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Enhanced ChatGPT Clone: Features Agents, MCP, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active.
https://librechat.ai/
aianthropicartifactsawsazurechatgptchatgpt-cloneclaudeclonedeepseekgeminigooglegpt-5librechatmcpo1openairesponses-apivisionwebui
24e29aa8cb
* 🌱 fix: Seed Code Tool Files Into Graph Sessions on First Call
Files attached to an agent's `tool_resources.execute_code` (user uploads
or generated artifacts from a prior turn) were silently dropped on the
first `execute_code` invocation of a turn. The agents-side `ToolNode`
populates `_injected_files` only when its `sessions` map already has an
`EXECUTE_CODE` entry — but that entry is only written by a previous
successful execution, so call #1 had nothing to inject. CodeExecutor
then fell back to a `/files/{session_id}` fetch, but `session_id` was
also empty on call #1, leaving the sandbox without the primed files.
Mirror the existing skill-priming pattern (`primeInvokedSkills` →
`initialSessions`) for code-resource files: eagerly call `primeFiles`
before `createRun` and merge the result into `initialSessions` via a
new `seedCodeFilesIntoSessions` helper. Skill files and code-resource
files now share the same `EXECUTE_CODE` entry; the prior representative
`session_id` is preserved on merge.
* 🔬 chore: Add Diagnostic Logging for Code-Files Seeding
Temporary debug logs to diagnose why first-call file injection is not
firing in real agent runs. Logs `wantsCodeExec`, available tool-resource
keys, primed file count, and the seeded EXECUTE_CODE entry. Will revert
once the failure mode is identified.
* 🪛 refactor: Capture primedCodeFiles per-agent at init, merge across run
Replace the client.js eager `primeFiles` call with a per-agent capture at
initialization time so every agent in a multi-agent run (primary +
handoff + addedConvo) contributes its `tool_resources.execute_code`
files to the shared `Graph.sessions` seed.
- handleTools.js (eager loadTools): the `execute_code` factory closes
over a `primedCodeFiles` slot and surfaces it in the return.
- ToolService.js loadToolDefinitionsWrapper (event-driven): captures
`files` from the existing `primeCodeFiles` call (was dropping them
while only keeping `toolContext`) and surfaces them.
- packages/api initialize.ts: the loadTools callback contract now
includes `primedCodeFiles`, threaded onto `InitializedAgent`.
- client.js: iterate `[primary, ...agentConfigs.values()]` and merge
each agent's `primedCodeFiles` into `initialSessions`. Drop the
primary-only `primeCodeFiles` call and diagnostic logs from the prior
attempt — wrong layer (single-agent), wrong gate (`agent.tools`
contained Tool instances after init, so the `.includes("execute_code")`
string check always failed).
* 🔬 chore: Add per-agent diagnostic logs for code-files seeding
Logs `tool_resources` keys + file counts inside loadToolDefinitionsWrapper
and per-agent `primedCodeFiles` + final initialSessions inside
AgentClient. Will revert once the failure mode is confirmed.
* 🔬 chore: Add file-lookup diagnostics inside initializeAgent
Logs the inputs and intermediate counts of the conversation-file lookup
chain (convo file ids, thread message ids, code-generated and
user-code file counts) so we can pinpoint why `tool_resources.execute_code`
is arriving empty at `loadToolDefinitionsWrapper` despite the agent
having `execute_code` in its tools list.
* 🔬 chore: Probe execute_code files without messageId filter
Adds a relaxed `getFiles({conversationId, context: execute_code})` probe
that runs only when `getCodeGeneratedFiles` returns empty. Lists what's
actually in the DB for this conversation so we can confirm whether the
file is missing entirely or whether the messageId filter is rejecting it.
* 🔬 chore: Fix probe getFiles arg order (sort vs projection)
Probe was passing a projection object as the sort arg, which mongoose
rejected with `Invalid sort value`. Move it to the third arg
(selectFields) so the probe actually runs.
* 🪢 fix: Preserve Original messageId on Code-Output File Update
Each `processCodeOutput` call was overwriting the persisted file's
`messageId` with the *current* run's id. When a turn re-creates an
existing file (filename + conversationId match → `claimCodeFile`
returns the existing record, `isUpdate=true`), the file's link to
the assistant message that originally produced it gets clobbered.
`initializeAgent` later runs `getCodeGeneratedFiles({messageId: $in: <thread>})`
to seed `tool_resources.execute_code` from prior-turn artifacts. With a
stale `messageId` (e.g. from a failed read attempt that re-shelled the
same filename), the file no longer matches the parent-walk thread, so
`tool_resources` arrives empty at agent init, the new
`primedCodeFiles` channel has nothing to seed, and the LLM can't see
its own prior-turn artifacts on the next turn — defeating the
just-added Graph-sessions seeding fix.
Preserve the existing `claimed.messageId` on update; first-creation
behavior is unchanged. The runtime return value still includes the
current run's `messageId` (via `Object.assign(file, { messageId })`)
so the artifact is correctly attributed to the live tool_call.
* 🧹 chore: Remove diagnostic logs from code-files seeding path
Drops the temporary debug logs added to trace the empty-tool_resources
failure mode. Production code paths (loadToolDefinitionsWrapper,
client.js seed loop, initializeAgent file lookup) are left as the
permanent shape: capture primedCodeFiles, merge across agents, seed
initialSessions before run start.
* 🪛 feat: read_file Sandbox Fallback for /mnt/data + Non-Skill Paths
When the model called `read_file` with a code-execution path (e.g.
`/mnt/data/sentinel.txt`), the handler returned a misleading
`Use format: {skillName}/{path}` error. Adds a sandbox-aware fallback:
- Short-circuit `/mnt/data/...` (can never be a skill reference) →
route to a sandbox `cat` via the new host-provided `readSandboxFile`
callback, which POSTs to the codeapi `/exec` endpoint.
- Skip the skill resolver entirely when `accessibleSkillIds` is empty
— the resolved-output of `resolveAgentScopedSkillIds` already
collapses the admin capability + ephemeral badge + persisted
`skills_enabled` chain, so an empty value is the authoritative
"skills aren't in scope for this agent" signal.
- For `{firstSegment}/...` paths, consult the catalog-derived
`activeSkillNames` Set (no DB read) to detect non-skill names and
fall through to the sandbox before the model has to retry with
`bash_tool`.
`activeSkillNames` is captured from `injectSkillCatalog`, threaded onto
`InitializedAgent`, into `agentToolContexts`, then through
`enrichWithSkillConfigurable` into `mergedConfigurable` for the handler.
The host implementation of `readSandboxFile` lives in
`api/server/services/Files/Code/process.js` and shells `cat <path>`
through the seeded sandbox session — `tc.codeSessionContext`
(emitted by ToolNode for `read_file` calls in `@librechat/agents`
v3.1.72+) provides the `session_id` + `_injected_files` so the read
lands in the same sandbox that holds prior-turn artifacts. When the
seeded context isn't available (older agents version, no codeapi
configured), the handler returns a model-visible error pointing at
`bash_tool` instead of silently failing.
Tests: 8 new `handleReadFileCall` cases cover the new short-circuits,
the skills-not-enabled gate, the activeSkillNames lookup, the
sandbox-fallback success path, and the bash_tool retry hint on
fallback failure. Existing `read_file` tests now opt into "skills are
in scope" via a `skillsInScope()` fixture (production wouldn't reach
the skill lookup with empty `accessibleSkillIds`).
* 🔧 chore: Update @librechat/agents dependency to version 3.1.72
Bumps the version of the @librechat/agents package across package-lock.json and relevant package.json files to ensure compatibility with the latest features and fixes.
* 🪛 refactor: Centralize Tool-Session Seed in buildInitialToolSessions Helper
Addresses review feedback on the per-agent merge in client.js:
- **Run-wide semantics, named explicitly.** The merge into a single
`Graph.sessions[EXECUTE_CODE]` was a deliberate match to the
agents-library design (`Graph.sessions` is shared across every
`ToolNode` in the run), but the inline `for (const a of agents)`
loop in `AgentClient.chatCompletion` made it look per-agent. Move
the logic to a TS helper `buildInitialToolSessions` that documents
the run-wide-by-design contract in one place. The CJS controller
now contains a single call site, no business logic.
- **Subagent walk (P2).** The previous loop only iterated
`[primary, ...agentConfigs.values()]`. Pure subagents are pruned
out of `agentConfigs` after init and retained on each parent's
`subagentAgentConfigs`, so their primed code files were silently
dropped from the seed. The helper now walks recursively, with a
visited-Set keyed on object identity that terminates safely on a
malformed agent graph (cycle).
- **`jest.setup.cjs` polyfill for undici `File`.** Reviewer hit
`ReferenceError: File is not defined` running the targeted spec on
WSL — a known Node 18 issue where `globalThis.File` from
`node:buffer` isn't auto-exposed. Polyfill it inside a Jest setup
file so the suite boots regardless of Node patch version.
Helper test coverage (8 new): skill-only / agent-only / both,
recursive subagent walk, cycle-safe walk, primary+subagent
deduplication, undefined/null entries in the agents iterable, and
representative session_id preservation across the merge.
16 tests pass total in `codeFilesSession.spec.ts` (8 prior + 8 new).
No behavior change vs. the previous commit for the existing
primary+agentConfigs case — subagent inclusion is the only new
behavior, and it matches what the existing seeding logic would have
done if subagents had been in `agentConfigs`.
* 🪛 fix: FIFO Walk Order in buildInitialToolSessions (P3 review)
The traversal used `Array.pop()` (LIFO), which visited the LAST
top-level agent first. The docstring says "primary first"; the code
contradicted it. When no skill seed exists the first-visited agent's
first file supplies the representative `session_id` written to
`Graph.sessions[EXECUTE_CODE]` — so a LIFO walk silently flipped which
agent that came from. `ToolNode` ultimately uses per-file `session_id`s
for runtime injection (so behavior was indistinguishable for current
callers), but the discrepancy was a footgun for any future consumer
that read the representative.
Switch to FIFO via `Array.shift()` to match both the docstring and the
existing `loadSubagentsFor` walk pattern in
`Endpoints/agents/initialize.js`. Add a regression test that asserts
the primary's `session_id` is the representative (and that all three
agents' files still contribute, with per-file `session_id`s preserved).
* 🔬 test: Lock In Code-Files Bug Fixes Per Comprehensive Review
Addresses MAJOR + MINOR + NIT findings from the multi-pass review:
**Finding #4 (MINOR) — empty relativePath misses sandbox fallback.**
A model calling `read_file("output/")` where "output" isn't a skill
name dead-ended with `Missing file path after skill name` instead of
being routed to the sandbox like every other malformed-path branch.
Add the same `codeEnvAvailable → handleSandboxFileFallback` pattern,
plus two regression tests.
**Finding #7 (NIT) — duplicate `skillsInScope()` helper.**
Hoist the identical helper out of two nested describe blocks to
module scope. Single source of truth.
**Finding #1 (MAJOR) — `persistedMessageId` had zero test coverage.**
The fix preserves a file's original `messageId` on update so
`getCodeGeneratedFiles` can still match it on subsequent turns. A
regression in the `isUpdate ? (claimed.messageId ?? messageId) : messageId`
ternary would silently re-introduce the original cross-turn priming
bug. Five new tests cover:
- UPDATE preserves `claimed.messageId` in the persisted record
- UPDATE falls back to current run id when `claimed.messageId` is
absent (legacy records predating the field)
- CREATE uses current run id (no claimed record exists)
- The runtime return value uses the LIVE id (artifact attribution)
even when the persisted record kept the original
- The image branch follows the same contract (would silently regress
if the ternary diverged across the two file-build branches)
The tests use a `snapshotCreateFileArgs()` helper because
`processCodeOutput` mutates the file object after `createFile`
returns (`Object.assign(file, { messageId, toolCallId })`) and a
naive `createFile.mock.calls[0][0]` would reflect the post-mutation
state instead of what was actually persisted.
**Finding #2 (MAJOR) — `readSandboxFile` had no direct tests.**
The model-controlled `file_path` flows through a POSIX single-quote
escape into a shell `cat` command, making this a security boundary.
A quoting regression would let a malicious filename break out of the
quoted argument and inject arbitrary shell. 20 new tests across:
- Shell quoting (7): plain filenames, embedded `'`, `$()`, backticks,
newlines, shell metachars, multiple consecutive single-quotes
- Payload shape (6): /exec URL, bash language, conditional
session_id / files inclusion, dedicated keepAlive:false agents
- Response handling (6): `{content}` on success, null on missing
base URL or absent stdout, throws on stderr-only, partial-success
returns stdout, transport errors are logged then rethrown
- Timeout (1): matches processCodeOutput's 15s SLA
Audited findings #5 (acknowledged tech debt — readSandboxFile in JS
workspace), #6 (pre-existing positional-args debt on
enrichWithSkillConfigurable), and #8 (cosmetic JSDoc style) — no
action taken per the reviewer's own assessment.
Audited finding #3 (walk order vs docstring) — already addressed in
commit
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LibreChat
English · 中文
✨ 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
-
- 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
-
🔦 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
- Compatible with Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, Google, Vertex AI, Responses API, and more
- Model Context Protocol (MCP) Support for Tools
- LibreChat Agents:
-
🔍 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 allow creation of React, HTML, and Mermaid diagrams directly in chat
-
🎨 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
-
- 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
-
⚙️ Configuration & Deployment:
- Configure Proxy, Reverse Proxy, Docker, & many Deployment options
- 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
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
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
Keep up with the latest updates by visiting the releases page and notes:
⚠️ Please consult the changelog for breaking changes before updating.
⭐ Star History
✨ Contributions
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
🎉 Special Thanks
We thank Locize for their translation management tools that support multiple languages in LibreChat.