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

5 adapter hooks (automatic — no configuration needed)

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

Hook	What it does
continuationHint	Tells the model to continue after tool results while required tools are pending
errorRecovery	Recovery prompt appended to the observation on tool errors
synthesisPrompt	Final answer synthesis guidance on the research→produce transition
qualityCheck	Post-step quality assessment (fires once before the final answer)
parseToolCalls	Normalizes malformed native tool calls (e.g. qwen3 stringified arguments) in every provider complete()/stream() response

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