{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"librechat","slug":"librechat","name":"LibreChat","type":"repo","url":"https://github.com/danny-avila/LibreChat","page_url":"https://unfragile.ai/librechat","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"librechat__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.api","name":"multi-provider llm abstraction with unified api","description":"LibreChat implements a BaseClient architecture that abstracts OpenAI, Anthropic, Google, Azure, AWS Bedrock, and local models (Ollama, LM Studio) behind a single interface. Each provider has a dedicated implementation class that translates the unified message format into provider-specific API calls, handling differences in authentication, streaming, function calling schemas, and response formats. The system uses a factory pattern to instantiate the correct provider client based on configuration, enabling seamless provider switching without application-level changes.","intents":["I want to support multiple AI providers without rewriting chat logic for each one","I need to switch between OpenAI and Anthropic models without changing my UI code","I want to use local models alongside cloud providers in the same application"],"best_for":["teams building multi-provider AI applications","organizations avoiding vendor lock-in","developers prototyping with different model families"],"limitations":["Provider-specific features (e.g., vision capabilities, function calling) require conditional logic in agent/tool layers","Token counting and pricing calculations differ per provider — requires per-provider implementations","Streaming response handling varies by provider — adds complexity to real-time features"],"requires":["API keys for at least one provider (OpenAI, Anthropic, Google, Azure, or AWS)","Node.js 18+","Environment variables configured per provider"],"input_types":["text messages","images (for vision-capable models)","file uploads (converted to text/embeddings)"],"output_types":["streamed text responses","structured function calls","tool/agent execution results"],"categories":["tool-use-integration","multi-provider-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_1","uri":"capability://automation.workflow.yaml.based.configuration.system.with.schema.validation","name":"yaml-based configuration system with schema validation","description":"LibreChat uses a declarative YAML configuration system (librechat.yaml) that defines AI providers, models, endpoints, token pricing, and feature flags without code changes. The system includes a schema validator that ensures configuration correctness at startup, supporting environment variable interpolation for sensitive values. Configuration is loaded into a centralized config service that exposes typed accessors, enabling runtime feature toggles and multi-tenant model availability without redeployment.","intents":["I want to add new AI models without modifying code or redeploying","I need to configure different model sets for different user groups","I want to set token limits and pricing per model dynamically"],"best_for":["DevOps teams managing multi-environment deployments","organizations with frequent model additions/removals","self-hosted deployments requiring configuration flexibility"],"limitations":["YAML syntax errors cause startup failures — requires validation before deployment","Complex conditional logic in configuration is not supported — use environment variables or code for dynamic behavior","Configuration changes require application restart (no hot-reload)"],"requires":["librechat.yaml file in project root or specified via CONFIG_PATH env var","Valid YAML syntax","Environment variables for API keys and secrets"],"input_types":["YAML configuration files","environment variables"],"output_types":["typed configuration objects","provider/model availability lists","token pricing and limit metadata"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_10","uri":"capability://memory.knowledge.rag.system.with.vector.embeddings.and.semantic.search","name":"rag system with vector embeddings and semantic search","description":"LibreChat includes a Retrieval-Augmented Generation (RAG) system that converts documents into vector embeddings, stores them in a vector database, and retrieves relevant documents based on semantic similarity to user queries. The RAG pipeline includes document chunking, embedding generation (using OpenAI, Anthropic, or local embeddings), and vector storage (Pinecone, Weaviate, Milvus, or local vector DB). Retrieved documents are injected into agent context, enabling agents to answer questions grounded in custom knowledge bases.","intents":["I want agents to answer questions based on custom documents","I need to ground agent responses in proprietary knowledge bases","I want to reduce hallucinations by providing relevant context from documents"],"best_for":["knowledge management and documentation systems","customer support with product documentation","enterprise search and Q&A over internal documents"],"limitations":["Embedding quality depends on model choice — poor embeddings reduce retrieval accuracy","Vector database synchronization is manual — document updates require re-embedding and re-indexing","Semantic search may retrieve irrelevant documents if query is ambiguous — requires query expansion or reranking","Chunking strategy affects retrieval quality — fixed-size chunks may split important context","Vector database costs scale with document volume — large knowledge bases require expensive infrastructure"],"requires":["Vector database (Pinecone, Weaviate, Milvus, or local)","Embedding model (OpenAI, Anthropic, or local)","Documents in supported formats (PDF, DOCX, TXT, Markdown)","Vector database credentials and connection string"],"input_types":["documents (PDF, DOCX, TXT, Markdown, code files)","user queries (text)","embedding parameters (model, chunk size, overlap)"],"output_types":["vector embeddings","retrieved documents with relevance scores","augmented agent context","Q&A responses grounded in documents"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_11","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.per.provider","name":"token counting and cost estimation per provider","description":"LibreChat implements per-provider token counting and cost estimation that calculates API costs based on input/output tokens, model pricing, and usage patterns. Token counts are computed using provider-specific tokenizers (OpenAI's tiktoken, Anthropic's token counter, etc.) before API calls, enabling cost prediction and budget enforcement. Cost data is stored per conversation and user, enabling usage analytics and billing integration. This allows operators to track spending and implement cost controls.","intents":["I want to estimate API costs before making requests","I need to track spending per user or conversation","I want to implement budget limits to control costs"],"best_for":["cost-conscious organizations using paid APIs","multi-tenant deployments requiring usage tracking","teams implementing chargeback or billing systems"],"limitations":["Token counting is approximate — actual API usage may differ due to provider-specific tokenization","Pricing data must be manually updated when providers change rates — no automatic price sync","Cost estimation doesn't account for streaming overhead or retries — actual costs may be higher","Budget enforcement is soft (warnings only) — no hard limits on API calls","Cost data is stored locally — no integration with provider billing APIs"],"requires":["Provider pricing configuration (tokens per $1, input/output rates)","Token counter library for each provider (tiktoken, anthropic-tokenizer, etc.)","Database to store cost and usage data"],"input_types":["messages (text)","model selection","provider selection"],"output_types":["token counts (input, output, total)","cost estimates ($)","usage analytics (tokens/cost per user, conversation, time period)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_12","uri":"capability://memory.knowledge.conversation.branching.and.message.editing.with.version.history","name":"conversation branching and message editing with version history","description":"LibreChat supports conversation branching, allowing users to explore alternative response paths by regenerating messages or creating branches from any point in a conversation. Message editing enables users to modify previous messages and regenerate subsequent responses. The system maintains version history for all messages and branches, enabling users to navigate between different conversation paths and restore previous versions. This is implemented through a tree-based conversation model where each message can have multiple children (branches).","intents":["I want to explore alternative responses without losing the original conversation","I need to edit previous messages and regenerate responses","I want to compare different conversation paths"],"best_for":["iterative AI-assisted workflows (writing, coding, analysis)","exploration and experimentation with different prompts","users refining agent outputs through multiple iterations"],"limitations":["Branching increases storage requirements — each branch stores full message history","UI complexity increases with deep branching — navigation becomes difficult with many branches","Regenerating long conversations is expensive (API costs) — users may hesitate to explore alternatives","Version history is not pruned automatically — old branches accumulate over time","Merging branches is not supported — users must manually copy content between branches"],"requires":["Database supporting tree-based conversation models","UI components for branch visualization and navigation","Message versioning and history tracking"],"input_types":["edited message content","regeneration requests","branch creation requests"],"output_types":["alternative responses","conversation branches","version history","branch comparison views"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_13","uri":"capability://text.generation.language.internationalization.i18n.with.multi.language.ui.support","name":"internationalization (i18n) with multi-language ui support","description":"LibreChat includes comprehensive internationalization support with translations for the UI, agent responses, and system messages in multiple languages. Language selection is configurable per user and persists across sessions. The i18n system uses JSON translation files organized by language code, with fallback to English for missing translations. This enables global deployments where users interact in their preferred language.","intents":["I want to deploy LibreChat for international users","I need the UI to display in users' preferred languages","I want to add translations for new languages"],"best_for":["global organizations with multilingual user bases","international SaaS deployments","open-source projects with diverse contributors"],"limitations":["Agent responses are in the model's training language — language selection only affects UI, not model outputs","Translation maintenance requires community contributions — incomplete translations fall back to English","Right-to-left (RTL) languages require additional CSS and layout adjustments — not all components support RTL","Translation files must be manually updated for new features — no automatic translation generation"],"requires":["i18n library (e.g., i18next)","Translation files for each supported language (JSON format)","Language detection or user preference storage"],"input_types":["language code (e.g., 'en', 'es', 'fr')","translation strings (JSON)"],"output_types":["localized UI text","language-specific formatting (dates, numbers, currency)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_14","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.production.configuration","name":"docker and kubernetes deployment with production configuration","description":"LibreChat provides production-ready Docker images and Kubernetes manifests for containerized deployment. The Docker setup includes multi-stage builds for optimized image size, environment variable configuration for all services, and docker-compose orchestration for local development. Kubernetes deployment includes Helm charts for easy installation, ConfigMaps for configuration management, and support for horizontal scaling. This enables operators to deploy LibreChat in containerized environments with minimal configuration.","intents":["I want to deploy LibreChat in Docker for development and production","I need to scale LibreChat horizontally in Kubernetes","I want to manage configuration through environment variables and ConfigMaps"],"best_for":["DevOps teams managing containerized deployments","organizations using Kubernetes for orchestration","teams requiring production-grade deployment automation"],"limitations":["Database setup is manual — Kubernetes manifests don't include database provisioning","Persistent storage configuration is environment-specific — requires custom PersistentVolumeClaims","Horizontal scaling requires load balancing and session affinity configuration — not included in base Helm chart","Image size is large (~1GB) — slow to pull in bandwidth-constrained environments","Multi-region deployment requires additional networking and replication setup"],"requires":["Docker installed (for local development)","Kubernetes cluster (for production deployment)","Helm 3+ (for Kubernetes deployment)","Container registry (Docker Hub, ECR, GCR, etc.)","Persistent storage (PVC, EBS, GCS, etc.)"],"input_types":["Docker image tags","environment variables","Kubernetes manifests (YAML)","Helm values (YAML)"],"output_types":["running Docker containers","Kubernetes pods and services","deployed LibreChat instances"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_15","uri":"capability://automation.workflow.monorepo.architecture.with.turbo.build.system","name":"monorepo architecture with turbo build system","description":"LibreChat uses a monorepo structure (managed with Turbo) that organizes the codebase into packages: api (Node.js backend), client (React frontend), data-provider (shared data layer), and data-schemas (shared type definitions). Turbo enables efficient incremental builds, caching, and parallel task execution across packages. This architecture allows independent development and deployment of frontend and backend while sharing types and data models, reducing duplication and improving consistency.","intents":["I want to develop frontend and backend independently","I need to share type definitions between frontend and backend","I want to optimize build times with incremental builds and caching"],"best_for":["teams with separate frontend and backend developers","projects requiring type safety across services","organizations optimizing CI/CD pipeline performance"],"limitations":["Monorepo complexity increases with package count — requires careful dependency management","Turbo caching can cause issues if tasks have side effects — requires careful task configuration","Shared type definitions require coordination between packages — breaking changes affect multiple services","Monorepo tooling (Turbo, pnpm) adds learning curve for new contributors"],"requires":["Node.js 18+","pnpm package manager","Turbo build system","Understanding of monorepo structure and package dependencies"],"input_types":["source code changes in any package","build configuration (turbo.json)"],"output_types":["built packages (compiled JavaScript, type definitions)","cached build artifacts"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_2","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.external.tools","name":"model context protocol (mcp) integration for external tools","description":"LibreChat integrates the Model Context Protocol (MCP) via the @modelcontextprotocol/sdk, enabling agents to discover and invoke external tools and context sources (e.g., file systems, databases, web APIs) through a standardized interface. The MCP layer handles tool schema negotiation, request/response serialization, and error handling, allowing agents to use tools without hardcoding provider-specific integrations. Tools are registered in configuration and exposed to the LLM as callable functions with JSON schemas.","intents":["I want agents to access external tools (databases, APIs, file systems) without custom code","I need to standardize how agents discover and call third-party services","I want to add new tools to agents without modifying the core application"],"best_for":["teams building extensible agent systems","organizations with diverse tool ecosystems","developers implementing tool marketplaces"],"limitations":["MCP server availability is required at runtime — tool failures cascade to agent execution failures","Tool schema mismatches between MCP server and LLM expectations can cause function calling errors","No built-in caching of tool schemas — each agent initialization may require schema negotiation"],"requires":["MCP server running and accessible (local or remote)","Tool schema definitions matching LLM function calling format","Network connectivity between LibreChat and MCP servers"],"input_types":["tool schema definitions (JSON)","agent prompts with tool references","user messages triggering tool use"],"output_types":["tool execution results","structured tool responses","agent-generated tool calls"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_3","uri":"capability://code.generation.editing.sandboxed.code.interpreter.with.multi.language.support","name":"sandboxed code interpreter with multi-language support","description":"LibreChat includes a sandboxed code execution environment supporting Python, Node.js, Go, C/C++, Java, PHP, Rust, and Fortran. The code interpreter receives code snippets from agents, executes them in isolated containers or processes with resource limits, and returns stdout/stderr output. This enables agents to perform calculations, data transformations, and system operations safely without exposing the host system. Execution is stateless by default but can maintain session state across multiple code blocks within a conversation.","intents":["I want agents to execute code for calculations and data processing without external tools","I need to safely run user-provided code without compromising system security","I want agents to generate and test code in multiple programming languages"],"best_for":["data science and analytics workflows","educational AI assistants","development environments requiring code execution"],"limitations":["Execution timeout and memory limits prevent long-running or resource-intensive code","Network access from sandboxed code is restricted — cannot make external API calls from executed code","Language-specific libraries must be pre-installed in the sandbox — dynamic package installation is limited","Stateless execution by default — session state requires explicit management"],"requires":["Docker or native language runtimes installed (Python 3.9+, Node.js 18+, Go, etc.)","Resource limits configured (CPU, memory, timeout)","Sandbox environment properly isolated from host system"],"input_types":["code snippets (Python, Node.js, Go, C/C++, Java, PHP, Rust, Fortran)","input data (files, variables)","execution parameters (timeout, memory limits)"],"output_types":["stdout/stderr output","execution results","error messages","generated files"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_4","uri":"capability://search.retrieval.web.search.integration.with.content.scraping.and.reranking","name":"web search integration with content scraping and reranking","description":"LibreChat integrates web search capabilities that allow agents to query the internet, scrape page content, and rerank results based on relevance. The search system uses configurable search providers (e.g., SerpAPI, Bing, Google Custom Search) and implements a content extraction pipeline that converts HTML to structured text. Results are reranked using semantic similarity or LLM-based relevance scoring before being passed to agents, reducing noise and improving answer quality.","intents":["I want agents to access current information beyond their training data","I need to ground agent responses in real-time web data","I want to reduce hallucinations by verifying claims against web sources"],"best_for":["news and research assistants","customer support agents requiring current information","fact-checking and verification workflows"],"limitations":["Search provider API rate limits and costs apply — high-volume searches incur charges","Content extraction from complex HTML can fail or produce incomplete results","Reranking adds latency (100-500ms per search) — impacts real-time chat responsiveness","Search results are time-sensitive — cached results may become stale"],"requires":["API key for search provider (SerpAPI, Bing, Google Custom Search, etc.)","Network connectivity to search provider","Configuration of search provider endpoint and credentials"],"input_types":["search queries (text)","search parameters (number of results, language, region)"],"output_types":["search results (title, URL, snippet)","scraped page content (text)","reranked results with relevance scores"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_5","uri":"capability://memory.knowledge.conversation.persistence.and.search.with.full.text.indexing","name":"conversation persistence and search with full-text indexing","description":"LibreChat stores conversations in a database (MongoDB, PostgreSQL, or MySQL) with full-text indexing, enabling users to search across conversation history by message content, metadata, or timestamps. The conversation model includes message threading, user associations, and metadata tags. Search queries are executed against indexed fields, returning matching conversations and messages with context. This enables users to retrieve past interactions and agents to access conversation history for context.","intents":["I want users to search their conversation history","I need to persist conversations across sessions for continuity","I want agents to access relevant past conversations for context"],"best_for":["multi-session chat applications","knowledge management systems","customer support platforms with conversation history"],"limitations":["Full-text indexing requires database support (MongoDB text indexes, PostgreSQL tsvector, MySQL FULLTEXT) — not all databases support it equally","Search performance degrades with very large conversation datasets (millions of messages) — requires database optimization","Conversation deletion is permanent — no soft-delete or archival by default","Search results are limited to indexed fields — complex queries require custom database queries"],"requires":["Database configured (MongoDB, PostgreSQL, or MySQL)","Full-text indexing enabled on message content fields","Database connection string and credentials"],"input_types":["search queries (text)","conversation IDs","user IDs","date ranges"],"output_types":["conversation objects with message history","search results with relevance scores","conversation metadata (created date, participant count, etc.)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_6","uri":"capability://planning.reasoning.no.code.agent.builder.with.visual.configuration.ui","name":"no-code agent builder with visual configuration ui","description":"LibreChat provides a visual agent builder interface that allows non-technical users to create custom agents by configuring system prompts, selecting models, attaching tools, and setting execution parameters without writing code. Agent definitions are stored as configuration objects that specify the agent's behavior, available tools, model selection, and response formatting. The UI generates agent configurations that are persisted and can be shared via a marketplace, enabling reuse across teams.","intents":["I want non-technical users to create custom AI agents","I need to share agent configurations across teams","I want to version control agent definitions without code changes"],"best_for":["non-technical business users","teams building agent marketplaces","organizations with diverse agent use cases"],"limitations":["Complex agent logic (conditional branching, multi-step reasoning) is difficult to express in UI — requires code for advanced workflows","Agent performance tuning (prompt engineering, model selection) requires trial-and-error through the UI","No built-in version control for agent configurations — requires external version management","Agent testing is limited to manual interaction — no automated testing framework"],"requires":["LibreChat UI accessible","User permissions to create agents","At least one AI model configured"],"input_types":["agent name and description","system prompt text","model selection","tool attachments","execution parameters"],"output_types":["agent configuration objects","shareable agent definitions","agent execution results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_7","uri":"capability://code.generation.editing.artifacts.system.for.generative.ui.components","name":"artifacts system for generative ui components","description":"LibreChat's Artifacts feature enables agents to generate and render interactive UI components (React components, HTML, Mermaid diagrams) directly in the chat interface. When an agent generates code marked as an artifact, LibreChat renders it in a sandboxed iframe or component renderer, allowing users to interact with generated interfaces without leaving the chat. This enables use cases like data visualization, form generation, and interactive documentation.","intents":["I want agents to generate interactive visualizations in chat","I need to create dynamic forms or UI components through conversation","I want to render diagrams and flowcharts generated by agents"],"best_for":["data visualization and analytics workflows","interactive documentation generation","UI prototyping and design assistance"],"limitations":["Artifact rendering is limited to React, HTML, and Mermaid — other frameworks require custom rendering","Sandboxed rendering prevents access to external APIs or host system resources","Complex interactive components may have performance issues in iframe context","Artifact state is not persisted — refreshing the page loses interactive state"],"requires":["React or HTML code generation capability in agent","Browser support for iframe sandboxing","Mermaid.js library for diagram rendering"],"input_types":["React component code (JSX)","HTML markup","Mermaid diagram syntax"],"output_types":["rendered React components","interactive HTML interfaces","visual diagrams"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_8","uri":"capability://image.visual.multimodal.input.processing.with.image.analysis.and.file.upload","name":"multimodal input processing with image analysis and file upload","description":"LibreChat supports multimodal inputs including image uploads, file attachments, and text-to-speech/speech-to-text conversion. Images are processed through vision-capable models (GPT-4V, Claude 3 Vision, Gemini Vision) for analysis and description. Files are converted to text or embeddings depending on type (PDFs, documents, code files). Speech input is transcribed to text via speech-to-text APIs, and agent responses can be converted to speech for accessibility. This enables richer interaction patterns beyond text-only chat.","intents":["I want agents to analyze images and diagrams","I need to upload documents for agents to process","I want voice input/output for accessibility and hands-free interaction"],"best_for":["accessibility-focused applications","document analysis and summarization workflows","visual content understanding tasks"],"limitations":["Vision model availability varies by provider — not all models support image input","Large file uploads are limited by API constraints (typically 20MB per file)","Document parsing quality depends on file format and complexity — PDFs with images may fail","Speech-to-text accuracy depends on audio quality and language — accents and background noise reduce accuracy","Text-to-speech latency adds 1-3 seconds per response"],"requires":["Vision-capable model (GPT-4V, Claude 3, Gemini Vision, etc.)","File upload storage (local filesystem or cloud storage)","Speech-to-text API (Whisper, Google Speech-to-Text, etc.) for voice input","Text-to-speech API (ElevenLabs, Google TTS, etc.) for voice output"],"input_types":["images (PNG, JPG, WebP, GIF)","documents (PDF, DOCX, TXT, code files)","audio files (MP3, WAV, M4A)","text"],"output_types":["image analysis results (text descriptions, extracted data)","document summaries and extracted information","audio responses (speech synthesis)","text responses"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__cap_9","uri":"capability://safety.moderation.enterprise.authentication.with.oauth2.openid.ldap.and.saml","name":"enterprise authentication with oauth2, openid, ldap, and saml","description":"LibreChat supports multiple enterprise authentication methods including OAuth2 (Google, GitHub, Discord), OpenID Connect, LDAP directory integration, and SAML 2.0 for single sign-on. Authentication is abstracted through a pluggable provider system that handles credential validation, token management, and user provisioning. This enables organizations to integrate LibreChat with existing identity management systems without managing separate credentials.","intents":["I want to integrate LibreChat with our corporate identity provider","I need single sign-on (SSO) for enterprise deployments","I want to sync user groups and permissions from LDAP/Active Directory"],"best_for":["enterprise deployments with existing identity infrastructure","organizations requiring SSO and centralized user management","teams with compliance requirements (SAML, LDAP audit trails)"],"limitations":["LDAP/SAML configuration is complex and error-prone — requires IT expertise","User provisioning from LDAP is one-way (LDAP → LibreChat) — no reverse sync","OAuth2 provider configuration requires registering LibreChat as an application with each provider","Token refresh and expiration handling varies by provider — requires careful session management","No built-in role-based access control (RBAC) — permissions must be managed separately"],"requires":["OAuth2 provider credentials (client ID, secret) or OpenID Connect configuration","LDAP server accessible (if using LDAP authentication)","SAML 2.0 IdP metadata (if using SAML)","Network connectivity to identity provider"],"input_types":["user credentials (username/password for LDAP)","OAuth2 authorization codes","SAML assertions","OpenID Connect tokens"],"output_types":["authenticated user sessions","user profile information","group/role memberships","access tokens"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"librechat__headline","uri":"capability://chatbots.assistants.self.hosted.ai.chat.platform","name":"self-hosted ai chat platform","description":"LibreChat is an open-source, self-hosted AI chat platform that integrates multiple AI providers into a single interface, allowing users to maintain control over their data and customize their chat experience.","intents":["best self-hosted AI chat platform","AI chat platform for multiple providers","open-source ChatGPT alternative","self-hosted chat solution for privacy","AI chat tool with plugin support"],"best_for":["developers seeking privacy","organizations needing custom AI solutions"],"limitations":["requires self-hosting knowledge","may need technical setup"],"requires":["Docker","basic programming knowledge"],"input_types":["text","files"],"output_types":["text","code execution results"],"categories":["chatbots-assistants"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one provider (OpenAI, Anthropic, Google, Azure, or AWS)","Node.js 18+","Environment variables configured per provider","librechat.yaml file in project root or specified via CONFIG_PATH env var","Valid YAML syntax","Environment variables for API keys and secrets","Vector database (Pinecone, Weaviate, Milvus, or local)","Embedding model (OpenAI, Anthropic, 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query expansion or reranking","Chunking strategy affects retrieval quality — fixed-size chunks may split important context","builder identity is not verified yet","no observed match outcomes 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