Conclusion
- Agent workloads amplify small compatibility gaps through retries and loops.
- Tool calls, JSON shape, streaming chunks, and rate-limit errors must be tested before launch.
- A cheap primary model plus stronger fallback is usually safer than one ultra-cheap endpoint.
- Budget caps and per-run logs are mandatory for scheduled or autonomous agents.
What to do next
- List the agent features you need: tools, JSON, streaming, long context, vision, embeddings, or code edits.
- Run a fixed benchmark across at least two providers and one fallback route.
- Measure task success, retry count, invalid JSON, latency, context failures, and total accepted-task cost.
- Keep base_url, key, model, and route policy in config instead of hard-coding them.
- Use OpenLLMAPI when one endpoint, fallback, logs, and budget attribution matter more than manual provider switching.
Recommended paths
| Provider | Free / credits | Best for |
|---|---|---|
| DeepSeek | Pricing/credits vary | Low-cost reasoning and coding primary route |
| Qwen DashScope | Signup credits vary | China-friendly compatible-mode agents |
| Zhipu GLM | Signup tokens vary | Domestic fallback and GLM experiments |
| OpenRouter | Free routes vary | Broad model testing through one API |
| OpenLLMAPI | Trial varies | Production routing, fallback, logs, and budgets |
Global developer checklist
- Confirm whether signup, billing, and API keys work from your country before writing production code.
- Prefer OpenAI-compatible endpoints when you may need to switch models, regions, or providers later.
- Test free credits with a real smoke prompt and record latency, error shape, streaming behavior, and quota burn.
- Keep at least one fallback route for provider outages, model deprecations, and regional access changes.
Production handoff
Give agents one compatible endpoint with guardrails
Route cheap tasks, fallback failures, and track spend per agent run without changing every tool config.
FAQ
Is OpenAI compatibility enough for agents?
No. Basic chat compatibility is only the first test. Agents also need reliable tool calls, JSON, streaming, context handling, retries, and clear errors.
Which model should be primary?
Choose by accepted task cost on your workload. DeepSeek, Qwen, and GLM are common low-cost tests; stronger models can be fallback.
Can I use one endpoint for Cline, RooCode, Cursor, and OpenClaw?
Yes if the endpoint is OpenAI-compatible and each tool lets you configure base_url, key, and model. Test each tool separately.
What should I log?
Provider, model, route, prompt/completion tokens, latency, retry count, tool-call result, status code, and final task outcome.