AI工作流平台

aigate

A self-hosted AI platform. One docker-compose up.

Everything an AI-powered workflow needs — inference, tool use, browser automation, image generation, speech synthesis, transcription, object storage, agentic code execution, web search, an email gateway, a Telegram client, time-series forecasting, an async job queue, and a web UI — behind a single OpenAI-compatible endpoint at http://localhost:4000. Point any existing client at it and it works.

Tools and capabilities

MCP tools across multiple servers. Any model with function calling can invoke them autonomously — the model orchestrates, you just prompt.

• Stealth browser cluster — 5 Camoufox replicas behind HAProxy, real OS-level mouse and keyboard input, zero CDP exposure. Passes Cloudflare, CreepJS, BrowserScan, and every other bot detector we've thrown at it.
• Two agentic coding agents — full shell access, persistent workspaces, file I/O, tool use. Claude Code (your subscription or API key) and pi-coding-agent pointed at z.ai for GLM models. Both expose REST API, OpenAI-compatible endpoint, and MCP server.
• Sandboxed code execution — piston at /piston/. Multi-language code execution in nsjail-isolated sandboxes (own user namespace, chroot, seccomp, cgroups, no network). Default install: Python + Node. Add Bash / Deno / Go / Rust / TypeScript / 40+ other languages via PISTON_LANGUAGES + rebuild. REST API + execute_code MCP tool any function-calling LLM can call to compute deterministic results (hashes, parses, transforms) instead of guessing.
• Object storage — S3-compatible, public-read uploads, presigned URLs, auto-expiry. Plain HTTP and boto3.
• Image generation — FLUX, DALL-E, Stable Diffusion (cloud + local CPU/CUDA via stable-diffusion.cpp) via MCP tools that return persistent URLs, not base64 blobs. Local CPU: sd-turbo, sdxl-turbo. Local CUDA adds: sdxl-lightning, flux-schnell, juggernaut-xi.
• Text-to-speech — Kokoro (CPU + CUDA-ONNX, English+Chinese, voice presets), Qwen3-TTS (CUDA, voice cloning + voice-design + emotion control via the instructions field), OpenAI TTS.
• Transcription — Whisper (cloud + local CPU/CUDA), Parakeet (~3400× real-time on CPU).
• Web search — SearXNG self-hosted meta-search (Google, Bing, DuckDuckGo, Wikipedia) via MCP search_web tool.
• Telegram client — Telethon at /telethon/. Send/read/edit/delete messages, list dialogs, forward, send files from URL, manage group membership. REST API + MCP tools.
• Email gateway — mailbox at /mailbox/. Stateless IMAP+SMTP across N accounts from one YAML config — unified inbox, per-account list/search/CRUD, SMTP send. REST API + flat MCP tool set (mailbox parameter selects account).
• Time-series forecasting + tabular ML — predictalot at /predictalot/. Five foundation forecasters (chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m) across six forecast types (univariate, multivariate, past/future covariates, samples) with per-type weighted ensembles at /v1/timeseries/<type>/… + a sibling /v1/tabular/* family in v1.0.0 — 9 supervised backends (lightgbm, xgboost, hist-gbt, random-forest, logistic, mlp, svm-rbf, knn, naive-bayes) and 3 meta-learners (calibrated / stacking / diversified). REST API for both families + 26 MCP tools (foundation models only — tabular is REST-only). CPU or CUDA.
• Audio production — audiolla at /audiolla/. Stem separation (Demucs / UVR), restoration (UVR de-reverb / de-echo / de-noise), mastering (matchering / pedalboard chains + curated presets like master-for-spotify, podcast-cleanup, vocal-cleanup), MIR analysis (BPM / key / LUFS / beats / onsets / melody / chords / segments), DSP transforms (sox + ffmpeg), loudness normalization, speech enhancement (DeepFilterNet), VAD (silero), diarization (pyannote), CLAP embeddings + zero-shot classification, AudioSet tagging (AST), audio→MIDI (basic-pitch), MIDI compose / inspect / transform / render via fluidsynth, text-to-audio generation (stable-audio-open / musicgen / riffusion / audioldm2 — CUDA only), ad-hoc op-chain pipelines, async jobs + webhooks. REST API + MCP tools. v1.0+ contract: JSON body on every audio endpoint, raw bytes only at PUT /v1/files/{path}, output_path xor output_url mandatory for any audio-producing call.
• Video toolkit — flickies at /flickies/. Lipsync via LatentSync 1.5 (ByteDance, Apache-2.0, ~8 GB VRAM, default on CUDA) and Wav2Lip / Wav2Lip-GAN (LRS2 non-commercial, gated). Face restore via GFPGAN v1.4. ffmpeg ops — trim, concat, transcode (incl. gif + fps + codec change), scale, mux audio, extract audio, thumbnail grid. ffprobe info. Async jobs + webhooks. REST API + 11 MCP tools. Same JSON-body + output_path xor output_url contract as audiolla. GFPGAN + LatentSync 1.5 are CUDA-only; CPU image runs ffmpeg ops + slow Wav2Lip-CPU.

Ask a Groq model to research something and it opens a browser, reads pages, screenshots them, uploads to storage, and comes back with a summary and links. The model decides what tools to use and in what order.