{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-1panel-dev-maxkb","slug":"mcp-1panel-dev-maxkb","name":"MaxKB","type":"repo","url":"https://github.com/1Panel-dev/MaxKB","page_url":"https://unfragile.ai/mcp-1panel-dev-maxkb","categories":["ai-agents"],"tags":["agent","agentic-ai","chatbot","deepseek-r1","knowledgebase","langchain","llama3","llm","maxkb","mcp-server","ollama","pgvector","qwen3","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-1panel-dev-maxkb__cap_0","uri":"capability://memory.knowledge.rag.powered.multi.document.knowledge.base.indexing.with.vector.embeddings","name":"rag-powered multi-document knowledge base indexing with vector embeddings","description":"MaxKB implements a document ingestion pipeline that processes uploaded files (PDF, Word, TXT, Markdown) into paragraph-level chunks, generates vector embeddings using configurable embedding models (BERT-based or API-backed), and stores them in PostgreSQL with pgvector extension for semantic search. The system handles batch vectorization asynchronously via Celery workers, tracks embedding status per document, and supports incremental re-indexing when documents are updated. Paragraph management includes problem-solution pairing for enhanced retrieval context.","intents":["I need to upload enterprise documents and make them searchable by semantic meaning, not just keywords","I want to build a knowledge base that can answer questions grounded in my company's documentation","I need to process large document batches without blocking the UI, with visibility into embedding progress","I want to update documents and have the knowledge base automatically re-index only changed content"],"best_for":["Enterprise teams building internal knowledge bases for customer support or employee onboarding","Organizations migrating from keyword-search to semantic search without rewriting infrastructure","Teams needing on-premise or self-hosted RAG without reliance on external embedding APIs"],"limitations":["Paragraph chunking strategy is fixed (no configurable chunk size or overlap in current architecture)","Embedding generation is synchronous per document in batch mode — large documents may timeout","No built-in deduplication across documents — duplicate content creates redundant embeddings","pgvector similarity search has no native reranking — relies on downstream LLM for relevance filtering","Batch operations lack granular error recovery — single failed document can stall entire batch"],"requires":["PostgreSQL 12+ with pgvector extension installed","Python 3.9+","Embedding model endpoint (local BERT, OpenAI, or Ollama)","Celery worker process for async embedding tasks","Sufficient disk space for document storage and vector indices"],"input_types":["PDF files","Microsoft Word documents (.docx)","Plain text files (.txt)","Markdown files (.md)","Web URLs (for web scraping integration)"],"output_types":["Vector embeddings (float arrays, dimension varies by model)","Paragraph metadata (text, source document, position)","Batch operation status (queued, processing, completed, failed)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.management.with.unified.provider.abstraction","name":"multi-provider llm model management with unified provider abstraction","description":"MaxKB abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, Qwen, DeepSeek, Llama3) behind a unified model configuration interface. The system stores provider credentials securely, supports model-specific parameters (temperature, max_tokens, system prompts), and routes inference requests through provider-specific adapters built on LangChain. Model configurations are workspace-scoped and can be switched at runtime without code changes. The architecture supports both cloud-hosted and self-hosted models (via Ollama).","intents":["I want to switch between different LLM providers (OpenAI to Anthropic) without rewriting my agent logic","I need to run open-source models locally (Ollama) for cost control and data privacy","I want to configure model-specific parameters (temperature, max tokens) per application without hardcoding","I need to support multiple LLM providers in the same workspace for A/B testing or fallback scenarios"],"best_for":["Teams evaluating multiple LLM providers and wanting to avoid vendor lock-in","Enterprises requiring on-premise LLM deployment for compliance or data residency","Builders prototyping agents and wanting to experiment with different model capabilities"],"limitations":["No built-in model fallback or retry logic — if primary provider fails, request fails immediately","Provider-specific features (vision, function calling) require custom adapter code per provider","Model parameter validation is minimal — invalid parameters may fail at inference time, not config time","No cost tracking or usage metering per provider — difficult to optimize spend across models","Streaming responses are provider-dependent — not all providers support consistent streaming behavior"],"requires":["API keys for cloud providers (OpenAI, Anthropic, etc.) OR Ollama instance running locally","LangChain library (included in dependencies)","Network access to provider endpoints or local Ollama service on port 11434"],"input_types":["Model configuration JSON (provider name, API key, model ID, parameters)","Inference requests (prompt text, system message, parameters)"],"output_types":["Model responses (text, streaming tokens)","Usage metadata (tokens consumed, cost estimates)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_10","uri":"capability://safety.moderation.prompt.injection.detection.and.content.filtering.for.safety","name":"prompt injection detection and content filtering for safety","description":"MaxKB implements content filtering and prompt injection detection before sending user messages to LLMs. The system uses pattern matching and heuristics to detect common prompt injection techniques (e.g., 'ignore previous instructions', 'system prompt override'). Filtered messages are logged for analysis. The system also supports custom content filters per workspace. Responses from LLMs are optionally filtered for sensitive content (PII, profanity) before returning to users.","intents":["I want to prevent users from manipulating my agent via prompt injection attacks","I need to filter sensitive content (PII, profanity) from agent responses","I want to log and analyze attempted prompt injections for security insights","I need to customize content filters based on my organization's policies"],"best_for":["Organizations deploying agents in untrusted environments (public chatbots)","Teams with compliance requirements (PII filtering, content moderation)","Builders needing visibility into prompt injection attempts"],"limitations":["Prompt injection detection is heuristic-based — sophisticated attacks may bypass filters","Content filtering is regex-based — no semantic understanding of context (e.g., 'bank' in 'riverbank' vs 'financial bank')","No built-in PII detection — requires external PII detection service or custom regex","Filtering is applied uniformly — no per-user or per-context filtering policies","False positives are common — legitimate queries may be filtered incorrectly","No real-time filter updates — new attack patterns require manual filter updates"],"requires":["Content filter definitions (regex patterns or heuristic rules)","Optional: External PII detection service (e.g., AWS Macie, Microsoft Presidio)","Logging infrastructure for filtered messages"],"input_types":["User message (text)","Filter rules (regex patterns, heuristics)","LLM response (text)"],"output_types":["Filtered message (with injections removed)","Filter decision (allowed/blocked)","Filter log entry (message, filter rule matched, timestamp)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_11","uri":"capability://safety.moderation.operation.audit.logging.with.user.attribution.and.resource.tracking","name":"operation audit logging with user attribution and resource tracking","description":"MaxKB logs all significant operations (create, update, delete, execute) with user attribution, timestamp, resource ID, and operation details. Audit logs are stored in PostgreSQL and queryable via API. The system supports filtering logs by user, resource type, operation type, and date range. Audit logs are immutable (append-only) and cannot be deleted by regular users. This enables compliance auditing and forensic analysis of system changes.","intents":["I need to audit who created, modified, or deleted applications and knowledge bases for compliance","I want to track when agents were executed and by whom for usage analytics","I need to investigate security incidents by reviewing operation history","I want to generate audit reports for compliance auditors"],"best_for":["Organizations with compliance requirements (SOC2, HIPAA, GDPR)","Teams needing forensic analysis of system changes","Builders requiring usage analytics and operation tracking"],"limitations":["Audit logs are not encrypted — sensitive operation details may be visible to database admins","Log retention is unlimited — audit table can grow very large over time","No built-in log archival or compression — old logs consume database space","Audit log queries are not optimized — large date ranges may be slow","No real-time alerting on suspicious operations — requires manual log review","Audit logs do not include LLM API calls or external tool invocations — incomplete operation tracking"],"requires":["PostgreSQL for audit log storage","Audit logging middleware in Django application","User authentication system for user attribution"],"input_types":["Operation type (create, update, delete, execute)","Resource ID (application, knowledge base, etc.)","User ID (authenticated user)","Operation details (changed fields, parameters)"],"output_types":["Audit log entry (operation, user, timestamp, resource, details)","Audit log query result (filtered logs)","Audit report (summary of operations by user/resource/type)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_12","uri":"capability://text.generation.language.internationalization.and.multi.language.ui.support","name":"internationalization and multi-language ui support","description":"MaxKB implements internationalization (i18n) via Django's translation framework, supporting multiple languages (English, Chinese, etc.) in the UI. Language selection is per-user and persisted in user preferences. The system uses gettext for translation string extraction and management. Frontend components use i18n libraries (Vue i18n) to render translated strings. API responses include language-specific content (error messages, labels). This enables global deployment without separate language-specific instances.","intents":["I want to deploy MaxKB to users in different countries with localized UI","I need to support multiple languages without maintaining separate instances","I want to customize language translations for my organization's terminology"],"best_for":["Organizations deploying MaxKB globally with multi-language user bases","Teams needing localized UI for different regions"],"limitations":["Translation coverage is incomplete — some UI strings may not be translated","Right-to-left (RTL) languages require custom CSS — not automatically supported","Translation updates require redeployment — no runtime translation management","No built-in translation management UI — translations are managed via gettext files","LLM responses are not automatically translated — only UI is localized"],"requires":["Django i18n framework","gettext tools for translation extraction","Translation files (.po, .mo) for each language","Frontend i18n library (Vue i18n)"],"input_types":["Language code (e.g., 'en', 'zh')","Translation strings (gettext format)"],"output_types":["Localized UI (HTML, JSON)","Translated error messages","Translated labels and buttons"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_2","uri":"capability://automation.workflow.node.based.workflow.orchestration.engine.with.conditional.branching.and.tool.integration","name":"node-based workflow orchestration engine with conditional branching and tool integration","description":"MaxKB implements a visual workflow designer backed by a node-based execution engine that supports sequential and conditional execution paths. Workflow nodes include LLM inference, tool calling, knowledge base retrieval, code execution, and branching logic. The engine executes workflows via a state machine pattern, passing context between nodes and supporting loops and error handling. Workflows are stored as JSON definitions and executed asynchronously via Celery, with execution history and step-level logging for debugging. Tool nodes integrate with the code sandbox for safe custom code execution.","intents":["I want to design multi-step agent workflows visually without writing code","I need to route execution based on LLM output (e.g., if intent is 'refund', call refund tool)","I want to combine knowledge base retrieval, LLM reasoning, and tool execution in a single workflow","I need to debug workflow execution by inspecting intermediate outputs at each node"],"best_for":["Non-technical domain experts designing agent workflows for customer support or internal processes","Teams building complex multi-step agents that require conditional logic and tool orchestration","Organizations needing workflow auditability and step-level execution logging for compliance"],"limitations":["No built-in loop constructs — conditional branching exists but iterative workflows require workarounds","Node-to-node context passing is implicit (via shared execution state) — difficult to reason about data flow","Error handling is basic — no retry policies or circuit breakers at node level","Workflow versioning is not enforced — no rollback mechanism if workflow definition is corrupted","Execution latency scales with node count — no parallel node execution, only sequential or branched paths","Debugging requires inspecting execution logs — no built-in breakpoints or step-through debugging"],"requires":["Django application running with workflow engine module","Celery worker for async workflow execution","PostgreSQL for workflow definition and execution history storage","Tool sandbox (sandbox.so) if using code execution nodes"],"input_types":["Workflow definition JSON (node types, connections, parameters)","User input (initial prompt or form data)","Context variables (user ID, session data)"],"output_types":["Workflow execution result (final LLM response or tool output)","Execution trace (node-by-node outputs and timing)","Execution status (completed, failed, timeout)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_3","uri":"capability://code.generation.editing.sandboxed.custom.tool.code.execution.with.system.call.interception","name":"sandboxed custom tool code execution with system call interception","description":"MaxKB provides a secure code execution environment for custom tools via a C-based sandbox (sandbox.so) that intercepts system calls and restricts file system access, network calls, and process spawning. Python code submitted as tool definitions is executed within this sandbox, allowing builders to extend agent capabilities with custom logic while preventing malicious code from accessing sensitive resources. The ToolExecutor class manages code compilation, sandboxing, and error handling. Execution results are captured and returned to the workflow engine.","intents":["I want to create custom tools that execute Python code without exposing my system to arbitrary code execution","I need to allow non-technical users to define tools via a UI without worrying about security","I want to integrate custom business logic (data transformation, API calls) into agent workflows safely"],"best_for":["Teams building agents with custom business logic that can't be expressed via existing tools","Organizations with strict security policies requiring code execution isolation","Builders needing to support user-defined tools in a multi-tenant environment"],"limitations":["Sandbox is Linux-only (C library compiled for Linux) — no Windows or macOS support","System call interception has performance overhead (~5-10% per call) — CPU-intensive tools are slower","No built-in timeout enforcement — long-running code can block workflow execution","Limited debugging visibility — errors inside sandbox are captured but stack traces may be truncated","Network access is restricted — tools cannot make HTTP requests or access external APIs","File system access is restricted to temporary directories — no persistent state between executions"],"requires":["Linux operating system (sandbox.so compiled for Linux only)","Python 3.9+ with ctypes for sandbox library loading","sandbox.so compiled and available in system library path","Tool code must be valid Python syntax"],"input_types":["Python code string (tool implementation)","Tool parameters (arguments passed to tool function)","Execution context (workflow variables, user data)"],"output_types":["Tool execution result (return value from tool function)","Execution error (if code raises exception)","Execution metadata (runtime, memory usage)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_4","uri":"capability://safety.moderation.multi.tenant.workspace.isolation.with.role.based.access.control","name":"multi-tenant workspace isolation with role-based access control","description":"MaxKB implements workspace-scoped multi-tenancy where each workspace is an isolated container for applications, knowledge bases, models, and users. Access control is enforced via role-based permissions (admin, editor, viewer) with fine-grained resource-level checks. User authentication uses JWT tokens, and workspace membership is tracked in a separate relation. The system supports workspace-level configuration (model defaults, embedding settings) and audit logging of all operations. Workspace data is logically isolated in the database but shares the same PostgreSQL instance.","intents":["I want to host multiple teams or customers in a single MaxKB instance with complete data isolation","I need to control who can create applications, edit knowledge bases, and invoke agents within my workspace","I want to audit all operations (who created what, when) for compliance and security","I need to configure workspace-level defaults (default LLM model, embedding settings) without affecting other workspaces"],"best_for":["SaaS platforms built on MaxKB serving multiple customers","Enterprises with multiple teams needing isolated agent environments","Organizations with compliance requirements (SOC2, HIPAA) needing audit trails and access control"],"limitations":["Workspace isolation is logical, not physical — shared database means potential for SQL injection to leak cross-workspace data","No built-in workspace quotas — one workspace can consume all resources, starving others","Permission checks are scattered across views and serializers — easy to miss authorization checks in new features","Audit logging is not comprehensive — some operations (internal tool calls) may not be logged","No workspace-level backup or restore — recovery requires database-level operations","Cross-workspace operations (sharing knowledge bases between workspaces) are not supported"],"requires":["Django application with workspace middleware","PostgreSQL database with audit logging tables","JWT token generation and validation (via Django REST Framework)","User authentication system (local or LDAP/OAuth)"],"input_types":["User credentials (username/password or OAuth token)","Workspace ID (in request context)","Resource ID (application, knowledge base, etc.)"],"output_types":["JWT token (for authenticated requests)","Authorization decision (allowed/denied)","Audit log entry (operation, user, timestamp, resource)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_5","uri":"capability://text.generation.language.streaming.chat.interface.with.real.time.token.delivery.and.multi.platform.support","name":"streaming chat interface with real-time token delivery and multi-platform support","description":"MaxKB implements a streaming chat interface that delivers LLM responses token-by-token to clients via Server-Sent Events (SSE) or WebSocket, providing real-time feedback without waiting for full response generation. The chat system supports multiple platforms (web, mobile, embedded widgets) via a unified backend API. Chat messages are persisted with full history, and the system supports file uploads and speech-to-text transcription within chat sessions. Message processing includes prompt injection detection and content filtering before sending to LLM.","intents":["I want to provide a responsive chat experience where users see LLM responses appearing in real-time","I need to embed a chat widget in my website without building a custom frontend","I want to support file uploads and voice input in chat conversations","I need to maintain chat history for context in multi-turn conversations"],"best_for":["Teams building customer-facing chatbots requiring responsive UX","Organizations embedding agents in websites or mobile apps","Builders needing multi-turn conversation context for complex reasoning tasks"],"limitations":["Streaming is provider-dependent — not all LLM providers support consistent token streaming","SSE connections are unidirectional — no server-to-client backpressure if client is slow","Chat history is stored in database — no built-in compression or archival for long-running conversations","File upload size limits are fixed — no configurable per-workspace limits","Speech-to-text requires external API (e.g., OpenAI Whisper) — no local speech recognition","No built-in rate limiting per user or session — vulnerable to abuse via rapid message submission"],"requires":["Django application with streaming response support","LLM provider with streaming API support (OpenAI, Anthropic, Ollama)","Frontend client supporting SSE or WebSocket (browser, mobile app)","PostgreSQL for chat history storage","Optional: Speech-to-text API (OpenAI Whisper, Google Cloud Speech)"],"input_types":["User message (text)","File upload (PDF, image, audio)","Chat session ID (for context retrieval)","User metadata (ID, workspace)"],"output_types":["Streaming tokens (via SSE or WebSocket)","Complete message (after streaming finishes)","Chat history (previous messages in session)","File metadata (upload status, processing result)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_6","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.for.standardized.tool.calling","name":"mcp (model context protocol) server integration for standardized tool calling","description":"MaxKB integrates with the Model Context Protocol (MCP) standard, allowing agents to discover and invoke tools via a standardized interface. The system exposes MaxKB tools (knowledge base search, workflow execution) as MCP resources and supports external MCP servers for third-party integrations. Tool schemas are automatically generated from function signatures and validated before execution. This enables interoperability with other MCP-compatible systems and reduces vendor lock-in for tool definitions.","intents":["I want to use tools from external MCP servers (e.g., Anthropic's file system tools) in my MaxKB agents","I need to expose MaxKB capabilities (knowledge base search) to other MCP-compatible systems","I want to standardize tool definitions across multiple agent platforms using MCP","I need to dynamically discover available tools without hardcoding tool lists"],"best_for":["Teams building multi-system agent architectures with MCP-compatible tools","Organizations standardizing on MCP for tool interoperability","Builders integrating MaxKB with other MCP servers (Claude, other LLM platforms)"],"limitations":["MCP support is partial — not all MaxKB tools are exposed as MCP resources","External MCP server integration requires manual configuration — no auto-discovery","MCP schema validation is basic — complex tool schemas may not translate correctly","No built-in MCP server authentication — assumes trusted MCP servers on same network","Streaming responses through MCP have latency overhead — not suitable for real-time applications","Tool execution errors in MCP context may not propagate clearly to calling system"],"requires":["MCP server implementation (Anthropic MCP SDK or compatible)","Tool schema definitions in JSON Schema format","Network connectivity to external MCP servers (if using remote tools)","Python MCP client library"],"input_types":["MCP tool schema (JSON Schema)","Tool invocation request (tool name, parameters)","MCP server configuration (endpoint, credentials)"],"output_types":["Tool execution result (structured data)","MCP resource listing (available tools)","Tool schema documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_7","uri":"capability://automation.workflow.asynchronous.task.processing.with.celery.for.long.running.operations","name":"asynchronous task processing with celery for long-running operations","description":"MaxKB uses Celery for asynchronous task processing, offloading long-running operations (document embedding, workflow execution, batch operations) from the request-response cycle. Tasks are queued in Redis or RabbitMQ and executed by worker processes, with status tracking and result storage. The system supports task retries, timeouts, and error callbacks. Embedding tasks are prioritized and can be monitored via a task status API. This architecture enables responsive UI even during heavy processing loads.","intents":["I want to upload large documents and have them embedded in the background without blocking the UI","I need to execute long-running workflows without timing out HTTP requests","I want to monitor the progress of batch operations (embedding, document processing)","I need to retry failed tasks automatically without manual intervention"],"best_for":["Teams processing large document batches or running complex workflows","Organizations needing responsive UI during heavy background processing","Builders requiring task monitoring and retry logic for reliability"],"limitations":["Celery adds operational complexity — requires separate worker processes and message broker","Task status is eventually consistent — UI may show stale status if workers are slow","No built-in task prioritization — all tasks are processed FIFO regardless of importance","Worker failures can cause task loss if not configured with persistent queues","Debugging Celery tasks is difficult — errors may be hidden in worker logs, not visible in UI","Scaling workers horizontally requires load balancing and monitoring infrastructure"],"requires":["Celery library (included in dependencies)","Message broker (Redis or RabbitMQ)","Celery worker process(es) running separately from Django application","Task result backend (Redis, database, or file system)"],"input_types":["Task definition (function name, arguments)","Task configuration (retry policy, timeout, priority)"],"output_types":["Task ID (for status tracking)","Task status (queued, processing, completed, failed)","Task result (output data or error message)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_8","uri":"capability://search.retrieval.paragraph.level.knowledge.base.search.with.semantic.and.keyword.hybrid.retrieval","name":"paragraph-level knowledge base search with semantic and keyword hybrid retrieval","description":"MaxKB implements hybrid search combining semantic similarity (via vector embeddings) and keyword matching to retrieve relevant paragraphs from the knowledge base. The search engine queries pgvector for semantic matches and PostgreSQL full-text search for keyword matches, then ranks results by relevance. Search results include source document metadata and paragraph context. The system supports filtering by document, knowledge base, or custom metadata. Reranking can be applied via LLM to improve result quality.","intents":["I want to search my knowledge base using natural language queries, not just keywords","I need to retrieve paragraphs with high relevance to user questions for RAG context","I want to combine semantic and keyword search to handle both conceptual and exact-match queries","I need to filter search results by document or knowledge base without re-querying"],"best_for":["Teams building RAG systems with large document collections","Organizations needing high-quality retrieval for question-answering agents","Builders requiring hybrid search to handle diverse query types"],"limitations":["Semantic search quality depends on embedding model — poor embeddings lead to poor retrieval","Keyword search is basic (PostgreSQL full-text) — no advanced NLP like stemming or synonym expansion","No built-in reranking — top-k results may include low-relevance matches","Search latency scales with knowledge base size — no indexing optimization for large collections","Filtering is applied post-retrieval — inefficient for large result sets","No search analytics — difficult to identify low-quality queries or missing content"],"requires":["PostgreSQL with pgvector extension","Embedding model (local or API-based)","Knowledge base with indexed paragraphs","Optional: LLM for reranking results"],"input_types":["Query text (natural language or keyword)","Search filters (document ID, knowledge base ID, metadata)","Search parameters (top-k results, similarity threshold)"],"output_types":["Ranked paragraph list (text, source, relevance score)","Document metadata (title, URL, upload date)","Search metadata (query time, result count)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-1panel-dev-maxkb__cap_9","uri":"capability://automation.workflow.application.configuration.and.deployment.with.multi.channel.publishing","name":"application configuration and deployment with multi-channel publishing","description":"MaxKB allows builders to create applications (agents, chatbots) with configurable settings (model, knowledge base, system prompt, tools) and deploy them across multiple channels (web chat, API, embedded widget, Slack, WeChat). Each application has a unique configuration stored in the database and can be published to different channels with channel-specific settings. Applications can be versioned and rolled back. The system generates shareable links and API endpoints for each application.","intents":["I want to create a chatbot application and deploy it to my website, Slack, and mobile app simultaneously","I need to configure different system prompts or knowledge bases for different channels (customer support vs internal)","I want to version my application configuration and roll back to a previous version if needed","I need to generate API endpoints and shareable links for my application without writing code"],"best_for":["Teams building multi-channel chatbots (web, Slack, WeChat, etc.)","Organizations needing to deploy the same agent across multiple platforms with minimal effort","Builders requiring application versioning and rollback capabilities"],"limitations":["Channel-specific customization is limited — some channels may not support all features (e.g., file upload in Slack)","No built-in A/B testing — difficult to compare performance across channel variants","Application versioning is manual — no automatic version management or diff tracking","Channel integration requires manual setup per channel — no auto-discovery of available channels","No built-in analytics per channel — difficult to identify which channels are most effective","Rollback is database-level — no atomic rollback across all channels simultaneously"],"requires":["Django application with application management module","PostgreSQL for application configuration storage","Channel-specific integrations (Slack API, WeChat API, etc.)","Web server for hosting chat widget or API endpoint"],"input_types":["Application configuration (name, model, knowledge base, system prompt, tools)","Channel selection (web, API, Slack, WeChat, etc.)","Channel-specific settings (webhook URL, API key, etc.)"],"output_types":["Application ID (unique identifier)","Shareable link (for web chat)","API endpoint (for programmatic access)","Channel-specific credentials (webhook URL, token)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["PostgreSQL 12+ with pgvector extension installed","Python 3.9+","Embedding model endpoint (local BERT, OpenAI, or Ollama)","Celery worker process for async embedding tasks","Sufficient disk space for document storage and vector indices","API keys for cloud providers (OpenAI, Anthropic, etc.) OR Ollama instance running locally","LangChain library (included in dependencies)","Network access to provider endpoints or local Ollama service on port 11434","Content filter definitions (regex patterns or heuristic rules)","Optional: External PII detection service (e.g., AWS Macie, Microsoft Presidio)"],"failure_modes":["Paragraph chunking strategy is fixed (no configurable chunk size or overlap in current architecture)","Embedding generation is synchronous per document in batch mode — large documents may timeout","No built-in deduplication across documents — duplicate content creates redundant embeddings","pgvector similarity search has no native reranking — relies on downstream LLM for relevance filtering","Batch operations lack granular error recovery — single failed document can stall entire batch","No built-in model fallback or retry logic — if primary provider fails, request fails immediately","Provider-specific features (vision, function calling) require custom adapter code per provider","Model parameter validation is minimal — invalid parameters may fail at inference time, not config time","No cost tracking or usage metering per provider — difficult to optimize spend across models","Streaming responses are provider-dependent — not all providers support consistent streaming behavior","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.746514750448346,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-04-30T06:39:05Z"},"community":{"stars":20871,"forks":2812,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-1panel-dev-maxkb","compare_url":"https://unfragile.ai/compare?artifact=mcp-1panel-dev-maxkb"}},"signature":"YAaxc0/M26pCl2E7AQz4x7bjr5vcVWsAtBXggHjmmL3f5gIYuYRpa8a0A3glsmO9DifWCbW5heqHtyvZosXyBg==","signedAt":"2026-06-21T16:58:18.755Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-1panel-dev-maxkb","artifact":"https://unfragile.ai/mcp-1panel-dev-maxkb","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-1panel-dev-maxkb","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"}}