Cleo - The AI Product Manager vs Thinking Orbs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cleo - The AI Product Manager and Thinking Orbs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Cleo - The AI Product Manager
kryptobaseddev / CleoCode
Agent-first task orchestration platform with persistent memory and multi-provider coordination for long-running developer workflows.
Key features
- Persistent Session Memory: Stores and surfaces long-lived context across agent sessions so work survives interruptions and agents can resume with historical state.
- Multi-Provider Coordination (CAAMP): Unified provider registry and managed agent packages (@cleocode/caamp) to integrate and coordinate multiple model providers and plugins.
- Long-Running Runtime Layer: Runtime supporting polling, server-sent events (SSE), and heartbeat mechanisms to run long-lived processes and background tasks reliably.
- Core Business Logic SDK: @cleocode/core provides task, session, memory, orchestration and lifecycle primitives for building structured agent workflows and pipelines.
- Command-Line Product: @cleocode/cleo offers a thin CLI wrapper to interact with the framework, run agents, and manage tasks from developer terminals.
- Batteries-Included Distribution: @cleocode/cleo-os bundles CANT bridge and TUI extensions for an out-of-the-box experience with additional tooling and interfaces.
- Agent-Managed Packages: Support for modular agent packages that encapsulate provider configurations, capabilities and reusable behaviors for consistent orchestration.
- Lifecycle & Interruption Resilience: Built-in structures for task lifecycles and state transitions so agent workflows can pause, resume, and recover deterministically.
- Agent-first task orchestration (structure, lifecycle, session management)
- Persistent memory across sessions and interruptions
- Multi-provider coordination and unified provider registry (CAAMP)
- Business logic SDK for tasks, sessions, memory and orchestration (@cleocode/core)
- Long-running process support with runtime layer (polling, SSE, heartbeat) (@cleocode/runtime)
- Command-line interface for developer workflows (@cleocode/cleo)
- Batteries-included distribution with bridge and TUI extensions (@cleocode/cleo-os)
- Provider & MCP management for multi-provider setups (@cleocode/caamp)
- Modular monorepo packaging (npm/pnpm, package.json, TypeScript) and native components (Cargo manifest present)
Best for
- Coordinating Multi-Agent Development Workflows: Orchestrate specialist agents (e.g., code-writing, testing, documentation) across a project while preserving shared context.
- Long-Horizon Project Work: Run background, long-running tasks (research, refactors, data processing) with runtime heartbeats and automatic resumption after interruptions.
- Provider-Agnostic Agent Pipelines: Build pipelines that combine outputs from multiple model providers and managed packages via a unified registry for redundancy and best-of-breed selection.
- CLI-Based Agent Operations: Enable developers to run, inspect, and manage agent tasks locally from the terminal using the CLEO command-line interface.
- Stateful Session Automation: Maintain project-specific memory and session history to let agents make decisions based on accumulated context and prior outputs.
- Packaging and Reuse of Agent Capabilities: Create and share modular agent packages for common tasks (e.g., QA, summarization, code generation) to standardize workflows.
- Embedding Agents into Products: Use the cleo-os distribution and CANT bridge to integrate orchestrated agent workflows into TUI tools or product demos.
- Coordinating multiple LLM/agent providers in complex software projects
- Building long-running agent processes that survive interruptions and maintain state
- Developer workflows requiring persistent task memory and session lifecycle
- Integrating provider registries and managing multiple model/connector providers (MCPs)
- Embedding agent orchestration into CLIs, TUI tools or server-side runtimes
Thinking Orbs
Yogesh
Thinking Orbs is a lightweight React library of animated orb status indicators for AI interfaces such as thinking, searching and compacting.
Key features
- State-Based Orbs: An Orb component with states like reasoning, searching, working, retrying, compacting and waiting to mirror what the agent is doing.
- Zero Dependencies: The React orb needs nothing but React and the plain JS version needs nothing at all, at about 3.8 KB gzipped.
- Customizable Props: Control variant, size, speed, density, dot size, tilt, pause and screen-reader label to fit any interface.
- Opt-in Shapes and Renders: Import extra shapes like cube and renders like halftone separately so only used code is bundled.
- Flexible Installation: Install from npm, pnpm, yarn or bun, or copy the component into a project with shadcn.
- Tintable with CSS: Color orbs with text-* utility classes so they match the host UI theme.
- Interactive Playground: A web playground to preview all states, shapes and renders before integrating.
- llms.txt Support: Publishes an llms.txt so coding assistants can integrate the library correctly.
Best for
- Chat Interface Status: Show a thinking or reasoning animation while an LLM responds.
- Agent Progress Display: Indicate searching, working or background-task states in an agentic coding UI.
- Context Compaction Feedback: Visualize compacting states when conversation history is summarized.
- Retry and Waiting States: Signal retries or waiting-for-input states with distinct orb variants.
- Design System Components: Add a polished AI loading indicator to an in-house React component library.
- Prototype AI Products: Quickly drop a refined status animation into demos and prototypes.