{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-agentpilot","slug":"agentpilot","name":"AgentPilot","type":"agent","url":"https://github.com/jbexta/AgentPilot","page_url":"https://unfragile.ai/agentpilot","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-agentpilot__cap_0","uri":"capability://automation.workflow.multi.agent.orchestration.and.lifecycle.management","name":"multi-agent orchestration and lifecycle management","description":"Manages creation, configuration, and execution of multiple AI agents within a unified desktop environment. Implements agent state persistence, parameter management, and inter-agent communication patterns through a centralized agent registry that tracks agent instances, their configurations, and execution contexts across sessions.","intents":["Build and configure multiple specialized agents for different tasks without writing boilerplate code","Switch between agents and maintain separate conversation contexts for each","Persist agent configurations and reuse them across sessions","Organize agents by purpose and manage their lifecycle from creation to retirement"],"best_for":["Teams building multi-agent systems who need visual management without CLI overhead","Researchers prototyping agent architectures and comparing agent behaviors","Solo developers building LLM-powered applications with multiple specialized agents"],"limitations":["Agent state is local to desktop app — no built-in cloud sync or multi-device sharing","No native agent-to-agent communication framework — requires manual message passing setup","Limited to agents running sequentially or in simple parallel patterns, no advanced DAG scheduling"],"requires":["Python 3.8+","PyQt5 or PyQt6 for desktop UI rendering","At least one LLM provider API key (OpenAI, Anthropic, or local Ollama)"],"input_types":["agent configuration JSON/YAML","natural language prompts","system instructions and parameters"],"output_types":["agent instance objects","conversation transcripts","agent metadata and state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_1","uri":"capability://text.generation.language.conversational.chat.interface.with.multi.agent.context.switching","name":"conversational chat interface with multi-agent context switching","description":"Implements a unified chat UI that maintains separate conversation histories per agent while allowing seamless switching between agents without losing context. Uses a message buffer architecture that stores conversation turns with metadata (agent ID, timestamp, token count) and retrieves relevant context on agent switch, enabling agents to reference prior exchanges.","intents":["Chat with different agents sequentially while keeping their conversation histories isolated","Switch agents mid-conversation and have the new agent understand the conversation context","View and edit conversation history for debugging or refinement","Export conversations for analysis or documentation"],"best_for":["Interactive AI application builders who need rich chat UX without building from scratch","Non-technical users managing multiple AI assistants through a single interface","Developers debugging agent behavior by reviewing full conversation traces"],"limitations":["No built-in conversation branching — cannot explore alternative agent responses from a single point","Context window management is manual — developers must specify max_tokens or implement sliding window logic","No native support for streaming responses in multi-agent scenarios — may cause UI blocking"],"requires":["PyQt5/PyQt6 for UI rendering","Message storage backend (SQLite or in-memory buffer)","LLM provider with streaming API support (optional but recommended)"],"input_types":["text messages","multi-line prompts","agent selection commands"],"output_types":["formatted chat messages","conversation JSON exports","token usage statistics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_10","uri":"capability://memory.knowledge.agent.memory.and.context.window.management","name":"agent memory and context window management","description":"Manages agent context windows by maintaining conversation history and implementing strategies for context truncation when conversations exceed token limits. Supports configurable context window sizes per agent and implements sliding window or summarization strategies to preserve relevant context.","intents":["Maintain conversation context across multiple turns without exceeding token limits","Automatically truncate or summarize old messages when context window is full","Configure different context window sizes for different agents","Preserve important context (e.g., system instructions) while removing less relevant messages"],"best_for":["Developers building long-running agent conversations","Teams optimizing token usage and costs in multi-turn interactions","Builders needing agents to remember context across many conversation turns"],"limitations":["Context truncation is basic — no intelligent selection of which messages to keep","Summarization is not built-in — requires manual implementation or external summarization service","No support for external memory stores (e.g., vector databases) — context is limited to conversation history","Context window size must be manually configured — no automatic optimization"],"requires":["Token counting library","Conversation history storage","Configurable context window size per agent"],"input_types":["conversation history","context window size limit","truncation strategy (sliding window, summarization, etc.)"],"output_types":["truncated conversation history","context window metadata","messages selected for inclusion"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.and.multi.provider.routing","name":"llm provider abstraction and multi-provider routing","description":"Abstracts LLM API calls behind a unified interface supporting OpenAI, Anthropic, and local Ollama models. Routes requests based on agent configuration, handles provider-specific request/response formatting, manages API keys securely in encrypted config storage, and implements fallback logic when a provider is unavailable or rate-limited.","intents":["Use different LLM providers for different agents without rewriting agent code","Switch between cloud and local models without changing agent logic","Manage multiple API keys securely in a desktop environment","Implement cost optimization by routing expensive tasks to cheaper models"],"best_for":["Developers building multi-model agent systems who want provider flexibility","Teams evaluating different LLM providers without vendor lock-in","Privacy-conscious builders wanting to run local models alongside cloud APIs"],"limitations":["No automatic model capability detection — developers must manually specify which models support function calling or vision","Fallback logic is basic — no intelligent retry with exponential backoff or circuit breaker patterns","Provider-specific features (e.g., OpenAI's vision, Anthropic's extended thinking) require manual wrapper code"],"requires":["API keys for OpenAI and/or Anthropic (optional if using Ollama only)","Ollama running locally on port 11434 (for local model support)","Python requests library or equivalent HTTP client"],"input_types":["agent configuration specifying provider and model name","prompt text and parameters","function definitions for tool calling"],"output_types":["LLM response text","token usage metadata","structured function calls"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_3","uri":"capability://tool.use.integration.tool.function.calling.with.schema.based.registration","name":"tool/function calling with schema-based registration","description":"Enables agents to call external tools and functions through a schema-based registry system. Agents define available tools as JSON schemas with input/output specifications, and the system translates LLM function-calling responses into actual Python function invocations with argument validation and error handling.","intents":["Give agents the ability to call external APIs, databases, or local functions","Define tool schemas once and reuse them across multiple agents","Validate tool inputs before execution to prevent runtime errors","Log and audit all tool calls for debugging and compliance"],"best_for":["Developers building agents that need to interact with external systems","Teams implementing agent-based automation workflows with tool dependencies","Builders creating specialized agents for specific domains (e.g., data analysis, API integration)"],"limitations":["No built-in tool discovery — tools must be manually registered in schema format","Error handling is basic — tool failures may not trigger intelligent retry or fallback logic","No native support for async tool execution — blocking calls can slow agent response time","Schema validation is strict — complex nested types require careful schema definition"],"requires":["Python 3.8+ with typing support","JSON schema validation library (jsonschema or pydantic)","LLM provider supporting function calling (OpenAI, Anthropic, or compatible)"],"input_types":["tool schema definitions (JSON)","function signatures with type hints","LLM function-calling responses"],"output_types":["function execution results","tool call logs","error messages and stack traces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_4","uri":"capability://text.generation.language.agent.prompt.templating.and.system.instruction.management","name":"agent prompt templating and system instruction management","description":"Provides a templating system for agent prompts that supports variable substitution, conditional logic, and reusable instruction blocks. System instructions are stored per-agent with version history, enabling A/B testing of prompts and rollback to previous versions without code changes.","intents":["Define and manage system prompts for agents without hardcoding them","Use variables in prompts (e.g., {{user_name}}, {{current_date}}) that are resolved at runtime","Create prompt templates that can be reused across multiple agents","Track prompt changes over time and compare agent behavior across prompt versions"],"best_for":["Non-technical prompt engineers optimizing agent behavior through UI","Teams running prompt experiments and A/B tests on agents","Developers building agents that need dynamic prompts based on context"],"limitations":["Template syntax is basic — no support for complex logic like loops or nested conditionals","Version history is local only — no collaborative prompt editing or conflict resolution","No built-in prompt evaluation metrics — developers must manually measure prompt quality"],"requires":["Template engine (Jinja2 or custom string substitution)","Agent configuration storage with version tracking","UI for prompt editing (PyQt5/PyQt6)"],"input_types":["prompt template text with variables","variable values at runtime","system instruction blocks"],"output_types":["resolved prompt text","prompt version history","variable substitution logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_5","uri":"capability://automation.workflow.agent.configuration.persistence.and.import.export","name":"agent configuration persistence and import/export","description":"Serializes agent configurations (model, provider, tools, prompts, parameters) to JSON/YAML files and stores them in a local database. Supports importing configurations from files or templates, enabling agent sharing and version control through standard file formats.","intents":["Save agent configurations and reload them in future sessions","Share agent configurations with team members via files or version control","Create agent templates for common use cases (e.g., code reviewer, data analyst)","Version control agent configurations alongside code in Git"],"best_for":["Teams collaborating on agent development and sharing configurations","Developers building reusable agent templates for their organization","Builders wanting to version control agent configurations in Git"],"limitations":["No built-in conflict resolution for concurrent agent edits","API keys are stored in config files — requires careful handling to avoid accidental exposure in version control","No schema validation on import — malformed configs may cause runtime errors","Import/export is file-based only — no cloud sync or collaborative editing"],"requires":["JSON or YAML serialization library","Local file system access","SQLite or equivalent for agent metadata storage"],"input_types":["agent configuration objects","JSON/YAML files","configuration templates"],"output_types":["JSON/YAML configuration files","agent metadata snapshots","configuration diffs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_6","uri":"capability://automation.workflow.desktop.native.ui.with.pyqt5.pyqt6.rendering","name":"desktop-native ui with pyqt5/pyqt6 rendering","description":"Builds a native desktop application using PyQt5/PyQt6 with a tabbed interface for agent management, chat windows, and configuration editing. Implements responsive UI patterns including async message handling to prevent blocking on LLM calls, and native file dialogs for import/export operations.","intents":["Interact with agents through a native desktop application without web browser overhead","Manage multiple agents and conversations in a single window with tabbed navigation","Edit agent configurations through forms and text editors without touching code","Import/export agent configurations using native file dialogs"],"best_for":["Desktop-first users who prefer native apps over web interfaces","Teams building internal tools that need to run offline or on-premise","Developers prototyping agents locally before deploying to cloud"],"limitations":["Desktop-only — no web or mobile access to agents","PyQt5/PyQt6 dependency adds ~50MB to application size","UI responsiveness depends on proper async handling — blocking LLM calls will freeze UI","Cross-platform support requires testing on Windows, macOS, and Linux"],"requires":["Python 3.8+","PyQt5 (5.15+) or PyQt6 (6.0+)","Qt runtime libraries (included in PyQt packages)"],"input_types":["user interactions (clicks, text input, file selection)","keyboard shortcuts","drag-and-drop operations"],"output_types":["rendered UI components","file dialogs and system notifications","chat message rendering"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_7","uri":"capability://memory.knowledge.conversation.history.storage.and.retrieval","name":"conversation history storage and retrieval","description":"Persists conversation messages to a local SQLite database with metadata (agent ID, timestamp, token count, message role). Implements efficient retrieval of conversation history with filtering by agent, date range, or search terms, and supports exporting conversations to JSON or markdown formats.","intents":["Maintain persistent conversation history across application restarts","Search and filter past conversations by agent or date","Export conversations for documentation, analysis, or sharing","Analyze agent behavior by reviewing full conversation transcripts"],"best_for":["Teams auditing agent behavior and maintaining compliance records","Researchers analyzing agent performance across multiple conversations","Developers debugging agent issues by reviewing conversation history"],"limitations":["No built-in conversation anonymization — sensitive data in conversations is stored in plaintext","Search is basic text matching — no semantic search or similarity-based retrieval","No automatic cleanup — conversation database can grow large without manual pruning","Export formats are limited to JSON and markdown — no integration with external analytics tools"],"requires":["SQLite3 (included in Python standard library)","Local file system access for database storage","Sufficient disk space for conversation history"],"input_types":["message text and metadata","agent ID and timestamp","search queries and filters"],"output_types":["conversation records from database","JSON/markdown exports","search results with metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_8","uri":"capability://automation.workflow.parameter.and.hyperparameter.configuration.per.agent","name":"parameter and hyperparameter configuration per agent","description":"Allows per-agent configuration of LLM parameters (temperature, max_tokens, top_p, frequency_penalty) and other hyperparameters through a configuration UI. Parameters are stored in agent config and passed to the LLM provider at request time, enabling fine-tuning of agent behavior without code changes.","intents":["Adjust agent creativity/determinism by tuning temperature without code changes","Control response length by setting max_tokens per agent","Experiment with different parameter combinations to optimize agent behavior","Apply different parameters to different agents for specialized behaviors"],"best_for":["Non-technical users tuning agent behavior through UI","Teams running hyperparameter experiments on agents","Developers optimizing agent responses for specific use cases"],"limitations":["No automatic hyperparameter optimization — tuning is manual trial-and-error","Parameter validation is basic — invalid combinations may cause LLM errors","No built-in parameter history or A/B testing framework","Some parameters are provider-specific — not all parameters work with all LLM providers"],"requires":["Agent configuration storage","UI for parameter editing (PyQt5/PyQt6)","LLM provider supporting parameter customization"],"input_types":["parameter names and values","parameter ranges and constraints","agent configuration"],"output_types":["parameter values passed to LLM","parameter validation results","parameter history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-agentpilot__cap_9","uri":"capability://text.generation.language.agent.execution.and.response.streaming","name":"agent execution and response streaming","description":"Executes agent logic by sending prompts to the configured LLM provider and streaming responses back to the UI in real-time. Implements token counting for cost tracking, handles streaming response buffering, and manages request timeouts and error recovery.","intents":["Send prompts to agents and receive responses in real-time","Stream agent responses to UI as they are generated","Track token usage for cost monitoring and optimization","Handle timeouts and errors gracefully without crashing the application"],"best_for":["Developers building interactive agent applications with real-time feedback","Teams monitoring agent costs through token usage tracking","Builders needing responsive UI during long-running LLM calls"],"limitations":["Streaming is provider-dependent — not all LLM providers support streaming","Token counting is approximate — actual token usage may vary from estimates","No built-in request queuing — concurrent requests may exceed rate limits","Timeout handling is basic — no intelligent retry with exponential backoff"],"requires":["LLM provider with streaming API support","Async/await support in Python (3.7+)","Token counting library (tiktoken for OpenAI, custom for other providers)"],"input_types":["prompt text","agent configuration","LLM provider credentials"],"output_types":["streamed response text","token usage metadata","error messages"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyQt5 or PyQt6 for desktop UI rendering","At least one LLM provider API key (OpenAI, Anthropic, or local Ollama)","PyQt5/PyQt6 for UI rendering","Message storage backend (SQLite or in-memory buffer)","LLM provider with streaming API support (optional but recommended)","Token counting library","Conversation history storage","Configurable context window size per agent","API keys for OpenAI and/or Anthropic (optional if using Ollama only)"],"failure_modes":["Agent state is local to desktop app — no built-in cloud sync or multi-device sharing","No native agent-to-agent communication framework — requires manual message passing setup","Limited to agents running sequentially or in simple parallel patterns, no advanced DAG scheduling","No built-in conversation branching — cannot explore alternative agent responses from a single point","Context window management is manual — developers must specify max_tokens or implement sliding window logic","No native support for streaming responses in multi-agent scenarios — may cause UI blocking","Context truncation is basic — no intelligent selection of which messages to keep","Summarization is not built-in — requires manual implementation or external summarization service","No support for external memory stores (e.g., vector databases) — context is limited to conversation history","Context window size must be manually configured — no automatic optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:02.370Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agentpilot","compare_url":"https://unfragile.ai/compare?artifact=agentpilot"}},"signature":"UyM3lwIMzEulgj4TAloAXE4thuNXlo0Y5um/B90P6P06pzbB6ZlklNO2QICe8Pm3OU0pPqpy9H62V48kdII9Dw==","signedAt":"2026-06-22T12:32:23.367Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentpilot","artifact":"https://unfragile.ai/agentpilot","verify":"https://unfragile.ai/api/v1/verify?slug=agentpilot","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"}}