex persists plan state, execution history, and context metadata in a relational database (SQLite, PostgreSQL) using a migration-based schema management system. The database tracks plan lifecycle events, stores file modifications, maintains context caching metadata, and enables plan resumption after server restarts. Schema migrations are versioned and applied automatically on server startup, ensuring compatibility across releases.","intents":["I want to resume a plan execution if the server crashes or restarts","I need to audit the history of all plans executed on a repository","I want to query plan execution metrics and performance data","I need to back up and restore plan state"],"best_for":["production deployments requiring plan persistence","teams needing audit trails and execution history","organizations with strict data retention requirements"],"limitations":["Database adds operational complexity — requires database administration and backups","Schema migrations can be slow on large databases — may cause downtime during upgrades","Database performance becomes bottleneck at scale — requires indexing and optimization","SQLite is not suitable for multi-server deployments — requires PostgreSQL or similar"],"requires":["Relational database (SQLite, PostgreSQL, MySQL, etc.)","Database credentials and network access","Plandex server with database driver compiled"],"input_types":["plan execution events","file modifications","context metadata"],"output_types":["persisted plan state","execution history","audit logs","performance metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_12","uri":"capability://automation.workflow.git.integration.with.automatic.conflict.detection.and.merge.strategies","name":"git integration with automatic conflict detection and merge strategies","description":"Plandex integrates with git to track plan-generated changes, detect conflicts with concurrent modifications, and apply merge strategies when necessary. The system checks for uncommitted changes before applying plans, detects conflicts between plan modifications and repository state, and provides options for conflict resolution (abort, merge, overwrite). Git history is preserved through explicit commits, and plans can be reverted by reversing commits.","intents":["I want to detect conflicts if the repository changes while a plan is executing","I need to revert a plan's changes by reversing git commits","I want to ensure plan changes are tracked in git history","I need to handle merge conflicts between plan modifications and concurrent changes"],"best_for":["teams using git for version control and collaboration","projects requiring change tracking and audit trails","environments where concurrent modifications are possible"],"limitations":["Conflict detection is file-level only — cannot detect line-level conflicts","Merge strategies are basic (abort, overwrite) — no three-way merge support","Git integration assumes standard git workflow — may conflict with advanced strategies","Requires git to be installed and accessible in PATH"],"requires":["Git repository initialized in project directory","Git binary installed and accessible","Write permissions to .git directory"],"input_types":["git repository state","file modifications","conflict resolution options"],"output_types":["git commits","conflict reports","merge results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_2","uri":"capability://tool.use.integration.role.based.ai.model.assignment.with.model.packs","name":"role-based ai model assignment with model packs","description":"Plandex assigns specialized AI models to different development roles (planner, builder, verifier) through configurable model packs. Developers can define which model handles planning tasks, code generation, and verification, allowing optimization for cost, speed, or quality. The system supports multiple LLM providers (OpenAI, Anthropic, Ollama) and enables switching between models without changing plan logic.","intents":["I want to use a fast, cheap model for planning and a more capable model for code generation","I need to switch from GPT-4 to Claude mid-project without rewriting my workflow","I want to use a local Ollama model for privacy-sensitive code tasks","I need to optimize costs by using different models for different plan phases"],"best_for":["teams with cost-sensitive requirements needing model flexibility","organizations requiring local/on-premise LLM execution","projects where planning and code generation have different quality/speed tradeoffs"],"limitations":["Model packs are static per plan — cannot dynamically switch models within a single plan execution","Cross-model context passing may lose information if models have different context window sizes","No automatic model selection based on task complexity — requires manual configuration","Provider API compatibility varies (function calling, vision, streaming support differs)"],"requires":["API keys for at least one supported LLM provider (OpenAI, Anthropic, Ollama)","Plandex configuration file with model pack definitions","Network access to LLM provider endpoints (or local Ollama instance)"],"input_types":["model pack configuration (JSON/YAML)","provider API credentials"],"output_types":["model assignment metadata","provider-specific API calls","token usage per model"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_3","uri":"capability://code.generation.editing.sandbox.based.file.modification.pipeline.with.git.backed.reversibility","name":"sandbox-based file modification pipeline with git-backed reversibility","description":"Plandex maintains AI-generated code changes in a sandbox environment separate from the actual project files until explicitly applied. The system uses git to track modifications, enabling developers to review diffs, revert changes, and apply modifications selectively. The build phase converts plan responses into file modifications stored in the sandbox, and the apply phase writes changes to the repository with full git integration for commit tracking.","intents":["I want to review AI-generated code changes before they touch my actual files","I need to revert a plan's changes if they break the build","I want to see diffs of all modifications before applying them","I need to apply changes incrementally and track them in git history"],"best_for":["teams requiring code review before AI changes are committed","projects with strict change control requirements","developers who want to experiment with AI suggestions without risk"],"limitations":["Sandbox isolation adds latency — changes must be built before review","No automatic conflict resolution if project files change during plan execution","Sandbox state is lost if server restarts — requires persistent storage configuration","Git integration assumes standard git workflow — may conflict with advanced git strategies (rebase, squash)"],"requires":["Git repository initialized in project directory","Plandex server with persistent storage (SQLite or external database)","Write permissions to project directory for apply phase"],"input_types":["AI-generated code responses","file modification instructions","git repository state"],"output_types":["file diffs (unified format)","sandbox modification metadata","git commit objects","conflict reports"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_4","uri":"capability://automation.workflow.streaming.terminal.ui.with.real.time.plan.execution.feedback","name":"streaming terminal ui with real-time plan execution feedback","description":"Plandex renders plan execution progress through a streaming terminal UI that displays AI responses, token usage, model assignments, and phase transitions in real-time. The UI uses Go's terminal rendering libraries to create interactive displays that update as the server streams responses, providing developers with immediate feedback on plan execution status without polling.","intents":["I want to see real-time progress as the AI executes my plan","I need to monitor token usage and costs during plan execution","I want to understand which model is handling each phase of execution","I need to interrupt or adjust a plan based on intermediate results"],"best_for":["developers using Plandex in terminal-based workflows","teams monitoring long-running plan executions","users who need immediate feedback on AI decision-making"],"limitations":["Terminal UI is text-only — cannot display rich formatting or images","Streaming updates require persistent connection to server — network interruptions break UI","Terminal width/height constraints limit information density","No persistent log of streamed output — requires manual capture for audit trails"],"requires":["Terminal emulator with ANSI color support","Plandex CLI client","Network connectivity to Plandex server"],"input_types":["server-streamed responses","plan execution events"],"output_types":["rendered terminal UI","real-time progress updates","token usage metrics"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_5","uri":"capability://code.generation.editing.polyglot.code.generation.with.language.agnostic.plan.execution","name":"polyglot code generation with language-agnostic plan execution","description":"Plandex generates code across 40+ programming languages supported by tree-sitter, with language detection and syntax-aware modification handling. The system uses language-specific AST parsing to understand code structure and generate modifications that respect language conventions. Plans are language-agnostic — a single plan can generate code in Python, Go, JavaScript, and other languages within the same project.","intents":["I want to generate code in multiple languages within a single plan","I need the AI to understand language-specific syntax and conventions","I want to add features to a polyglot project without managing language-specific workflows","I need to refactor code while preserving language idioms"],"best_for":["polyglot projects with multiple programming languages","teams building microservices in different languages","full-stack development where frontend and backend use different languages"],"limitations":["Language support limited to tree-sitter's language bindings (40+ languages, but not all)","Cross-language type inference not supported — each language is analyzed independently","Language-specific build systems (Maven, Cargo, npm) not integrated — requires manual build verification","Syntax errors in generated code may not be caught until build phase"],"requires":["Project files in supported tree-sitter languages","Plandex server with tree-sitter bindings for target languages","LLM with knowledge of target programming languages"],"input_types":["code files in multiple languages","language-specific context (imports, dependencies)"],"output_types":["generated code in target languages","language-specific modifications","syntax-aware diffs"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_6","uri":"capability://tool.use.integration.http.api.with.request.response.streaming.and.server.side.plan.persistence","name":"http api with request/response streaming and server-side plan persistence","description":"Plandex exposes a REST HTTP API that handles plan creation, execution, context management, and model configuration. The API supports streaming responses for long-running operations (plan execution, code generation) and persists plan state in a database, enabling clients to reconnect and resume interrupted operations. The server maintains active plan locks to prevent concurrent modifications and uses a request handler pattern to route API calls to appropriate business logic.","intents":["I want to integrate Plandex into my CI/CD pipeline via HTTP API","I need to build a custom UI or IDE plugin that communicates with Plandex","I want to run Plandex server in a cloud environment and access it remotely","I need to resume a plan execution if my client connection drops"],"best_for":["teams building custom integrations with Plandex","organizations deploying Plandex as a shared service","CI/CD pipelines requiring programmatic access to plan execution"],"limitations":["API is HTTP/REST only — no WebSocket support for bidirectional streaming","Plan state persistence requires external database — no in-memory mode for stateless deployments","Authentication is basic (API keys) — no OAuth or SAML support","Streaming responses require long-lived connections — incompatible with some load balancers"],"requires":["Plandex server running and accessible over HTTP","API key for authentication","HTTP client library supporting streaming responses","Database backend for plan persistence (SQLite, PostgreSQL, etc.)"],"input_types":["JSON request bodies","query parameters","file uploads"],"output_types":["JSON responses","streaming response bodies","HTTP status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_7","uri":"capability://automation.workflow.active.plan.management.with.repository.locking.and.concurrent.access.control","name":"active plan management with repository locking and concurrent access control","description":"Plandex maintains a single active plan per repository and uses file-based or database-backed locking to prevent concurrent modifications. The system tracks active plan state, enforces exclusive access during build and apply phases, and prevents multiple clients from executing plans simultaneously on the same repository. Lock acquisition is checked before each plan operation and released after completion or timeout.","intents":["I want to prevent team members from accidentally running conflicting plans on the same repository","I need to ensure only one plan is modifying files at a time","I want to know which plan is currently active and who initiated it","I need to force-release a lock if a plan execution hangs"],"best_for":["teams sharing a single Plandex server instance","projects with strict change control requiring sequential plan execution","environments where concurrent modifications would cause conflicts"],"limitations":["Lock mechanism is repository-level only — cannot parallelize independent plans","Lock timeout requires manual configuration — no automatic deadlock detection","Distributed locking (across multiple servers) not supported — requires single server instance","Lock state is not visible in CLI — requires API access to check active plan"],"requires":["Plandex server with persistent storage for lock state","Git repository initialized in project directory","Coordination mechanism (database or file system) for lock management"],"input_types":["plan execution requests","lock acquisition/release commands"],"output_types":["lock status","active plan metadata","lock timeout errors"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_8","uri":"capability://memory.knowledge.context.aware.token.counting.and.budget.management","name":"context-aware token counting and budget management","description":"Plandex tracks token usage across context loading, plan execution, and model responses, providing developers with real-time token counts and cost estimates. The system implements context caching to reduce token consumption on repeated requests and allows developers to set token budgets per plan. Token counting is performed before API calls to prevent unexpected overages, and the system provides detailed breakdowns of token usage by context type (files, directories, images, notes).","intents":["I want to understand how many tokens my context is consuming before executing a plan","I need to optimize context loading to stay within my API budget","I want to see cost estimates for different model configurations","I need to track token usage across multiple plans for billing purposes"],"best_for":["teams with strict API budgets or cost constraints","organizations tracking AI spending per project or team","developers optimizing context efficiency"],"limitations":["Token counting is approximate — actual usage may vary by 5-10% due to tokenizer differences","Context caching reduces token count but adds latency on first request","Budget enforcement is soft — system warns but does not block execution","Token usage tracking requires database logging — adds overhead to each request"],"requires":["Plandex server with token counting implementation","LLM provider API supporting token count estimation","Database for token usage logging (optional, for historical tracking)"],"input_types":["context files and directories","plan execution requests"],"output_types":["token count estimates","cost projections","token usage breakdowns","budget warnings"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-plandex-ai--plandex__cap_9","uri":"capability://automation.workflow.repl.based.interactive.plan.refinement.with.command.history","name":"repl-based interactive plan refinement with command history","description":"Plandex provides a REPL (Read-Eval-Print Loop) interface where developers can interactively execute commands (chat, tell, continue, build, apply) within a single plan context. The REPL maintains command history, allows editing previous commands, and preserves plan state across multiple commands. Developers can iterate on plans without exiting and re-entering the tool, enabling rapid experimentation and refinement.","intents":["I want to refine a plan iteratively without exiting and re-entering the CLI","I need to try multiple approaches and compare results within the same plan","I want to review command history and re-execute previous commands with modifications","I need to maintain context across multiple interactions without reloading files"],"best_for":["developers who prefer interactive, exploratory workflows","rapid prototyping and experimentation with AI-generated code","iterative refinement of complex plans"],"limitations":["REPL state is in-memory — exiting the REPL loses command history unless explicitly saved","No multi-plan REPL — each REPL session is tied to a single plan","Command history is not persisted across server restarts","REPL interface is text-only — cannot display rich formatting or interactive elements"],"requires":["Plandex CLI client","Terminal emulator with line editing support (readline or similar)","Active Plandex server connection"],"input_types":["CLI commands (chat, tell, continue, build, apply)","command arguments and options"],"output_types":["command results","plan state updates","command history"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Go 1.18+ (for CLI)","API key for OpenAI, Anthropic, or other supported LLM provider","Git repository initialized in project directory","Plandex server running (local or cloud-hosted)","Project files in supported tree-sitter languages (Go, Python, JavaScript, TypeScript, Rust, C++, etc.)","Plandex server with tree-sitter bindings compiled","LLM provider supporting context caching (OpenAI, Anthropic)","API keys for at least one supported LLM provider","Plandex server with provider adapters compiled","Network access to provider endpoints (or local Ollama instance)"],"failure_modes":["Plan state is tied to a single repository context — cannot easily transfer plans between projects","Chat phase requires manual prompting; no automatic task decomposition from natural language","Build and apply phases are sequential — no parallel execution of independent tasks","Tree-sitter maps provide signatures only — semantic understanding requires full file loading","Context caching reduces costs but adds latency on first request (cache population)","Supported languages limited to tree-sitter's language bindings (40+ languages, but not all)","Map generation requires parsing entire codebase on each context refresh","Provider abstraction adds latency — each request goes through adapter layer","Function calling support varies by provider — some providers have limited tool support","Context window sizes differ across providers — may require different context strategies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6949159797847092,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"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":"2026-05-03T13:57:16.560Z","last_commit":"2025-10-03T21:49:58Z"},"community":{"stars":15322,"forks":1128,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=plandex-ai--plandex","compare_url":"https://unfragile.ai/compare?artifact=plandex-ai--plandex"}},"signature":"EvJK9bmU22sifEFQXiRw69S0zgwADYhtuCHFW6Z3m118dvq5ZD/wlu654BXayzQrdBUFNAehGhUp3AT/JQF7Ag==","signedAt":"2026-06-22T06:46:47.186Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/plandex-ai--plandex","artifact":"https://unfragile.ai/plandex-ai--plandex","verify":"https://unfragile.ai/api/v1/verify?slug=plandex-ai--plandex","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"}}