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your agents, not your LLM calls.","maintainers":[{"name":"warpmetrics","email":"nikolai@warpmetrics.com"}],"readme":"# warp\n\nMeasure your agents, not your LLM calls.\n\nWarp is a lightweight SDK that wraps your existing OpenAI or Anthropic client and gives you full observability over your AI agent's execution — runs, groups, costs, and outcomes — with zero config changes to your LLM calls.\n\n## Install\n\n```bash\nnpm install @warpmetrics/warp\n```\n\n## Quick start\n\n```js\nimport OpenAI from 'openai';\nimport { warp, run, group, call, trace, outcome } from '@warpmetrics/warp';\n\nconst openai = warp(new OpenAI(), { apiKey: 'wm_...' });\n\nconst r = run('Code Review', { name: 'Review PR #42' });\nconst planning = group(r, 'Planning');\n\nconst response = await openai.chat.completions.create({\n  model: 'gpt-4o',\n  messages: [{ role: 'user', content: 'Review this PR...' }],\n});\n\ncall(planning, response);\noutcome(r, 'Completed', { reason: 'Approved' });\n```\n\nEvery LLM call is captured by `warp()` but only sent to the API when you explicitly `call()` it into a run or group. Unclaimed responses are never transmitted.\n\n## API\n\n### `warp(client, opts?)`\n\nWrap an OpenAI or Anthropic client. Every call to `.chat.completions.create()` or `.messages.create()` is automatically intercepted and buffered.\n\n```js\nconst openai = warp(new OpenAI(), { apiKey: 'wm_...' });\nconst anthropic = warp(new Anthropic(), { apiKey: 'wm_...' });\n```\n\nOptions are only needed on the first call. After that, config is shared across all wrapped clients.\n\n| Option | Type | Default | Description |\n|---|---|---|---|\n| `apiKey` | `string` | `WARPMETRICS_API_KEY` env var | Your Warpmetrics API key |\n| `baseUrl` | `string` | `https://api.warpmetrics.com` | API endpoint |\n| `enabled` | `boolean` | `true` | Disable tracking entirely |\n| `debug` | `boolean` | `false` | Log events to console |\n| `flushInterval` | `number` | `1000` | Auto-flush interval in ms |\n| `maxBatchSize` | `number` | `100` | Max events per batch |\n\n### `run(label, opts?)`\n\nCreate a run — the top-level unit that tracks one agent execution.\n\n```js\nconst r = run('Code Review', { name: 'PR #42', link: 'https://github.com/org/repo/pull/42' });\n```\n\n### `run(act, label, opts?)`\n\nCreate a follow-up run from an act (the result of acting on an outcome).\n\n```js\nconst r2 = run(a, 'Code Review', { name: 'Retry' });\n```\n\n### `group(target, label, opts?)`\n\nCreate a group — a logical phase or step inside a run or group.\n\n```js\nconst planning = group(r, 'Planning', { name: 'Planning Phase' });\nconst coding = group(r, 'Coding');\nconst subStep = group(planning, 'Sub Step');  // groups can nest\n```\n\n### `call(target, response, opts?)`\n\nTrack an LLM call by linking a buffered response to a run or group.\n\n```js\nconst response = await openai.chat.completions.create({ model: 'gpt-4o', messages });\ncall(r, response);\ncall(g, response, { label: 'extract' });  // with opts\n```\n\n### `trace(target, data)`\n\nManually record an LLM call for providers not wrapped by `warp()`.\n\n```js\ntrace(r, {\n  provider: 'google',\n  model: 'gemini-2.0-flash',\n  messages: [{ role: 'user', content: 'Hello' }],\n  response: 'Hi there!',\n  tokens: { prompt: 10, completion: 5 },\n  latency: 230,\n  cost: 0.0001,\n});\n```\n\n| Field | Type | Required | Description |\n|---|---|---|---|\n| `provider` | `string` | Yes | Provider name (e.g. `\"google\"`, `\"cohere\"`) |\n| `model` | `string` | Yes | Model identifier |\n| `messages` | `any` | No | Request messages/input |\n| `response` | `string` | No | Response text |\n| `tools` | `string[]` | No | Tool names available |\n| `toolCalls` | `{ id, name, arguments }[]` | No | Tool calls made |\n| `tokens` | `{ prompt?, completion?, total? }` | No | Token usage |\n| `latency` | `number` | No | Duration in milliseconds |\n| `timestamp` | `string` | No | ISO 8601 timestamp (auto-generated if omitted) |\n| `status` | `string` | No | `\"success\"` (default) or `\"error\"` |\n| `error` | `string` | No | Error message |\n| `cost` | `number` | No | Cost in USD |\n| `opts` | `Record<string, any>` | No | Custom metadata |\n\n### `outcome(target, name, opts?)`\n\nRecord an outcome on any tracked target.\n\n```js\noutcome(r, 'Completed', { reason: 'All checks passed', source: 'ci' });\n```\n\n### `act(target, name, opts?)`\n\nRecord an action taken on an outcome. Returns an act handle that can be passed to `run()` for follow-ups.\n\n```js\nconst oc = outcome(r, 'Failed', { reason: 'Tests failed' });\nconst a = act(oc, 'Retry', { strategy: 'fix-and-rerun' });\nconst r2 = run(a, 'Code Review');\n```\n\n### `ref(target)`\n\nResolve any target (run, group, outcome, act, or LLM response) to its string ID. Also accepts raw ID strings (e.g. `\"wm_run_...\"` loaded from a database) and registers them locally.\n\n```js\nref(r)         // 'wm_run_01jkx3ndek0gh4r5tmqp9a3bcv'\nref(response)  // 'wm_call_01jkx3ndef8mn2q7kpvhc4e9ws'\nref('wm_run_01jkx3ndek0gh4r5tmqp9a3bcv')  // adopts and returns the ID\n```\n\n### `flush()`\n\nManually flush pending events. Events are auto-flushed on an interval and on process exit, but you can force it.\n\n```js\nawait flush();\n```\n\n## Entity Model\n\nSix entity types form an execution DAG. Every entity is created client-side, batched, and sent atomically to `POST /v1/events`.\n\n### Entities\n\n| Entity | Prefix | Purpose |\n|--------|--------|---------|\n| **Run** | `wm_run_` | Top-level execution unit. Has a label, opts, and startedAt. |\n| **Group** | `wm_grp_` | Logical phase/step inside a run or group. Nestable. |\n| **Call** | `wm_call_` | Single LLM API call with tokens, cost, duration. Always a leaf node. Created by `call()` (intercepted) or `trace()` (manual) — same entity either way. |\n| **Link** | — | Parent→child edge. Connects runs/groups to their children. No ID of its own. |\n| **Outcome** | `wm_oc_` | Named result recorded on a run, group, or call. |\n| **Act** | `wm_act_` | Action taken on an outcome. Can trigger follow-up runs. |\n\nIDs are monotonic ULIDs: `wm_{prefix}_{ulid}`.\n\n### Hierarchy (Links)\n\n```\nRun ──→ Group ──→ Group  (nestable)\n │        │\n │        └──→ Call\n └──→ Call\n```\n\n- Links are directional (parent → child)\n- A child has exactly one parent\n- Calls are always leaf nodes\n- Groups can nest arbitrarily\n\n### Outcome → Act → Run chain\n\nThe mechanism for multi-step workflows:\n\n```\nRun / Group / Call\n     │\n     └─ outcome(\"Needs Review\")     → Outcome  (refId = entity)\n              │\n              └─ act(\"Review\")      → Act      (refId = outcome)\n                       │\n                       └─ run(act)  → Run      (refId = act)\n```\n\n- An outcome references exactly one entity (run, group, or call) via `refId`\n- An act references exactly one outcome via `refId`\n- A follow-up run references exactly one act via `refId`\n- Multiple outcomes can target the same entity\n- Multiple acts can target the same outcome\n- An act with a follow-up run is \"resolved\"; without one it's \"pending\"\n\n### Runner pattern\n\nA graph runner processes the entity model by declaring a workflow graph and walking it:\n\n```\ndefine graph:\n  ActName → {\n    executor: string | null     # null = phase group (auto-transition)\n    results: {\n      resultType → [{ outcome, on?, next? }]\n    }\n  }\n\ndefine states:\n  OutcomeName → BoardColumn\n\nalgorithm processRun(run):\n  act = findPendingAct(run)\n  while act:\n    node = graph[act.name]\n\n    if node.executor == null:                    # phase group\n      group = createGroup(run, node.label)\n      result = { type: \"created\" }\n    else:                                        # work act\n      result = execute(node.executor, run, act)\n\n    edges = node.results[result.type]            # [{outcome, on?, next?}]\n    for edge in edges:\n      container = resolveContainer(edge.on, run) # run or phase group\n      oc = recordOutcome(container, edge.outcome)\n      if edge.next:\n        act = emitAct(oc, edge.next, result.nextActOpts)\n\n    syncBoard(run, states[lastOutcome])\n\n    if not edge.next:\n      break                                      # terminal\n```\n\nKey concepts:\n\n- **Phase groups** (`executor: null`): create a group, auto-resolve with `created`, and immediately transition to the next act. No external work.\n- **Work acts** (`executor: string`): call an executor, get a typed result, map it to graph edges.\n- **`findPendingAct`**: walk the run's outcomes and group outcomes (newest first) to find the last act with no follow-up run.\n- **`resolveContainer`**: outcomes target either the top-level run (for board tracking) or a phase group (for scoped tracking). A single result can produce outcomes on both.\n- **Board sync**: map the last outcome name to a board column via the `states` map.\n\n### Event batch format\n\nAll entities are sent atomically in a single `POST /v1/events` payload:\n\n```json\n{\n  \"runs\":     [{ \"id\", \"label\", \"opts\", \"refId\", \"startedAt\" }],\n  \"groups\":   [{ \"id\", \"label\", \"opts\", \"startedAt\" }],\n  \"calls\":    [{ \"id\", \"provider\", \"model\", \"messages\", \"response\", \"tokens\", \"duration\", ... }],\n  \"links\":    [{ \"parentId\", \"childId\", \"type\", \"timestamp\" }],\n  \"outcomes\": [{ \"id\", \"refId\", \"name\", \"opts\", \"timestamp\" }],\n  \"acts\":     [{ \"id\", \"refId\", \"name\", \"opts\", \"timestamp\" }]\n}\n```\n\nThe body is base64-encoded: `{ \"d\": \"<base64>\" }`.\n\n## Supported providers\n\n- **OpenAI** — `client.chat.completions.create()` and `client.responses.create()`\n- **Anthropic** — `client.messages.create()`\n\nNeed another provider? [Open an issue](https://github.com/warpmetrics/warp/issues).\n\n## Environment variables\n\n| Variable | Description |\n|---|---|\n| `WARPMETRICS_API_KEY` | API key (fallback if not passed to `warp()`) |\n| `WARPMETRICS_API_URL` | Custom API endpoint |\n| `WARPMETRICS_DEBUG` | Set to `\"true\"` to enable debug logging |\n\n## Development\n\n### Running tests\n\n```bash\nnpm install\nnpm test              # unit tests only (integration tests auto-skip)\nnpm run test:coverage # with coverage report\nnpm run test:watch    # watch mode\n```\n\n### Integration tests\n\nIntegration tests make real API calls to OpenAI and Anthropic. They are **automatically skipped** unless the corresponding API keys are set.\n\nTo run them:\n\n```bash\ncp .env.example .env\n# Edit .env with your API keys\nnpm run test:integration\n```\n\n> **Note:** Integration tests make a small number of API calls with `max_tokens: 5`, so costs are minimal (fractions of a cent per run).\n\n## License\n\nMIT\n","readmeFilename":"README.md"}