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, typed decisions, 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/. 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 + 27 MCP tools (foundation-model forecasting plus unload_models, tabular is REST-only). CPU or CUDA.
• Typed decisions: decidealot at /decidealot/. Local Laya, Von, and CLM are enabled by default. Hosted TypeSafe Jev enables when its key is configured. Send a state plus named choice, score, or noul questions and get typed answers with model probabilities, so the caller sets its own threshold. Laya routes between English and multilingual checkpoints and has a checkpoint tuned for policy, routing, and triage decisions. Von is English-only, for short questions with clear criteria. CLM calls AIGate's internal Qwen3-8B embeddings route; make run-bg starts its CUDA encoder automatically. Set DECIDEALOT_CLM_ENABLED=false to remove that GPU dependency. REST API, batch decisions, and 4 MCP tools. 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.