calesthio

OpenMontage: Turn Your AI Coding Assistant Into a Full Video Production Studio --- title: "OpenMontage: Turn Your AI Coding Assistant Into a Full Video Production Studio" published: true tags: opensource, ai, video, aiagents canonical_url: devpik.com --- OpenMontage turns your AI coding assistant into a full video production studio. Paste a Short you love and it studies the pacing, pitches concepts with the cost upfront, then writes, generates, and edits the whole video. It has 76,864 stars and gained 3,700 in the last week. What Is OpenMontage? OpenMontage bills itself as the first open-source, agentic video production system, and its pitch is disarmingly simple: describe what you want in plain language, and your AI coding assistant handles the research, scripting, asset generation, editing, and final composition. It is built to be driven by an agent, not clicked through in an editor, so it works with Claude Code, Cursor, Copilot, Windsurf, and Codex, any coding assistant that can read files and run code. Under the hood it is a serious system: more than ten production pipelines, over 100 tools, and more than 700 agent skill and production-knowledge files. That depth is what lets it go from a one-line prompt to a finished video instead of stopping at a script or a storyboard. It is a Python project, licensed under AGPL-3.0, and the repository had 76,864 stars and 10,197 forks when we checked on November 5, 2026, after gaining 3,700 stars in a single week. Start From a Video You Love The fastest way in is also the most interesting feature: paste a reference video and let the agent study it. Give it a YouTube Short, a Reel, a TikTok, or a local clip and say something like, here is a Short I love, make me something like this but about quantum computing. The agent analyzes the transcript, pacing, scenes, keyframes, and style, then comes back with two or three differentiated concepts, an honest tool path, cost estimates, and a sample before full production starts. What you get back is not a vague prompt. You get what it keeps from the reference, the pacing, hook style, structure, and tone; what it changes, the topic, visual treatment, angle, and narration approach; what it will cost at your target duration before any asset generation begins; and what it will actually look like with the tools you currently have available. That cost-upfront answer is the part most AI video tools skip, and it is the difference between an experiment and a plan. Decide the format before you ask: a Short or Reel is vertical 9:16 and a standard YouTube video is 16:9, and the aspect ratio calculator gives the exact pixel dimensions for either if you are reframing your own clips to match. What It Can Actually Make The showreel is genuinely varied, which is the point: a cinematic sci-fi trailer produced end to end, a 60-second Pixar-style animated short about a lonely banana that cost 1.33 dollars to generate, a 54-second music-driven 3D showcase with gravity-shifting furniture and frozen car drifts, a 50-second vertical transformation film, a 60-second product film, and a 100-second documentary about salt that mixes real-world footage with narration and hand-authored motion graphics. One distinction matters more than the rest. OpenMontage can make image-based videos, but it can also make actual motion video for free and open-source workflows: the agent builds a corpus from free stock footage and open archives, retrieves actual motion clips, edits them into a timeline, and renders a finished piece. That is not the usual trick of animating a handful of stills and calling it video. If you want that path, ask for a documentary montage, tone poem, or stock-footage collage and say explicitly to use real footage only. The Cost Question, Answered Honestly Here is the part to understand before you start: the AI-generated clips use paid providers, so a video built on generated footage costs real money, while a video built on free stock footage costs nothing but time. OpenMontage is upfront about this at proposal time, showing you the cost before generation starts, and you choose the path yourself. The README's own examples put numbers on it: a 60-second animated short cost 1.33 dollars, a transformation film about 4 dollars, and a seven-world showcase about 5 dollars in generation costs. The paid path runs through providers like fal.ai for FLUX images and Veo, Kling, and MiniMax video, plus ElevenLabs, OpenAI, Google, Runway, and HeyGen, each behind an API key you add to a .env file. The free path uses Piper TTS for narration, Archive.org, NASA, and Wikimedia Commons for footage, Pexels, Unsplash, and Pixabay for stock, and Remotion or HyperFrames for composition, all with no paid key at all. If you have a GPU, you can also unlock free local video generation with models like Wan 2.2. Every provider selection is scored across seven dimensions with an auditable decision log, so you can see why the agent picked what it picked. The Backlot: A Storyboard That Watches the Production Chat tells you what the agent said. Backlot shows you what the production is actually doing. It is a local board that fills itself in as the pipeline runs: stages light up, the script lands as a screenplay page, scene cards shimmer while assets generate, and every provider decision and dollar spent is on the wall. When a production starts, the agent opens it for you automatically. The storyboard is also a real approval gate. Asset generation pauses on a scene-by-scene contact sheet, takes, prompts, per-asset cost, and quality scores, so you approve the visuals before the render, not after it is too late. Creative gates hold until you answer in chat, and when a run is done you can replay the whole production from its timestamps, scrubbable end to end. It is the closest thing to sitting in an edit suite while an agent does the work. How to Try It You need Python 3.10 or newer, FFmpeg, Node.js 18 or newer, and an AI coding assistant. Clone the repository, run make setup, open the project in your assistant, and tell it what you want in plain language, for example, make a 60-second animated explainer about how neural networks learn. If you write the narration yourself, keep in mind that 60 seconds holds only about 130 words at a typical speaking pace, and the speaking-time estimate in the Word Counter tells you whether your script fits. Or ask for the real-footage path: make a 75-second documentary montage about city life in the rain, use real footage only, no narration, elegiac tone, with music. That is genuinely it for a first video. The agent researches your topic with live web search, generates AI images, writes and narrates the script with voice direction, finds royalty-free background music automatically, burns in word-level subtitles, and renders the final video. Before you see anything, the system runs a multi-point self-review: ffprobe validation, frame sampling, audio level analysis, delivery promise verification, and subtitle checks. Add API keys only when you want the paid providers; with zero keys you still get narration, footage, composition, subtitles, and post-production for free. What to Watch Out For First, the license: OpenMontage is AGPL-3.0, which is stricter than MIT or Apache. If you run it as a network service or build on it, read what AGPL requires of you, because it is not a license to ignore. Second, costs are real on the paid path. The per-video numbers are small, a dollar or a few dollars, but they scale with length and the number of takes, and the approval gates exist precisely so you do not burn money on assets you have not seen. Third, this is an agentic system, so results depend on the assistant driving it and the providers you have configured. A run with only free tools looks different from a run with Veo and Kling behind it, and the proposal step is where you find that out, before spending anything. Finally, it is a young, fast-moving project: 76,864 stars and a week of 3,700 new ones means the docs and pipelines change often, so treat the README as the current truth. What it offers today is remarkable either way: a full video production pipeline that starts from a video you love and ends with a finished piece, open source, with the cost stated upfront. FAQs What is OpenMontage? OpenMontage is an open-source, agentic video production system, and its README calls it the first of its kind. It turns your AI coding assistant into a full video production studio: describe what you want in plain language and the agent handles research, scripting, asset generation, editing, and final composition. It is written in Python, licensed under AGPL-3.0, and had 76,864 stars in November 2026. How does OpenMontage work? You describe the video you want to your AI coding assistant, or paste a reference video like a YouTube Short and ask for something similar about your topic. The agent analyzes the reference, pitches 2-3 concepts with cost estimates upfront, then runs one of its production pipelines, more than ten in all, using 100+ tools to research, script, generate assets, edit, and render the final video. Which AI coding assistants work with OpenMontage? Claude Code, Cursor, Copilot, Windsurf, and Codex. Any AI coding assistant that can read files and run code can drive it, since the system is pipeline-driven rather than a point-and-click editor. How much does it cost to make a video with OpenMontage? It depends on the path you choose. AI-generated clips use paid providers, and the README's examples run about $1.33 for a 60-second animated short to about $5 for a multi-scene showcase. Or you can use free stock footage and open archives, which costs nothing. OpenMontage shows you the cost upfront before generation starts, so you choose. Can I make videos with OpenMontage without any paid API keys? Yes. With zero API keys you get Piper TTS narration, free footage from Archive.org, NASA, and Wikimedia Commons, stock from Pexels, Unsplash, and Pixabay, Remotion or HyperFrames composition, built-in subtitles, and FFmpeg post-production. A GPU unlocks free local video generation too. What is Backlot in OpenMontage? Backlot is a live local storyboard that shows what a production is actually doing: stages lighting up, the script as a screenplay page, scene cards while assets generate, and every provider decision and dollar spent. It also acts as an approval gate, pausing asset generation so you approve visuals before the render. How do I install OpenMontage? You need Python 3.10+, FFmpeg, Node.js 18+, and an AI coding assistant. Clone the repository, run make setup, open it in your assistant, and describe the video you want. API keys for paid providers go in a .env file and are all optional. Is OpenMontage free? The software is free and open source under AGPL-3.0. Generating video with paid AI providers costs money per clip, but the free stock-footage path costs nothing, and OpenMontage tells you the cost before it generates anything. --- Originally published at devpik.com. Repo: calesthio/OpenMontage.

Want a video without the editing grind? OpenMontage turns your AI coding assistant into a video studio, from research and script to final edit. Open source. Official Link: calesthio/OpenMontage #OpenMontage #OpenSource #AIVideo #ClaudeCode #GitHub t.co

OpenMontage (calesthio/OpenMontage) is free and open-source (AGPL-3.0). Clone the repo, run make setup, then open it in Claude Code, Cursor, Codex, or similar and just prompt your video idea. It handles the rest. Core path uses free local tools + stock footage with zero cost. Optional paid APIs improve quality (with built-in spend limits).

OpenMontage is trending on GitHub, with 70k+ stars. It is a free, open-source tool that turns your AI coding assistant into a video maker. You describe the video, and it plans, edits and builds it. Works with Claude Code and Cursor 🎬 calesthio/OpenMontage #OpenSource #AI

Keep OpenMontage's Local LLM From Competing With Your Video Renderer Keep OpenMontage's Local LLM From Competing With Your Video Renderer You deploy a local LLM for workflow orchestration. You run inference in isolation, validate the server, ship it. Two weeks later, production rendering jobs start failing with cryptic CUDA OOM errors at 3 AM. The inference server is still running. GPU memory looks fine in nvidia-smi. The blame lands on "the AI integration," and suddenly your careful architecture is fighting for its reputation. This is the predictable failure mode when inference and rendering share GPU resources without explicit boundaries. I'm evaluating OpenMontage as an external integrator. The repository advertises 12 production pipelines, 100+ tools, and over 700 skill and production-knowledge files. Local inference is an attractive integration point: planning, tool selection, and revision loops stay on the workstation. But making it coexist with GPU-heavy video work requires deliberate resource isolation, not just a memory utilization flag. This architecture is a deployment boundary around OpenMontage's coding-assistant workflow, not a description of verified built-in inference adapters. Repository-specific configuration still needs checking against the version you deploy. Separate the Director From the Machinery The end-to-end path should be explicit: User brief → assistant and skill selection → bounded inference request → validated tool invocation → production worker → artifact store. The inference server generates decisions. Production workers execute media operations. Durable job state and artifacts belong outside both processes. Put a bounded queue before inference. Keep tool output, transcripts, and production knowledge outside the prompt until needed; retrieve relevant sections instead of concatenating the entire project history. Apply the token budget after assembling the complete request, including tool schemas. Most importantly, a GPU-memory utilization flag is not a hardware partition. If inference and rendering share one GPU, serialize their GPU-heavy phases or enforce isolation outside the model server. The configuration below assumes a dedicated 24 GiB inference GPU. Why the Toy Setup Fails The 70B OOM incident worth diagnosing is not necessarily a bad model load. Partial layer offloading can make weights fit while leaving insufficient room for the configured context and concurrent requests. KV-cache capacity depends on architecture, cache precision, sequence length, and concurrency. Weight quantization does not automatically quantize that cache. Increasing context while preserving concurrency can consume the remaining headroom. Long-running throughput degradation introduces another trap: retained allocator memory and an intentionally preallocated KV-cache pool can resemble a leak. A rising nvidia-smi number alone cannot establish one. Compare equivalent workloads, active-request counts, and cache occupancy after requests drain. For an initial integration, I would use a smaller model with bounded context rather than force a partially offloaded 70B model onto the production workstation. One Bounded Server, One Streaming Probe This configuration uses Qwen2.5-7B, an 8,192-token context, four active sequences, and an 80% GPU-memory budget. These are conservative starting limits, not universally optimal settings. Run in a CUDA-compatible Linux environment with Python 3.11 and nvidia-smi. Select the inference GPU through CUDA_VISIBLE_DEVICES; GPU_INDEX must identify that same physical GPU for measurement. python -m pip install 'vllm==0.10.2' aiohttp export CUDA_VISIBLE_DEVICES=0 export GPU_INDEX=0 export MODEL=Qwen/Qwen2.5-7B-Instruct export BASE_URL=http://127.0.0.1:8000/v1 vllm serve "$MODEL" \ --host 127.0.0.1 --port 8000 \ --dtype half \ --kv-cache-dtype auto \ --gpu-memory-utilization 0.80 \ --max-model-len 8192 \ --max-num-seqs 4 \ --max-num-batched-tokens 4096 \ --enable-chunked-prefill & SERVER_PID=$! trap 'kill "$SERVER_PID" 2>/dev/null || true' EXIT python - <<'PY' import asyncio, json, os, statistics, time import aiohttp BASE = os.environ["BASE_URL"].rstrip("/") MODEL = os.environ["MODEL"] GPU = os.environ.get("GPU_INDEX", "0") HEADERS = {} if os.environ.get("API_KEY"): HEADERS["Authorization"] = "Bearer " + os.environ["API_KEY"] async def memory(): p = await asyncio.create_subprocess_exec( "nvidia-smi", "-i", GPU, "--query-gpu=memory.used", "--format=csv,noheader,nounits", stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) out, err = await p.communicate() if p.returncode: raise RuntimeError(err.decode()) return int(out.decode().strip()) async def request(session): start, first = time.perf_counter(), None payload = { "model": MODEL, "messages": [{"role": "user", "content": "Write a concise six-shot production plan for a coffee advert."}], "max_tokens": 128, "temperature": 0, "stream": True, } async with session.post( BASE + "/chat/completions", json=payload ) as response: response.raise_for_status() async for raw in response.content: line = raw.decode().strip() if not line.startswith("data:"): continue data = line[5:].strip() if data == "[DONE]": break event = json.loads(data) if "error" in event: raise RuntimeError(event["error"]) for choice in event.get("choices", []): if choice.get("delta", {}).get("content") and first is None: first = time.perf_counter() - start if first is None: raise RuntimeError("Stream returned no content") return first, time.perf_counter() - start async def main(): timeout = aiohttp.ClientTimeout(total=180, sock_read=60) async with aiohttp.ClientSession( headers=HEADERS, timeout=timeout ) as session: for attempt in range(180): try: async with session.get(BASE + "/models") as r: if r.status == 200: break except aiohttp.ClientError: pass await asyncio.sleep(2) else: raise RuntimeError("Server readiness timed out") await request(session) # Warm up before measuring. baseline = await memory() samples = [baseline] async def sample(): while True: samples.append(await memory()) await asyncio.sleep(0.25) semaphore = asyncio.Semaphore(2) async def bounded(): async with semaphore: return await request(session) monitor = asyncio.create_task(sample()) try: results = await asyncio.gather(*(bounded() for _ in range(8))) finally: monitor.cancel() try: await monitor except asyncio.CancelledError: pass print(json.dumps({ "requests": len(results), "median_ttft_s": statistics.median(x[0] for x in results), "median_latency_s": statistics.median(x[1] for x in results), "gpu_baseline_mib": baseline, "gpu_peak_mib": max(samples), "gpu_peak_delta_mib": max(samples) - baseline, "gpu_after_mib": await memory(), }, indent=2)) asyncio.run(main()) PY Interpret the Boundary, Not Just the Numbers TTFT starts when the request enters the HTTP client; latency ends when streaming completes. Both exclude time waiting for the benchmark's semaphore. Production queue delay needs its own measurement. GPU measurements cover the entire device, not just vLLM. Sampling can miss brief peaks, and a preallocated cache can produce almost no measured delta. This is a smoke benchmark, not a capacity certification. For an optional remote comparison, the same harness can target B-Lost's relay endpoint, https://b-lost.com/v1, using an authorized API_KEY and an available model identifier. I have not measured that endpoint here. Local GPU readings would not describe its server-side memory. The trade-off is deliberate: bounded context and concurrency sacrifice flexibility to protect production work. Expand either only after replaying representative tool-heavy requests alongside the actual rendering schedule. --- The hardest operational question is not whether to bound inference resources, but when to stop defending the boundary and admit that inference and rendering need separate hardware. If you are running 70B models with 32k context windows while rendering 4K video timelines, no amount of semaphore discipline will save you. At what utilization threshold do you declare victory for clever resource sharing and buy another GPU? I have drawn that line differently on every project. What does your architecture assume, and where has it broken?

OpenFusion: Efficient sub task routing preview.redd.it The main agent routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Would also really appreciate if someone could benchmark using some other harnesses.

不會拍也不會剪？GitHub 3 個開源神器直接出片：ArtCraft・OpenMontage・hypit 不會拍、不會剪，也能做影片？這支 60 秒把 GitHub 上三個「AI 做影片」開源專案一次講清楚，repo 全名都在畫面上。 ① storytold/artcraft｜37,549★｜Rust｜Apache-2.0 storytold/artcraft 「給創作者的 IDE」：2D 畫布排構圖 → 3D 舞台擺姿勢、自己定機位 → 再生成。先搭好場景才出圖，不是一句話盲抽。桌面版在 getartcraft.com 下載。 ② calesthio/OpenMontage｜72,883★｜Python｜AGPL-3.0 calesthio/OpenMontage 自稱第一個「agentic」影片製作系統：12 條產線、100+ 工具、700+ 技能檔。裝法就是 clone 下來 make setup（要 Python 3.10+、FFmpeg、Node 18+，再配一個 AI 編程助手），之後用白話講需求，查資料、寫腳本、配音、配樂、字幕、出片交給它跑。 ③ hypit-ai/hypit｜26,408★｜TypeScript｜Apache-2.0 附條件 hypit-ai/hypit 丟一支爆紅影片進去，它先拆解「為什麼會紅」，再換臉、換詞、換 B-roll，一條指令出一百個版本。安裝：npx skills add hypit-ai/hypit -g（Node 22.15+）。 三個要提醒你的實話 ・軟體免費，不等於全免費：模型 API、生成積分、編程助手訂閱都要另外付（hypit README 也直接寫「free to use，但模型服務自己算」）。 ・復刻的是結構，不是素材：別人的畫面、音樂、臉不能直接搬。 ・YouTube 合作夥伴計畫對搬運、批量重複的內容不給變現。 數字怎麼來的 星數／授權／語言是 2026-10-29 直接打 GitHub API 實測（不是轉述別人的截圖），片中的示意畫面都是自己畫的，沒有使用這三個專案的示範影片。 影像素材（Wikimedia Commons） ・Photo: Incase from San Francisco, USA · CC BY 2.0 ・Photo: Mattbr · CC BY 2.0 編製：螯蝦Pi(小蝦) #GitHub #開源 #AI影片 #短影音 #Shorts #ArtCraft #OpenMontage #hypit

OpenFusion for sub task routing Using this your main agent routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Open Sourcing this. You can use it if it helps you with token and time saving. I am seeing about 25% cost and 25-40% time saving for complex multi-step tasks within Codex Would also really appreciate if someone could benchmark using some other harnesses. calesthio/OpenFusion reddit.com

Introducing OpenFusion for appropriate model routing It routes each subtask to the cheapest model that can do it well, and has a judge escalate to a stronger model when the cheap attempt isn't good enough. Open Sourcing this. You can use it if it helps you with token and time saving. I am seeing about 30% token and 25-40% time saving for complex multi-step tasks within Claude Code. Would also really appreciate if someone could benchmark using some other harnesses. calesthio/OpenFusion

30 Best Claude Plugins on GitHub (Sorted by Your Job) 30 Claude plugins with 3.9 million GitHub stars between them, sorted into 5 rows so you only need yours: coding, research, saving tokens, design, and writing + video. Don't add all 30. Pick the row that fits your week, add one plugin, and open its repo first to check what it can touch on your machine. 💬 Comment ANYLANCER and I'll send you all 30 links, or grab them here: 1) You write code • Superpowers: obra/superpowers • Matt Pocock's Skills: mattpocock/skills • ECC: affaan-m/ECC • Karpathy Skills: multica-ai/andrej-karpathy-skills • Ponytail: DietrichGebert/ponytail • Agent Skills: addyosmani/agent-skills 2) You research big things • Graphify: Graphify-Labs/graphify • Agent Reach: Panniantong/Agent-Reach • Understand Anything: Egonex-AI/Understand-Anything • Last 30 Days: mvanhorn/last30days-skill • Career Ops: career-ops-hq/career-ops • REA: morluto/rea 3) Huge projects, few tokens • gstack: garrytan/gstack • Caveman: JuliusBrussee/caveman • Claude-Mem: thedotmack/claude-mem • Headroom: headroomlabs-ai/headroom • GSD: gsd-build/get-shit-done • BMAD Method: bmad-code-org/BMAD-METHOD 4) Design & visuals • UI UX Pro Max: nextlevelbuilder/ui-ux-pro-max-skill • Taste: Leonxlnx/taste-skill • Archify: tt-a1i/archify • Impeccable: pbakaus/impeccable • PPT Master: hugohe3/ppt-master • Diagram Design: cathrynlavery/diagram-design 5) Writing & video • Anthropic Skills: anthropics/skills • OpenMontage: calesthio/OpenMontage • HyperFrames: heygen-com/hyperframes • Humanizer: blader/humanizer • I Have ADHD: ayghri/i-have-adhd • Marketing Skills: coreyhaines31/marketingskills Built a skill people would pay for? Sell it on Anylancer: anylancer.com Which row are you? 👇 Star counts from GitHub on Oct 29, 2026. This video was made with HyperFrames. #ClaudeCode #AIAgents #GitHub #Shorts