tylerjrbuell/provider-patterns

Provider Patterns

Agent objective

Produce a builder with the correct provider + model + any provider-specific configuration; know which providers need special handling for streaming and tool calls.

When to load this skill

• Configuring a non-Anthropic provider
• Debugging tool call or streaming issues specific to one provider
• Using local models (Ollama) or proxy routing (LiteLLM)
• Enabling extended thinking or provider-specific model options

Implementation baseline

// Anthropic — highest quality, native FC, prompt caching
const agent = await ReactiveAgents.create()
  .withProvider("anthropic")
  .withModel("claude-sonnet-4-6")
  .withReasoning({ defaultStrategy: "adaptive" })
  .withTools()
  .build();

// Local Ollama model
const agent = await ReactiveAgents.create()
  .withProvider("ollama")
  .withModel("qwen2.5:7b")
  .withReasoning({ defaultStrategy: "reactive", maxIterations: 6 })
  .withTools({ allowedTools: ["web-search"] })
  .build();

Provider selection guide

| Provider | Best for | Key notes | |----------|----------|-----------| | "anthropic" | Production, highest quality | Native FC, prompt caching, streaming | | "openai" | GPT-4o, broad compatibility | Native FC, streaming | | "gemini" | Multimodal, long context | Native FC; functionResponse.name quirk | | "ollama" | Local, privacy-first | Tool calls arrive on chunk.done | | "litellm" | Proxy routing, cost optimization | OpenAI-compatible; use for Groq, OpenRouter, etc. | | "test" | Unit tests, CI | Returns deterministic mock responses |

Key patterns

Extended thinking (Anthropic)

.withProvider("anthropic")
.withModel({ model: "claude-opus-4-6", thinking: true })
// Enables extended thinking — model reasons before responding
// Higher quality on complex reasoning tasks; adds latency and cost

LiteLLM for provider routing

// Groq, OpenRouter, Bedrock, Vertex — all through LiteLLM
.withProvider("litellm")
.withModel("groq/llama-3.1-70b-versatile")
// Model name format: "provider/model-name" as per LiteLLM docs

Circuit breaker for unreliable providers

.withProvider("ollama")
.withModel("llama3:8b")
.withCircuitBreaker({
  failureThreshold: 3,    // open after 3 consecutive failures
  cooldownMs: 30_000,     // wait 30s before half-open probe
  halfOpenRequests: 1,
})
.withRateLimiting({ requestsPerMinute: 10 })

Enabling temperature and sampling

.withModel({ model: "gpt-4o", temperature: 0.2 })  // more deterministic
.withModel({ model: "claude-sonnet-4-6", temperature: 0.9 })  // more creative

7 adapter hooks (automatic — no configuration needed)

These hooks run automatically and adapt prompts/behavior for each provider's strengths:

| Hook | Method name | What it does | |------|-------------|-------------| | taskFraming | adapter.frameTask(task, tier) | Wraps task in provider-optimal framing | | toolGuidance | adapter.injectToolGuidance(prompt) | Injects provider-specific tool-use instructions | | continuationHint | adapter.getContinuationHint(lastMsg) | Tells model to continue after tool results | | errorRecovery | adapter.getErrorRecoveryPrompt(error) | Recovery prompt on tool errors | | synthesisPrompt | adapter.getSynthesisPrompt(context) | Final answer synthesis guidance | | qualityCheck | adapter.checkQuality(output, task) | Post-step quality assessment | | systemPromptPatch | adapter.patchSystemPrompt(base) | Provider-specific system prompt additions |

Adapter selection is automatic via selectAdapter(capabilities, tier). Each provider (Anthropic, OpenAI, Gemini, Ollama) has an adapter with specialized implementations.

Builder API reference

| Method | Key params | Notes | |--------|-----------|-------| | .withProvider(p) | "anthropic"\|"openai"\|"gemini"\|"ollama"\|"litellm"\|"test" | Required | | .withModel(m) | string \| { model, thinking?, temperature? } | thinking: true = extended reasoning | | .withCircuitBreaker(cfg?) | { failureThreshold?, cooldownMs?, halfOpenRequests? } | Auto-retry with backoff | | .withRateLimiting(cfg) | { requestsPerMinute?, tokensPerMinute?, maxConcurrent? } | |

Pitfalls

• "groq" and "openrouter" are not valid provider names — use "litellm" with the appropriate model prefix
• Gemini: functionResponse.name must use msg.toolName, not hard-coded "tool" — framework handles this but custom tool parsers must follow the same pattern
• Ollama: tool_calls arrive on chunk.done, not during the stream — don't parse mid-stream chunks for tool calls
• Anthropic streaming: use raw streamEvent, not helper events (inputJson fires before contentBlock in streaming FC)
• thinking: true requires a model that supports extended thinking — verify model capability before enabling
• LiteLLM model names are "provider/model" format — check LiteLLM docs for exact names