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Haim"},"license":"MIT","homepage":"https://github.com/asf-hm/agentlog#readme","keywords":["ai","agent","audit","logging","vercel-ai","ai-sdk","tamper-evident"],"repository":{"type":"git","url":"git+https://github.com/asf-hm/agentlog.git"},"description":"Vercel AI SDK adapter for agentlog — audit logging for generateText and streamText","maintainers":[{"name":"asafhm","email":"asi.h42@gmail.com"}],"readme":"# agentlog\n\nLocal tamper-evident audit logs for AI agent runs.\n\nagentlog records prompts, tool calls, responses, and errors as hash-chained JSONL files on disk. Use it to debug what happened, inspect runs locally, and verify that a log was not edited after the fact.\n\nNot a transcript viewer. Each run is a verifiable chain — any edit or deletion breaks verification.\n\n- Local files, no cloud service\n- Hash-chained events for tamper evidence\n- Vercel AI SDK integration\n- CLI verification and timeline view\n- Local Studio UI for browsing runs\n\nRuns are written to `.agentlog/runs/*.jsonl` and gitignored by default.\n\n---\n\n## 2-minute start\n\n```bash\nnpm install @asafhm/agentlog-core @asafhm/agentlog-vercel-ai\n```\n\n```ts\nimport { generateText } from 'ai';\nimport { openai } from '@ai-sdk/openai';\nimport { createAgentLogger } from '@asafhm/agentlog-vercel-ai';\n\nconst { telemetry, onError } = createAgentLogger({ agentName: 'my-agent' });\n\ntry {\n  const { text } = await generateText({\n    model: openai('gpt-4o-mini'),\n    prompt: 'Explain SQL joins',\n    experimental_telemetry: telemetry,\n  });\n  console.log(text);\n} catch (err) {\n  onError(err);\n  throw err;\n}\n// → .agentlog/runs/<runId>.jsonl\n```\n\nInspect the run:\n\n```bash\nnpx agentlog view .agentlog/runs/<runId>.jsonl\n```\n\n```\n[0] 2026-04-28 12:00:00Z  run_start\n[1] 2026-04-28 12:00:01Z  prompt\n      model: {\"provider\":\"openai\",\"modelId\":\"gpt-4o-mini\"}\n[2] 2026-04-28 12:00:03Z  response  (1821ms)\n      usage: {\"inputTokens\":89,\"outputTokens\":18}\n[3] 2026-04-28 12:00:03Z  run_end\n      status: \"success\"\n```\n\nVerify it wasn't edited:\n\n```bash\nnpx agentlog verify .agentlog/runs/<runId>.jsonl\n```\n\n```\n✓ Valid — 4 events verified\n```\n\n---\n\n## Why not just logs?\n\n- Console logs are unstructured and hard to replay\n- Observability tools usually require an external service\n- Normal log files can be silently edited after the fact\n- agentlog writes one local, hash-chained timeline per agent run — readable by humans, verifiable by machines\n\n---\n\n## Packages\n\n| Package | Description |\n|---|---|\n| `@asafhm/agentlog-core` | Core logger — create runs, append events, verify files |\n| `@asafhm/agentlog-vercel-ai` | Vercel AI SDK adapter for `generateText` and `streamText` |\n| `@asafhm/agentlog` | CLI — `verify`, `view`, and `studio` commands |\n\n---\n\n## Vercel AI SDK\n\n### generateText\n\n```ts\nimport { generateText } from 'ai';\nimport { openai } from '@ai-sdk/openai';\nimport { createAgentLogger } from '@asafhm/agentlog-vercel-ai';\n\nconst { telemetry, onError } = createAgentLogger({ agentName: 'my-agent' });\n\ntry {\n  const { text } = await generateText({\n    model: openai('gpt-4o-mini'),\n    prompt: 'Explain SQL joins',\n    experimental_telemetry: telemetry,\n  });\n  console.log(text);\n} catch (err) {\n  onError(err);\n  throw err;\n}\n```\n\n### streamText\n\n`onError` plugs in directly — no try/catch needed:\n\n```ts\nimport { streamText } from 'ai';\nimport { openai } from '@ai-sdk/openai';\nimport { createAgentLogger } from '@asafhm/agentlog-vercel-ai';\n\nconst { telemetry, onError } = createAgentLogger({ agentName: 'stream-agent' });\n\nconst result = streamText({\n  model: openai('gpt-4o-mini'),\n  prompt: 'Explain SQL joins',\n  experimental_telemetry: telemetry,\n  onError,\n});\n\nfor await (const chunk of result.textStream) {\n  process.stdout.write(chunk);\n}\n```\n\nBy default, agentlog records metadata only: timestamps, event types, durations, and tool names. No prompt content is captured unless you opt in — see [Capture modes](#capture-modes).\n\n---\n\n## Core API\n\nFor use outside the Vercel AI SDK:\n\n```ts\nimport { createRun, verifyFile } from '@asafhm/agentlog-core';\n\nconst run = createRun({ agentName: 'my-agent' });\nrun.append('prompt', { model: 'gpt-4o' });\nrun.append('response', { usage: { promptTokens: 5, completionTokens: 20 } });\nrun.end('success');\n\nconst result = await verifyFile(`.agentlog/runs/${run.runId}.jsonl`);\nconsole.log(result); // { valid: true, eventsChecked: 4 }\n```\n\n---\n\n## Inspect and verify\n\n```bash\nnpx agentlog view .agentlog/runs/<runId>.jsonl    # human-readable timeline\nnpx agentlog verify .agentlog/runs/<runId>.jsonl  # exits 1 if tampered\nnpx agentlog studio                               # browser UI at http://127.0.0.1:3001\n```\n\n---\n\n## Studio\n\n```bash\nnpx agentlog studio\n```\n\nOpens `http://127.0.0.1:3001` — a local UI showing all runs, event timelines, and verification status.\n\n**Run list** — every run with its verify status at a glance:\n\n![agentlog studio run list](docs/studio-runs.png)\n\n**Event timeline** — what the agent actually did, step by step:\n\n![agentlog studio timeline](docs/studio-timeline.png)\n\n---\n\n## Capture modes\n\n| Mode | What is recorded |\n|---|---|\n| `metadata` (default) | Timestamps, event types, durations, tool names |\n| `full` | Everything above plus prompts, responses, tool inputs/outputs |\n\n```ts\nconst { telemetry, onError } = createAgentLogger({\n  agentName: 'my-agent',\n  captureMode: 'full',\n});\n```\n\n`full` mode keeps all data local but may capture sensitive content. Review what your prompts and tool outputs contain before enabling it in production.\n\n---\n\n## How it works\n\nEach event is hashed with SHA-256 over its fields plus the previous event's hash, forming a chain. `verifyFile` recomputes every hash and checks:\n\n- Sequence numbers are contiguous from 0\n- Each `prevHash` matches the previous event's hash\n- Each stored hash matches the recomputed hash\n\nAny modification, insertion, or deletion breaks every subsequent hash. Completed runs also write a `.head.json` sidecar — if present, tail truncation is detected too.\n\n`verifyFile` returns structured details on failure:\n\n```ts\nresult.details?.code          // e.g. \"HASH_MISMATCH\"\nresult.details?.lastValidSeq  // last sequence number known good\n```\n\n---\n\n## Auditability\n\nagentlog is not a compliance product by itself, but it is designed for teams that need trustworthy records of AI system behavior. Its append-only, hash-chained logs can support audit, debugging, incident review, and regulated AI workflows.\n\nUseful for auditability and Article 12-style automatic logging requirements.\n\n---\n\n## CLI reference\n\n```bash\nnpx agentlog verify <run.jsonl>   # verify integrity — exits 1 if invalid\nnpx agentlog view <run.jsonl>     # human-readable event timeline\nnpx agentlog studio               # local browser UI at http://127.0.0.1:3001\n```\n\n`AGENTLOG_DIR` — storage location (default: `.agentlog`)\n`AGENTLOG_STUDIO_PORT` — studio port (default: `3001`)\n\n---\n\n## Event types\n\n| Event | When |\n|---|---|\n| `run_start` | Created automatically by `createRun()` |\n| `prompt` | Before an LLM call |\n| `tool_call` | When a tool is invoked |\n| `tool_result` | When a tool returns |\n| `response` | After an LLM call |\n| `late_error` | Error after run completed |\n| `error` | On caught errors |\n| `run_end` | Created by `run.end(status)` |\n\n---\n\n## Status\n\n| | |\n|---|---|\n| Core logger | Stable enough for local experiments |\n| CLI (`verify`, `view`, `studio`) | Supported |\n| Vercel AI SDK — `generateText` | Supported |\n| Vercel AI SDK — `streamText` | Supported |\n| Cryptographic signing (private key) | Planned |\n| Cloud backend / remote storage | Not planned |\n\nAPI is stable; expect changes before 1.0.\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}