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Changelog

All notable changes to this project will be documented here. Format follows Keep a Changelog; versions follow SemVer.

[Unreleased]

[0.2.0] - 2026-06-15

Added

  • Price mode — emit computed dollar cost instead of token counts. New pricing_mode config ("tokens" default | "price"), plus markup, cost_metric_code (default llm_cost), pricing_ttl_seconds, and bedrock_default_region. In price mode the SDK emits one llm_cost event per call carrying a top-level precise_total_amount_cents (cost in cents, after markup) for Lago's dynamic charge model, with a full per-field breakdown in properties (value in USD, base, markup, source, per-field tokens/unit_price/cost). Live unit prices come from public, no-auth sources: OpenRouter (/api/v1/models) for native anthropic/openai/mistral/gemini, and the AWS Bedrock Price List Bulk API for Bedrock. Prices are fetched + cached on the background queue thread (never blocking the customer's call); a missing price falls back to token events and calls on_error (never silently under-bills). Mode and markup are overridable per-call via extra_lago={"mode": "price", "markup": 1.5}. Money is computed with Decimal floored to 12 dp, identical to the JS implementation (cross-repo golden fixture). New pricing.py module + PricingProvider; default pricing_mode="tokens" keeps existing behavior unchanged.

Fixed

  • Anthropic messages.create(stream=True) under-billed input tokens. The stream wrapper read only top-level usage, which on a basic stream appears only on message_delta as {output_tokens: N} — the authoritative input_tokens / cache_* counts arrive nested under message.usage on the message_start event and were ignored, so input billed 0. The wrapper now merges usage from message_start (input/cache) and message_delta (cumulative output). Sync + async paths; regression tests use the realistic wire shape (delta carries no input echo).
  • Legacy google-generativeai SDK silently emitted no events. The detector matched both the new google-genai and the deprecated google-generativeai SDKs, but the wrapper only instruments the unified Client.models / .aio surface — a legacy GenerativeModel routed through and wrapped nothing. wrap() now rejects legacy clients with a clear pointer to migrate to google-genai.

Security

  • Hardened the publish workflow: least-privilege permissions: contents: read default (only publish gets id-token: write, only release gets contents: write), and every third-party action pinned to a full commit SHA so a re-pointed tag can't inject code into the OIDC-token-minting job.
  • Added if: startsWith(github.ref, 'refs/tags/v') to the publish job as defense-in-depth — it refuses to run on a non-tag ref even if the environment's protected-tag rule is misconfigured.
  • Added .github/dependabot.yml (github-actions ecosystem) so the SHA pins stay fresh — Dependabot bumps the SHA and version comment together rather than letting actions silently age.
  • RELEASING.md now documents pypi environment protection (required reviewers + protected-tag restriction) as a required setup step, not optional, since trusted publishing is only as strong as that environment's rules.

Documentation

  • README: clarified that cache_read, audio_input, and image_input are subsets of input for OpenAI and Gemini (not additive) — summing them with llm_input_tokens double-counts.

Added

  • Native google-genai SDK support covering client.models.generate_content + generate_content_stream, sync + async (client.aio.models).
  • extract_gemini_native adapter maps usage_metadata: prompt_token_count → input, candidates_token_count → output, cached_content_token_count → cache_read, thoughts_token_count → reasoning, prompt_tokens_details[modality=AUDIO/IMAGE] → audio_input/image_input, candidates_tokens_details[modality=AUDIO] → audio_output, count of candidates[0].content.parts[].function_call → tool_calls.
  • Gemini 2.5 surfaces reasoning tokens by default (thoughts_token_count) — fires llm_reasoning_tokens automatically. Note the semantic difference vs OpenAI: Gemini's reasoning is ADDITIVE to output (candidates + thoughts = total billable output); OpenAI's reasoning is a SUBSET of completion_tokens. Documented in adapter docstring + README.
  • gemini optional dependency group: pip install 'lago-agent-sdk[gemini]'.
  • 21 new unit tests (15 adapter + 6 wrapper) and 4 live integration tests (gated on GEMINI_API_KEY). Total: 304 unit tests.
  • 5 captured response fixtures from the real Gemini API (plain, tool use, streaming, thinking, multi-turn).
  • Detector now returns gemini (was google) for google-genai clients.

Added (OpenAI — earlier in this branch)

  • Native openai SDK support covering both APIs: chat.completions.create and responses.create, each with sync + streaming. Same coverage on AsyncOpenAI.
  • extract_openai_native adapter handles both API shapes with auto-detection:
    • Chat Completions: prompt_tokens, completion_tokens, prompt_tokens_details.{cached_tokens, audio_tokens}, completion_tokens_details.{reasoning_tokens, audio_tokens}, count of choices[0].message.tool_calls.
    • Responses API: input_tokens, output_tokens, input_tokens_details.cached_tokens, output_tokens_details.reasoning_tokens, count of output[].type == "function_call".
  • First provider to populate llm_reasoning_tokens — OpenAI o-series models (o4-mini, o1, etc.) surface reasoning token counts separately.
  • Auto-injection of stream_options={"include_usage": True} when the customer sets stream=True without it, so streamed Chat Completions emit usage on the final chunk.
  • audio_output field added to CanonicalUsage (maps to llm_audio_output_tokens), populated by GPT-4o-audio responses.
  • openai optional dependency group: pip install 'lago-agent-sdk[openai]'.
  • 27 new unit tests (18 adapter + 9 wrapper) and 5 live integration tests (gated on OPENAI_API_KEY). Total: 283 unit tests.
  • 10 captured response fixtures from the real OpenAI API (plain chat, tool use, auto-caching, streaming with usage, o-series reasoning, multi-turn, Responses API plain + tool use + reasoning).

Previously in unreleased (Anthropic)

  • Native anthropic SDK support. Wraps Anthropic.messages.create (including stream=True) and Anthropic.messages.stream(...) context manager. Same coverage on AsyncAnthropic (sync + async variants).
  • extract_anthropic_native adapter with the full Anthropic field map: input_tokens, output_tokens, cache_creation_input_tokens, cache_read_input_tokens, cache_creation.ephemeral_5m_input_tokens, cache_creation.ephemeral_1h_input_tokens, content[].type == "tool_use".
  • anthropic optional dependency group: pip install 'lago-agent-sdk[anthropic]'.
  • 19 unit tests (adapter + wrapper) and 3 live integration tests (gated on ANTHROPIC_API_KEY).
  • 9 captured response fixtures from the real Anthropic API (plain, tool use, 5m + 1h prompt caching, extended thinking, streaming, multi-turn).

[0.1.0] — initial release

Added

  • LagoSDK core with batched async event queue, exponential backoff, bounded buffer, async-local subscription resolution.
  • boto3 Bedrock wrapper covering Converse, ConverseStream, InvokeModel, InvokeModelWithResponseStream.
  • 7 InvokeModel family adapters (anthropic, opus_4_7, nova, pixtral, mistral_legacy, openai_compat_basic, openai_compat_with_details) with substring-match dispatch.
  • mistralai native wrapper covering chat.complete, chat.stream, async variants.
  • Three subscription-resolution tiers: per-call extra_lago, context-bound set_subscription, init-time default.
  • 245 tests: 237 unit + 8 integration; verified against 159 fixtures captured from real provider responses.
  • p99 wrap-overhead ≤ 5 ms benchmark.