{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-astrbotdevs--astrbot","slug":"astrbotdevs--astrbot","name":"AstrBot","type":"agent","url":"https://astrbot.app","page_url":"https://unfragile.ai/astrbotdevs--astrbot","categories":["ai-agents"],"tags":["agent","ai","chatbot","chatgpt","discord","docker","gemini","gpt","llama","llm","mcp","openai","python","qq","qqbot","telegram"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-astrbotdevs--astrbot__cap_0","uri":"capability://tool.use.integration.multi.platform.unified.message.routing.and.normalization","name":"multi-platform unified message routing and normalization","description":"AstrBot implements a platform adapter abstraction layer that normalizes incoming messages from Discord, Telegram, QQ, and web chat into a unified internal message format, then routes responses back through platform-specific adapters. The system uses a connection mode abstraction supporting both webhook and polling patterns, with message component transformation that converts platform-native rich content (embeds, reactions, files) into a standardized AST-like structure for processing. This enables a single agent pipeline to serve heterogeneous chat platforms without duplicating business logic.","intents":["Deploy a single AI agent across multiple chat platforms without rewriting message handling logic","Support both webhook-based and polling-based platform connections with unified configuration","Transform platform-specific message formats (Discord embeds, Telegram inline keyboards) into a canonical representation"],"best_for":["Teams building multi-platform chatbots who want to avoid platform-specific branching logic","Developers migrating from single-platform agents to multi-channel deployments"],"limitations":["Platform-specific features (Discord threads, Telegram inline queries) may lose fidelity during normalization","Webhook mode requires public endpoint exposure; polling mode adds latency and API quota consumption","Rich message components (buttons, carousels) must be manually reconstructed per platform in response stage"],"requires":["Python 3.9+","Platform API credentials (Discord bot token, Telegram API key, QQ bot credentials, etc.)","Network connectivity for webhook endpoints or polling intervals"],"input_types":["platform-native message objects (discord.py Message, telegram.Update, QQ message JSON)","webhook payloads","polling API responses"],"output_types":["unified Message data structure with normalized components","platform-specific response objects for transmission"],"categories":["tool-use-integration","platform-adapters"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.streaming.and.context.compression","name":"multi-provider llm abstraction with streaming and context compression","description":"AstrBot implements a provider abstraction layer that unifies access to multiple LLM backends (OpenAI, Anthropic, Gemini, Ollama, local models) through a common interface. The system manages provider lifecycle (initialization, authentication, model selection), handles streaming responses with token-level granularity, implements context compression strategies to fit conversations within token limits, and provides automatic retry logic with exponential backoff. Provider configuration separates sources (API credentials) from instances (model + parameter combinations), enabling multi-model deployments and A/B testing without credential duplication.","intents":["Switch between different LLM providers (OpenAI → Anthropic) without changing agent code","Stream LLM responses token-by-token for real-time UI updates while maintaining conversation history","Automatically compress conversation context when approaching token limits using configurable strategies","Configure multiple model instances with different parameters (temperature, max_tokens) pointing to the same API credentials"],"best_for":["Teams building LLM-powered agents who want provider flexibility and cost optimization","Developers needing streaming responses for low-latency user experiences","Applications with long-running conversations requiring intelligent context management"],"limitations":["Context compression strategies are lossy; summarization may drop important details from early conversation turns","Streaming adds ~50-200ms latency per token due to network round-trips and buffering","Provider-specific features (vision, function calling schemas) require adapter-level implementation; not all features available across all providers","Retry logic uses fixed exponential backoff; no adaptive strategies based on error type"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Network connectivity to provider endpoints or local Ollama instance"],"input_types":["conversation history (list of Message objects with role, content, tool_calls)","system prompts","tool definitions (for function calling)","provider configuration (model name, temperature, max_tokens, etc.)"],"output_types":["streaming text tokens (via async generator)","complete response text","tool call objects with arguments","usage statistics (input_tokens, output_tokens, total_cost)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_10","uri":"capability://automation.workflow.configuration.system.with.dynamic.reloading.and.environment.variable.interpolation","name":"configuration system with dynamic reloading and environment variable interpolation","description":"AstrBot implements a hierarchical configuration system that loads settings from YAML/JSON files, environment variables, and runtime API calls. The system supports configuration hot-reloading without application restart, environment variable interpolation (e.g., `${OPENAI_API_KEY}`), configuration validation against schemas, and configuration versioning. Configuration is organized into sections (platform settings, provider settings, feature flags, etc.), with defaults provided for all settings. The configuration API allows runtime updates to settings, which are persisted to disk and applied immediately.","intents":["Load configuration from files, environment variables, or API without hardcoding values","Update configuration at runtime without restarting the application","Validate configuration against schemas to catch errors early","Support multiple configuration profiles (development, staging, production)"],"best_for":["Teams deploying AstrBot across multiple environments (dev, staging, prod) with different configurations","Developers who want to update configuration without restarting the application","Organizations using containerized deployments that rely on environment variables for configuration"],"limitations":["Configuration hot-reloading may cause race conditions if configuration is accessed during reload; requires careful synchronization","Environment variable interpolation is string-based; no type coercion (e.g., `${PORT}` is always a string)","Configuration validation is schema-based; complex validation logic must be implemented separately","No configuration rollback mechanism; failed updates may leave the system in an inconsistent state"],"requires":["Python 3.9+","Configuration files (YAML or JSON) in the expected directory structure","Environment variables for sensitive settings (API keys, database URLs, etc.)"],"input_types":["configuration files (YAML, JSON)","environment variables","runtime API calls with configuration updates"],"output_types":["configuration objects (Python dicts or dataclasses)","configuration validation errors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_11","uri":"capability://data.processing.analysis.media.handling.and.file.service.with.platform.specific.attachment.transformation","name":"media handling and file service with platform-specific attachment transformation","description":"AstrBot implements a media handling layer that normalizes file uploads and attachments across platforms, stores files in a configurable backend (local filesystem, S3, etc.), and transforms media for platform-specific requirements. The system handles file type validation, size limits, virus scanning (optional), and generates platform-specific attachment objects (Discord embeds, Telegram InputFile, etc.). The file service provides a unified API for uploading, downloading, and deleting files, with support for temporary files and automatic cleanup.","intents":["Accept file uploads from users across different platforms (Discord, Telegram, QQ, web)","Store files in a configurable backend (local, S3, cloud storage) with automatic cleanup","Transform files for platform-specific requirements (image resizing, format conversion)","Validate file types and sizes to prevent abuse"],"best_for":["Chatbots that need to handle user file uploads (documents, images, etc.)","Teams deploying across multiple platforms with different file handling requirements","Applications requiring file storage with automatic cleanup and quota management"],"limitations":["File transformation (resizing, format conversion) adds latency; large files may timeout","Virus scanning requires external service integration; no built-in scanning","File storage backend must be configured at startup; no runtime backend switching","Temporary file cleanup is best-effort; orphaned files may accumulate if cleanup fails"],"requires":["Python 3.9+","File storage backend (local filesystem, S3, Azure Blob Storage, etc.)","Optional: image processing library (Pillow) for image transformation","Optional: virus scanning service (ClamAV, VirusTotal, etc.)"],"input_types":["file uploads (multipart/form-data or platform-native file objects)","file metadata (filename, size, MIME type)","transformation configuration (resize dimensions, format, quality)"],"output_types":["file storage URLs","platform-specific attachment objects (Discord Embed, Telegram InputFile, etc.)","file metadata (size, type, hash)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_12","uri":"capability://text.generation.language.internationalization.and.theming.system.with.dynamic.language.switching","name":"internationalization and theming system with dynamic language switching","description":"AstrBot implements an i18n system that supports multiple languages for UI, agent responses, and system messages. Language packs are loaded from JSON/YAML files, with support for pluralization, variable interpolation, and context-specific translations. The system detects user language from platform metadata (Discord locale, Telegram language_code) or explicit user preference, and applies translations at the UI and agent level. Theming system allows customization of dashboard appearance (colors, fonts, layout) via configuration files.","intents":["Support multiple languages for UI and agent responses without code changes","Detect user language from platform metadata and apply appropriate translations","Allow users to explicitly set language preference","Customize dashboard appearance (colors, fonts, layout) via configuration"],"best_for":["Teams building global chatbots that serve users in multiple languages","Organizations with multi-language support requirements","Developers who want to customize dashboard appearance without CSS knowledge"],"limitations":["Translation quality depends on translation files; incomplete translations fall back to default language","Agent responses are translated at the system prompt level; LLM may ignore or override translations","Language detection from platform metadata is not always accurate; explicit user preference is more reliable","Theming system is limited to predefined variables; complex customizations require CSS knowledge"],"requires":["Python 3.9+","Translation files (JSON or YAML) for each supported language","Language detection mechanism (platform metadata or user preference)"],"input_types":["translation files (JSON, YAML)","user language preference","theme configuration (colors, fonts, layout)"],"output_types":["translated UI strings","translated agent responses","themed dashboard HTML/CSS"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_2","uri":"capability://tool.use.integration.function.tool.system.with.mcp.server.integration.and.sandboxed.execution","name":"function tool system with mcp server integration and sandboxed execution","description":"AstrBot implements a dual-mode tool execution system: native function tools defined via Python decorators or JSON schemas, and remote MCP (Model Context Protocol) servers for standardized tool discovery and execution. The system maintains a tool registry, validates tool call arguments against schemas, executes tools in an isolated sandbox context with restricted access to system resources, and handles tool results with error recovery. MCP integration enables tools to be defined in any language and discovered dynamically, while native tools provide low-latency execution for performance-critical operations.","intents":["Define custom tools as Python functions with automatic schema generation for LLM function calling","Integrate external MCP servers (e.g., Claude's file tools, web search) without reimplementing tool logic","Execute tools in a sandboxed environment with controlled access to system resources and APIs","Iterate on tool execution with automatic retry and error handling when tools fail"],"best_for":["Developers building agents that need custom business logic tools (database queries, API calls, file operations)","Teams adopting MCP standard for tool interoperability across AI platforms","Applications requiring tool execution isolation for security and resource control"],"limitations":["Sandbox isolation adds ~100-500ms overhead per tool call due to context switching and validation","MCP server discovery is static (configured at startup); no dynamic server registration at runtime","Tool result serialization to JSON may lose fidelity for complex Python objects (custom classes, binary data)","No built-in tool versioning; breaking changes to tool schemas require manual migration"],"requires":["Python 3.9+","Tool definitions as Python functions with type hints or JSON schemas","MCP server endpoints (if using remote tools) with stdio or HTTP transport","Appropriate API credentials for tools that call external services"],"input_types":["tool definitions (Python functions with @tool decorator or JSON schema objects)","tool call requests from LLM (tool_name + arguments)","MCP server configurations (endpoint, transport type)"],"output_types":["tool execution results (JSON-serializable objects)","error messages with stack traces","tool call history for agent iteration"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_3","uri":"capability://tool.use.integration.event.driven.plugin.system.with.hot.reload.and.marketplace.distribution","name":"event-driven plugin system with hot reload and marketplace distribution","description":"AstrBot implements a plugin architecture (called 'Stars') built on an event bus that decouples plugins from core systems. Plugins register event handlers and commands at startup, can be loaded/unloaded dynamically without restarting the application, and persist configuration in a plugin-specific storage layer. The system includes a plugin marketplace for discovery and installation, automatic dependency resolution, and a context API that provides plugins with access to agent state, configuration, and platform adapters. Hot reload enables rapid iteration during development by reloading plugin code without losing application state.","intents":["Extend AstrBot with custom features (commands, event handlers, integrations) without modifying core code","Develop and test plugins in isolation with automatic hot reload on file changes","Distribute plugins via a marketplace for community discovery and installation","Access agent state, configuration, and platform adapters from plugin code via a stable context API"],"best_for":["Teams building extensible AI agents with community-contributed features","Developers iterating on custom plugins who want rapid feedback loops","Organizations distributing internal tools as plugins across multiple AstrBot deployments"],"limitations":["Hot reload does not persist plugin state across reloads; stateful plugins must use external storage","Plugin dependencies must be manually managed; no automatic dependency resolution for Python packages","Event bus is in-process only; plugins cannot be distributed across multiple machines","Plugin marketplace requires manual curation; no automated security scanning or version compatibility checking"],"requires":["Python 3.9+","Plugin code as Python module with entry point in pyproject.toml or plugin.yaml","Event handler registration via @on_event or @on_command decorators","Access to AstrBot's context API (injected at plugin initialization)"],"input_types":["plugin source code (Python modules)","plugin metadata (name, version, dependencies, entry points)","event payloads (Message, Command, etc.)","plugin configuration (YAML or JSON)"],"output_types":["event handler results (responses, side effects)","command outputs (text, structured data)","plugin state (persisted to storage layer)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_4","uri":"capability://planning.reasoning.message.processing.pipeline.with.security.filtering.and.result.decoration","name":"message processing pipeline with security filtering and result decoration","description":"AstrBot implements a multi-stage message processing pipeline that routes incoming messages through security/filtering stages (content moderation, rate limiting, permission checks), a main agent processing stage (LLM inference + tool execution), and result decoration stages (formatting, embedding generation, response assembly). Each stage is pluggable and can be extended or replaced. The pipeline uses an async/await pattern for non-blocking I/O and supports streaming responses where intermediate results are sent to the user before the full response is complete. Pipeline stages have access to a shared context object containing message metadata, agent state, and configuration.","intents":["Filter incoming messages for spam, profanity, or policy violations before agent processing","Apply rate limiting and permission checks to prevent abuse and unauthorized access","Process messages through an agent (LLM + tools) with streaming results for real-time feedback","Decorate agent responses with formatting, citations, or metadata before sending to users"],"best_for":["Teams building production chatbots that need content moderation and rate limiting","Developers who want to customize message processing without forking the core codebase","Applications requiring streaming responses for low-latency user experiences"],"limitations":["Pipeline stages are sequential; no parallel execution of independent stages","Streaming responses require client support (Server-Sent Events or WebSocket); not all platforms support streaming","Security filtering stages add ~50-200ms latency per message; high-volume deployments may need caching","Custom pipeline stages must be registered at startup; no dynamic stage injection at runtime"],"requires":["Python 3.9+","Async-compatible event loop (asyncio)","Custom pipeline stage implementations (if extending beyond built-in stages)"],"input_types":["incoming Message objects from platform adapters","user context (user_id, permissions, rate limit state)","agent configuration (model, tools, system prompt)"],"output_types":["processed Message objects with agent response","streaming response tokens (for streaming-enabled clients)","decorated response with metadata (citations, tool calls, etc.)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_5","uri":"capability://planning.reasoning.agent.orchestration.with.subagent.routing.and.skill.composition","name":"agent orchestration with subagent routing and skill composition","description":"AstrBot implements a hierarchical agent system where a main agent can delegate tasks to subagents based on intent classification or explicit routing rules. Agents are composed of skills (reusable task templates) and tools, with configuration-driven agent instantiation. The system manages agent lifecycle (initialization, context setup, execution), handles agent-to-agent communication via a message queue, and provides a unified execution interface that abstracts whether a task is handled locally or delegated to a subagent. Agent context includes conversation history, user metadata, and access to platform adapters.","intents":["Route user requests to specialized subagents (e.g., customer support agent, billing agent) based on intent","Compose complex workflows by chaining multiple agents with shared context","Reuse agent configurations and skills across multiple deployments via configuration files","Monitor and log agent execution for debugging and performance analysis"],"best_for":["Teams building large-scale chatbots with multiple specialized agents (support, sales, billing, etc.)","Organizations with complex workflows that benefit from agent specialization and skill reuse","Developers who want to test agent behavior in isolation before integrating into the main system"],"limitations":["Subagent routing is synchronous; no parallel execution of multiple subagents","Agent context is copied between agents; changes in subagent context do not propagate back to parent agent","Skill composition is static (defined at startup); no dynamic skill injection at runtime","No built-in load balancing or failover for subagent execution; single subagent failure blocks the parent agent"],"requires":["Python 3.9+","Agent configuration files (YAML or JSON) defining agent name, model, tools, skills, routing rules","Skill definitions (Python functions or external tool definitions)"],"input_types":["user messages","agent configuration (model, tools, skills, routing rules)","skill definitions"],"output_types":["agent response (text, tool calls, subagent delegation)","execution trace (agent name, tools called, subagents invoked)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_6","uri":"capability://memory.knowledge.knowledge.base.system.with.semantic.search.and.rag.integration","name":"knowledge base system with semantic search and rag integration","description":"AstrBot implements a knowledge base layer that stores documents, embeddings, and metadata in a vector database. The system supports semantic search via embedding similarity, retrieval-augmented generation (RAG) where relevant documents are injected into the LLM context, and configurable chunking strategies for long documents. Knowledge bases can be populated via file upload, web scraping, or API integration. The RAG pipeline automatically retrieves relevant documents based on user queries and injects them into the agent's system prompt or conversation history, enabling the agent to answer questions grounded in custom knowledge.","intents":["Build a custom knowledge base from documents (PDFs, web pages, API responses) for domain-specific Q&A","Retrieve relevant documents for a user query and inject them into the LLM context for grounded answers","Configure chunking and embedding strategies to optimize retrieval quality and latency","Update knowledge bases dynamically without retraining or restarting the agent"],"best_for":["Teams building domain-specific chatbots (customer support, product documentation, internal knowledge bases)","Developers who want to ground LLM responses in custom data without fine-tuning","Organizations with frequently-updated knowledge that needs to be reflected in agent responses"],"limitations":["Semantic search quality depends on embedding model quality; poor embeddings lead to irrelevant retrievals","RAG adds ~200-500ms latency per query due to embedding generation and vector search","Chunking strategies are lossy; splitting documents may break semantic coherence","Vector database must be kept in sync with source documents; no automatic change detection or incremental updates"],"requires":["Python 3.9+","Vector database (e.g., Pinecone, Weaviate, Milvus, or local FAISS)","Embedding model (OpenAI, Hugging Face, or local)","Document sources (files, web URLs, APIs)"],"input_types":["documents (PDF, text, markdown, web pages)","user queries (text)","chunking configuration (chunk_size, overlap, strategy)"],"output_types":["retrieved documents (with relevance scores)","augmented LLM context (documents injected into system prompt or conversation history)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_7","uri":"capability://text.generation.language.persona.system.with.dynamic.personality.and.response.style.customization","name":"persona system with dynamic personality and response style customization","description":"AstrBot implements a persona system that allows configuration of agent personality, tone, response style, and behavioral constraints via YAML/JSON configuration files. Personas are injected into the system prompt and influence agent behavior across all interactions. The system supports persona switching at runtime, persona composition (combining multiple personas), and persona-specific tool restrictions. Personas can be versioned and shared across multiple agents, enabling consistent personality across deployments.","intents":["Configure agent personality (friendly, professional, technical, etc.) without modifying code","Switch agent personas at runtime based on user context or explicit requests","Compose multiple personas to create nuanced behavior (e.g., friendly + professional)","Restrict tool access based on persona (e.g., billing agent cannot access user deletion tools)"],"best_for":["Teams building customer-facing chatbots that need consistent personality across channels","Organizations with multiple agents that share a common personality framework","Developers who want to A/B test different agent personalities without code changes"],"limitations":["Persona injection via system prompt is not guaranteed; LLM may ignore or override persona instructions","Persona composition (multiple personas) may create conflicting instructions; no conflict resolution","Tool restrictions based on persona are enforced at the agent level; no fine-grained permission system","Personas are static after agent initialization; no dynamic persona updates without agent restart"],"requires":["Python 3.9+","Persona configuration files (YAML or JSON) defining personality traits, tone, constraints","System prompt template that includes persona placeholders"],"input_types":["persona configuration (name, traits, tone, constraints, tool restrictions)","user context (for persona selection)"],"output_types":["system prompt with persona injected","agent responses influenced by persona"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_8","uri":"capability://automation.workflow.web.dashboard.with.provider.management.platform.configuration.and.system.monitoring","name":"web dashboard with provider management, platform configuration, and system monitoring","description":"AstrBot includes a web-based dashboard (built with modern frontend framework) that provides UI for managing LLM providers, configuring platform connections, installing/managing plugins, viewing conversation history, and monitoring system health. The dashboard uses dynamic configuration components that adapt to provider-specific settings (e.g., OpenAI vs. Anthropic parameters). The backend exposes REST APIs for all dashboard operations, enabling programmatic configuration management. The dashboard supports real-time updates via WebSocket for monitoring agent execution and system metrics.","intents":["Configure LLM providers and model instances without editing YAML files","Connect and configure platform adapters (Discord, Telegram, QQ) via UI","Install and manage plugins from the marketplace","Monitor agent execution, conversation history, and system health in real-time","View and manage system configuration (settings, feature flags, etc.)"],"best_for":["Non-technical users who want to configure AstrBot without editing configuration files","Teams managing multiple AstrBot deployments who need centralized configuration management","Developers debugging agent behavior who want to inspect conversation history and execution traces"],"limitations":["Dashboard is single-instance; no multi-user access control or role-based permissions","Real-time updates via WebSocket add complexity and may not scale to high-concurrency deployments","Dynamic configuration components require provider-specific UI implementations; adding new providers requires dashboard updates","No audit logging for configuration changes; no rollback mechanism for configuration errors"],"requires":["Python 3.9+","Node.js 16+ (for frontend build)","Web browser with WebSocket support (for real-time updates)","Network connectivity to AstrBot backend"],"input_types":["provider configuration (model name, API key, parameters)","platform configuration (bot token, webhook URL, etc.)","plugin installation requests","system configuration updates"],"output_types":["configuration state (JSON)","conversation history (paginated)","system metrics (CPU, memory, API latency)","execution traces (agent calls, tool invocations)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-astrbotdevs--astrbot__cap_9","uri":"capability://memory.knowledge.conversation.history.persistence.with.umop.session.routing.and.context.management","name":"conversation history persistence with umop session routing and context management","description":"AstrBot implements a conversation persistence layer that stores message history in a database, with support for multi-turn conversations, session management, and context routing via UMOP (User-Message-Operation-Platform) keys. The system automatically manages conversation context (selecting relevant history for LLM context), implements sliding window and summarization strategies for long conversations, and supports conversation search and retrieval. Sessions are scoped by user, platform, and conversation ID, enabling multi-platform conversation continuity (e.g., user starts conversation on Discord, continues on Telegram).","intents":["Persist conversation history across agent restarts and platform disconnections","Retrieve relevant conversation context for multi-turn interactions without exceeding token limits","Search conversation history by user, platform, or content","Support multi-platform conversation continuity (same user, different platforms)"],"best_for":["Teams building stateful chatbots that need conversation persistence","Applications with long-running conversations requiring intelligent context management","Organizations supporting users across multiple platforms who want unified conversation history"],"limitations":["Database queries for conversation retrieval add ~50-200ms latency per message","Context selection strategies (sliding window, summarization) are heuristic-based; may miss important context","Multi-platform conversation continuity requires consistent user identification across platforms; no built-in cross-platform user linking","Conversation search is full-text only; no semantic search across conversation history"],"requires":["Python 3.9+","Database (SQLite, PostgreSQL, MySQL, etc.) for conversation storage","User identification mechanism (user_id, email, etc.) for session scoping"],"input_types":["Message objects with user_id, platform, conversation_id","context management configuration (window size, summarization strategy)"],"output_types":["conversation history (list of Message objects)","selected context (subset of history for LLM)","search results (matching messages with relevance scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Platform API credentials (Discord bot token, Telegram API key, QQ bot credentials, etc.)","Network connectivity for webhook endpoints or polling intervals","API keys for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","Network connectivity to provider endpoints or local Ollama instance","Configuration files (YAML or JSON) in the expected directory structure","Environment variables for sensitive settings (API keys, database URLs, etc.)","File storage backend (local filesystem, S3, Azure Blob Storage, etc.)","Optional: image processing library (Pillow) for image transformation","Optional: virus scanning service (ClamAV, VirusTotal, etc.)"],"failure_modes":["Platform-specific features (Discord threads, Telegram inline queries) may lose fidelity during normalization","Webhook mode requires public endpoint exposure; polling mode adds latency and API quota consumption","Rich message components (buttons, carousels) must be manually reconstructed per platform in response stage","Context compression strategies are lossy; summarization may drop important details from early conversation turns","Streaming adds ~50-200ms latency per token due to network round-trips and buffering","Provider-specific features (vision, function calling schemas) require adapter-level implementation; not all features available across all providers","Retry logic uses fixed exponential backoff; no adaptive strategies based on error type","Configuration hot-reloading may cause race conditions if configuration is accessed during reload; requires careful synchronization","Environment variable interpolation is string-based; no type coercion (e.g., `${PORT}` is always a string)","Configuration validation is schema-based; complex validation logic must be implemented separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7662963388265973,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-05-03T12:41:50Z"},"community":{"stars":31227,"forks":2153,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=astrbotdevs--astrbot","compare_url":"https://unfragile.ai/compare?artifact=astrbotdevs--astrbot"}},"signature":"Sv0/MW1XmrKm1N25ckxpSJ/FCDMYR7vCEa5w6gt40LvStNVPwpN9GEhyOfYSOHe/UAYZElJnNQ1r4mzga3rlCg==","signedAt":"2026-06-22T18:32:33.556Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/astrbotdevs--astrbot","artifact":"https://unfragile.ai/astrbotdevs--astrbot","verify":"https://unfragile.ai/api/v1/verify?slug=astrbotdevs--astrbot","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"}}