* feat(ai-agent): add autocompacted memory that summarizes older context Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): compact on final-answer turns and count what a turn appended Address the pre-push review findings on the compaction path: - A turn the model answers without a tool call left the agent loop on its first iteration, so a chat-shaped step never compacted and reloaded the whole conversation on every later turn. Compaction now also runs after the loop. - The trigger measured only the last request, so a single large tool result could carry the next one past the window without ever crossing 80%. - The summarization call re-sent the usage-tracking request shape on endpoints the loop had already learned to drop it for. - The flat 8000-token summary reserve swallowed the whole target on a small context window, leaving one message in the tail and summarizing the rest. - A response cut off inside the <analysis> scratchpad was accepted as a summary. - The chat-mode memory default was a shared object the step form edited in place. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): keep Anthropic prompt counts and compact once per response Address the first CI review round on the compaction path: - Anthropic's streaming parser dropped `message_start`, the only event carrying the prompt-side counts, so a native Anthropic run reported no input tokens at all and compaction fell back to a character estimate. - A loop that exits without issuing another request — a structured-output turn does — reached the post-loop pass still holding the previous measurement and compacted a second time, or retried a failure with nothing changed. - The summarization call inherited the step's `max_completion_tokens`; a low one truncates the summary inside its scratchpad, which counts as a failure and disables compaction after three of them. - A fired trigger that found nothing to summarize said nothing. - Memory already over the window — a lowered `context_window`, or a step moved over from `auto` — had no way back, since compaction only ran after an accepted request. It now also runs once before the first one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): state the summary's own completion cap and drop the pre-flight pass - The summarization call asked for no completion cap at all, which is "uncapped" only on the OpenAI-shaped providers: Anthropic substitutes 64000, over several Claude models' output ceiling, and Bedrock leaves the model's own small default, short enough to cut the response off inside its scratchpad. It now asks for the reserve the split already set aside, raised to the step's cap when that is larger. - Compaction no longer runs before the first request. The fallbacks the loop learns from a rejection are not known that early, so on exactly the endpoints that need them the summarization was malformed by construction: it failed, spent a strike, and the first agent request still carried the oversized conversation. A memory already past the window is repaired on the turn after a request the endpoint accepts, rather than by a pass that cannot succeed there. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): ask the summary for exactly the room the split reserved The split scales its reserve down on a small window while the request asked for a flat 8000, so the two diverged below an 80k window: on a 4k/8k model the cap alone exceeded the window and every summarization was refused, and on a 20k one a full-length summary could land the conversation back over the trigger and compact its own previous summary on the next response. Both now read one `summary_reserve_tokens`. The call also no longer inherits the step's reasoning effort. Every provider counts thinking against that same budget, so a high-effort model could spend the whole reserve before writing anything and return a summary cut off inside its scratchpad; the compaction prompt asks for an `<analysis>` block, which is the reasoning this call needs. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): charge the compaction budget for tools and the system prompt The tail budget was the whole target, but a request also carries the system prompt compaction keeps and the tool definitions, which are not in the message list at all. On a small window those are most of it: a tail sized to the full target left the next request back over the trigger, compacting again every response, and the no-usage estimate missed the tool schemas entirely so it could fail to trigger at all. Both now account for them. The reserve also gains a floor. It is the summary's output cap as well as the room the split leaves, and scaled down without one a small window gave a structured nine-section summary a few hundred tokens — truncated inside its scratchpad every time, which is discarded, which switches the mode off after three. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): count Gemini's tool-use prompt tokens in an agent step's usage Gemini splits a tool-using turn's input across `promptTokenCount` and a disjoint `toolUsePromptTokenCount`, and its thinking apart from `candidatesTokenCount`. The agent step's parser read only the headline fields, so every tool-using turn under-reported both — and the compaction trigger, which runs off the reported prompt, could not see the tool results that grew it. It now goes through the same helpers the proxy path already used. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): calibrate the compaction estimate against the measured prompt Two rounds running, the finding was "the character estimate cannot see input X" — tool schemas, then S3 attachments, which are short paths in the message list and whole images by the time a provider counts them. Enumerating those is a list that only grows, so the estimate is now scaled to the one number that is ground truth: what the provider charged for the last request. Attachments, tokenizer drift and whatever comes next fall out of that, because the estimate is only ever used relative to itself. Also stop the Gemini helpers turning an absent count into `Some(0)`. Downstream, absent means "fall back to estimating the conversation" while zero reads as an empty prompt and would hold the trigger below its threshold for the whole run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): charge attachments what they cost and let a heavy short prefix compact The calibration conserved the conversation's total cost but spread it by character count, so an attachment — a short S3 path in the message list, a whole image or PDF once a provider expands it — was charged to the text messages around it and stayed nearly free in the split. It now carries a nominal cost of its own, which the calibration corrects a residual on rather than the whole gap. The four-message minimum also refused exactly the case that fix is for: an attachment arriving on the first or second turn can pass the trigger before four removable messages exist, and summarizing even one of them saves most of the prompt. A prefix worth a quarter of the window is now enough on its own. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): never summarize a prefix holding only a previous summary The message-count floor was carrying a second job: a fresh summary sits in a one or two message prefix, so requiring four declined it. The share threshold added last commit admits it, and a summary is reserve-sized by construction — so the post-compaction shape could spend one summarization per response swapping a summary for another the same size, shrinking nothing and losing fidelity each time. A previous summary no longer counts towards that threshold. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): take the context window from the model and drop the estimate calibration Brings compaction in line with how the AI session does the same job, which had already answered these three questions. - The window is looked up from the model. `MODEL_CONTEXT_WINDOWS` in `windmill-ai/src/model_context.rs` mirrors the session's table in `copilot/modelConfig.ts`, entry for entry and with the same matching rules; each side points at the other, since a model added to one and not the other compacts at two different sizes. A step's `context_window` becomes the override for what the lookup cannot serve, and chat mode writes none. - Provider usage is normalized where the provider's quirk is, not at the consumer. `TokenUsage::with_cache_beside_input` raises `input_tokens` to the whole prompt for Anthropic and Bedrock, which report their cached prefix beside it; the OpenAI shape already counts it inside. `prompt_tokens()` is then just `input_tokens`, rather than inferring the shape from whether a write count is present. - The estimator is no longer calibrated against the measured prompt. The session uses the provider's count when it has one and a chars/4 estimate otherwise, with nothing in between, and a tail sized a little wrong only compacts again a turn later. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * feat(ai-agent): summarize memory down to what the database can store Without an instance object store, memory is a 100KB database row cut from its oldest message, the summary included, so compaction on a mainstream model never got to keep anything across runs. A step that persists there now runs its post-loop compaction pass against the smaller of the model's window and the cap at chars/4, about 25k tokens: the loop keeps the whole window, and what is written is a summary plus a tail that fits. The run logs when that pass summarizes, and how many messages the write dropped when one still overshoots. The editor's storage warning on the option is removed: nothing exposes the instance storage to it, so it keyed on the workspace S3 setting, which is unrelated to where memory goes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): get a complete, billed summary out of every provider Compaction against the real providers turned up four things the stub could not: Gemini and OpenAI's reasoning models think by default and bill it against the same cap the summary must fit in, so the summarization request now asks them for their least (none, low); an OpenAI Responses call that hits max_output_tokens ends in response.incomplete, whose usage the parser dropped, so that summarization went unbilled; a summary that quotes </summary> when it describes its own instruction was cut off at the quote, on the agent step and the AI session alike; and the prefix could end on an unanswered user message, after which the instruction reads as part of that turn (Anthropic merges the two outright). The tail now starts on a user message, and both prompts tell the model the instruction is not part of the conversation. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): compact down to half the window, on the agent step and the AI session The gap between the 80% trigger and the target is what one compaction buys, and every summarization request carries most of the window. At a 70% target a 128k model summarized about 13k tokens of prefix for a summary of up to 8k, so each ~100k-token request bought a few turns of room before the next one re-summarized the previous summary. At 50% the same request frees about 30k. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): drop the workspace-S3 memory hint and state the database bound in the tooltip The memory field warned that memory is kept in the database whenever the workspace had no S3 storage. That setting has no bearing on where memory goes: the instance object store decides, and nothing exposes it to the editor. The field's tooltip now describes both memory kinds and states the database bound unconditionally; the run log says what happened. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): send the summarizer its tool history as text The summarization request carries no tool definitions, and Bedrock's Converse API rejects toolUse/toolResult blocks that arrive without them, so on Bedrock every summarization of a prefix holding a tool call failed silently until the breaker tripped. The prefix's tool calls and results now reach the summarizer rendered as text, on the agent step and in the AI session's compaction, which goes through the same proxy. Also drops the TokenUsage::prompt_tokens accessor, which had become a plain read of the normalized input_tokens, and shortens the context window field's description. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): price attachments from the provider count, bound storage in bytes, effort per pro model Addresses two Codex rounds and a leftovers audit. - Attachments were priced at a flat 1500 tokens in the split, so a multi-page PDF (tens of thousands of tokens to the provider, a short S3 path in the message list) could be kept in the tail or leave no prefix worth summarizing. They are now priced from the provider's count for the request that carried them, less that request's text, with the 1500 floor where nothing was counted. - The database storage bound measured the provider's token count, but the 100KB cap is bytes and repetitive text packs several characters per token. The persist pass now measures the serialized conversation. - The summarizer forced `low` on every reasoning model, which the pro variants reject (gpt-5-pro takes only high, gpt-5.2-pro starts at medium); they now get no effort. - Dropped the unused prompt_tokens accessor and its orphaned assert, an unused PartialEq, a needlessly public lookup, and fully-qualified Gemini calls; refreshed stale comments and the memory_id schema doc; regenerated the flow schema artifacts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): evict a heavy attachment into the summarized prefix, not the tail Pricing attachments from the provider count was not enough on its own: a leading attachment is a user message, and the boundary rule pulled the last unanswered user turn back into the kept tail to keep it with its answer. For a heavy attachment that dragged it into the tail — or, at the front, emptied the prefix — so it was never summarized and rode every request. The boundary now moves forward instead, keeping that user turn and its answer in the summarized prefix. Verified on the running instance: a 25k-token PDF on a 30k window is summarized out on the turn it overflows, and later turns drop from 26k to ~1.5k tokens. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): keep the forward boundary move off tool results and the prefix start The forward move that keeps an unanswered user turn out of the tail had two edges the third Codex round found: advancing past the user could land the boundary on a tool result (its tool_calls then summarized away, orphaning it), and with no system prompt the summarizable prefix starts at 0, so a trigger firing while the tail estimate fit everything indexed below the start and panicked the task. The forward scan now skips tool-opening boundaries, and the move is guarded above the prefix start. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): drop the step temperature from the summary request OpenAI's reasoning models (gpt-5-mini, gpt-5.1, gpt-5.2) reject `temperature` alongside any reasoning effort but their own default, so a step configured with a temperature made every summarization fail once the summarizer forced a low effort — history then grew unchecked. The internal summary call now omits the step's temperature: a structured extraction does not need a set one, and omitting it sidesteps each provider's temperature-versus-reasoning rules. Confirmed against the API that low + temperature is refused on those models. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): compact an oversized loaded memory before the first request Compaction was reactive, taken only after a request the endpoint accepted, so the fallbacks the loop learns from a rejection are known first. But a memory loaded from an earlier run can already exceed this run's window — the step was switched to a smaller model, or a run under a wider one persisted more than fits — and that first request then overflows and fails the run, with every retry reloading the same history and failing again. A pass is now taken up front, off the character estimate, before the first request. It uses the default request shape; an endpoint needing a fallback may reject this one summary, which is non-fatal, and mainstream providers need none. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): under the storage bound, trigger on the max of bytes and model tokens The storage-bound pass measured only the serialized row size, so an attachment — a few bytes as an S3 path but nearly the whole model context — read as tiny and the pass skipped a compaction the model needed. It now takes the larger of the byte measure and the model's token count, since repetitive text is few tokens but many bytes and an attachment is the reverse; either being over must fire a pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): drop oldest turns when a summary cannot fit the window, as the AI session does An oversized loaded memory (a step switched to a smaller model, or an object-store run that persisted more than a later model's window holds) left a prefix larger than the summarizer's own window, so the summary request overflowed and failed, the memory was untouched, and every retry failed the same way. The AI session handles this by falling back from summarization to dropping the oldest turns down to the target; compaction here now does the same. When a summary cannot run — it failed, the breaker is tripped, or nothing is worth folding — the oldest turns are dropped until the conversation fits and opens on a user message, keeping the newest turn. The next request then always fits. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): drop whole turns only, keep the storage pass to bytes, refresh the count after a rewrite Three edges the seventh Codex round found, all in the drop-oldest fallback and the storage-bound measure: - drop_oldest_to_fit dropped to any point that freed enough, which could strand a tool result whose tool_calls went with the messages before it. It now drops whole turns only, always landing the boundary on a user message and never splitting the newest turn; a lone turn too big for the window is left whole rather than broken. - The storage-bound pass measured the whole model prompt against the shrunk 25k window, so a large tool roster and the system prompt — neither written to the row — tripped it on a conversation the row easily held. It measures the serialized bytes alone now; the model's own window is enforced by the in-loop passes and the pre-first-request pass, so the persisted size is all this pass is for. - A compaction rewrites the message list, so the provider's count for the request that produced it no longer lines up. The count is now cleared after any pass that rewrites the conversation, so a later pass measures the estimate over the actual messages instead of a stale, larger prompt (which could decline a summary that already fit and then drop it). The step temperature, no longer sent to the summarizer on any path, is dropped from the request struct. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): measure only the persisted messages against the storage cap Persistence strips the system prompt before writing the memory row, but the storage pass was serializing every message including it, so a large system prompt with a tiny conversation reported far over the storage trigger, and the fallback dropped the one real turn, run after run. The storage measure now serializes only the non-system messages, matching what the row actually holds. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix(ai-agent): run the model-window pass before the storage-bytes pass post-loop A turn the model answered without a tool call broke before the in-loop compaction check, so on database-backed memory its only pass was the storage one, which measures bytes. An attachment fills the model context but is a few bytes in the row, so that turn never compacted and a follow-up could overflow the model. The post-loop now runs a model-window pass first, off the provider's count, then the storage-bytes pass when the row is smaller than the model — both limits enforced for a chat-shaped step, not just the one that happens to bind. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix: simplify agent compaction and preserve execution history * fix: remove unused compaction history setting * fix: preserve answers and recover rejected agent context * refactor: make agent compaction transactional * fix: skip agent summaries that cannot fit retained context * fix: explain skipped agent context compaction * fix: retain recent agent memory when storage compaction cannot fit * fix: start retained agent memory at a user turn * fix: reject unsafe agent memory truncation on storage fallback * docs: clarify agent context window override scope * fix: keep recent turns verbatim when compaction memory outgrows storage Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix: keep the compaction summary out of the agent's answers Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * fix: shorten the agent context window help text Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01ViJyjUmidDYV2m6ifQdLeH * chore: update ee-repo-ref to 942d4013f36edac1fc9a9addbdb02198db1c7a05 This commit updates the EE repository reference after PR #812 was merged in windmill-ee-private. Previous ee-repo-ref: 8ca1682ce6106ba6ea96894fbe606dac64102eb6 New ee-repo-ref: 942d4013f36edac1fc9a9addbdb02198db1c7a05 Automated by sync-ee-ref workflow. --------- Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: windmill-internal-app[bot] <windmill-internal-app[bot]@users.noreply.github.com> Co-authored-by: Ruben Fiszel <ruben@windmill.dev>
Open-source developer platform for internal code: APIs, background jobs, workflows and UIs. Self-hostable alternative to Retool, Pipedream, Superblocks and a simplified Temporal with autogenerated UIs and custom UIs to trigger workflows and scripts as internal apps.
Scripts are turned into sharable UIs automatically, and can be composed together into flows or used into richer apps built with low-code. Supported languages: Python, TypeScript, Go, Bash, SQL, GraphQL, PowerShell, Rust, and more.
Try it - Website - Docs - Discord - Hub - Contributing
Windmill - Developer platform for APIs, background jobs, workflows and UIs
Windmill is fully open-sourced (AGPLv3) and Windmill Labs offers dedicated instances and commercial support and licenses.
https://github.com/user-attachments/assets/d80de1d9-64de-4d89-aacd-6df23fa81fc4
- Windmill - Developer platform for APIs, background jobs, workflows and UIs
Main Concepts
- Define a minimal and generic script in Python, TypeScript, Go or Bash that solves a specific task. The code can be defined in the provided Web IDE or synchronized with your own GitHub repo (e.g. through VS Code extension): provided Web IDE or synchronized with your own GitHub repo (e.g. through VS Code extension):
- Your scripts parameters are automatically parsed and generate a frontend.
- Make it flow! You can chain your scripts or scripts made by the community shared on WindmillHub.
- Build complex UIs on top of your scripts and flows.
Scripts and flows can be triggered by schedules, webhooks, HTTP routes, Kafka, WebSockets, emails, and more.
Build your entire infra on top of Windmill!
Show me some actual script code
//import any dependency from npm
import * as wmill from "windmill-client";
import * as cowsay from "cowsay@1.5.0";
// fill the type, or use the +Resource type to get a type-safe reference to a resource
type Postgresql = {
host: string;
port: number;
user: string;
dbname: string;
sslmode: string;
password: string;
};
export async function main(
a: number,
b: "my" | "enum",
c: Postgresql,
d = "inferred type string from default arg",
e = { nested: "object" }
//f: wmill.Base64
) {
const email = process.env["WM_EMAIL"];
// variables are permissioned and by path
let variable = await wmill.getVariable("f/company-folder/my_secret");
const lastTimeRun = await wmill.getState();
// logs are printed and always inspectable
console.log(cowsay.say({ text: "hello " + email + " " + lastTimeRun }));
await wmill.setState(Date.now());
// return is serialized as JSON
return { foo: d, variable };
}
Local Development
Windmill supports multiple ways to develop locally and sync with your instance:
| Tool | Description |
|---|---|
| CLI | Sync scripts from local files or GitHub, run scripts/flows from the command line |
| VS Code Extension | Edit and test scripts & flows directly from VS Code / Cursor with full IDE support |
| Git Sync | Two-way sync between Windmill and your Git repository |
| Claude Code | AI-assisted development with Claude for scripts, flows, and apps |
https://github.com/user-attachments/assets/c541c326-e9ae-4602-a09a-1989aaded1e9
You can run scripts locally by passing the right environment variables for the wmill client library to fetch resources and variables from your instance. See local development docs.
Stack
- Database: Postgres (compatible with Aurora, Cloud SQL, Neon, Azure PostgreSQL)
- Backend: Rust - stateless API servers and workers pulling jobs from a Postgres queue
- Frontend: Svelte 5
- Sandboxing: nsjail and PID namespace isolation
- Runtimes:
- TypeScript/JavaScript: Bun (default) and Deno
- Python: python3 with uv for dependency management
- Go, Bash, PowerShell, PHP, Rust, C#, Java, Ansible
Fastest Self-Hostable Workflow Engine
We have compared Windmill to other self-hostable workflow engines (Airflow, Prefect & Temporal) and Windmill is the most performant solution for both benchmarks: one flow composed of 40 lightweight tasks & one flow composed of 10 long-running tasks.
All methodology & results on our Benchmarks page.
Security
- Sandboxing: nsjail for filesystem/resource isolation, and PID namespace isolation (enabled by default) to prevent jobs from accessing worker process memory
- Secrets: One encryption key per workspace for credentials stored in Windmill's K/V store. We recommend encrypting the Postgres database as well.
See Security documentation for details.
Performance
Once a job started, there is no overhead compared to running the same script on the node with its corresponding runner (Deno/Go/Python/Bash). The added latency from a job being pulled from the queue, started, and then having its result sent back to the database is ~50ms. A typical lightweight deno job will take around 100ms total.
Architecture
How to self-host
For detailed setup options, see Self-Host documentation.
Docker compose
Deploy Windmill with 3 files (docker-compose.yml, Caddyfile, .env):
curl https://raw.githubusercontent.com/windmill-labs/windmill/main/docker-compose.yml -o docker-compose.yml
curl https://raw.githubusercontent.com/windmill-labs/windmill/main/Caddyfile -o Caddyfile
curl https://raw.githubusercontent.com/windmill-labs/windmill/main/.env -o .env
docker compose up -d
Go to http://localhost - default credentials: admin@windmill.dev / changeme
Using an external database: Set DATABASE_URL in .env to point to your managed Postgres (AWS RDS, GCP Cloud SQL, Azure, Neon, etc.) and set db replicas to 0.
Kubernetes (Helm charts)
helm repo add windmill https://windmill-labs.github.io/windmill-helm-charts/
helm install windmill-chart windmill/windmill --namespace=windmill --create-namespace
See windmill-helm-charts for configuration options.
Cloud providers
Windmill works on AWS (EKS/ECS), GCP, Azure, Ubicloud, Fly.io, Render.com, Hetzner, Digital Ocean, and others. Rule of thumb: 1 worker per 1vCPU and 1-2 GB RAM.
OAuth, SSO & SMTP
Configure OAuth and SSO (Google Workspace, Microsoft/Azure, Okta) directly from the superadmin UI. See documentation.
License
The Community Edition is free to use internally. For commercial redistribution or managed services, contact sales@windmill.dev. See LICENSE and Pricing for details.
The "Community Edition" of Windmill available in the docker images hosted under ghcr.io/windmill-labs/windmill and the github binary releases contains the files under the AGPLv3 and Apache 2 sources but also includes proprietary and non-public code and features which are not open source and under the following terms: Windmill Labs, Inc. grants a right to use all the features of the "Community Edition" for free without restrictions other than the limits and quotas set in the software and a right to distribute the community edition as is but not to sell, resell, serve Windmill as a managed service, modify or wrap under any form without an explicit agreement.
The binary compilable from source code in this repository without the "enterprise" feature flag is open-source under the LICENSE-AGPLv3 License terms and conditions.
To re-expose directly any Windmill parts to your users as a feature of your product, with the exception of iframed public Windmill "apps", or to build a feature on top of "Windmill Community Edition" that you sell commercially or embed in a distributable product or binary, you must get a commercial license. Contact us at sales@windmill.dev if you have any questions. To do the same from the binary compiled from the source code in this repository without the "enterprise" feature flag, you must comply with the AGPLv3 license terms and conditions or get a commercial license from Windmill Labs, Inc.
To use Windmill "Community Edition" as is internally in your organization, or to use its APIs as is, you do NOT need a commercial license.
Integrations
In Windmill, integrations are referred to as resources and resource types. Each Resource has a Resource Type that defines the schema that the resource needs to implement.
On self-hosted instances, you might want to import all the approved resource types from WindmillHub. A setup script will prompt you to have it being synced automatically everyday.
Environment Variables
| Environment Variable name | Default | Description | Api Server/Worker/All |
|---|---|---|---|
| DATABASE_URL | The Postgres database url. | All | |
| WORKER_GROUP | default | The worker group the worker belongs to and get its configuration pulled from | Worker |
| MODE | standalone | The mode if the binary. Possible values: standalone, worker, server, agent | All |
| METRICS_ADDR | None | (ee only) The socket addr at which to expose Prometheus metrics at the /metrics path. Set to "true" to expose it on port 8001 | All |
| JSON_FMT | false | Output the logs in json format instead of logfmt | All |
| BASE_URL | http://localhost:8000 | The base url that is exposed publicly to access your instance. Is overriden by the instance settings if any. | Server |
| ZOMBIE_JOB_TIMEOUT | 30 | The timeout after which a job is considered to be zombie if the worker did not send pings about processing the job (every server check for zombie jobs every 30s) | Server |
| RESTART_ZOMBIE_JOBS | true | If true then a zombie job is restarted (in-place with the same uuid and some logs), if false the zombie job is failed | Server |
| NATIVE_MODE | false | Enable native mode: sets NUM_WORKERS=8, rejects non-native jobs (nativets, postgresql, mysql, etc.) | Worker |
| SLEEP_QUEUE | 50 | The number of ms to sleep in between the last check for new jobs in the DB. It is multiplied by NUM_WORKERS such that in average, for one worker instance, there is one pull every SLEEP_QUEUE ms. | Worker |
| KEEP_JOB_DIR | false | Keep the job directory after the job is done. Useful for debugging. | Worker |
| EXIT_AFTER_N_JOBS | None | Exit the worker process after it has executed that many jobs, so that a supervisor restarts it and no process runs more than that many, bar the steps of a same-worker flow it has started, which it always finishes (set it to 1 for a process per job; jobs handed to a dedicated worker, and the worker's own init and periodic scripts, do not count). Not counting the init and periodic scripts means they run again on every restart: an init script's runtime is added to the latency of every batch of that many jobs, and a periodic script fires once per process start whatever its interval says. The worker's shell in the workers page also starts backed off rather than after the two minutes it otherwise takes, since a process due to be recycled cannot count on living that long: the first command of a session can wait up to 15s, later ones are immediate. For deployments that isolate executions by process lifetime rather than with nsjail; note that a container restart resets the process, not the container filesystem, so caches and /tmp survive it. The worker name is then derived from the hostname instead of being random, so the restarted worker keeps its row in the workers list (an agent worker keeps the row but restarts its job count). Use one worker per process: workers of one process share its environment, so the first to reach the limit shuts the others down too. |
Worker |
| WORKER_SUFFIX | None | Pins the last part of the worker name, which is otherwise random, so that a restarted worker keeps its row in the workers list. Only needed when several worker processes of the same worker group run on one host, since the name is derived from the hostname: give each of them a distinct value, as two processes sharing one must never happen. At most 64 letters, digits and underscores; anything else is refused at startup. | Worker |
| LICENSE_KEY (EE only) | None | License key checked at startup for the Enterprise Edition of Windmill | Worker |
| SLACK_SIGNING_SECRET | None | The signing secret of your Slack app. See Slack documentation | Server |
| COOKIE_DOMAIN | None | The domain of the cookie. If not set, the cookie will be set by the browser based on the full origin | Server |
| DENO_PATH | /usr/bin/deno | The path to the deno binary. | Worker |
| PYTHON_PATH | The path to the python binary if wanting to not have it managed by uv. | Worker | |
| GO_PATH | /usr/bin/go | The path to the go binary. | Worker |
| GOPRIVATE | The GOPRIVATE env variable to use private go modules | Worker | |
| GOPROXY | The GOPROXY env variable to use | Worker | |
| NETRC | The netrc content to use a private go registry | Worker | |
| PY_CONCURRENT_DOWNLOADS | 20 | Sets the maximum number of in-flight concurrent python downloads that windmill will perform at any given time. | Worker |
| PATH | None | The path environment variable, usually inherited | Worker |
| HOME | None | The home directory to use for Go and Bash , usually inherited | Worker |
| DATABASE_CONNECTIONS | 50 (Server)/3 (Worker) | The max number of connections in the database connection pool | All |
| SUPERADMIN_SECRET | None | A token that would let the caller act as a virtual superadmin superadmin@windmill.dev | Server |
| TIMEOUT_WAIT_RESULT | 20 | The number of seconds to wait before timeout on the 'run_wait_result' endpoint | Worker |
| QUEUE_LIMIT_WAIT_RESULT | None | The number of max jobs in the queue before rejecting immediately the request in 'run_wait_result' endpoint. Takes precedence on the query arg. If none is specified, there are no limit. | Worker |
| DENO_AUTH_TOKENS | None | Custom DENO_AUTH_TOKENS to pass to worker to allow the use of private modules | Worker |
| DISABLE_RESPONSE_LOGS | false | Disable response logs | Server |
| CREATE_WORKSPACE_REQUIRE_SUPERADMIN | true | If true, only superadmins can create new workspaces | Server |
| MIN_FREE_DISK_SPACE_MB | 15000 | Minimum amount of free space on worker. Sends critical alert if worker has less free space. | Worker |
| RUN_UPDATE_CA_CERTIFICATE_AT_START | false | If true, runs CA certificate update command at startup before other initialization | All |
| RUN_UPDATE_CA_CERTIFICATE_PATH | /usr/sbin/update-ca-certificates | Path to the CA certificate update command/script to run when RUN_UPDATE_CA_CERTIFICATE_AT_START is true | All |
| GOOGLE_APPLICATION_CREDENTIALS | None | (ee only) Credentials file for GCP Pub/Sub triggers that authenticate as the instance rather than through a gcloud resource (workspace admins only). Application default credentials also resolve the gcloud well-known file and the GCE metadata server. Workload Identity Federation files work with the file, url and aws credential sources; the executable source is not supported. |
Server |
Run a local dev setup
We recommend using Nix. See ./frontend/README_DEV.md for all options.
Frontend only
Uses the backend of https://app.windmill.dev with local frontend (hot-reload):
cd frontend
npm install
npm run generate-backend-client # or generate-backend-client-mac on Mac
npm run dev
Windmill available at http://localhost/
Backend + Frontend
See the ./frontend/README_DEV.md file for all running options.
- Start a local Postgres database using for instance the
start-dev-db.shscript which will make a database available atpostgres://postgres:changeme@localhost:5432/windmillThen run the migrations using the following command:This will also avoid compile time issue with sqlx'scargo install sqlx-cli env DATABASE_URL=<YOUR_DATABASE_URL> sqlx migrate runquery!macro. - (optional, linux only) Install nsjail and have it accessible in your PATH
- Install bun, deno and python3 (+ any languages you want to use), have the bins at
/usr/bin/bun,/usr/bin/deno, and/usr/local/bin/python3or set the corresponding environment variables. - (optional) Install the lld linker
- Go to
frontend/:npm install,npm run generate-backend-clientthenREMOTE=http://localhost:8000 npm run dev- You might need to set some extra heap space for the node runtime
export NODE_OPTIONS="--max-old-space-size=4096" - Create an empty
frontend/buildfolder usingmkdir frontend/build
- Go to
backend/:env DATABASE_URL=<YOUR_DATABASE_URL> RUST_LOG=info cargo run- You can specify any feature flag you want to enable, for example
cargo run --features pythonto enable the python executor.
- Windmill should be available at
http://localhost:3000
Contributing
At this time, we are not seeking outside contribution. Bug reports and feature requests remain very welcome, and small, trivially-verified PRs that fix a problem are still accepted. See CONTRIBUTING.md for the full policy.
Contributors
Copyright
© 2023-2026 Windmill Labs, Inc.






