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\n

🧠 Agent Diaries Core

\n

Stop AI agents from doing the same work twice.

\n

One function call. Any agent framework. Exactly-once execution across your entire swarm.

\n\n[![NPM Version](https://img.shields.io/npm/v/@agent-diaries/core?style=for-the-badge&logo=npm&color=CB3837)](https://www.npmjs.com/package/@agent-diaries/core)\n[![NPM Downloads](https://img.shields.io/npm/dm/@agent-diaries/core?style=for-the-badge&logo=npm&color=44CC11)](https://www.npmjs.com/package/@agent-diaries/core)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](https://opensource.org/licenses/MIT)\n[![TypeScript](https://img.shields.io/badge/TypeScript-3178C6?style=for-the-badge&logo=typescript&logoColor=white)](https://www.typescriptlang.org/)\n[![Tests](https://img.shields.io/badge/Tests-191%2F191%20Passing-brightgreen?style=for-the-badge&logo=vitest&logoColor=white)](https://github.com/swapwarick/agent-diaries-core/actions)\n\n
\n\n---\n\n## 🛑 The Problem\n\nAutonomous agents duplicate work when scaling up.\n\nWhen 50 agents run concurrently, they repeatedly fetch identical web pages, execute redundant LLM reasoning, and trigger duplicate API calls.\n\n- **Token & API Bloat:** You pay 50× the cost for 1× the result.\n- **Swarm Race Conditions:** Multiple agents attempt to process or mutate the same task at once.\n- **Memory Growth & OOM Risk:** Unbounded in-memory caches grow indefinitely as swarms run 24/7.\n- **Framework Blindness:** Frameworks like LangChain, AutoGen, CrewAI, or LlamaIndex lack cross-process coordination out of the box.\n\n---\n\n## ⚡ The Solution\n\n**Agent Diaries** is a lightweight, framework-agnostic coordination layer that guarantees **exactly-once execution** for your AI agents.\n\n### Core Benefits\n\n- 🎯 **Exactly-Once Execution:** Guarantee tasks execute only once, no matter how many agents attempt them concurrently.\n- 🌐 **Distributed Coordination:** Safely coordinate agents across processes, worker nodes, or serverless functions with zero-polling FIFO mutexes.\n- 🚫 **Duplicate Prevention:** Intercept and deduplicate identical LLM calls, web scrapes, and API requests before they happen.\n- 🚀 **Multi-Tiered LRU Caching:** Automatic bounded in-memory L1 cache with configurable eviction prevents memory bloat and delivers sub-millisecond lookups.\n- 📦 **Binary Serialization:** Built-in MessagePack encoding for compact network transmission and fast disk/DB persistence.\n- 💾 **Persistent History:** Store execution records in-memory, Redis, PostgreSQL, MongoDB, or SQLite so past work survives restarts.\n\n---\n\n## 🚀 30-Second Quick Start\n\n### Installation\n\n```bash\nnpm install @agent-diaries/core\n```\n\nZero required setup or external database drivers. Works in-memory out of the box.\n\n---\n\n## 💡 One Memorable Code Example\n\nWrap any expensive agent operation in `executeOnce()`.\n\n```typescript\nimport { AgentDiary } from \"@agent-diaries/core\";\n\nconst diary = new AgentDiary({ agentId: \"research-agent\" });\n\n// 100 agents call executeOnce() concurrently.\n// Exactly 1 agent executes the LLM call. The remaining 99 reuse the cached result instantly.\nconst summary = await diary.executeOnce(\"research:openai-q4-2024\", async () => {\n const page = await fetchWebPage(\"https://openai.com/blog/q4-2024\");\n return await summarizeWithLLM(page);\n});\n```\n\n> `executeOnce` — if this task already ran, returns the cached result. If another agent is running it right now, waits, then returns their result. If it's new, runs your function exactly once.\n\n---\n\n## ⚡ Performance & Scalability Upgrades\n\nAgent Diaries is engineered for high-throughput multi-agent swarms:\n\n| Feature / Metric | Without Agent Diaries | With Agent Diaries | Performance Impact |\n|---|---|---|---|\n| **Hot Key Read Latency** | Direct Database / Network Roundtrip | In-Memory `O(1)` L1 Cache Hit | **< 1 ms (Sub-millisecond)** |\n| **Memory Footprint** | Unbounded growth (OOM leak risk) | Strictly bounded LRU eviction | **Guaranteed constant max memory** |\n| **Payload Size (Traces/State)** | Verbose UTF-8 JSON strings | Binary MessagePack buffers | **30–60% reduction in byte size** |\n| **Concurrency Locking** | Spinlocks / Polling loops (`sleep`) | Chained-Promise FIFO Mutex | **Zero CPU spin & zero lock-theft races** |\n| **Concurrent Redundant Calls** | 50× redundant LLM/API invocations | Deduplicated down to 1 call | **Up to 98% token & cost savings** |\n\n---\n\n## 🌍 Real-World Use Cases\n\n| Use Case | How Agent Diaries Solves It |\n|---|---|\n| 🌐 **Browser Agents** | Playwright/Puppeteer swarms deduplicate URL visits so identical web pages are never scraped twice. |\n| 🔬 **Research Agents** | Multi-agent swarms researching 100 topics execute exactly 1 LLM call per topic, preventing redundant synthesis. |\n| 🛡️ **GitHub Security Scanning** | 200 security bots scanning 50 repos execute exactly 50 scans, skipping 9,950 duplicate calls and saving $150+ in LLM tokens. |\n| 🔌 **MCP Server Swarms** | Model Context Protocol tools coordinate execution across agents without repeating expensive tool calls. |\n| 📚 **Multi-Agent RAG** | Vector retrieval and document embedding pipelines prevent duplicate index queries and chunk processing across workers. |\n\n---\n\n## ❓ Why Not Redis?\n\n> \"Can't I just build this with Redis?\"\n\nBuilding coordination yourself requires writing distributed locks, Lua scripts, expiration handling, crash recovery, and result serialization.\n\n| Outcome | DIY with Redis | Agent Diaries |\n|---|---|---|\n| **Atomic Task Ownership** | Complex Lua scripts + `SET NX` | `diary.executeOnce()` |\n| **Crash-Safe Locks** | Manual dead-letter queue + cron cleanup | Built-in |\n| **Execution History** | Custom key schemas & TTL management | Built-in |\n| **Duplicate Interception** | Manual checks before every LLM call | Automatic |\n| **Zero Infrastructure Setup** | ❌ Requires Redis running | ✅ Works in-memory out of the box |\n| **Distributed Scale** | Manual backend wiring | 1 line to enable Redis / Postgres |\n\n---\n\n## 📖 API Reference & Examples\n\n### Primary API (Start Here)\n\n`executeOnce()` handles claiming, executing, error handling, result saving, and cache lookup in a single call:\n\n```typescript\nconst result = await diary.executeOnce(taskId, async () => {\n return await expensiveFunction();\n});\n```\n\n---\n\n### Bounded Memory Caching with `LruMemoryProvider`\n\nWrap any cache provider with an LRU layer to enforce strict memory bounds:\n\n```typescript\nimport { StorageManager, MemoryCacheProvider } from \"@agent-diaries/core\";\nimport { LruMemoryProvider } from \"@agent-diaries/core/memory\";\n\n// Initialize with a maximum capacity (or configure via AG_DIARIES_CACHE_SIZE env var)\nconst boundedCache = new LruMemoryProvider(new MemoryCacheProvider(), 1000);\n\nconst storage = new StorageManager({\n cache: boundedCache,\n});\n\n// Cache writes support optional TTL (in milliseconds)\nawait boundedCache.set(\"agent:context:101\", { state: \"active\" }, 60_000);\n```\n\n---\n\n### Binary MessagePack Serialization (`codec`)\n\nFor high-frequency telemetry, binary persistence, or network streaming of complex agent records:\n\n```typescript\nimport { encode, decode } from \"@agent-diaries/core\";\n\n// Fast binary encoding to Uint8Array\nconst tracePayload = {\n workflowId: \"wf-9812\",\n agentId: \"summarizer\",\n timestamp: Date.now(),\n metadata: { tokensUsed: 1420, model: \"gemini-2.5-flash\" },\n};\n\nconst binaryData: Uint8Array = encode(tracePayload);\n\n// Binary decoding back to typed object\nconst decoded = decode(binaryData);\nconsole.log(decoded.workflowId); // \"wf-9812\"\n```\n\n---\n\n### Advanced Manual Lifecycle Control\n\nWhen you need granular control over the lifecycle:\n\n```typescript\n// 1. Manually claim task\nconst claimed = await diary.claimTask(\"task-id\");\n\nif (claimed) {\n try {\n const result = await doWork();\n // 2. Complete task & store result\n await diary.writeTaskResult(\"task-id\", result);\n } catch (err) {\n await diary.failTask(\"task-id\", err.message);\n }\n} else {\n // 3. Retrieve existing result from winning agent\n const cached = await diary.getTaskResult(\"task-id\");\n}\n```\n\n---\n\n### Batch Operations\n\n```typescript\nconst newTasks = await diary.filterNewTasks(taskList);\nconst claimedList = await diary.batchClaimTasks(taskTitles);\n```\n\n---\n\n### Distributed Backends (Redis, PostgreSQL, MongoDB, SQLite)\n\nSwitch from in-memory to Redis or PostgreSQL when scaling to distributed nodes:\n\n```typescript\nimport { StorageManager } from \"@agent-diaries/core\";\nimport { RedisCacheProvider, RedisLockProvider } from \"@agent-diaries/core/redis\";\n\nconst storageManager = new StorageManager({\n cache: new RedisCacheProvider(redisClient),\n lock: new RedisLockProvider(redisClient),\n});\n\nconst diary = new AgentDiary({ agentId: \"distributed-agent\", storageManager });\n```\n\n---\n\n## 📊 Performance Metrics & Monitoring\n\nThe library ships with a lightweight **MetricsEngine** that records key operational counters (workflow successes/failures, cache hits/misses, etc.). You can plug any metrics exporter (Prometheus, StatsD, OpenTelemetry) by subscribing to the `MetricsEngine` events.\n\n### Example: Exporting to Prometheus\n\n```ts\nimport { MetricsEngine } from \"@agent-diaries/core\";\nimport client from \"prom-client\";\n\nconst register = new client.Registry();\nconst workflowSuccess = new client.Counter({\n name: \"agent_diaries_workflow_success_total\",\n help: \"Total successful workflow executions\",\n});\nregister.registerMetric(workflowSuccess);\n\nconst metricsEngine = new MetricsEngine(/* repository instance */);\n\nmetricsEngine.on(\"recorded\", ({ name, value }) => {\n if (name === \"workflow_success_count\") workflowSuccess.inc(value);\n if (name === \"workflow_failure_count\") workflowFailure.inc(value);\n if (name === \"cache_hit_count\") cacheHit.inc(value);\n if (name === \"cache_miss_count\") cacheMiss.inc(value);\n});\n\n// expose HTTP endpoint\nimport http from \"http\";\nhttp.createServer(async (_, res) => {\n res.setHeader(\"Content-Type\", register.contentType);\n res.end(await register.metrics());\n}).listen(9464);\n```\n\n> **Tip:** The `MetricsEngine` emits a generic `recorded` event for every metric you record, letting you map names to your own monitoring schema.\n\n---\n\n## 📚 Advanced Documentation\n\n- [Architecture & Design](./docs/architecture.md) — How in-memory locks, distributed mutexes, and storage facades work\n- [Workflow Coordinator](./docs/advanced.md) — Enterprise multi-step pipeline orchestration\n- [Distributed Tracing](./docs/tracing.md) — OpenTelemetry-style span tracking and metrics\n- [Plugin Framework](./docs/plugins.md) — Custom storage adapters and middleware\n- [Benchmarks & Performance](./BENCHMARKS.md) — Comprehensive latency and throughput methodology\n\n---\n\n## 📄 License\n\nMIT © [swapwarick_n](https://github.com/swapwarick)\n","readmeFilename":"README.md"}