OpenAI.Response 0.7.0 improvement ideas using various CLI tools (Claude Code, Codex, Gemini)

claude code - opus 4.1

Based on my analysis of your library, here are comprehensive improvement suggestions organized by priority and impact:

  🎯 High Priority Improvements

  1. Enhanced Error Handling & Resilience

  - Add automatic retry with exponential backoff for transient failures (429, 500, 503 errors)
  - Implement circuit breaker pattern to prevent cascading failures
  - Add configurable timeout strategies per operation type
  - Provide better error context with request details in errors

  2. Type Safety & Specifications

  - Add comprehensive typespecs to all public functions
  - Define custom types for commonly used structures (e.g., @type model :: String.t())
  - Add dialyzer checks to CI pipeline
  - Create behavior modules for extensibility points

  3. Observability & Monitoring

  - Integrate :telemetry for metrics and instrumentation
  - Add request/response logging with configurable levels
  - Track API usage metrics (tokens, costs, latencies)
  - Provide hooks for custom monitoring solutions

  🚀 Performance Optimizations

  4. Connection Management

  - Implement connection pooling using Finch instead of bare Req
  - Add HTTP/2 support for multiplexing
  - Enable keep-alive connections for better throughput
  - Add request deduplication for identical concurrent requests

  5. Rate Limiting & Throttling

  - Built-in rate limiter respecting OpenAI's limits
  - Request queuing with priority support
  - Adaptive throttling based on response headers
  - Token budget management per time window

  6. Caching Strategy

  - Response caching with configurable TTL
  - ETS-based cache for in-memory storage
  - Cache key normalization for better hit rates
  - Conditional requests using ETags

  📚 Developer Experience

  7. Enhanced Streaming API

  - Add GenStage/Flow support for backpressure handling
  - Provide Broadway producer for stream processing
  - Add stream transformation helpers (buffer, batch, throttle)
  - Implement stream resumption on connection failures

  8. Testing Improvements

  - Add property-based tests using StreamData
  - Create test helpers module for mocking responses
  - Implement contract testing against OpenAI API specs
  - Add performance benchmarks using Benchee

  9. Configuration Flexibility

  - Support multiple API keys for different contexts
  - Environment-specific configurations
  - Runtime configuration updates without restart
  - Configuration validation on startup

  🔧 Architectural Enhancements

  10. Middleware/Plugin System

  defmodule OpenAI.Responses.Middleware do
    @callback before_request(map()) :: {:ok, map()} | {:error, term()}
    @callback after_response(Response.t()) :: {:ok, Response.t()} | {:error, term()}
  end

  11. Batch Operations Support

  - Batch request API for multiple operations
  - Parallel execution with configurable concurrency
  - Transaction-like semantics for related operations

  12. Schema Validation

  - Compile-time schema validation
  - Runtime input validation before API calls
  - Better error messages for schema mismatches
  
  ⏺ Example: Retry with Exponential Backoff

  defmodule OpenAI.Responses.Retry do
    def with_retry(fun, opts \\ []) do
      max_attempts = Keyword.get(opts, :max_attempts, 3)
      base_delay = Keyword.get(opts, :base_delay, 1000)

      do_retry(fun, 1, max_attempts, base_delay)
    end

    defp do_retry(fun, attempt, max_attempts, base_delay) do
      case fun.() do
        {:error, error} when attempt < max_attempts and Error.retryable?(error) ->
          delay = base_delay * :math.pow(2, attempt - 1) |> round()
          Process.sleep(delay)
          do_retry(fun, attempt + 1, max_attempts, base_delay)

        result -> result
      end
    end
  end

  Example: Telemetry Integration

  defmodule OpenAI.Responses.Telemetry do
    def setup do
      :telemetry.attach_many(
        "openai-responses-handler",
        [
          [:openai, :request, :start],
          [:openai, :request, :stop],
          [:openai, :request, :exception]
        ],
        &handle_event/4,
        nil
      )
    end

    def emit_request_start(metadata) do
      :telemetry.execute([:openai, :request, :start], %{system_time: System.system_time()}, metadata)
    end
  end

  Example: Connection Pool with Finch

  defmodule OpenAI.Responses.Client do
    def child_spec(opts) do
      name = Keyword.get(opts, :name, __MODULE__)

      Finch.child_spec(
        name: name,
        pools: %{
          "https://api.openai.com" => [
            size: 10,
            count: 2,
            protocol: :http2,
            conn_opts: [transport_opts: [timeout: 30_000]]
          ]
        }
      )
    end
  end

  🎁 Additional Features to Consider

  1. Assistant API Support - Add first-class support for OpenAI Assistants
  2. File Upload Handling - Streamlined file upload for vision/document processing
  3. Token Counting - Pre-flight token counting to estimate costs
  4. Response Validation - Validate API responses against expected schemas
  5. Async Job Queue - Background job processing for long-running operations
  6. WebSocket Support - For real-time streaming (when OpenAI adds it)
  7. GraphQL-like Query Language - Simplified response field selection
  8. Response Transformers - Pluggable response transformation pipeline

  📊 Metrics to Track

  - Request latency percentiles (p50, p95, p99)
  - Token usage by model and operation
  - Error rates by error type
  - Cache hit rates
  - Retry success rates
  - Cost per operation type

  🏗️ Implementation Priority

  1. Phase 1 (Quick wins): Type specs, basic retry logic, telemetry
  2. Phase 2 (Core improvements): Connection pooling, rate limiting, enhanced error handling
  3. Phase 3 (Advanced features): Caching, middleware system, batch operations
  4. Phase 4 (Ecosystem): Testing helpers, monitoring dashboards, example applications

  These improvements would transform your library into a production-grade, enterprise-ready OpenAI client for Elixir applications while maintaining its current simplicity and ease of use.

codex cli - gpt-5-high

Here’s a focused architectural review with concrete, high‑impact improvements, tied to specific modules and flows.

Strengths

- Clear API surface: create/1,2,!, stream/1, run/2, call_functions/2, list_models/0,1, request/1.
- Good separation: core (lib/responses.ex) vs. helpers (Response, Stream, Schema, Pricing, Internal).
- Sensible ergonomics: map/keyword/string-or-atom keys accepted; streaming helpers (delta/1, text_deltas/1, json_events/1).
- Cost tracking via Decimal with centralized pricing.
- Function-calling loop (run/2) is minimal and easy to extend.

Gaps To Address

- SSE parsing is brittle and cannot truly stop early.
- Internal.prepare_payload/1 overwrites text settings when schema is present.
- Option normalization is duplicated and inconsistent across modules.
- Error handling in a few public APIs can crash or be inflexible to missing config.
- Limited extensibility for HTTP and telemetry; retries aren’t available even when errors are retryable.
- Types/specs are sparse; Dialyzer value could be much higher.

Core Improvements

- SSE robustness and control:
    - Implement a real SSE parser with buffering across chunks and support for multi-line data: records, comments, id:, and optional retry:; preserve partials between into callbacks in lib/
responses/stream.ex.
    - Respect callback backpressure: if callback returns {:error, reason}, stop streaming (close the connection / halt into), and still surface the captured final response if present.
    - Make @streaming_timeout configurable (e.g., stream_timeout option, default via Application.get_env/3).
    - Make @streaming_timeout configurable (e.g., stream_timeout option, default via Application.get_env/3).
-
Preserve text settings alongside schema:
    - In OpenAI.Responses.Internal.prepare_payload/1, merge text: %{format: ...} into existing text instead of replacing it. Today, schema clobbers user text options (e.g. verbosity).
    - Same for create/2 preserved options: ensure downstream schema usage doesn’t silently discard preserved text fields.
-
Unify option normalization:
    - Introduce OpenAI.Responses.Options to normalize and access options once (stringify keys, convert keyword to map, deep-normalize). Then replace get_option/2, has_option?/2, and ad‑hoc
conversions in Responses and Internal.
    - Provide helpers for “preserved LLM options” merging to avoid scattering key-handling logic.
-
Safer configuration and error surfaces:
    - Replace System.fetch_env! in Internal.get_api_key/0 with a graceful error: if missing, return {:error, %Error{message: "Missing OPENAI_API_KEY", status: 401}} from request/1 instead of
raising.
    - In list_models/0,1, avoid pattern matching {:ok, response} = request(...) — return {:error, err} on failure (non-breaking: add list_models!/1 if you want a bang that raises).
-
HTTP client adaptability and retries:
    - Extract HTTP to OpenAI.Responses.Client with behavior and a default Req adapter. Allow dependency injection (configurable adapter: ...) to mock or swap clients.
    - Add simple retry with backoff for Error.retryable?/1 in request/1 (configurable max retries/jitter).
    - Make base URL and timeouts configurable (env/config).
-
Streaming API ergonomics:
    - Add stream!/1 (raises), and allow Responses.stream(options, on_chunk: ..., on_error: ...) in addition to returning an Enumerable.
    - Document that Responses.Stream.text_deltas/1 and json_events/1 compose with Responses.stream/1.

Response/Schema Enhancements

- Response:
    - Add @type t and @specs across Response + public API (helps mix dialyzer).
    - Consider convenience fields (model, usage) mirroring body for quick access.
    - Optionally support atom-key parsed output (opt-in) from extract_json/1 to integrate easier with Elixir code, while defaulting to strings.
    - Optionally support atom-key parsed output (opt-in) from extract_json/1 to integrate easier with Elixir code, while defaulting to strings.
-
Schema:
    - Validate option keys (e.g., detect typos like max_items vs maxItems), and provide clear errors.
    - Factor large normalization pipeline into smaller internal modules to reduce cognitive load; keep a single entry (build_output/1, build_function/3).
    - Provide a tiny “schema dsl” helper (optional) for readability in user code.

Function Calling

- run/2:
    - Allow a user-provided “policy” for how to merge function outputs (e.g., append vs. replace), and a max-iterations guard.
    - Support async/multiple tool calls in parallel if the API returns several (batch function execution with Task.async_stream/3).
- call_functions/2:
    - Add structured error outputs with machine-readable codes (not just strings), to let callers decide retry vs. abort per tool.
    - Keep current atom safety for keyword functions; optionally accept a translations map to map external names → internal atoms.

Pricing

- Keep static pricing but:
    - Expose price version date via Pricing.version/0.
    - Add “fallback pricing” or a warning when the model is unknown; optionally let users inject pricing for private models.
    - Consider simple memoization for get_pricing/1 (though current is constant-time).

Telemetry and Observability

- Emit Telemetry events:
    - [:openai_responses, :request, :start/stop/exception]
    - [:openai_responses, :stream, :chunk], :complete, :error.
    - Include fields: model, endpoint, request_id (if available), byte sizes, timings.
- Optional log metadata for correlation IDs from OpenAI responses.

DX, Testing, and Stability

- Types and Specs:
    - Add @spec and @type throughout (Responses, Stream, Response, Schema, Pricing, Error).
- Tests:
    - Unit-test the new SSE parser, especially multi-line data: and partial chunk boundaries.
    - Add tests ensuring schema merges into existing text fields.
    - Add request retry tests using a mock adapter.
    - Add tests for missing API key handling that return {:error, %Error{}} (no raise).
- Lint/format/dialyzer:
    - Run mix credo --strict and add missing specs; keep functions narrow and pattern-match where needed as you already do.

Smaller Code-Level Fixes

- Responses.list_models/0,1: return {:error, error} on request failure; add list_models!/1 for raising.
- Stream.start_streaming/1: consider linking the Task to the stream owner and gracefully cancel on cleanup_stream/1 rather than :kill, so into cleanup runs.
- Stream.parse_stream_chunk/1: tolerate events with only data: (SSE spec allows missing event: default) and accumulate multiple data: lines per event.
- Responses.create!/1,2: ensure raise always raises an exception struct (wrap non-exception terms in Error).

If you’d like, I can:

- Implement the safe text-merge with schema (low-risk, high-value).
- Add an SSE buffer-based parser and early-stop handling.
- Introduce a small Options module and refactor call sites incrementally.
- Add retry/backoff to request/1 behind config flags.
- Wire basic Telemetry around request/stream lifecycle.

Tell me which subset you want first, and I’ll patch the code and add focused tests.

gemini - gemini-2.5-pro

I've thoroughly reviewed the openai_responses library and have a clear plan for improvement. The current modular architecture is a good starting point, but I've identified
  several areas to enhance its robustness, extensibility, and maintainability.

  My plan is as follows:

  1. Core API Refactoring (`OpenAI.Responses`): I'll simplify the create functions by introducing a private do_create/1 helper to handle normalized options, reducing code
  duplication. The run/2 function will be streamlined to accept only maps, and I'll improve error messages in run!/2. A request!/1 function will be added for consistency.

  2. Configuration & Extensibility: I'll make the API base URL configurable and modularize the request logic to pave the way for supporting other OpenAI APIs like Embeddings
  and Fine-tuning, likely by introducing a shared OpenAI.Client module.

  3. Response Handling (`Response` Module): To improve efficiency, I'll implement memoization for the extract_* functions and consolidate parsing logic into a single
  parse_response/1 function.

  4. Schema Generation (`Schema` Module): I'll refactor the complex build_property and normalize_spec functions for better readability and add more robust validation for schema
  inputs.

  5. Pricing Data (`Pricing` Module): I'll create a script to automate updates to the pricing data, ensuring it stays current with OpenAI's changes.

  6. Streaming (`Stream` Module): I'll simplify the stream_with_callback/2 function by exploring alternatives to the Agent-based approach and improve error handling in
  json_events/1.

  7. Code Quality: I'll reduce boilerplate by replacing custom helpers with more generic solutions and expand the test suite to cover new features and refactoring, focusing on
  error handling and edge cases.

  I will begin with a small, safe refactoring: modifying the run function in lib/responses.ex to exclusively accept a map of options, which will improve API consistency.

prompt.txt

Think deeply about the current package implementation and architecture, and suggest ways to improve it.