scale-agentexScale AI's open agent platform: scaffold agents with a CLI, run them behind the ACP protocol with a dev UI, and graduate from sync chat to durable Temporal-backed long-running workflows.
Why switchBoth are open control planes for building and deploying agents as long-running services with routing, queues and durable state. AgentField wraps plain Python/Go/TS functions as microservice agents; Agentex standardizes on the ACP protocol with Temporal underneath and a scaffolding CLI + dev UI.
Full comparison → paperclipOpen-source control plane for running fleets of heterogeneous AI agents as a "company" — bring your own agent, assign goals, org charts, budgets, governance, and an audited ticket system.
Why switchBoth are open control planes for fleets of agents, attacking opposite ends: paperclip is the governance layer over agents you already have (BYO agent, goals, org charts, budgets, audited tickets); agentfield is the build-and-run backend where agents are SDK-written microservices with REST endpoints, queues and retries. Govern existing agents → paperclip; build the agent backend itself → agentfield.
Full comparison → planoAI-native Envoy-based proxy for agentic apps: agent orchestration via a 4B routing model, smart LLM routing, guardrail filter chains and zero-code OTEL traces. Rust, framework-agnostic.
Why switchSame 'production infrastructure layer for agents' job from opposite ends: agentfield is a control plane that wraps your functions in routing/queues/retries, Plano is a data-plane proxy your unmodified HTTP agents sit behind.
Full comparison → LangGraphBuild stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming.
Why switchSame job — production multi-agent systems — opposite shape. LangGraph is an in-process library: model your workflow as a stateful graph with durable execution. agentfield is an out-of-process control plane: plain functions become REST microservices and the platform handles fan-out, queues and retries, explicitly rejecting graph wiring. Library and embedded → LangGraph; platform and service-oriented → agentfield.
Full comparison → hiveYC-backed multi-agent harness for production: state an objective and the runtime compiles a graph DAG of specialized agents — role-based memory, crash recovery, cost limits, human-in-the-loop.
Why switchTwo production-runtime bets: AgentField runs agents as microservices — REST, queues, retries, your functions; Hive runs them as a goal-compiled DAG with role-based memory and human-in-the-loop gates.
Full comparison → simVisual workspace to build, deploy and orchestrate AI agents — 1,000+ integrations, knowledge bases, built-in tables and files, schedules and run monitoring. Self-host via npx simstudio or Docker.
Why switchSame 'run your AI workforce' ambition, opposite audiences: AgentField turns plain Python/Go/TS functions into agent microservices for engineers; Sim gives teams a visual workspace where agents are built and operated without touching the runtime.
Full comparison → trueforgeTrueFoundry's open agent harness: the runtime loop — model calls, MCP tools, SKILL.md packs, sandboxing, approvals, compaction — behind a chat UI, HTTP API, TypeScript SDK and embeddable UI.
Why switchagentfield runs agents as microservices — REST endpoints, queues, retries, fleet scale; TrueForge runs one well-instrumented agent loop with a UI in front of it. Pick by whether your problem is scaling deployments or getting a single agent production-ready.
Full comparison →