{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nano-collective--nanocoder","slug":"nano-collective--nanocoder","name":"nanocoder","type":"agent","url":"https://docs.nanocollective.org/nanocoder","page_url":"https://unfragile.ai/nano-collective--nanocoder","categories":["code-editors"],"tags":["ai","ai-agents","ai-coding","coding-agents","llm","llm-inference","ollama","openai","openrouter"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nano-collective--nanocoder__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.client.abstraction.with.openai.compatible.api.routing","name":"multi-provider llm client abstraction with openai-compatible api routing","description":"Nanocoder implements a client-factory pattern (source/client-factory.ts) that abstracts multiple LLM providers (Ollama, LM Studio, OpenRouter, OpenAI, Anthropic) behind a unified interface. The factory detects provider type from configuration, instantiates the appropriate client, and routes all chat completions through a standardized handler that normalizes streaming responses and function-calling schemas across providers. This enables seamless switching between local and cloud models without code changes.","intents":["Switch between local Ollama models and cloud APIs without reconfiguring the agent","Run the same agent code against multiple LLM providers for cost/latency optimization","Support both streaming and non-streaming responses transparently across different provider APIs"],"best_for":["developers building privacy-first coding agents","teams wanting to avoid vendor lock-in with a single LLM provider","organizations testing multiple models before committing to one"],"limitations":["Function-calling schema normalization adds ~50-100ms overhead per tool invocation due to schema translation between providers","Some provider-specific features (e.g., vision capabilities, extended context windows) require manual configuration","No automatic fallback if primary provider is unavailable — requires explicit configuration of backup providers"],"requires":["Node.js 18+","API key or local endpoint for at least one provider (Ollama, OpenAI, Anthropic, OpenRouter, LM Studio)","Configuration file with provider credentials and model selection"],"input_types":["chat messages with role/content structure","tool/function definitions in OpenAI format","streaming request flags"],"output_types":["streaming text chunks","complete chat completions","tool/function calls with arguments","usage statistics (tokens, cost)"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_1","uri":"capability://safety.moderation.approval.gated.tool.execution.with.risk.assessment.workflow","name":"approval-gated tool execution with risk assessment workflow","description":"Nanocoder implements a risk-and-approval system that intercepts tool calls (file operations, bash commands, web fetches) before execution, displays the intended action to the user with context, and requires explicit approval before proceeding. The system categorizes operations by risk level (read-only vs destructive), shows diffs for file modifications, and logs all executed actions for audit trails. This is enforced through a middleware layer in the tool execution flow that blocks execution until user confirmation is received.","intents":["Prevent accidental destructive operations (file deletion, overwriting) by requiring user confirmation","Audit all tool invocations and understand what the agent is doing before it happens","Implement organizational policies that require human-in-the-loop for sensitive operations"],"best_for":["teams working in regulated environments requiring audit trails","developers new to agentic workflows who want to understand agent behavior","organizations with strict change-control policies"],"limitations":["Approval workflow blocks agent execution — adds latency proportional to user response time","No granular permission model — all users see all operations, no role-based filtering","Batch operations require individual approval per action, not bulk approval for multi-step workflows"],"requires":["Interactive terminal session (non-interactive mode disables approvals)","User presence to respond to approval prompts","Configuration enabling approval system (enabled by default)"],"input_types":["tool invocation requests with parameters","file paths and content for modifications","bash commands with arguments"],"output_types":["approval prompts with operation details","execution logs with timestamps and user decisions","diff previews for file modifications"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_10","uri":"capability://tool.use.integration.built.in.tool.set.for.file.operations.bash.execution.and.web.fetching","name":"built-in tool set for file operations, bash execution, and web fetching","description":"Nanocoder provides a set of built-in tools that the agent can invoke: file read/write/delete operations, bash command execution with output capture, and HTTP web fetching. Each tool is implemented as a function that validates inputs, executes the operation, and returns results or errors. Tools are registered in a tool registry and exposed to the LLM via function-calling schemas. All tool invocations go through the approval system before execution.","intents":["Allow the agent to read and modify files in the project","Enable the agent to run bash commands for testing, building, or analysis","Fetch external resources (documentation, APIs) to inform code generation"],"best_for":["developers wanting the agent to autonomously modify code","teams automating code generation and testing workflows","users wanting the agent to execute arbitrary bash commands"],"limitations":["File operations are limited to the project directory — no access outside the project root","Bash execution runs with the same permissions as the nanocoder process — no sandboxing","Web fetching has no timeout or size limits — can hang on slow/large responses","Tool output is captured as text — binary data is not supported"],"requires":["File system write permissions for project directory","Bash shell available on the system","Network access for web fetching (optional)"],"input_types":["file paths and content","bash commands and arguments","URLs for web fetching"],"output_types":["file content (read operations)","bash command output and exit codes","HTTP response bodies"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_11","uri":"capability://memory.knowledge.state.management.with.in.memory.conversation.history.and.session.persistence","name":"state management with in-memory conversation history and session persistence","description":"Nanocoder maintains application state through React hooks (useAppInitialization, custom hooks) that manage conversation history, configuration state, and tool execution state. Conversation history is stored in memory as an array of messages with roles and content. Session state persists for the duration of the CLI session but is lost on exit. The system uses React's state management patterns to ensure UI updates reflect state changes in real-time.","intents":["Maintain conversation context across multiple agent interactions in a single session","Track tool execution history and results for debugging","Manage application state (configuration, context) consistently"],"best_for":["developers using nanocoder for extended interactive sessions","teams wanting to review conversation history during a session","users debugging agent behavior by examining execution state"],"limitations":["Conversation history is in-memory only — lost when agent exits","No persistence mechanism for long-term history — each session starts fresh","Memory usage grows with conversation length — very long sessions may consume significant RAM","No export mechanism for conversation history"],"requires":["Node.js process with sufficient memory","Active terminal session"],"input_types":["user messages","LLM responses","tool execution results"],"output_types":["conversation history array","session state object","UI updates reflecting state changes"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_12","uri":"capability://automation.workflow.application.lifecycle.management.with.async.initialization.phases","name":"application lifecycle management with async initialization phases","description":"Nanocoder implements a structured application lifecycle (source/hooks/useAppInitialization.tsx) with distinct initialization phases: configuration loading, client creation, tool system setup, and external integrations. Each phase reports progress asynchronously and failures in later phases don't prevent application startup. The system uses async/await patterns to manage dependencies between phases and provides error handling that allows partial initialization. The UI displays initialization progress to the user.","intents":["Initialize the application with clear progress feedback to the user","Handle initialization failures gracefully without crashing the entire application","Manage dependencies between initialization phases (e.g., config must load before client creation)"],"best_for":["developers wanting transparent startup behavior","teams debugging initialization issues","users wanting to understand what the agent is doing during startup"],"limitations":["Initialization is sequential — phases don't run in parallel, adding startup latency","Partial initialization may leave the agent in an inconsistent state if later phases fail","No rollback mechanism — if a phase fails, earlier phases aren't undone","Error messages from failed phases may not be user-friendly"],"requires":["Configuration file accessible","LLM provider available (at least one)","File system access for project analysis"],"input_types":["configuration file","project directory","provider credentials"],"output_types":["initialization progress messages","error messages for failed phases","initialized application state"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_2","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.extensible.tool.systems","name":"model context protocol (mcp) server integration for extensible tool systems","description":"Nanocoder integrates with the Model Context Protocol to dynamically load and execute tools from external MCP servers. The system maintains a registry of MCP server configurations, establishes connections at startup, discovers available tools from each server, and routes tool invocations through the MCP protocol. This allows users to extend the agent's capabilities by adding custom MCP servers without modifying the core codebase. Tool discovery, schema validation, and execution are handled through the MCP client library.","intents":["Add custom tools and integrations without forking or modifying nanocoder source code","Reuse existing MCP servers (e.g., for database access, API integrations, specialized domain tools)","Build a plugin ecosystem where third-party developers can contribute tools"],"best_for":["teams building domain-specific coding agents with custom tools","organizations with existing MCP server infrastructure","developers wanting to extend nanocoder without core contributions"],"limitations":["MCP server discovery and connection happens at startup — adding new servers requires agent restart","Tool schema validation errors in MCP servers can crash the agent if not properly handled","Network latency for remote MCP servers adds per-tool-call overhead (typically 50-200ms)"],"requires":["MCP server implementation (local or remote)","MCP server configuration in nanocoder config file with connection details","MCP client library compatible with nanocoder version"],"input_types":["MCP server configuration (host, port, authentication)","tool invocation requests matching MCP tool schemas"],"output_types":["tool discovery results (available tools and schemas)","tool execution results from MCP servers","error responses if MCP server is unavailable"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_3","uri":"capability://memory.knowledge.project.aware.context.tagging.with.git.history.and.file.analysis","name":"project-aware context tagging with git history and file analysis","description":"Nanocoder automatically analyzes the project structure at startup, tags files by type/purpose (source code, tests, config, docs), and integrates git history to understand recent changes and file ownership. This context is maintained in memory and used to prioritize which files to include in LLM prompts, reducing token usage and improving relevance. The system uses file extension matching, directory patterns, and git blame/log data to build a semantic understanding of the codebase without requiring manual configuration.","intents":["Automatically include relevant project context in prompts without manually specifying files","Reduce token usage by filtering to only relevant files based on project structure","Understand code ownership and recent changes to make better refactoring decisions"],"best_for":["developers working in large codebases where manual context selection is tedious","teams wanting to reduce LLM token usage through intelligent context filtering","projects with clear structure (monorepos, standard layouts)"],"limitations":["File tagging is heuristic-based (extension/path matching) — may misclassify files in non-standard projects","Git history analysis requires a git repository — non-git projects get reduced context","Initial project analysis can be slow for very large codebases (10k+ files) — adds startup latency","Context tagging is static per session — changes to project structure during execution aren't reflected"],"requires":["Project directory with detectable structure (source code files with standard extensions)","Git repository (optional but recommended for full context)","File system read permissions for project directory"],"input_types":["project root directory path","git repository metadata"],"output_types":["file tags (source, test, config, docs, etc.)","file relevance scores","git history summaries (recent changes, authors)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_4","uri":"capability://text.generation.language.markdown.based.custom.command.system.with.parameter.substitution","name":"markdown-based custom command system with parameter substitution","description":"Nanocoder supports defining reusable prompts as markdown files with template variables (e.g., {{filename}}, {{language}}) that are substituted at runtime. Users can create custom commands that encapsulate multi-step workflows (e.g., 'refactor-function', 'add-tests') as markdown templates, invoke them via CLI, and pass parameters that are interpolated into the prompt. The command system integrates with the chat handler to execute the resulting prompt as a normal agent interaction.","intents":["Create reusable prompt templates for common coding tasks without writing code","Standardize workflows across a team by sharing markdown command definitions","Parameterize prompts to handle variations (e.g., different languages, file types)"],"best_for":["non-technical users wanting to define agent workflows","teams standardizing coding practices through prompt templates","developers building domain-specific command sets"],"limitations":["Parameter substitution is simple string replacement — no conditional logic or loops","No validation of parameters before substitution — invalid values are passed to LLM as-is","Commands are stateless — no way to reference outputs from previous commands in a sequence"],"requires":["Markdown files in the commands directory","Template variables in {{variable}} format","Command invocation via CLI with parameter arguments"],"input_types":["markdown template files","command parameters as CLI arguments"],"output_types":["interpolated prompt text","agent response to the prompt"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_5","uri":"capability://text.generation.language.interactive.terminal.ui.with.streaming.message.display.and.autocomplete","name":"interactive terminal ui with streaming message display and autocomplete","description":"Nanocoder builds its UI using the Ink framework (React for terminals), rendering an interactive terminal interface with real-time streaming message display, user input handling with autocomplete suggestions, and status indicators. The UI layer (App.tsx, UserInput.tsx, ChatQueue.tsx components) manages message queuing, renders streaming responses character-by-character, and provides keyboard-driven navigation. The system maintains a message history and renders it incrementally as the agent responds, providing immediate visual feedback.","intents":["Interact with the coding agent in a natural terminal-based conversation","See agent responses stream in real-time as they're generated","Use autocomplete to discover available commands and reduce typing"],"best_for":["developers comfortable with terminal interfaces","teams using CI/CD pipelines where terminal interaction is natural","users wanting a lightweight alternative to GUI IDEs"],"limitations":["Terminal rendering is limited to text — no rich formatting (colors are basic ANSI)","Streaming display adds latency before first token appears (typically 100-500ms for API calls)","Autocomplete is basic — no semantic understanding of context, just keyword matching","Terminal size constraints limit how much context can be displayed at once"],"requires":["Terminal emulator supporting ANSI escape codes","Node.js 18+ with TTY support","Sufficient terminal width (minimum 80 columns recommended)"],"input_types":["keyboard input (text, arrow keys, special keys)","streaming chat completion responses"],"output_types":["rendered terminal UI with messages","status indicators and progress bars","autocomplete suggestions"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_6","uri":"capability://code.generation.editing.vs.code.extension.integration.with.live.diff.previews.and.diagnostics","name":"vs code extension integration with live diff previews and diagnostics","description":"Nanocoder provides a VS Code extension that integrates with the CLI agent, enabling users to invoke the agent from within the editor and see results (diffs, diagnostics, suggestions) directly in the editor UI. The extension communicates with the nanocoder CLI via IPC or network, sends file context and user requests, and renders responses as inline diffs, code actions, and diagnostic markers. This bridges the terminal-based agent with the GUI editor workflow.","intents":["Use the nanocoder agent without leaving VS Code","See agent-suggested changes as diffs before applying them","Integrate agent capabilities into existing editor-based workflows"],"best_for":["VS Code users wanting agentic coding without switching to terminal","teams using VS Code as their primary development environment","developers wanting to compare agent suggestions with existing code"],"limitations":["Extension requires VS Code 1.80+ — older versions not supported","Communication between extension and CLI adds latency (typically 100-300ms per request)","Diff preview is read-only — changes must be explicitly applied by user","Extension only works with VS Code — no support for other editors (Vim, Emacs, etc.)"],"requires":["VS Code 1.80+","nanocoder CLI installed and configured","VS Code extension installed from marketplace"],"input_types":["file content from editor","user selections and cursor position","agent requests from editor UI"],"output_types":["diff previews in editor","diagnostic markers and code actions","inline suggestions"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_7","uri":"capability://automation.workflow.non.interactive.batch.execution.mode.for.ci.cd.pipelines","name":"non-interactive batch execution mode for ci/cd pipelines","description":"Nanocoder supports a non-interactive mode where it reads prompts from stdin or files, executes them without requiring user approval, and outputs results to stdout/files. This mode disables the approval workflow, removes interactive UI elements, and formats output for machine consumption (JSON, plain text). The mode is triggered via CLI flags and is designed for integration into CI/CD pipelines, automated testing, and batch processing workflows.","intents":["Run nanocoder in CI/CD pipelines to automate code generation or analysis tasks","Batch process multiple files or prompts without manual intervention","Integrate nanocoder into automated workflows where interactive approval isn't feasible"],"best_for":["CI/CD pipeline engineers automating code generation","teams running batch code analysis or refactoring jobs","organizations automating code quality checks with LLM assistance"],"limitations":["Approval workflow is completely disabled — no safety checks on destructive operations","No interactive feedback or error recovery — failures terminate execution","Output formatting is basic (plain text or JSON) — no rich terminal formatting","Requires explicit prompt input (stdin or file) — no interactive chat history"],"requires":["Prompt input via stdin, file, or CLI argument","Non-interactive mode flag (e.g., --non-interactive)","Output destination specified (stdout, file, etc.)"],"input_types":["prompts from stdin or files","command-line arguments","file paths for batch processing"],"output_types":["plain text results","JSON-formatted output","exit codes for success/failure"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_8","uri":"capability://automation.workflow.configuration.system.with.wizard.guided.setup.and.provider.detection","name":"configuration system with wizard-guided setup and provider detection","description":"Nanocoder implements a configuration system (source/config/index.ts, source/config/preferences.ts) that manages LLM provider settings, tool configurations, and user preferences. The system includes an interactive configuration wizard that guides users through setup, auto-detects available providers (Ollama, LM Studio, OpenAI), and validates configurations before saving. Configuration is stored in a standard format (YAML/JSON) and can be edited manually or through the wizard. The system supports project-specific overrides and global defaults.","intents":["Set up nanocoder with minimal manual configuration through an interactive wizard","Switch between different LLM providers and models without editing config files","Store and manage API keys and provider credentials securely"],"best_for":["new users setting up nanocoder for the first time","teams managing multiple provider configurations","developers wanting to avoid manual YAML editing"],"limitations":["Configuration wizard is interactive — requires terminal presence, not suitable for automated setup","API keys are stored in plain text in config files — no encryption at rest","Provider auto-detection only works for local providers (Ollama, LM Studio) — cloud providers require manual entry","Configuration changes require agent restart — no hot-reload support"],"requires":["Interactive terminal for wizard","Write permissions to config directory","At least one LLM provider available (local or cloud)"],"input_types":["user input via wizard prompts","configuration file (YAML/JSON)","provider credentials and API keys"],"output_types":["validated configuration file","provider detection results","configuration validation errors"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nano-collective--nanocoder__cap_9","uri":"capability://text.generation.language.prompt.system.with.role.based.message.formatting.and.context.injection","name":"prompt system with role-based message formatting and context injection","description":"Nanocoder implements a prompt system that formats user messages with role information (user, assistant, system), injects project context (file tags, git history), and manages conversation history. The system constructs prompts by combining user input with automatically-discovered context, formats them according to the LLM provider's requirements, and maintains a conversation history for multi-turn interactions. System prompts define the agent's behavior and capabilities.","intents":["Automatically include relevant project context in prompts without manual specification","Maintain conversation history across multiple interactions","Format prompts correctly for different LLM providers and models"],"best_for":["developers wanting automatic context injection in prompts","teams building multi-turn agentic workflows","users wanting consistent prompt formatting across providers"],"limitations":["Context injection is automatic but not always accurate — may include irrelevant files","Conversation history is in-memory only — lost when agent exits","Prompt formatting is provider-specific — some providers may not support all message roles","No explicit control over context inclusion — users can't easily exclude files"],"requires":["Project context (files, git history)","LLM provider with chat completion support","Message history storage (in-memory)"],"input_types":["user messages","project context (files, tags, history)","system prompts"],"output_types":["formatted prompt messages","conversation history","LLM provider-specific message format"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","API key or local endpoint for at least one provider (Ollama, OpenAI, Anthropic, OpenRouter, LM Studio)","Configuration file with provider credentials and model selection","Interactive terminal session (non-interactive mode disables approvals)","User presence to respond to approval prompts","Configuration enabling approval system (enabled by default)","File system write permissions for project directory","Bash shell available on the system","Network access for web fetching (optional)","Node.js process with sufficient memory"],"failure_modes":["Function-calling schema normalization adds ~50-100ms overhead per tool invocation due to schema translation between providers","Some provider-specific features (e.g., vision capabilities, extended context windows) require manual configuration","No automatic fallback if primary provider is unavailable — requires explicit configuration of backup providers","Approval workflow blocks agent execution — adds latency proportional to user response time","No granular permission model — all users see all operations, no role-based filtering","Batch operations require individual approval per action, not bulk approval for multi-step workflows","File operations are limited to the project directory — no access outside the project root","Bash execution runs with the same permissions as the nanocoder process — no sandboxing","Web fetching has no timeout or size limits — can hang on slow/large responses","Tool output is captured as text — binary data is not supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4852758543021005,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-05-03T12:45:34Z"},"community":{"stars":1853,"forks":177,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nano-collective--nanocoder","compare_url":"https://unfragile.ai/compare?artifact=nano-collective--nanocoder"}},"signature":"Vh6hxVOYZNDK2VEtIV1NW5FFKc9w2dOCPFnXm6E5F48+HNPHmFGr5z1ftNTw5uShdCO20kDit09mvS07g5MIAQ==","signedAt":"2026-06-20T06:57:42.223Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nano-collective--nanocoder","artifact":"https://unfragile.ai/nano-collective--nanocoder","verify":"https://unfragile.ai/api/v1/verify?slug=nano-collective--nanocoder","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"}}