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replay, and score evaluation primitives for Beach applications — built on the event log.","maintainers":[{"name":"johncandrew","email":"johncandrew@yahoo.co.uk"}],"readme":"# @cool-ai/beach-evals\n\nOptional evaluation primitives for Beach-based agents. Test case schema, deterministic replay-based runner, reference scorers, pluggable `Scorer` interface.\n\n> **Home:** [cool-ai.org](https://cool-ai.org) · **Documentation:** [cool-ai.org/docs](https://cool-ai.org/docs)\n\n## Install\n\n```sh\nnpm install --save-dev @cool-ai/beach-evals\n```\n\n## API\n\n### EvalRecorder\n\nRecords live turn data as a replayable `EvalTestCase`.\n\n```typescript\nimport { EvalRecorder } from '@cool-ai/beach-evals';\n\nconst recorder = new EvalRecorder();\n\n// Wrap specialist tools to capture their recorded outputs\nconst wrappedTools = recorder.wrapRegistry(toolDefinitions);\n\n// After a turn completes, record it\nconst testCase = recorder.record({\n  handlerId: 'concierge',\n  handlerSystemPrompt: systemPrompt,\n  mailboxSnapshot,\n  specialistExecutions,\n  expected: {\n    conversationState: 'complete',\n    parts: [{ partType: 'domain-data' }],\n  },\n});\n```\n\n### EvalDataset\n\nStores test cases on disk — one JSON file per case.\n\n```typescript\nimport { EvalDataset } from '@cool-ai/beach-evals';\n\nconst dataset = new EvalDataset();\ndataset.add(testCase);\nawait dataset.save('./evals/cases');\n\n// Load from disk\nconst loaded = await EvalDataset.load('./evals/cases');\n```\n\n### EvalRunner\n\nReplays test cases and scores them.\n\n```typescript\nimport { EvalRunner, ConversationStateScorer, PartPresentScorer, ExactMatchScorer, SchemaValidScorer } from '@cool-ai/beach-evals';\n\nconst runner = new EvalRunner({ handlerConfig, provider, toolDefinitions });\n\nconst result = await runner.run(testCase, {\n  scorers: [\n    new ConversationStateScorer('complete'),\n    new PartPresentScorer('domain-data'),\n    new ExactMatchScorer('response', 'Flights found'),\n    new SchemaValidScorer((data) => Array.isArray((data as any).flights)),\n  ],\n  promptHashMismatch: 'warn',  // 'warn' | 'error' | 'ignore'\n});\n\nconsole.log(result.passed, result.scores);\n```\n\n### Reference scorers\n\n| Scorer | Purpose |\n|--------|---------|\n| `ConversationStateScorer` | Assert the replay settled with the expected `conversationState` |\n| `PartPresentScorer` | Assert a part of a given type is present (optionally non-empty) |\n| `ExactMatchScorer` | Assert a `response` part's text matches exactly |\n| `SchemaValidScorer` | Assert a `domain-data` part passes a caller-supplied predicate |\n\nBring your own `Scorer` implementation for LLM-as-judge, rubric-based, or domain-specific checks.\n\n## Rationale\n\nBeach's audit-first claim is undercut without evaluation — auditability without assertions is just logging. Consumers need to:\n\n- **Regression-test** their agents: \"given this input, does the agent produce this output?\"\n- **Compare models**: \"same input against different models, compare outputs.\"\n- **Iterate prompts**: \"does changing the prompt change behaviour in the ways I expected?\"\n- **Score outputs**: equality, regex, schema-validation, rubric, LLM-as-judge.\n\n`@cool-ai/beach-evals` ships the primitives for this without being a full evaluation platform. Consumers with more sophisticated needs (rubric-based scoring, human-judged evals, continuous CI with dataset management, cross-run dashboards) write their own scorers against Beach's `Scorer` interface, or integrate with an external eval platform (Mastra evals, LangSmith, Braintrust, PromptFoo, Logfire).\n\n## Concern\n\n`@cool-ai/beach-evals` provides:\n\n- **`EvalTestCase` schema** — `{ id, description, handlerId, handlerPromptHash, mailboxSnapshot, specialistExecutions, expected, recordedAt, tags? }`.\n- **`EvalRecorder`** — records live turn data as an `EvalTestCase`; `wrapRegistry()` intercepts specialist tool calls transparently so executions are captured without changing handler code.\n- **`EvalRunner`** — replays a test case by calling `runRespondLoop` directly with a mocked `ToolRegistry`, runs scorers against the result, returns an `EvalResult`.\n- **`EvalDataset`** — stores test cases on disk (one JSON file per case) with `save(dir)` / `static load(dir)`.\n- **Reference scorers** — `ConversationStateScorer`, `PartPresentScorer`, `ExactMatchScorer`, `SchemaValidScorer`.\n- **`Scorer` interface** — consumers plug in custom scorers (LLM-as-judge, rubric-based, domain-specific).\n- **`hashPrompt(prompt)`** — computes a SHA-256 hash over the handler's system prompt text; stored with the recording and compared at replay time to detect prompt drift.\n\n## Not in this package\n\n- CI integration (GitHub Actions, GitLab Pipelines, report uploaders).\n- Dashboards, trend charts, cross-run comparisons.\n- Human-in-the-loop eval UI.\n- LLM-as-judge reference implementation (consumers bring their own using the `Scorer` interface).\n\nFor any of these, integrate an external eval platform. Beach ships the primitives; the rest is a platform-choice concern.\n\n## Scorer interface\n\n```typescript\ninterface Scorer {\n  name: string;\n  score(result: ReplayResult): ScoreResult;\n}\n\ninterface ScoreResult {\n  scorer: string;\n  passed: boolean;\n  message?: string;\n  details?: Record<string, unknown>;\n}\n```\n\nA consumer's LLM-as-judge scorer would call an LLM with the actual output + a rubric, return a `passed` boolean plus the LLM's reasoning in `details`.\n\n## Determinism\n\n`@cool-ai/beach-evals` runs tests deterministically by calling `runRespondLoop` with a mocked registry:\n\n- LLM calls are mocked from recorded outputs during replay, or run live against a specified model for \"fresh\" runs.\n- Tool calls are mocked from recorded results during replay; specialist-scope tools are mocked from their `specialist_execution` log records (see principle 17).\n- Both modes are available; the test case declares which.\n\n### Prompt-change invalidates recorded outputs\n\nA subtle trap: **recorded LLM outputs are pinned to the prompt that produced them**. If the handler's system prompt changes between recording and replay, the recorded output was made against the old prompt. Replaying with the new prompt but mocking from the old output tests nothing about the new prompt's behaviour — it tests only that the old prompt's behaviour is still consistent.\n\n**Rules of use:**\n\n- **Deterministic-behaviour regressions** (data shape, schema conformance, conversation-state correctness) are valid against recorded outputs as long as the prompt is fixed.\n- **Prompt-sensitive regressions** (response quality, clarification thresholds, tool-choice heuristics) require **fresh runs against a live model**. A recorded replay won't catch prompt regressions.\n- **Rerecord when you change prompts.** Test runners should detect prompt hash mismatches and warn. `@cool-ai/beach-evals` computes a hash over the handler's resolved system prompt (snippet + consumer content) and stores it with the recording; mismatch at replay time surfaces a warning, not a silent pass.\n\nConsumer eval pipelines should interleave both modes — fresh runs for prompt iteration, replay for regression checks on unchanged prompts.\n\n## Consumers\n\nAny Beach-based agent serious enough to need regression testing. Prototype or throwaway agents can skip.\n\n## Related\n\n- [../inspect/](../inspect/) — dev UI that can show eval run results alongside session traces.\n- [https://cool-ai.org/docs/design-principles](https://cool-ai.org/docs/design-principles) — principle 3.7 (replay and evaluation are first-class).\n","readmeFilename":"README.md"}