Ciris vs OpenLegion

Ciris and OpenLegion are both ai agent apps tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Ciris

CIRIS runs a signed reasoning agent on your phone or a home device, with no warehouse in the middle for the closest privacy circles. The vendor describes two paths: fully on-device using a small model like Gemma 4, or free hosted inference for phones that can't run a local model — both paths produce cryptographically signed outputs. Every claim the agent makes carries an ed25519+post-quantum signature, so you can audit it, revoke trust, and re-open any conclusion built on a bad source. The architecture depends on a 'social circle' data model; data in your innermost circles never sends the network message that would let anyone request it. Teams needing broad third-party integrations or a hosted API endpoint will find neither here.

OpenLegion

Each agent gets its own isolated container, spend cap, and vault-proxied credentials — so a rogue agent can't drain your API budget or leak credentials to the next task in the queue. The platform deploys a coordinated fleet from a plain-English description of the function you need: a sales pipeline, a content studio, a research desk. Credential handling and per-agent budgets are locked down by default, which means you're not retrofitting security after something goes wrong. The ceiling appears when your workflow needs branching logic that the template model can't express — at that point you're describing edge cases in natural language and hoping the agent interprets them correctly. Teams with deterministic multi-step requirements often add a separate orchestration layer to compensate.

Attribute	Ciris	OpenLegion
Pricing	Free	Paid
Price	—	$19/mo
Free trial	No	7 days
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	iPhone, Android, desktop, pip	Web, Self-hosted (Docker)
Released	—	2026-02
Pros	On-device inference with no data center in the path for supported hardware, which means your input and the agent's reasoning never leave the device — no logs elsewhere, no third-party retention. Cryptographic signing on every agent output using ed25519 plus a post-quantum scheme, so you can trace exactly what the agent claimed, who agreed, who pushed back, and revoke trust retroactively if a source is found to be misleading. Seven-circle privacy model where innermost circles are structurally isolated — not by policy enforcement but by the absence of the outbound network message — which means there is no configuration mistake that can accidentally expose 'self' or 'family' data. Fully open-source under AGPL-3.0 with self-hosted option, so the vendor going dark does not kill your deployment and you can audit the signing and isolation logic yourself. Hosted inference path available at no cost for low-resource devices in 29 languages, which means teams can deploy to users whose hardware cannot run a local model without building separate infrastructure.	Per-agent spend caps enforce budget ceilings at the container level, so a misconfigured agent or a prompt injection that triggers excessive tool calls cannot consume your entire LLM budget before you notice. Vault-proxied credential handling means raw API keys and account credentials are never passed between agents in plaintext, which removes a common attack surface in multi-agent setups where credentials flow through shared memory. Support for over 100 LLM providers with no markup on usage, so switching the model backing a specific agent — say, moving a high-volume scraping agent from a premium model to a cheaper one — is a configuration change, not a rebuild. Container isolation per agent means a failure or security event in one agent's environment does not propagate to the rest of the fleet, so a single broken workflow doesn't take down concurrent production tasks. Native trigger integrations with Slack, Discord, Telegram, WhatsApp, and webhooks mean agents can be kicked off from tools your team already uses, so you avoid building a separate scheduling or event layer to connect the platform to your existing stack.
Cons	No API surface exists — there is no endpoint to call from an external pipeline, no webhook, no SDK. Any team building a product that needs to programmatically query the agent or integrate it into an existing backend hits a hard wall on day one and moves to a tool with an API. The CEWP fabric is a closed trust network; it does not bridge to standard enterprise identity systems, cloud storage, or third-party data sources. Teams expecting to connect the agent to a CRM, a document store, or an external knowledge base find no integration path and either abandon the tool or build outside the CEWP model entirely. On-device inference requires hardware capable of running a small local model. The vendor names Gemma 4 as an example. Devices that cannot meet this threshold fall back to hosted inference, reintroducing a data center into the path and partially negating the core privacy architecture for those users. The social circle and trust federation model is novel and not documented against standard compliance frameworks. Teams operating under HIPAA, SOC 2, or GDPR audit requirements cannot map CIRIS's architecture to their compliance checklists without significant interpretive work — and no audit trail export to standard formats is described.	Workflows that depend on precise conditional branching — route this lead differently based on company size, or skip invoice processing if the vendor field is blank — have to be described in natural language rather than defined in code. At production volume, the agent's interpretation drifts, and teams running exception-heavy operations report adding a rules layer outside the platform to catch the cases that fall through. There is no free tier. Evaluation requires a paid commitment with a money-back window. Teams that need to run a live proof-of-concept against their actual data before budgeting the tool will find the evaluation model friction — and some will default to an open-source alternative like n8n or a code-first framework they can run locally at zero cost. The platform is closed-source, which means teams with strict compliance requirements who need to audit the agent runtime itself — not just the action logs — cannot inspect the execution layer. Organizations in regulated industries that hit this wall during security review switch to a self-hostable, open-source orchestration framework where the full stack is auditable.

Bottom line

Ciris is free while OpenLegion is paid; Ciris is open source; only OpenLegion exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Ciris and OpenLegion?

Ciris is Free and open source, while OpenLegion is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Ciris better than OpenLegion?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.