Valmis
Open-source AI agent platform that runs agents in isolated Docker containers behind a host-side credential proxy.
Valmis solves the credential problem, and that is the whole reason to look at it — a host-side proxy plus per-agent Docker isolation means an agent can touch Salesforce or Google Workspace without ever seeing your keys. It is also young, self-hosted, and mostly documented through a README, so buy it as infrastructure you run. If you need managed onboarding or a support SLA, a hosted agent platform is the easier call; if data-residency rules rule those out, this is the credible open-source route.
Verified 9h ago · liveness 76/100 · cite: rightaichoice.com/tools/valmis
- Security-conscious engineering teams that need agents to touch Slack, Salesforce, or Google Workspace without exposing API keys
- Organizations with data-residency or compliance rules that rule out hosted agent platforms
- Developers building recurring multi-step workflows with conditions, loops, and per-step permissions
- Teams assigning different LLM providers per agent to match model cost against task value
- Non-technical users who want a plug-and-play agent product with a managed cloud
- Companies that require a vendor support SLA or an accountable hosted provider
- Teams expecting polished docs, templates, and onboarding beyond a README
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Skip Valmis if you want a plug-and-play agent product someone else hosts, patches, and supports — this is a Docker Compose deployment you own, and anything short of a README-plus-GitHub-issues support model will not do it for you.
The platform is $0, but you supply the compute — a host to run agents, workflow and LLM proxy containers, and disk for each agent's isolated file system.
Valmis itself is free: Apache 2.0 source, a single Docker Compose deployment, no per-seat or usage fees, and unlimited agents on your own hardware. The real cost is infrastructure plus your LLM provider keys, which means the total spend scales with agent count and model choice rather than a subscription. Compared with hosted agent platforms and Claude Cowork, that is cheaper on licenses and more expensive on your engineers' time.
In short
Valmis — Open-source AI agent platform that runs agents in isolated Docker containers behind a host-side credential proxy. Best for Security-conscious engineering teams that need agents to touch Slack, Salesforce, or Google Workspace without exposing API keys, Organizations with data-residency or compliance rules that rule out hosted agent platforms, Developers building recurring multi-step workflows with conditions, loops, and per-step permissions. Free to use.
What people actually say about Valmis — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
40 mentions across 7 sources (Reddit, Hacker News, YouTube, Product Hunt, Stack Overflow, GitHub, Lemmy) · researched Aug 7, 2026.
Average across the 7 sources that answered — each source counts once, not each post.
- +Credential proxy keeps raw API keys away from agents.
- +Docker container isolation sandboxes agents securely.
- +Supports 100+ integrations, including Slack and Notion.
- +Persistent memory with episodic and semantic recall.
- +Natural-language and visual workflow builder.
- −No managed cloud version; self-hosting required.
- −Documentation is thin beyond the README.
- −No official support or SLA for enterprises.
- −Steep learning curve for non-technical users.
- −Community is small, with low GitHub stars and upvotes.
- • Infrastructure costs for running Docker and agents
- • LLM API costs for model usage
- • Time cost for setup, maintenance, and security management
Viability Score
How well maintained and how widely used is Valmis? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Host-side credential proxy: agents request API calls by credential ID and never see raw keys
- LLM API calls routed through the same proxy instead of direct from the agent container
- Each agent runs in its own isolated Docker container with a separate file system
- Agent containers can run with internet access disabled and still execute app requests
- Build a fleet of agents with per-agent credentials, skills, and knowledge bases
- Per-agent credential access boundaries enforced at the code level
- Assign a different LLM provider per agent to control model cost against task value
- Agents can act as team lead and manage other agents' workflow as a decision tree
- Workflow builder canvas for multi-step jobs with conditions and loops
- Smart conditions judged by AI from plain-language descriptions
- Strict conditions comparing values with programming logic
- Per-step credential and tool limits plus output schema definitions
- Cron, webhook, and app-event triggers such as new email, form submission, or Slack mention
- Describe a workflow in natural language and have an agent build it
- Cross-session memory organized into episodic, semantic, procedural, and working
About Valmis
Valmis is an open-source (Apache 2.0) AI agent platform for shipping agents into production work, and its selling point is security rather than a fancier chat window. You build a fleet of agents, each with its own credentials, skills, knowledge base, and even its own LLM provider, then point them at 100+ business and productivity integrations including all Google Workspace apps, Slack, Notion, HubSpot, Salesforce, and Figma. Non-critical jobs can be routed to cheaper models per agent, which keeps cost tied to task value. What separates it from OpenClaw-style personal assistants is the proxy design. Agents run in dockerized runtimes with their own file systems, isolated from the host machine and from each other, and they never hold raw keys. To call an API, the container hands the host a credential ID; the host makes the request and returns only the JSON. Even LLM API calls go through the proxy, so you can switch off the agent container's internet access and it still completes app work. Credential boundaries are enforced at the code level, so one agent cannot borrow another's permissions. The workflow layer is built for repetition, not one-off prompts. A workflow builder canvas assembles multi-step jobs with conditions (smart, where AI judges plain-language criteria, or strict, comparing values with programming logic), loops, per-step credential and tool limits, and output schemas for cleaner data mapping. Triggers cover cron, webhooks, and app events such as a new email or a form submission, and you can also just describe a workflow and have an agent construct it. Cross-session memory is split into episodic, semantic, procedural, and working categories so agents accumulate context over time. Valmis is an Estonian-designed project — the name means "completed" or "done" — positioned as the work-oriented alternative to personal-assistant agents. It launched publicly in mid-2026 as an open-source answer to Claude Cowork and drew early Hacker News attention. Against
Behind the Verdict
Pick Valmis when the blocker is credentials and control, not features. If your security team has already vetoed an agent platform because keys would land in an agent's memory or a hosted vendor's tenancy, the proxy model is the argument that gets you unblocked: the container requests an API call by credential ID, the host executes it and returns JSON, and the agent container can run with internet access off. We'd reach for this in teams that need agents working inside Slack, HubSpot, or all the Google Workspace apps while keeping data in infrastructure they own. It also fits teams with genuinely recurring work. The workflow builder handles conditions, loops, and per-step credential limits, and triggering on cron, webhooks, or an app event such as a new email or form submission is the difference between a demo and something that runs unattended. Per-agent LLM provider assignment is a practical cost lever — let routine steps run on a cheaper model and reserve the expensive one for judgment calls. Memory across sessions, split into episodic, semantic, procedural, and working, is what makes a long-lived agent useful rather than amnesiac. When to pass. If nobody on your side wants to own a Docker deployment, patches, and upgrades, this is the wrong shape of product. There is no managed cloud to absorb that, and the documentation is largely a README, so expect to read code rather than follow a guide. Compared with Claude Cowork or a hosted agent platform, you are trading onboarding polish, templates, and an accountable vendor for isolation you control. One caveat worth stating plainly: Valmis launched publicly only in mid-2026 as an open-source alternative to Claude Cowork, so the ecosystem around it — examples, community answers, third-party writeups — is thin. Budget
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Real-world workflow fit
Concrete scenarios for the personas Valmis actually fits — and what changes day-one when you adopt it.
Stand up Valmis with Docker Compose on internal infrastructure, register Google Workspace, Slack, and Salesforce credentials in the host proxy, and run an agent container with internet access disabled while it still executes app requests by credential ID.
Outcome: Agents complete cross-app tasks without raw API keys ever entering the agent runtime, which is the audit story the compliance team needs.
Describe a multi-step workflow in plain language, let the agent build it on the canvas, add a strict condition plus a loop, set per-step credential and tool limits, then trigger it on a cron schedule and from a webhook.
Outcome: A recurring multi-step job runs on schedule with defined output schemas, and the agent keeps episodic and semantic memory so context persists across sessions.
Create separate agents for coding, data analysis, and documentation, assign each its own credentials, skills, knowledge base, and LLM provider, and keep the low-stakes agents on a cheaper model.
Outcome: Model spend tracks task value per agent, and code-level credential boundaries stop one agent from borrowing another's app permissions.
Use Cases
- Automate multi-step workflows across business apps with conditions and loops
- Self-host a private AI agent platform for sensitive or regulated data
- Build agent fleets that collaborate on coding, data analysis, and documentation
- Run an agent container with internet disabled and still execute app requests via the proxy
- Ingest knowledge from Google Drive or Notion to give agents persistent context
- Trigger agents from cron, webhooks, or app events like new email and form submissions
- Assign cheaper LLM providers to low-stakes agents and stronger models to critical ones
- Give an agent broad app access without exposing API keys to the agent runtime
Models Under the Hood
as of 2026-10-17
Limitations
- As an early-stage open-source project, Valmis is lightly documented — mostly a README — and support runs through GitHub issues rather than a vendor SLA.
- It ships as a Docker Compose deployment you own and patch yourself, so you carry deployment, hardening, and upgrade work.
- Its app reach runs through the proxy system (the project cites 100+ apps and an integrations folder), so verify your specific app before committing.
- The project surfaced on Hacker News in mid-2026 and is still young.
as of 2026-10-01
Verification history
We have re-verified Valmis 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Valmis tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source (Apache 2.0)
$0
Ideal for
Engineering teams with data-residency or audit rules that rule out hosted agent platforms, willing to run and patch their own Docker Compose deployment.
What this tier adds
Starting tier — the only tier: $0 for the full Apache 2.0 source, unlimited agents, 100+ integrations through the credential proxy, bring-your-own LLM keys, and no vendor support SLA.
Where the pricing makes sense
The company stage and team size where Valmis's pricing actually pencils out — and where peers do it cheaper.
Valmis itself is free: Apache 2.0 source, a single Docker Compose deployment, no per-seat or usage fees, and unlimited agents on your own hardware. The real cost is infrastructure plus your LLM provider keys, which means the total spend scales with agent count and model choice rather than a subscription. Compared with hosted agent platforms and Claude Cowork, that is cheaper on licenses and more expensive on your engineers' time.
Setup time & first value
How long it actually takes to get something useful out of Valmis — broken out by persona, not the marketing-page minute.
Plan for hours, not minutes: you need a host, Docker and Docker Compose, LLM provider keys, and credentials registered for each app the proxy will call. A single agent working against one app is an afternoon; a fleet with proxied credentials, workflow triggers, and per-step limits is closer to a day-plus, plus time to harden and document your own deployment.
Switching to or from Valmis
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenClaw personal assistants: move work agents onto Valmis so credentials go through the host proxy instead of sitting in agent memory
- →From hosted agent platforms: deploy Valmis via Docker Compose and keep data on your own hardware to satisfy residency rules
- →From manual chat-driven automation: rebuild recurring jobs as canvas workflows with cron, webhook, or app-event triggers
- ↗To a managed agent platform: export your workflow logic and re-point triggers if you would rather not own deployment and upgrades
- ↗To Claude Cowork or a hosted assistant: fall back to a turn-based assistant if a multi-step workflow engine is more than you need
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Valmis”, and we withheld 6: 6 could not be judged, because “Valmis” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Valmis.
Official links
Tools that pair well with Valmis
Common stack mates teams adopt alongside Valmis, with the specific reason each pairing earns its keep.
OpenAgents
OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing.
Cryptohopper
Cryptohopper runs an automated crypto trading bot in the cloud, with Copy Bot mirroring and an MCP server for AI trading agents.
Poolside AI
Poolside builds open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — for code that stays inside your security
Featured Head-to-Head Comparisons
Valmis vs Bito
If you are a solo developer or small team prioritizing data privacy and flexibility, Valmis's free open-source platform is a solid foundation. But for multi-repo enterprise engineering teams that rely on coding agents (Cursor, Claude Code, Codex) and need system-wide context, gap analysis, and automated scoping, Bito's knowledge graph and enterprise integrations are far more capable—though at a higher cost.
Valmis vs Poolside Ai
Valmis is a strong choice for privacy-conscious teams and open-source enthusiasts who want a free, customizable AI coding assistant without vendor lock-in. Poolside AI, on the other hand, is purpose-built for enterprise-grade, high-consequence software engineering in regulated industries, offering on-prem deployment, multi-agent orchestration, and governance. If you need a zero-cost, self-hosted tool with flexibility, pick Valmis; if your organization requires compliance, auditability, and robust model performance for critical tasks, Poolside AI is the clear winner.
Valmis vs Cognition Ai
Valmis is ideal for privacy-focused developers who want a free, open-source AI workspace they can fully customize and self-host. Cognition AI (Devin) is built for enterprise teams needing an autonomous engineer that writes production code, manages bugs, and offers a $10M productivity guarantee. If you need zero lock-in and total control, choose Valmis; if you need scalable, guaranteed productivity at scale, choose Cognition AI.
Alternatives to Valmis
View all
OpenAgents
OpenAgents is an open-source language agent platform for data analysis, 200+ plugins and autonomous web browsing.
Cryptohopper
Cryptohopper runs an automated crypto trading bot in the cloud, with Copy Bot mirroring and an MCP server for AI trading agents.
Poolside AI
Poolside builds open-weight agentic coding models — Laguna XS 2.1 (33B) and Laguna S 2.1 (118B) — for code that stays inside your security
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