THE COMPARISON / A CLOSER LOOK
Rivet vs AG2
Compare the listed plans, useful capabilities, and practical tradeoffs.
Prices and features reflect editorial records. Check vendor terms before you buy; documentary checks do not imply hands-on testing.
Rivet
🔴DeveloperAI Agent Frameworks
Rivet is an open-source visual programming environment for building AI agents with LLMs, created and maintained by Ironclad.
SOURCE-CHECK COVERAGE
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Starting Price
FreeAG2
🔴DeveloperAI Agent Frameworks
AG2 is an open-source, Python-native framework for building multi-agent systems with collaboration, state, human approvals, MCP and A2A interoperability.
SOURCE-CHECK COVERAGE
No scoped source-check record shown. Check current pricing and vendor terms before buying.
Starting Price
FreeFeature Comparison
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Rivet - Pros & Cons
Pros
- ✓Visual + code hybrid — design in the editor, ship the same graph inside a real Node.js app
- ✓Remote debugger is genuinely unique; watching a production graph execute step-by-step is a massive win when troubleshooting agent behavior
- ✓YAML-first storage means Git-native version control and normal PR review workflows
- ✓MIT-licensed, no hosted-service lock-in, no credit meter
- ✓TypeScript type safety extends into graph input/output contracts
- ✓Actively maintained by Ironclad's engineering team with production-grade usage
Cons
- ✗TypeScript/Node-only — no Python runtime, which excludes most ML-shaped teams
- ✗No hosted execution or SaaS tier — you deploy and monitor the runtime yourself
- ✗Smaller community and plugin ecosystem than LangChain/LangGraph
- ✗Documentation is thinner than the largest agent frameworks; some patterns you'll figure out by reading source
- ✗Not a fit for teams that want a fully no-code experience — you'll still write TypeScript to embed and deploy
- ✗No built-in evals or agent tracing beyond the live debugger (integrations exist via plugins)
AG2 - Pros & Cons
Pros
- ✓The core Python framework is free, open source and compatible with multiple model providers
- ✓AgentOS is designed to coordinate agents from several frameworks instead of forcing one stack
- ✓Human approvals, shared state and auditability address real production workflow needs
- ✓MCP and A2A support provide standards-based integration options
Cons
- ✗AG2 publishes no numerical pricing for Space, AgentOS, managed hosting or enterprise support
- ✗Multi-agent conversations can add latency, token cost and failure modes compared with one agent or deterministic code
- ✗The Python-first framework requires software engineering and operational expertise
- ✗Vendor-reported productivity results are not enough to predict performance on a buyer's own workload
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