Agency Agents vs Ciris

Agency Agents and Ciris 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.

Agency Agents

The project is a MIT-licensed, self-hostable collection of pre-defined agent definitions organized by domain — engineering, marketing, product, design, and more — built to be activated inside Claude Code, Cursor, and similar AI coding tools. Each agent carries a defined personality, a stated process, and expected deliverables, so the session opens with role context already loaded. The differentiator is breadth plus specificity: you are not configuring a blank agent; you are picking a specialist with an opinionated approach baked in. The ceiling appears when your workflow requires branching between agents at runtime or dynamic handoffs — the repo defines agents, it does not orchestrate them. Teams needing cross-agent coordination wire that logic themselves on top.

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.

Attribute	Agency Agents	Ciris
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	macOS, Linux, Windows	iPhone, Android, desktop, pip
Pros	Pre-defined personality and process per agent, so sessions open with role context already loaded rather than you spending the first exchanges re-establishing what the model should be doing. MIT license with self-hosted install options (install.sh and brew command documented in the repo), so the definitions stay on your infrastructure and are not gated behind a vendor's API or auth layer. Organized by domain directory — engineering, marketing, product, design, finance, and others — so a team can adopt only the agents relevant to their work without importing unrelated definitions. Community fork count and open contribution model mean the agent library grows through pull requests, so domain gaps can be filled without waiting for a vendor roadmap. Personality-driven definitions that go beyond bare system prompts, which means the model's tone and decision-making style stays consistent across sessions rather than varying with however the user frames the first message.	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.
Cons	No built-in runtime coordination between agents: when a task requires one agent to trigger or hand off to another based on output, you write that logic yourself — and at more than two or three agents, you are maintaining a separate orchestration layer that is not part of this repo. No API surface is provided, so any team that wants to call these agent definitions programmatically from their own application has to extract and adapt the definition files manually rather than consuming them via an endpoint. The definitions are only as current as the last accepted pull request — if a domain evolves (a new framework standard, a changed marketing platform) and the community has not merged an update, the agent's stated process drifts from reality, and you get confidently outdated guidance. Teams that need agents to run tasks autonomously across a multi-step pipeline — rather than as session-scoped personas — will exhaust what this repo offers and migrate to a full agent framework (LangGraph, Dify, or similar) that treats coordination and state as first-class concerns, at which point these definitions become input prompts to a larger system rather than the system itself.	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.

Bottom line

Agency Agents and Ciris are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Agency Agents and Ciris?

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

Is Agency Agents better than Ciris?

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.