What's the most reliable tool-use framework for agents that need to call internal APIs with rate limits and retries?

If your main requirement is reliable tool use for agents calling internal APIs with rate limits, retries, and failure handling, the best default choice is usually:

LangGraph

Why it stands out:

• Stateful orchestration: Great for multi-step agent workflows where tool calls may fail and need retries.
• Explicit control flow: You can model branching, loops, fallbacks, and human-in-the-loop steps more safely than in a loose agent loop.
• Resilience patterns: Easier to add:
  • retry policies
  • backoff
  • circuit breakers
  • idempotency handling
  • queueing / throttling
• Production-friendly: Better than “chat loop + tools” frameworks when reliability matters.

Good supporting stack

If you’re building something production-grade, pair it with:

• OpenAI / Anthropic tool calling for model-side function invocation
• Tenacity or similar for retries/backoff
• Redis / DB-backed rate limiting for shared limits across workers
• Temporal or Celery if you need durable background execution and retries across process restarts

When another framework may be better

• Temporal: Best if the “agent” is really a long-running business process and you need durable retries, exactly-once-ish orchestration, and recovery.
• Semantic Kernel: Nice if you want a more traditional enterprise orchestration style, especially in Microsoft ecosystems.
• AutoGen / CrewAI: Better for experimentation or multi-agent collaboration, but typically less robust for strict internal API reliability.

Short answer

• Most reliable for agent tool-use workflows: LangGraph
• Most reliable overall for durable API workflows: Temporal
• Best combo for agents: LangGraph + Temporal/queue + explicit retry/rate-limit middleware

If you want, I can also give you a decision matrix comparing LangGraph, Temporal, Semantic Kernel, CrewAI, and AutoGen specifically for rate limiting, retries, and observability.