LibreChat Documentation

File: README.md Copy Chapter

🚀 What's New in v0.8.8-rc1 Copy Chapter

• Agent run control: Interrupt or steer an Agent mid-run, queue follow-up messages, and reclaim, edit, or escalate pending steers.
• Human-in-the-loop Agents: Agents stream question progress, ask up to four related questions in one form, pause for input or tool approval, and resume.
• Unified Agent Builder: A redesigned Tools marketplace brings together Skills, MCP, Code Interpreter, orchestration, Programmatic Tool Calling, model-spec controls, and per-tool background and intent settings.
• Readable Agent activity: Generated activity-group headers, parent phase summaries, and live tool intent labels make long reasoning and tool runs easier to scan.
• Code Interpreter workflows: Code and shell tools can run in the background, sandbox images return as viewable artifacts, and highly experimental stateful sessions can reuse prewarmed conversation workspaces.
• Agent extensibility: Experimental Agent Plugins can bundle deployment Skills, MCP servers, and opt-in command hooks, while explicit subagents initialize only when selected.
• Memory, context, and identity: Agents can manage memory with optional per-agent isolation, expose support contacts safely, and show a more faithful Context Usage gauge.
• Sharing and files: Shared conversations show a badge and update at a stable URL, while signed-in viewers can continue them as personal copies.
• Artifact workflows: Open previews fullscreen, work with PowerPoint .potx templates across upload, search, and code execution, upload shell scripts across common MIME variants, export Mermaid diagrams as SVG or PNG, and download original Office files from the artifact panel.
• Models and reasoning: Added GPT-5.6 with Responses API reasoning controls, Claude Opus 5 and Sonnet 5, Gemini 3.7 and 3.6 Flash, and Gemini 3.5 Flash-Lite.
• Langfuse observability: Configure encrypted Langfuse connections in-app, let authorized admins open sampled sessions directly, optionally fan out traces by tenant, and suppress central export per run.
• Administration and security: Delegate config sections, encrypt registered secrets, enforce SSRF checks for speech, OCR, and web tools, and generate unique temporary credentials when secrets are blank.
• Messages and navigation: Right-aligned user turns, unified multi-part editing, full-message copy, a dock-style message rail, virtualized search, smooth streaming, and faster Agent startup.
• Streaming and tool reliability: Adaptive provider smoothing, Redis delta batching, dynamic MCP tool refresh, parsed MCP response media types, runtime OAuth recovery, and Agent stream circuit breakers improve long-running workflows.
• Deployment and reliability: Added configurable HTTP timeouts, Amazon DocumentDB 5.0+ support, low-noise Redis and browser observability, and a rolling-upgrade-safe generation protocol.

Read the full v0.8.8-rc1 changelog.

✨ Features

• 🖥️ UI & Experience inspired by ChatGPT with enhanced design and features

• 🤖 AI Model Selection:

  • Anthropic (Claude), AWS Bedrock, OpenAI, Azure OpenAI, Google, Vertex AI, OpenAI Responses API (incl. Azure)
  • Custom Endpoints: Use any OpenAI-compatible API with LibreChat, no proxy required
  • Compatible with Local & Remote AI Providers:
    • Ollama, groq, Cohere, Mistral AI, Apple MLX, koboldcpp, together.ai,
    • OpenRouter, Helicone, Perplexity, ShuttleAI, Deepseek, Qwen, and more

• 🔧 Code Interpreter API:

  • Secure, Sandboxed Execution in Python, Node.js (JS/TS), Go, C/C++, Java, PHP, Rust, and Fortran
  • Seamless File Handling: Upload, process, and download files directly
  • No Privacy Concerns: Fully isolated and secure execution
  • Open-Source & Self-Hostable: powered by ClickHouse/code-interpreter

• 🔦 Agents & Tools Integration:

  • LibreChat Agents:
    • No-Code Custom Assistants: Build specialized, AI-driven helpers
    • Agent Marketplace: Discover and deploy community-built agents
    • Collaborative Sharing: Share agents with specific users and groups
    • Flexible & Extensible: Use MCP Servers, tools, file search, code execution, and more
    • Skills: Create reusable SKILL.md instruction bundles for manual, automatic, or always-on agent workflows
    • Agent Plugins: Experimentally bundle deployment Skills and MCP servers into startup-loaded packages
    • Subagents: Delegate focused work to isolated child agent runs with their own context windows
    • Compatible with Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, Google, Vertex AI, Responses API, and more
    • Model Context Protocol (MCP) Support for Tools

• 🔍 Web Search:

  • Search the internet and retrieve relevant information to enhance your AI context
  • Combines search providers, content scrapers, and result rerankers for optimal results
  • Customizable Jina Reranking: Configure custom Jina API URLs for reranking services
  • Learn More →

• 🪄 Generative UI with Code Artifacts:

  • Code Artifacts create React, HTML, and Mermaid content directly in chat
  • Open previews fullscreen and export Mermaid diagrams as SVG or PNG

• 🎨 Image Generation & Editing

  • Text-to-image and image-to-image with GPT-Image-1
  • Text-to-image with DALL-E (3/2), Stable Diffusion, Flux, or any MCP server
  • Produce stunning visuals from prompts or refine existing images with a single instruction

• 💾 Presets & Context Management:

  • Create, Save, & Share Custom Presets
  • Switch between AI Endpoints and Presets mid-chat
  • Edit, Resubmit, and Continue Messages with Conversation branching
  • Create and share prompts with specific users and groups
  • Fork Messages & Conversations for Advanced Context control

• 💬 Multimodal & File Interactions:

  • Upload and analyze images with Claude 3, GPT-4.5, GPT-4o, o1, Llama-Vision, and Gemini 📸
  • Chat with Files using Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, & Google 🗃️

• 🌎 Multilingual UI:

  • English, 中文 (简体), 中文 (繁體), العربية, Deutsch, Español, Français, Italiano
  • Polski, Português (PT), Português (BR), Русский, 日本語, Svenska, 한국어, Tiếng Việt
  • Türkçe, Nederlands, עברית, Català, Čeština, Dansk, Eesti, فارسی
  • Suomi, Magyar, Հայերեն, Bahasa Indonesia, ქართული, Latviešu, ไทย, ئۇيغۇرچە

• 🧠 Reasoning UI:

  • Dynamic Reasoning UI for Chain-of-Thought/Reasoning AI models like DeepSeek-R1

• 🎨 Customizable Interface:

  • Customizable Dropdown & Interface that adapts to both power users and newcomers

• 🌊 Resumable Streams:

  • Never lose a response: AI responses automatically reconnect and resume if your connection drops
  • Multi-Tab & Multi-Device Sync: Open the same chat in multiple tabs or pick up on another device
  • Production-Ready: Works from single-server setups to horizontally scaled deployments with Redis

• 🗣️ Speech & Audio:

  • Chat hands-free with Speech-to-Text and Text-to-Speech
  • Automatically send and play Audio
  • Supports OpenAI, Azure OpenAI, and Elevenlabs

• 📥 Import & Export Conversations:

  • Import Conversations from LibreChat, ChatGPT, Chatbot UI
  • Export conversations as screenshots, markdown, text, json

• 🔍 Search & Discovery:

  • Search all messages/conversations

• 👥 Multi-User & Secure Access:

  • Multi-User, Secure Authentication with OAuth2, LDAP, & Email Login Support
  • Built-in Moderation, and Token spend tools

• 🎛️ Admin Panel:

  • Browser-based UI to manage users, groups, roles, and configuration overrides
  • Edit settings and per-role/group permissions live, without redeploying
  • Bundled with the Docker Compose stacks for one-command setup

• ⚙️ Configuration & Deployment:

  • Configure Proxy, Reverse Proxy, Docker, & many Deployment options
  • Use S3 with CloudFront for stable media links, edge delivery, signed cookies, and secured downloads
  • Use completely local or deploy on the cloud

• 📖 Open-Source & Community:

  • Completely Open-Source & Built in Public
  • Community-driven development, support, and feedback

For a thorough review of our features, see our docs here 📚

🪶 All-In-One AI Conversations with LibreChat Copy Chapter

LibreChat is a self-hosted AI chat platform that unifies all major AI providers in a single, privacy-focused interface.

Beyond chat, LibreChat provides AI Agents, Model Context Protocol (MCP) support, Artifacts, Code Interpreter, custom actions, conversation search, and enterprise-ready multi-user authentication.

Open source, actively developed, and built for anyone who values control over their AI infrastructure.

🌐 Resources Copy Chapter

GitHub Repo:

• RAG API: github.com/danny-avila/rag_api
• Website: github.com/LibreChat-AI/librechat.ai

Other:

• Website: librechat.ai
• Documentation: librechat.ai/docs
• Blog: librechat.ai/blog

📝 Changelog Copy Chapter

Keep up with the latest updates by visiting the releases page and notes:

• Releases
• Changelog

⚠️ Please consult the changelog for breaking changes before updating.

⭐ Star History Copy Chapter

✨ Contributions Copy Chapter

Contributions, suggestions, bug reports and fixes are welcome!

For new features, components, or extensions, please open an issue and discuss before sending a PR.

If you'd like to help translate LibreChat into your language, we'd love your contribution! Improving our translations not only makes LibreChat more accessible to users around the world but also enhances the overall user experience. Please check out our Translation Guide.

💖 This project exists in its current state thanks to all the people who contribute Copy Chapter

🎉 Special Thanks Copy Chapter

We thank Locize for their translation management tools that support multiple languages in LibreChat.

File: helm/librechat-rag-api/readme.md Copy Chapter

Librechat RAG API Helm CHart

[!WARNING]
This Helm-Chart is needed for LibreChat and Deployment information is documented in (the librechat chart)[../librechat/readme.md]

File: helm/librechat/readme.md Copy Chapter

LibreChat Helm Chart

This Librechat Helm Chart provides an easy, light weight template to deploy LibreChat on Kubernetes

Variables Copy Chapter

In this Chart, LibreChat will only work with environment Variables. You can Specify Vars and Secret using an existing Secret (This can be generated by creating an Env File and converting it to a Kubernetes Secret --from-env-file)

Setup Copy Chapter

1. Generate Variables
   Generate unique values for CREDS_KEY, JWT_SECRET, JWT_REFRESH_SECRET, and MEILI_MASTER_KEY using openssl rand -hex 32, and CREDS_IV using openssl rand -hex 16. Store them in the existing Kubernetes Secret so every replica uses the same values.
   place them in a secret like this (If you want to change the secret name, remember to change it in your helm values):

apiVersion: v1
kind: Secret
metadata:
  name: librechat-credentials-env
  namespace: <librechat-chart-namespace>
type: Opaque
stringData:
  CREDS_KEY: <generated value>
  CREDS_IV: <generated value>
  JWT_SECRET: <generated value>
  JWT_REFRESH_SECRET: <generated value>
  MEILI_MASTER_KEY: <generated value>

2. Add Credentials to the Secret
   Dependant of the Model you want to use, create Credentials in your provider and add them to the Secret:

apiVersion: v1
kind: Secret
. . . .

  OPENAI_API_KEY: <your secret value>

3. Apply the Secret to the Cluster

4. Fill out values.yaml and apply the Chart to the Cluster

Admin Panel SSO Copy Chapter

Set librechat.adminPanelUrl to the admin panel base URL used for OAuth/SSO
redirect, whether the admin panel is deployed on a separate origin
or on the same origin under an admin subpath.

It may include a path, but it should not
end with a trailing / because LibreChat appends /auth/... callback paths.

librechat:
  adminPanelUrl: https://admin.example.com/admin

This renders ADMIN_PANEL_URL for LibreChat's admin OAuth flow. For OpenID SSO,
also register this LibreChat callback URL with your identity provider:

https://<librechat-domain>/api/admin/oauth/openid/callback

Generation protocol rollout Copy Chapter

Redis-backed generation streams use protocol v1 by default during the first
rollout of a v2-capable image. This keeps a rolling deployment compatible with
replicas that still run the previous Redis queue, checkpoint, and recovery
scripts.

After every LibreChat replica is on the v2-capable image and all active
generations owned by the old image have drained, set
librechat.configEnv.GENERATION_PROTOCOL_VERSION="2" in a second rollout.
Keep the new image in place until v2 generations have drained; an older image
cannot safely operate on their Redis state. In-memory generation streams do not
share state across replicas and negotiate v2 without this cutover.

Langfuse Fanout Copy Chapter

The chart can optionally deploy a Langfuse fanout gateway with an internal
OpenTelemetry Collector sidecar. The gateway handles Langfuse media fanout and
proxies traces to the collector; the collector forwards tenant-scoped Langfuse
traces to both a central Langfuse project and the tenant Langfuse project. It is
disabled by default.

When enabled, the chart also sets LANGFUSE_FANOUT_ENABLED and
LANGFUSE_FANOUT_COLLECTOR_URL for the LibreChat app unless those values are
already provided in librechat.configEnv.

Set librechat.configEnv.LANGFUSE_FANOUT_TENANT_EXPORT_DISABLED=true to keep
central trace export flowing through the fanout gateway while disabling tenant trace
and score export. When omitted, false, or blank, tenant export remains available
if tenant keys and a known destination are configured.

Langfuse tenant base URLs are selected from the startup-configured destination
map rendered into LibreChat and the fanout gateway. Tenant API keys can still be added
through tenant app configuration at runtime without restarting either component.
The internal collector provides trace memory limiting, batching, tenant routing,
and removal of LibreChat-only routing attributes before export.

The fanout gateway stores one-time media upload plans in Redis so media create
and byte-upload requests can land on different gateway replicas. Set
langfuseFanout.redis.uri for an external Redis service, or enable the bundled
Redis chart with redis.enabled=true and let the chart derive the internal URI.
Scale the gateway manually with langfuseFanout.replicaCount; the chart does
not create a fanout HPA.
The internal collector receiver is bound to 127.0.0.1:4319 by default because
only the gateway sidecar should send traces to it.

The gateway exposes Prometheus metrics at /metrics. Configure
langfuseFanout.metrics.secret.name and .key to pass a bearer token secret to
the gateway; if omitted, /metrics returns 401. Use
langfuseFanout.service.annotations for scrape annotations when your cluster
uses annotation-based discovery. The gateway container also has configurable
/healthz liveness and readiness probes under langfuseFanout.

See otel/langfuse-fanout/README.md
for the central Langfuse secret and values example.

File: packages/data-schemas/README.md Copy Chapter

LibreChat Data Schemas Package

This package provides the database schemas, models, types, and methods for LibreChat using Mongoose ODM.

📁 Package Structure Copy Chapter

packages/data-schemas/
├── src/
│   ├── schema/         # Mongoose schema definitions
│   ├── models/         # Model factory functions
│   ├── types/          # TypeScript type definitions
│   ├── methods/        # Database operation methods
│   ├── common/         # Shared constants and enums
│   ├── config/         # Configuration files (winston, etc.)
│   └── index.ts        # Main package exports

🏗️ Architecture Patterns Copy Chapter

1. Schema Files (`src/schema/`) Copy Section

Schema files define the Mongoose schema structure. They follow these conventions:

• Naming: Use lowercase filenames (e.g., user.ts, accessRole.ts)
• Imports: Import types from ~/types for TypeScript support
• Exports: Export only the schema as default

Example:

import { Schema } from 'mongoose';
import type { IUser } from '~/types';

const userSchema = new Schema(
  {
    name: { type: String },
    email: { type: String, required: true },
    // ... other fields
  },
  { timestamps: true }
);

export default userSchema;

2. Type Definitions (`src/types/`) Copy Section

Type files define TypeScript interfaces and types. They follow these conventions:

• Base Type: Define a plain type without Mongoose Document properties
• Document Interface: Extend the base type with Document and _id
• Enums/Constants: Place related enums in the type file or common/ if shared

Example:

import type { Document, Types } from 'mongoose';

export type User = {
  name?: string;
  email: string;
  // ... other fields
};

export type IUser = User &
  Document & {
    _id: Types.ObjectId;
  };

3. Model Factory Functions (`src/models/`) Copy Section

Model files create Mongoose models using factory functions. They follow these conventions:

• Function Name: create[EntityName]Model
• Singleton Pattern: Check if model exists before creating
• Type Safety: Use the corresponding interface from types

Example:

import userSchema from '~/schema/user';
import type * as t from '~/types';

export function createUserModel(mongoose: typeof import('mongoose')) {
  return mongoose.models.User || mongoose.model<t.IUser>('User', userSchema);
}

4. Database Methods (`src/methods/`) Copy Section

Method files contain database operations for each entity. They follow these conventions:

• Function Name: create[EntityName]Methods
• Return Type: Export a type for the methods object
• Operations: Include CRUD operations and entity-specific queries

Example:

import type { Model } from 'mongoose';
import type { IUser } from '~/types';

export function createUserMethods(mongoose: typeof import('mongoose')) {
  async function findUserById(userId: string): Promise {
    const User = mongoose.models.User as Model;
    return await User.findById(userId).lean();
  }

  async function createUser(userData: Partial): Promise {
    const User = mongoose.models.User as Model;
    return await User.create(userData);
  }

  return {
    findUserById,
    createUser,
    // ... other methods
  };
}

export type UserMethods = ReturnType<typeof createUserMethods>;

5. Main Exports (`src/index.ts`) Copy Section

The main index file exports:

• createModels() - Factory function for all models
• createMethods() - Factory function for all methods
• Type exports from ~/types
• Shared utilities and constants

🚀 Adding a New Entity Copy Chapter

To add a new entity to the data-schemas package, follow these steps:

Step 1: Create the Type Definition Copy Section

Create src/types/[entityName].ts:

import type { Document, Types } from 'mongoose';

export type EntityName = {
  /** Field description */
  fieldName: string;
  // ... other fields
};

export type IEntityName = EntityName &
  Document & {
    _id: Types.ObjectId;
  };

Step 2: Update Types Index Copy Section

Add to src/types/index.ts:

export * from './entityName';

Step 3: Create the Schema Copy Section

Create src/schema/[entityName].ts:

import { Schema } from 'mongoose';
import type { IEntityName } from '~/types';

const entityNameSchema = new Schema(
  {
    fieldName: { type: String, required: true },
    // ... other fields
  },
  { timestamps: true }
);

export default entityNameSchema;

Step 4: Create the Model Factory Copy Section

Create src/models/[entityName].ts:

import entityNameSchema from '~/schema/entityName';
import type * as t from '~/types';

export function createEntityNameModel(mongoose: typeof import('mongoose')) {
  return (
    mongoose.models.EntityName || 
    mongoose.model<t.IEntityName>('EntityName', entityNameSchema)
  );
}

Step 5: Update Models Index Copy Section

Add to src/models/index.ts:

1. Import the factory function:

import { createEntityNameModel } from './entityName';

2. Add to the return object in createModels():

EntityName: createEntityNameModel(mongoose),

Step 6: Create Database Methods Copy Section

Create src/methods/[entityName].ts:

import type { Model, Types } from 'mongoose';
import type { IEntityName } from '~/types';

export function createEntityNameMethods(mongoose: typeof import('mongoose')) {
  async function findEntityById(id: string | Types.ObjectId): Promise {
    const EntityName = mongoose.models.EntityName as Model;
    return await EntityName.findById(id).lean();
  }

  // ... other methods

  return {
    findEntityById,
    // ... other methods
  };
}

export type EntityNameMethods = ReturnType<typeof createEntityNameMethods>;

Step 7: Update Methods Index Copy Section

Add to src/methods/index.ts:

1. Import the methods:

import { createEntityNameMethods, type EntityNameMethods } from './entityName';

2. Add to the return object in createMethods():

...createEntityNameMethods(mongoose),

3. Add to the AllMethods type:

export type AllMethods = UserMethods &
  // ... other methods
  EntityNameMethods;

📝 Best Practices Copy Chapter

1. Consistent Naming: Use lowercase for filenames, PascalCase for types/interfaces
2. Type Safety: Always use TypeScript types, avoid any
3. JSDoc Comments: Document complex fields and methods
4. Indexes: Define database indexes in schema files for query performance
5. Validation: Use Mongoose schema validation for data integrity
6. Lean Queries: Use .lean() for read operations when you don't need Mongoose document methods

🔧 Common Patterns Copy Chapter

Enums and Constants Copy Section

Place shared enums in src/common/:

// src/common/permissions.ts
export enum PermissionBits {
  VIEW = 1,
  EDIT = 2,
  DELETE = 4,
  SHARE = 8,
}

Compound Indexes Copy Section

For complex queries, add compound indexes:

schema.index({ field1: 1, field2: 1 });
schema.index(
  { uniqueField: 1 },
  { 
    unique: true, 
    partialFilterExpression: { uniqueField: { $exists: true } }
  }
);

Virtual Properties Copy Section

Add computed properties using virtuals:

schema.virtual('fullName').get(function() {
  return `${this.firstName} ${this.lastName}`;
});

🧪 Testing Copy Chapter

When adding new entities, ensure:

• Types compile without errors
• Models can be created successfully
• Methods handle edge cases (null checks, validation)
• Indexes are properly defined for query patterns

📚 Resources Copy Chapter

• Mongoose Documentation
• TypeScript Handbook
• MongoDB Indexes