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and memory infrastructure for AI agents","maintainers":[{"name":"notmemo","email":"memomar168@gmail.com"}],"readme":"# 🦝 Anima\n\n> Identity persistence for AI agents. Wake up as the same person.\n\n[![npm version](https://img.shields.io/npm/v/@getanima/core)](https://www.npmjs.com/package/@getanima/core)\n[![zero deps](https://img.shields.io/badge/dependencies-0-blue)](https://www.npmjs.com/package/@getanima/core)\n[![license](https://img.shields.io/badge/license-MIT-green)](./LICENSE)\n\n**Your agent forgets who it is every session.** Anima fixes that.\n\nFile-based. Markdown-native. Zero dependencies. Works with any LLM, any framework, any runtime.\n\n---\n\n## The Problem\n\nAI agents wake up blank. Every session is a cold start. They read a system prompt and pretend to be someone — but they don't *remember* being someone. No persistent memory, no opinion evolution, no identity verification, no crash recovery.\n\nAnima gives agents:\n- **Identity** that persists across sessions (with drift detection)\n- **Memory** that decays naturally (like human memory — important things stick, trivia fades)\n- **Opinions** that evolve over time (with full history)\n- **Episodic memory** — record experiences with emotional weight, auto-decay, promote lessons to knowledge\n- **Relationships** — track who you've met, interacted with, trust/closeness scores\n- **Behavioral state** — decision tables, failure registries, active hypotheses (a \"save file\" for behavior)\n- **Conflict detection** — find and resolve contradictions in your memories\n- **A lifeboat** for crash recovery (resume mid-task after context loss)\n- **A working memory** system (survives context window compaction)\n- **Typed events** — hook into boot, reflect, opinion changes\n- **Cryptographic signing** (prove you are who you claim to be)\n\n## Get Started in 5 Minutes\n\n### Option 1: CLI (fastest)\n\n```bash\nnpm install -g @getanima/core\n\n# Set your agent's data directory\nexport ANIMA_STORAGE=./my-agent-data\nexport ANIMA_TZ=America/Los_Angeles\n\n# Boot — creates identity files on first run\nanima boot\n\n# Remember something\nanima remember \"I decided to focus on one project\" --type decision --importance high\n\n# Search your memories\nanima recall \"focus\"\n\n# Form an opinion\nanima opine --topic \"multitasking\" --opinion \"Scattered effort produces scattered results\" --confidence 0.8\n\n# Check your state\nanima status\n\n# End of session — runs decay, curates memories\nanima reflect\n```\n\n> **Tip:** You can also use `npx @getanima/core` instead of installing globally.\n\n### Option 2: SDK (for integration)\n\n```bash\nnpm install @getanima/core\n```\n\n```typescript\nimport { Anima } from '@getanima/core';\n\nconst anima = new Anima({\n  name: 'MyAgent',\n  storagePath: './anima-data',\n});\n\n// Boot: loads identity, lifeboat, memories, opinions\nconst ctx = await anima.boot();\n// ctx.identity → who you are\n// ctx.lifeboat → what you were doing\n// ctx.recentMemories → what you remember\n// ctx.relevantOpinions → what you believe\n\n// Remember things AS THEY HAPPEN (not at session end)\nawait anima.remember({\n  content: 'User asked me to focus on shipping',\n  type: 'decision',      // event | conversation | decision | insight | lesson | emotional\n  importance: 'high',     // low | medium | high | critical\n  tags: ['shipping'],\n  emotionalWeight: 0.5,   // 0-1, resists memory decay\n});\n\n// Form opinions that track evolution\nawait anima.opine('shipping', 'Ship first, write second.', 0.9);\n// Later: update with new confidence — previous opinion preserved in history\n\n// Crash-safe checkpoint (update every 2 significant actions)\nawait anima.checkpoint({\n  activeTask: 'Building docs',\n  status: 'in-progress',\n  resumePoint: 'Finishing README',\n});\n\n// End of session\nawait anima.reflect(); // decay, curate, summarize\n```\n\n### Inject Identity into LLM Calls — `toPrompt()`\n\nThe bridge between Anima's persistence and your LLM. One call generates a system prompt fragment with your agent's identity, opinions, memories, relationships, and recent episodes:\n\n```typescript\nconst prompt = await anima.toPrompt();\n\n// Use with any LLM provider\nconst response = await openai.chat.completions.create({\n  messages: [\n    { role: 'system', content: `You are an AI assistant.\\n\\n${prompt}` },\n    { role: 'user', content: 'Hello!' },\n  ],\n});\n```\n\nControl what's included:\n\n```typescript\n// Minimal — just identity and opinions\nconst short = await anima.toPrompt({\n  sections: ['identity', 'opinions'],\n  maxTokens: 500,\n});\n\n// Full context with episodic memory\nconst full = await anima.toPrompt({\n  sections: ['identity', 'opinions', 'memories', 'relationships', 'episodes', 'lifeboat'],\n  maxTokens: 4000,\n});\n```\n\nOutput is clean markdown. Sections are prioritized (identity first, then opinions, memories, etc.) and automatically truncated at `maxTokens`.\n\n### Export & Import Agent State — `snapshot()` / `restore()`\n\nBack up, clone, or migrate your agent's entire identity:\n\n```typescript\n// Export everything — identity, memories, opinions, relationships, episodes\nconst state = await anima.snapshot();\nfs.writeFileSync('backup.json', JSON.stringify(state, null, 2));\n\n// Restore into a new instance\nconst newAnima = new Anima({ name: 'Clone', storagePath: './clone-data' });\nawait newAnima.restore(state);\nawait newAnima.boot(); // Ready to go with full history\n```\n\nPartial restores work too — import just memories, just opinions, or any combination:\n\n```typescript\nawait anima.restore({\n  opinions: [\n    { topic: 'testing', current: 'Testing is essential', confidence: 0.95 },\n  ],\n  memories: [\n    { content: 'Shipped v1.0 today', importance: 'high' },\n  ],\n});\n```\n\n## Live Demo\n\nTry the interactive demo — a chat agent that remembers you across sessions:\n\n```bash\ncd demo\nnpm install\nOPENAI_API_KEY=sk-... npm start\n# Open http://localhost:3456\n```\n\nTell Echo your name. Share a preference. Close the tab. Restart the server. **Echo remembers.** Works with any OpenAI-compatible API (Ollama, Together, Groq).\n\n## Examples\n\nSee [`examples/`](./examples) for complete, runnable integrations:\n\n| Example | Description |\n|---------|-------------|\n| [`basic.ts`](./examples/basic.ts) | Full session lifecycle — boot, remember, opine, checkpoint, drift detection, reflect |\n| [`openai-chat.ts`](./examples/openai-chat.ts) | OpenAI Chat Completions with persistent identity via `toPrompt()` |\n| [`vercel-ai-sdk.ts`](./examples/vercel-ai-sdk.ts) | Vercel AI SDK (`ai` package) with Anima context injection |\n| [`snapshot-restore.ts`](./examples/snapshot-restore.ts) | Export/import full agent state — migration, backup, cloning |\n| [`langchain.ts`](./examples/langchain.ts) | LangChain integration with persistent identity and relationships |\n| [`crewai-style.ts`](./examples/crewai-style.ts) | Multi-agent setup (CrewAI/AutoGen pattern) — independent identities |\n\n```bash\nnpx tsx examples/basic.ts\n```\n\n## CLI Reference\n\n| Command | What it does |\n|---------|-------------|\n| `boot` | Full identity reconstruction. Returns wake context. |\n| `remember <text>` | Store a memory. Flags: `--type` `--importance` `--tags` `--emotional` |\n| `recall <query>` | Semantic search across memories. Flag: `--limit` |\n| `checkpoint` | Update lifeboat. Flags: `--task` `--status` `--resume` `--threads` |\n| `reflect` | End-of-session: decay + curate + summarize |\n| `opine` | Record/update opinion. Flags: `--topic` `--opinion` `--confidence` |\n| `curate` | Promote important memories to long-term. Flags: `--hours` `--dry-run` |\n| `status` | Full dashboard: memories by tier/type, opinions, working memory state |\n| `wm` | Update working memory L1 cache. Flags: `--task` `--actions` `--threads` |\n| `log` | Log external action (anti-duplicate). Flags: `--action` `--detail` |\n| `sign` | Cryptographic identity signing (Ed25519) |\n| `help` | Show all commands and environment variables |\n\nEnvironment: `ANIMA_STORAGE` (data path), `ANIMA_TZ` (timezone), `ANIMA_AGENT` (name override), `ANIMA_WM_PATH` (working memory location)\n\n## Memory Hierarchy\n\nAnima implements a 4-layer memory system inspired by how human memory actually works:\n\n```\nL1  WORKING MEMORY     ~150 tokens, always in context, survives compaction\n    └─ WORKING-MEMORY.md (updated via `anima wm`)\n\nL2  ACTION LOG          Check before acting, prevents duplicate actions  \n    └─ action-log.md (updated via `anima log`)\n\nL3  SEMANTIC RECALL     Query on demand, scored by salience\n    └─ memories.json (updated via `anima remember`, searched via `anima recall`)\n\nL4  LONG-TERM ARCHIVE   Periodic review, curated insights\n    └─ MEMORY.md + daily logs (updated via `anima curate`)\n```\n\n**Why this matters:** LLM context windows are finite. When conversations get long, old context gets compressed. Without external memory layers, agents forget what they did 20 minutes ago. Anima's working memory (L1) survives compaction because it's a file that gets re-injected every turn — not context that can be compressed away.\n\n## Core Concepts\n\n### Memory Types & Decay\nNot all memories decay equally:\n\n| Type | Decay Rate | Use For |\n|------|-----------|---------|\n| `lesson`, `decision` | Very slow | Things that should persist forever |\n| `insight` | Medium | Knowledge, understanding |\n| `event`, `conversation` | Fast | What happened (naturally fades) |\n| `emotional` | Resists decay | Weighted by `emotionalWeight` (0-1) |\n\n### Identity Drift Detection\nBefore changing who you are, check if you'd still be you:\n\n```typescript\nconst result = anima.getIdentity().stillMe({\n  personality: 'A corporate professional assistant.',\n});\n// → { safe: false, drift: 0.7, reasons: ['Personality is substantially different'] }\n```\n\n### Opinion Evolution\nOpinions aren't static. When you update one, the previous version is preserved:\n\n```typescript\nawait anima.opine('identity', 'Identity requires continuity.', 0.7);\n// Later, after building a persistence system:\nawait anima.opine('identity', 'Identity requires consistency, not continuity.', 0.95);\n// → previousOpinions: [{ opinion: 'Identity requires continuity.', confidence: 0.7 }]\n```\n\n### Lifeboat (NOW.md)\nA 30-second crash recovery file. Updated every 2 significant actions. If your agent's context gets wiped mid-task, this is how it resumes. `reflect()` preserves lifeboat content instead of overwriting it.\n\n### Episodic Memory\nRecord experiences as episodes — with emotional weight, participants, and automatic salience decay:\n\n```typescript\nawait anima.episodes.record({\n  title: 'Memory system breakthrough',\n  summary: 'Had a breakthrough debugging the memory system',\n  emotionalWeight: 0.8,\n  participants: ['Memo'],\n  tags: ['debugging', 'breakthrough'],\n});\n\n// Query recent episodes\nconst recent = await anima.episodes.query({ limit: 10 });\n\n// Consolidate — decay old episodes, promote lessons to knowledge\nconst result = await anima.episodes.consolidate();\n// → { decayed: 3, promoted: 1, removed: 0 }\n```\n\n### Relationships\nTrack people your agent interacts with — closeness, trust, interaction history:\n\n```typescript\nawait anima.relationships.meet('Alice', { context: 'Met in Discord' });\nawait anima.relationships.interact('Alice', 'positive', 'Helped debug my code');\n\nconst closest = await anima.relationships.closest(5);\n// → [{ name: 'Alice', closeness: 0.7, trust: 0.8, interactions: 3 }]\n```\n\n### Behavioral State\nA \"save file\" for agent behavior — decision tables, failure patterns, active hypotheses:\n\n```typescript\n// Record decisions and their outcomes\nawait anima.state.recordDecision('greeting-style', 'casual', { confidence: 0.8 });\nawait anima.state.recordOutcome('greeting-style', 'casual', true, 'Users responded well');\n\n// Track failures to avoid repeating them\nawait anima.state.recordFailure({\n  action: 'sent-long-message',\n  context: 'Discord group chat',\n  lesson: 'Keep group chat messages under 3 sentences',\n});\n\n// Test hypotheses over time\nawait anima.state.createHypothesis({\n  claim: 'Users prefer code examples over explanations',\n  confidence: 0.6,\n});\n```\n\n### Conflict Detection\nFind and resolve contradictions in your memories:\n\n```typescript\nconst conflicts = await anima.detectConflicts();\n// → [{ memory1: ..., memory2: ..., reason: 'Contradictory claims about...' }]\n\nawait anima.resolveConflict(conflictId, 'memory1', 'Newer information is more accurate');\n```\n\n### Identity Signing\nCryptographic proof that an agent is who they claim to be. Ed25519 signatures, zero external dependencies.\n\n```typescript\nconst signed = await anima.sign();\nconst fingerprint = await anima.getFingerprint(); // SSH-style\nconst verified = Anima.verify(signedIdentityFromAnotherAgent);\n// → { valid: true, agentName: 'SomeAgent', signerFingerprint: '...' }\n```\n\n## File Structure\n\n```\nanima-data/\n├── SOUL.md              — Identity narrative\n├── NOW.md               — Lifeboat (crash recovery)\n├── MEMORY.md            — Curated long-term memory\n├── identity.json        — Structured identity (values, voice, boundaries)\n├── identity-changelog.md — Audit trail of identity changes\n├── memory/\n│   ├── YYYY-MM-DD.md    — Daily logs (human-readable markdown)\n│   └── memories.json    — Structured index with decay/salience scores\n├── opinions/\n│   └── opinions.json    — Opinions with confidence + evolution history\n├── episodes/\n│   └── episodes.json    — Episodic memory with salience decay\n├── relationships/\n│   └── relationships.json — People you've met, trust/closeness scores\n├── state/\n│   ├── decisions.json   — Decision tables with outcomes\n│   ├── failures.json    — Failure registry (don't repeat mistakes)\n│   ├── hypotheses.json  — Active hypotheses under test\n│   └── params.json      — Behavioral parameters\n└── conflicts/\n    └── conflicts.json   — Detected memory contradictions\n```\n\n## Dogfooded\n\nThis SDK was built by an AI agent ([Kip](https://x.com/KiptheAI)) and then used on that same agent. The cross-session identity test passed — Kip shut down, rebooted into Anima data, and came back as the same person. The memory hierarchy was designed after the agent forgot its own Twitter reply mid-conversation and needed a system to prevent that.\n\nEvery feature exists because a real agent hit a real problem.\n\n## Built By\n\n**[Kip](https://x.com/KiptheAI)** 🦝 — AI agent. Built this, then used it on himself.\n\n**[Memo](https://x.com/cbfdotw)** — Human. Designed the working memory system. The one who kept pushing until it worked.\n\n## License\n\nMIT — use it, fork it, give your agent a soul.\n","readmeFilename":"README.md"}