When Native SDK Primitives Are Enough (and When They Aren't)

Use this cheatsheet to decide whether OpenAI Agents SDK, Anthropic Messages tool loops, or Google ADK-style natives are sufficient - or whether you should adopt LangGraph, CrewAI, Microsoft Agent Framework, Pydantic AI, or a custom runtime.

How to Use This List

• Walk A (enough) and B (not enough) as opposing checklists on the same project.
• If three or more B items are true, plan a control-plane upgrade before production scale.
• Keep tools and schemas portable regardless of the decision.
• Re-run after major product changes (HITL, multi-cloud, multi-day jobs).

A - Native SDK Primitives Are Enough

Tick when true for your workload.

• 1. Single strategic model provider for this agent product (OpenAI or Anthropic or Gemini/Vertex), not all three.
• 2. Loop shape is tools + optional specialist handoffs, not complex joins across parallel branches.
• 3. State fits conversation + light app DB (user prefs, ticket id) without graph checkpoints.
• 4. Stop conditions are simple (max turns, final answer, guardrail tripwire).
• 5. Human approval is rare and can be a single gate (tool approval / ticket status), not multi-node resume.
• 6. Team can operate the provider dashboard (traces, keys, spend) as the main observability path.
• 7. Hosted tools or MCP cover integrations you would otherwise invent.
• 8. Latency budget tolerates sequential tool loops without custom scheduler complexity.
• 9. Compliance accepts provider data paths already reviewed for this workload.
• 10. You can name an exit strategy (pure tools + schemas) even if you stay native now.

If 1-7 are mostly true, ship on natives and invest in product quality, not framework shopping.

B - Native Primitives Are Not Enough

Tick when true; each item is a migration pressure signal.

• 1. Multi-cloud model routing is a hard requirement for cost, residency, or procurement.
• 2. Durable resume mid-workflow after process death is mandatory (days-long jobs, approvals).
• 3. Complex branching, fan-out/fan-in, or map-reduce over many subtasks dominates.
• 4. Multiple products need one shared orchestration platform with common ops.
• 5. Typed structured I/O culture is the product core and Pydantic AI (or similar) fits better than provider agents.
• 6. Role/crew authoring for non-orchestrator stakeholders is the main DX need (CrewAI-class).
• 7. Enterprise connectors/governance already standardize on another stack (for example Microsoft).
• 8. You are reinventing graphs, checkpoints, or memory stores on top of the native SDK weekly.
• 9. On-call cannot debug provider-only traces for your reliability bar.
• 10. Legal/security forbids the provider path for tool-connected production data.

If 2, 3, or 8 are true, prioritize LangGraph / MS workflows / custom state machines over more handoff prompts.

Quick Decision Tree

Is a single provider strategic for this agent?
├─ No → Framework or custom loop + portable tools
└─ Yes
   ├─ Need durable graph resume / complex joins?
   │  ├─ Yes → LangGraph / MS Agent Framework / custom
   │  └─ No
   │     ├─ Mostly tools + handoffs + guardrails?
   │     │  └─ Yes → Native SDK (OpenAI Agents / ADK / Anthropic loop)
   │     └─ Need strong schema-first Python DX only?
   │        └─ Consider Pydantic AI (± natives for hosted tools)

Capability Gap Table

Migration Triggers (write these in the ADR)

Upgrade off pure natives when any trigger fires:

1. Resume ticket: "We lost a 2-hour agent run on deploys" more than once.
2. Graph ticket: "We need join of N specialists with partial failure policies."
3. Routing ticket: "Procurement requires model failover across vendors in-agent."
4. Reinvention ticket: "We built a half framework inside utils/agent_os.py."
5. Ops ticket: "Support cannot see state machines, only chat transcripts."

Do not migrate because Twitter prefers another logo.

What to Keep Portable Either Way

Recommended Stack Combos

FAQs

Related

• What Native Provider Agent Primitives Give You Over a Framework
• Comparing OpenAI Agents SDK to Google's Agent Development Kit (ADK)
• Anthropic's Tool Use Primitives for Building Agents Directly
• Framework-Free vs Framework-Based: When to Roll Your Own
• When a Custom Runtime Stops Being Worth Maintaining
• OpenAI Agents SDK & Native Primitives Best Practices

Stack versions: Pins from the category manifest (verify at build): OpenRouter (~315+ models, July 2026 pricing/fees); LangGraph 1.0+; CrewAI 1.14+; Microsoft Agent Framework 1.0; Vercel AI SDK 6; Pydantic AI (latest); LlamaIndex (latest); OpenAI Agents SDK (latest + MCP); MCP (Linux Foundation governance); A2A (HTTP+SSE+JSON-RPC 2.0); Solana @solana/web3.js + @solana/spl-token.