Cycles
Runtime authority for autonomous agents: stop runaway spend and risky actions before they execute.
Cycles is a rare pre-execution enforcement layer that stops runaway agent spend and risky actions before they happen, not after. The Reserve-Commit-Deny model is a real differentiator, backed by practical tooling like shadow mode and CyclesEvidence. If you're self-hosting agents in production, it's worth a hard look; otherwise, wait for the managed cloud or stick with rate limiters.
Verified 2d ago · liveness 78/100 · cite: rightaichoice.com/tools/cycles
- Engineering teams deploying autonomous agents in production who need hard cost and action guardrails
- SaaS platforms requiring per-tenant spend isolation so one customer's runaway doesn't block others
- FinOps teams enforcing hard cost caps on LLM usage to prevent $4,200 overnight incidents
- Compliance officers needing pre-execution audit trails via CyclesEvidence for regulated environments
- Teams that only need basic token counting or rate limiting—that's not what Cycles does
- Users looking for a fully managed SaaS—the cloud (runcycles.ai) is planned but not yet available
- Simple chatbot use cases with no tool-calling or significant spend potential
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Skip Cycles if you don't need pre-execution budget and action enforcement, or if you're not prepared to self-host Docker and Redis and manage a fast-moving protocol.
Paid support plans are custom-priced and require contacting sales, so budget for negotiation and annual commitment if you need response targets or architecture review.
Cycles is free to self-host (Apache 2.0) with no per-seat or per-call fees, making it cheaper for high-volume agent operations than per-call gateways like LiteLLM or Helicone. Paid support is flat and custom, suitable for production teams needing assurance over cost predictability.
In short
Cycles — Runtime authority for autonomous agents: stop runaway spend and risky actions before they execute. Best for Engineering teams deploying autonomous agents in production who need hard cost and action guardrails, SaaS platforms requiring per-tenant spend isolation so one customer's runaway doesn't block others, FinOps teams enforcing hard cost caps on LLM usage to prevent $4,200 overnight incidents. Free to use.
What's new in Cycles
Checked 2 days agoAcross the latest 4 updates: 4 changelog entries.
TypeScript client 0.4.3 released
Updated TypeScript SDK to 0.4.3 with Node 20+ support and improved settlement handling for post-action failures.
Protocol spec and server patch releases
Protocol spec revision v0.1.25.16 and cycles-server v0.1.25.59 released with additive-only changes, keeping wire format stable within 0.1.x.
Governance spec, admin, dashboard updates
Governance spec v0.1.25.42, cycles-server-admin v0.1.25.55, and cycles-dashboard v0.1.25.85 shipped with ongoing refinement.
Events service patch release
cycles-server-events v0.1.25.25 released as part of the patch stream after the initial v0.1.25 Events & Webhooks launch.
What people actually say about Cycles — 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.
149 mentions across 8 sources (Hacker News, YouTube, Product Hunt, App Store, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 4, 2026.
- +Pre-execution budget control stops runaway agent costs cold.
- +Reserve-Commit-Deny lifecycle is an elegant solution for blast radius.
- +Open-source Apache 2.0 with Docker deploy in under 5 minutes.
- +Supports multiple LLMs and MCP for Claude, Cursor, Windsurf.
- +Verifiable audit trails via CyclesEvidence for compliance needs.
- −Extreme name collision makes finding real reviews nearly impossible.
- −No managed cloud yet; self-hosting required for now.
- −Community very small; hard to know if tool is production-ready.
- −Product Hunt comments hint at feature gaps (ajax, sticky nav).
- −Support availability and quality are unverified by users.
- • Self-hosting requires DevOps time and infrastructure
- • Paid support plans have no public pricing
Viability Score
How well maintained and how widely used is Cycles? 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: September 2026
How we score →Key Features
- Reserve-Commit-Deny lifecycle for pre-execution budget and action control
- Per-action cost caps with TTL and grace period
- Blast radius risk calculator with RISK_POINTS scoring
- Cost calculator comparing Claude vs GPT for LLM spend
- Shadow mode and dry run for policy testing
- Multi-tenant budget isolation and shared pools
- Tenant-close cascade prevention
- CyclesEvidence: verifiable audit trail of decisions, denials, commits
- Webhook event delivery with scope filtering
- Admin API for tenant, budget, and API key management
- Prometheus metrics and monitoring
- MCP server support for Claude Desktop, Claude Code, Cursor, Windsurf
- SDKs for Python, TypeScript, Java/Spring, Rust
- Configurable overage policies and debt management
- Decide: preflight checks for action governance
About Cycles
Cycles is a self-hosted, Apache 2.0 open-source platform that gives engineering teams protocol-level control over autonomous agents. It intercepts every agent action—LLM completions, tool calls, API requests—and applies a Reserve-Commit-Deny lifecycle: each action reserves budget, gets approved or denied, and only then executes. This pre-execution enforcement stops the $4,200 overnight incidents and dangerous tool calls that rate limiters and post-hoc dashboards miss. Built for teams deploying agents in production, Cycles quotes 22,822+ package installs and benchmarks like a 34ms p99 reserve time and 891 reserves/second. It ships with two interactive tools to quantify exposure: a Claude-vs-GPT cost calculator and an AI-agent blast-radius risk calculator with shareable URLs and embeddable exports. Shadow mode lets you dry-run policies against real traffic before flipping to hard enforcement, and CyclesEvidence generates a verifiable audit trail of every pre-execution decision, denial, and commit. Cycles integrates natively with major LLM providers (OpenAI, Anthropic, AWS Bedrock, Google Gemini, Groq, Ollama) and frameworks (LangChain, LangGraph, Vercel AI SDK, Spring AI, LlamaIndex, CrewAI, Pydantic AI, AutoGen, OpenAI Agents, OpenClaw). SDKs are available for Python, TypeScript, Java/Spring, and Rust, plus an MCP server for Claude Desktop, Claude Code, Cursor, and Windsurf. Deployment is Docker-based and takes under five minutes. The software is free to self-host with no license key, no seat fees, and no percentage of spend. Paid support plans add response targets, upgrade guidance, incident support, and architecture review. A managed cloud (runcycles.ai) is planned but not yet available. For teams running autonomous agents in production, Cycles fills the gap between rate limiting and full governance.
Behind the Verdict
Cycles attacks a specific, painful problem: autonomous agents that run away and cause financial or operational damage. Unlike rate limiters or observability dashboards that react after the fact, Cycles enforces budgets and risk policies at decision time. The Reserve-Commit-Deny lifecycle is a sound design—it reserves budget before an action runs, and denies it if the budget is exceeded or the action is flagged as risky. This stops the $4,200 overnight incidents and tool loops that token counters miss. Strengths: The protocol-level control is genuinely different. Shadow mode lets you test policies without blocking traffic, which is a huge operational advantage. CyclesEvidence provides a verifiable audit trail—critical for compliance. The tooling is thorough: calculators, SDKs for four languages, MCP support, and 28 integrations spanning providers and frameworks. The performance benchmarks (34ms p99 reserve time, 891 reserves/sec) suggest it can handle production load. Weaknesses: It's self-hosted only right now. You need DevOps capacity to run Docker and Redis, and manage a fast-moving protocol that's still on v0.1.25. The documentation is deep but steep—there are many concepts to absorb (scopes, caps, overage policies, debt). There's no managed cloud yet (planned as runcycles.ai), so it's not a set-and-forget solution. Where it fits: Engineering teams deploying autonomous agents in production, especially multi-tenant SaaS platforms that need per-customer spend isolation, and FinOps teams enforcing hard cost caps. Where it doesn't: teams without Docker/Redis expertise, or those needing a fully managed service. Compared to alternatives: Rate limiters (like provider caps) are simpler but can't gate tool calls or isolate tenants—they fire after the first action, not before. Guardrails AI focuses on output validation, not pre-execution budget control. LangSmith is observability, not enforcement. LiteLLM/Helicone/OpenRouter are gateways that bill per call, whereas Cycles charges flat support pricing with no percentage of spend. Overall, Cycles is a strong, focused tool for a serious problem. If you're self-hosting agents, it's worth evaluating. If you're not ready for self-hosting, wait for the cloud or use a simpler rate limiter.
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Real-world workflow fit
Concrete scenarios for the personas Cycles actually fits — and what changes day-one when you adopt it.
You deploy Cycles with Docker, wrap your agent's LLM calls with the @cycles decorator, set tenant-scoped budgets, and enable shadow mode to calibrate limits before enforcing.
Outcome: You isolate each customer's spend and action authority, preventing one runaway from blocking others.
You use the cost calculator to estimate Claude-vs-GPT spend, set a $15 cap per run, and enable CyclesEvidence for audit logs.
Outcome: You stop a potential $4,200 overnight incident and have a verifiable paper trail for compliance.
You integrate via MCP for Claude Code, assign RISK_POINTS to tools like email and deploy, and run shadow mode for a week.
Outcome: You identify risky actions and block them before they can cause damage, with minimal change to your existing workflow.
Use Cases
- Set hard cost caps per agent run to prevent runaway spend
- Block risky tool calls (email, deploy, mutations) before execution
- Isolate AI costs per tenant in a multi-tenant SaaS
- Provide verifiable audit evidence for compliance
- Test budget and risk policies in shadow mode
- Prevent retry storms and concurrent overspend
Models Under the Hood
as of 2026-08-26
Limitations
- Cycles is a self-hosted runtime authority tool that requires deployment and management of its own infrastructure.
- It is designed for developers and technical teams, with pricing not listed publicly and support available via contact.
- The platform integrates with major AI providers and frameworks, and offers SDKs and an API for programmatic control.
as of 2026-09-01
Verification history
We have re-verified Cycles 8 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-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
- — 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 8 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 Cycles 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
$0/mo
Ideal for
Engineering teams comfortable self-hosting who want free, unlimited runtime authority without license keys or seat fees.
What this tier adds
Starting tier: full Apache 2.0 software, no feature gating, community support via GitHub.
Paid Support
Custom
Ideal for
Production teams running Cycles as a control point needing response targets, upgrade guidance, and incident support.
What this tier adds
Adds response targets, upgrade guidance, incident support, architecture review, and audit-ready evidence configuration over the open-source tier.
Where the pricing makes sense
The company stage and team size where Cycles's pricing actually pencils out — and where peers do it cheaper.
Cycles is free to self-host (Apache 2.0) with no per-seat or per-call fees, making it cheaper for high-volume agent operations than per-call gateways like LiteLLM or Helicone. Paid support is flat and custom, suitable for production teams needing assurance over cost predictability.
Setup time & first value
How long it actually takes to get something useful out of Cycles — broken out by persona, not the marketing-page minute.
Run the local demo in ~5 minutes with a single script. Wrap your first LLM call in ~10 minutes (Python @cycles decorator or TypeScript withCycles wrapper). Wrap a tool call in ~30 minutes. Shadow mode takes 1+ day to calibrate budgets against real traffic; production enforcement follows.
Switching to or from Cycles
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From custom rate limiter: Replace your rate limiter with Cycles by wrapping your LLM calls and setting budgets—use the 'Migrate from Custom Rate Limiter' guide.
- →From provider caps: Add Cycles on top of provider caps to get per-run caps and tool gating before the provider's account-level limit fires.
- →From observability-only dashboards: Start with shadow mode to compare costs and actions against your current baseline before enforcing.
- ↗To a managed gateway: If you later need a hosted solution, export your policy definitions and budgets, then recreate them in your new gateway's UI.
- ↗To a simpler rate limiter: For basic token counting, you can drop Cycles and use provider-side limits, but you'll lose pre-execution action authority.
Integrations
Resources & Guides
Tutorials & Learning
![Synthesia [Piano Tutorial] DJVI - Cycles (Geometry Dash lvl 9)](https://img.youtube.com/vi/WxKbU98GLRY/mqdefault.jpg)
![[Tutorial] Slate + Ash Cycles Explained with Kirk Degiorgio](https://img.youtube.com/vi/KXFP9lmH1F0/mqdefault.jpg)
Official links
Featured Head-to-Head Comparisons
Cycles vs Spider Cloud
Don't compare apples to oranges. If you need to stop runaway agent spend and enforce tenant-level budgets before any action executes, choose Cycles. If you need to feed your AI agent fresh web data at low cost with structured output, choose Spider Cloud. They solve different problems and can even complement each other.
Cycles vs Temporal Ai
Choose Cycles if your primary concern is preventing runaway AI agent spend and enforcing granular cost/risk policies pre-execution, especially in multi-tenant SaaS settings. Choose Temporal if you need rock-solid durable execution for multi-step workflows that must survive crashes and retries, with a mature open-source ecosystem and cloud option.
Cycles vs Presto Voice
If you're building and deploying autonomous AI agents in production, Cycles provides the pre-execution budget control and blast radius isolation that post-hoc monitoring tools lack. Presto Voice is a specialized drive-thru voice AI solution that increases revenue and efficiency for QSR chains. These tools serve completely different markets — choose based on whether you need agent cost governance or restaurant automation.
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