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open-source TypeScript framework for autonomous AI agents. Unified graph orchestration, cognitive memory, runtime tool forging, multi-tier guardrails, voice pipeline, and 11 LLM providers.","maintainers":[{"name":"manicteam","email":"team@manic.agency"},{"name":"jdunnfive","email":"johnnyddunn@gmail.com"}],"readme":"<div align=\"center\">\n\n<a href=\"https://agentos.sh\">\n  <img src=\"https://raw.githubusercontent.com/framerslab/agentos/master/assets/agentos-primary-no-tagline-transparent-2x.png\" alt=\"AgentOS: TypeScript AI Agent Framework with Cognitive Memory\" height=\"100\" />\n</a>\n\n<br />\n\n# **AgentOS** · TypeScript AI Agent Framework\n\n**Agents that remember, forge their own tools, and survive long-running sessions.** Persistent cognitive memory, optional HEXACO personality, multi-agent orchestration, and one dispatch interface across 11 LLM providers. Apache-2.0.\n\n[![npm](https://img.shields.io/npm/v/@framers/agentos?style=flat-square&logo=npm&color=cb3837)](https://www.npmjs.com/package/@framers/agentos)\n[![CI](https://img.shields.io/github/actions/workflow/status/framerslab/agentos/ci.yml?branch=master&style=flat-square&logo=github&label=CI)](https://github.com/framerslab/agentos/actions/workflows/ci.yml)\n[![tests](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/framerslab/agentos/master/.github/badges/tests.json&style=flat-square&logo=vitest&logoColor=white)](https://github.com/framerslab/agentos/actions/workflows/ci.yml)\n[![codecov](https://codecov.io/gh/framerslab/agentos/graph/badge.svg)](https://codecov.io/gh/framerslab/agentos)\n[![TypeScript](https://img.shields.io/badge/TypeScript-5.4+-3178c6?style=flat-square&logo=typescript&logoColor=white)](https://www.typescriptlang.org/)\n[![License](https://img.shields.io/badge/License-Apache_2.0-blue?style=flat-square)](https://opensource.org/licenses/Apache-2.0)\n[![NVIDIA Inception](https://img.shields.io/badge/NVIDIA_Inception-Member-76B900?style=flat-square&logo=nvidia&logoColor=white)](https://www.nvidia.com/en-us/startups/)\n[![LongMemEval-S](https://img.shields.io/badge/LongMemEval--S-85.6%25-2ea043?style=flat-square)](https://docs.agentos.sh/blog/2026/04/27/longmemeval-s-83-with-semantic-embedder)\n[![LongMemEval-M](https://img.shields.io/badge/LongMemEval--M-70.2%25-2ea043?style=flat-square)](https://docs.agentos.sh/blog/2026/04/29/longmemeval-m-70-with-topk5)\n[![agentos-bench](https://img.shields.io/badge/bench-public-blue?style=flat-square)](https://github.com/framerslab/agentos-bench)\n[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?style=flat-square&logo=discord)](https://wilds.ai/discord)\n\n[**Benchmarks**](https://github.com/framerslab/agentos-bench/blob/master/results/LEADERBOARD.md) * [Website](https://agentos.sh) * [Docs](https://docs.agentos.sh) * [npm](https://www.npmjs.com/package/@framers/agentos) * [Discord](https://wilds.ai/discord) * [Blog](https://docs.agentos.sh/blog)\n\n</div>\n\n---\n\nAgentOS is an open-source TypeScript framework for AI agents that **remember, adapt, and write their own tools**.\n\n- **Top open-source memory benchmarks:** [85.6% on LongMemEval-S](https://github.com/framerslab/agentos-bench/blob/master/results/LEADERBOARD.md) at $0.0090/correct (gpt-4o), and 70.2% on LongMemEval-M, the only open-source library above 65% on M with reproducible methodology.\n- **Runtime tool forging.** An agent writes a TypeScript function with a Zod schema, an LLM judge approves it, and it runs in a hardened `node:vm` sandbox before joining the catalog for the rest of the session.\n- **Persistent [cognitive memory](https://docs.agentos.sh/features/cognitive-memory)** with 8 neuroscience-backed mechanisms: Ebbinghaus decay, retrieval-induced forgetting, reconsolidation, source-confidence decay.\n- **Optional [HEXACO personality](https://docs.agentos.sh/features/hexaco-personality)**, [6 orchestration strategies](https://docs.agentos.sh/features/agency-collaboration), [guardrails](https://docs.agentos.sh/features/guardrails-architecture), and [voice](https://docs.agentos.sh/features/voice-pipeline) across **11 LLM providers**; 100+ extensions and 88 skills auto-load at startup.\n\n---\n\n<div align=\"center\">\n\n<picture>\n  <source srcset=\"https://raw.githubusercontent.com/framerslab/agentos/master/assets/agentos-forge-demo.webp\" type=\"image/webp\" />\n  <img src=\"https://raw.githubusercontent.com/framerslab/agentos/master/assets/agentos-forge-demo.gif\"\n       alt=\"Three AgentOS agents with distinct HEXACO personalities collaborate on a code review, forge a new tool at runtime once they hit a gap their static toolkit can't cover, the LLM judge approves the spec, and all three invoke it on the next turn.\"\n       width=\"900\" />\n</picture>\n\n<sub>Runtime tool forging + multi-agent collaboration. Reproduce with <code>node <a href=\"https://github.com/framerslab/agentos/blob/master/examples/emergent-hierarchical-spawning.mjs\">examples/emergent-hierarchical-spawning.mjs</a></code>.</sub>\n\n</div>\n\n---\n\n## Install\n\n```bash\nnpm install @framers/agentos\n```\n\n```typescript\nimport { agent } from '@framers/agentos';\n\nconst tutor = agent({\n  provider: 'anthropic',                          // resolves to claude-sonnet-4-6 (provider default)\n  // model: 'claude-opus-4-8',                    // pin a specific model to override the default\n  instructions: 'You are a patient CS tutor.',\n  personality: { openness: 0.9, conscientiousness: 0.95 },\n  memory: { types: ['episodic', 'semantic'], working: { enabled: true } },\n});\n\n// Provider auto-detected from env when `provider` is omitted.\n\nconst session = tutor.session('student-1');\nawait session.send('Explain recursion with an analogy.');\nawait session.send('Can you expand on that?'); // remembers context\n```\n\n[Full quickstart](https://docs.agentos.sh/getting-started) * [Examples cookbook](https://docs.agentos.sh/getting-started/examples) * [API reference](https://docs.agentos.sh/api)\n\n**Sessions.** A session keeps the whole conversation whether memory is on or off: each `send()` records its tool calls, their results and the model's signed thinking, and every later request replays them. `stream()` records its turn as the prompt and the final text. History is capped at about 120K tokens by default. Set `history: false` for a stateless session, set `history: { maxTokens }` to change the cap, and call `reseed()` to replace the history with a shorter set of messages you build yourself. `close()` ends a session and frees its history: the next `session(id)` with that id starts empty, and `agent.usage(id)` still reports what the id spent.\n\n```ts\nconst stateless = agent({ model, memory: false, history: false }).session('job-1');\nconst bounded = agent({ model, history: { maxTokens: 60_000 } }).session('job-2');\nbounded.reseed([{ role: 'user', content: 'compact resume snapshot' }]);\n```\n\n---\n\n## Emergent Design\n\nThree things accumulate across a session and compose into behavior: **memory** (what was said, decided, retrieved), the **tool surface** (which grows when an agent forges a tool the judge approves), and an optional **HEXACO personality** vector that biases retrieval, routing, and decisions. Each is configurable and observable.\n\n**Runtime tool forging.** When no tool covers a sub-task, the agent writes a TypeScript function with a Zod schema; a separate LLM judge approves it; it runs in a hardened `node:vm` sandbox (5s wall clock, no `eval`/`require`/`process`), then joins a discoverable index for the rest of the session. First forge costs full tokens; reuse costs tens. Promoted tools export as `SKILL.md` skills. [Emergent capabilities ->](https://docs.agentos.sh/features/emergent-capabilities)\n\n**HEXACO personality (optional).** Off by default; the runtime behaves identically without it. When supplied, the kernel weights retrieval, specialist routing, and tool selection by trait values, so the same prompt and tools yield measurably different decision sequences. It lives in the kernel, not the prompt, so it persists under context pressure. [HEXACO docs ->](https://docs.agentos.sh/features/hexaco-personality)\n\n**Soul files.** Identity, voice, hard limits, and HEXACO scores can live in a `SOUL.md` workspace. Its `memory/` directory is a markdown wiki (an `index.md` catalog plus `entities/`, `concepts/`, `log/` pages with `[[wikilinks]]`) that *is* the agent's long-term memory: markdown is the source of truth, the vector/graph index is rebuilt from it, and [`souledAgent()`](https://docs.agentos.sh/getting-started/high-level-api) wires it end to end. [Soul Files ->](https://docs.agentos.sh/features/soul-files)\n\n```ts\nimport { souledAgent } from '@framers/agentos';\n\nconst aria = await souledAgent({ provider: 'anthropic', soul: '~/.agentos/agents/aria' });\n```\n\n---\n\n## Generalized Mind Instances (GMIs)\n\nOn the full runtime, every session is served by a **GMI**: a persistent agent with its own persona, mood, conversation history and reasoning trace. `agent()` is the lightweight helper; it calls the model with a prompt and keeps session history. A GMI runs a turn loop around the same model, tools, guardrails and cognitive memory:\n\n- **Sentiment → metaprompts.** When a persona enables sentiment tracking, every user turn is scored; sustained frustration or confusion fires recovery metaprompts, and a self-reflection metaprompt re-reads the GMI's mood and task context from evidence.\n- **Mood-weighted memory.** With cognitive memory attached, each exchange is encoded with the GMI's current mood and recalled with emotional congruence in the score.\n- **Self-modification tools.** With `selfImprovement.enabled`, the runtime registers `adapt_personality`, `manage_skills`, `create_workflow` and `self_evaluate`; `adapt_personality` changes the running GMI's traits within bounds, and a mutation store records the changes when a storage adapter and `persistWithDecay` are configured.\n- **A reasoning trace** of the last 500 decisions by default (`reasoningTraceConfig` on the persona or the runtime's default), and persona overlays per session.\n\n```ts\nimport { AgentOS, AgentOSResponseChunkType, BUILT_IN_PERSONAS } from '@framers/agentos';\n\n// AgentOS.create() reads persona files from ./personas by default. Personas can\n// also be given inline, as parsed JSON or code-built objects; here, the five the\n// package ships. A custom loader covers any other source.\nconst agentos = await AgentOS.create({ personas: BUILT_IN_PERSONAS });\nfor await (const chunk of agentos.processRequest({\n  userId: 'user-42', sessionId: 'research-q1', selectedPersonaId: 'v_researcher',\n  textInput: 'Summarize the open incidents from this week.',\n})) {\n  if (chunk.type === AgentOSResponseChunkType.TEXT_DELTA) process.stdout.write(chunk.textDelta);\n}\n```\n\n[What a GMI adds over a plain agent →](https://docs.agentos.sh/architecture/gmi)\n\n---\n\n## Memory Benchmarks\n\n`gpt-4o` reader, `gpt-4o-2024-08-06` judge, full N=500, single-CLI reproduction with bootstrap 95% CIs and per-benchmark judge-FPR probes.\n\n- **LongMemEval-S: 85.6%** at $0.0090/correct, 3,558 ms p50: +1.4 points over Mastra OM gpt-4o (84.23%), 0.4 behind Emergence.ai's closed-source 86%. The highest publicly reproducible open-source number at `gpt-4o`.\n- **LongMemEval-M: 70.2%** (1.5M-token haystacks, 500 sessions): the only open-source library above 65% on M with reproducible methodology.\n\n[Full leaderboard ->](https://github.com/framerslab/agentos-bench/blob/master/results/LEADERBOARD.md) * [Transparency audit ->](https://agentos.sh/en/blog/memory-benchmark-transparency-audit/) * [LongMemEval paper](https://arxiv.org/abs/2410.10813) (Wu et al., ICLR 2025)\n\n---\n\n## Why AgentOS\n\n| vs. | AgentOS differentiator |\n|---|---|\n| **LangChain / LangGraph** | Cognitive memory ([8 neuroscience-backed mechanisms](https://docs.agentos.sh/features/cognitive-memory)), HEXACO personality, runtime tool forging |\n| **Vercel AI SDK** | Multi-agent teams (6 strategies), 7 vector backends, [guardrails](https://docs.agentos.sh/features/guardrails-architecture), voice/telephony, [zero-config prompt caching](https://docs.agentos.sh/features/prompt-caching) |\n| **CrewAI / Mastra** | Unified orchestration (DAGs + graphs + missions), personality-driven routing, **published reproducible numbers on LongMemEval-S (85.6%) and LongMemEval-M (70.2%) with full methodology disclosure** |\n\n[Full framework comparison ->](https://docs.agentos.sh/blog/2026/02/20/agentos-vs-langgraph-vs-crewai)\n\n---\n\n## Key Features\n\n| Category | Highlights |\n|---|---|\n| **LLM Providers** | 11 (9 API-key + 2 local CLI): OpenAI, Anthropic, Gemini, Groq, Ollama, OpenRouter, Together, Mistral, xAI, Claude CLI, Gemini CLI. Plus image/video/audio generation providers. |\n| **Prompt Caching** | Zero config on every provider: automatic Anthropic breakpoints incl. multi-turn history (direct + OpenRouter) * OpenAI cache-key routing * normalized cache usage + leak detection * per-call TTL/opt-out * [guide](https://docs.agentos.sh/features/prompt-caching) |\n| **Cognitive Memory** | 8 mechanisms: reconsolidation, retrieval-induced forgetting, involuntary recall, FOK, gist extraction, schema encoding, source decay, emotion regulation |\n| **HEXACO Personality** | 6 traits modulate memory, retrieval bias, response style |\n| **GMI Runtime** | Per-session persona, mood and reasoning trace * sentiment-triggered metaprompts (opt-in per persona) * mood-weighted memory bridge * bounded `adapt_personality` trait changes (opt-in) |\n| **RAG Pipeline** | 7 vector backends * 4 retrieval strategies * GraphRAG * HyDE * Cohere rerank-v3.5 |\n| **Multi-Agent Teams** | 6 coordination strategies * manager delegation and specialist spawning * panel quorum on the parallel strategy * HITL approval gates |\n| **Orchestration** | `workflow()` DAGs * `AgentGraph` cycles * `mission()` goal-driven planning * checkpointing |\n| **Guardrails** | 5 security tiers * 6 packs (PII, ML classifiers, topicality, code safety, grounding, content policy) |\n| **Emergent Capabilities** | Runtime tool forging * 4 self-improvement tools * tiered promotion * skill export |\n| **Voice & Telephony** | ElevenLabs, Deepgram, Whisper * Twilio, Telnyx, Plivo |\n| **Channels** | 37 platform adapters (Telegram, Discord, Slack, WhatsApp, webchat, ...) |\n| **Observability** | OpenTelemetry * usage ledger * cost guard * circuit breaker |\n\n---\n\n## Multi-Agent in 6 Lines\n\n```typescript\nimport { agency } from '@framers/agentos';\n\nconst team = agency({\n  strategy: 'graph',\n  agents: {\n    researcher: { provider: 'anthropic', instructions: 'Find relevant facts.' },                            // -> claude-sonnet-4-6\n    writer:     { provider: 'openai',    instructions: 'Summarize clearly.', dependsOn: ['researcher'] },   // -> gpt-4o\n    reviewer:   { provider: 'gemini',    instructions: 'Check accuracy.',    dependsOn: ['writer'] },       // -> gemini-2.5-flash\n  },\n});\n\nconst result = await team.generate('Compare TCP vs UDP for game networking.');\n```\n\nStrategies: `sequential`, `parallel`, `debate`, `review-loop`, `hierarchical`, `graph`. With `hierarchical` + `emergent: { enabled: true }`, the manager forges new sub-agents at runtime. Every roster agent can set its own `provider`, `model`, `apiKey` and `effort`; a `parallel` agency can require a provider quorum (`quorum: { minProviders: 2 }`) before it synthesizes. [Multi-agent docs ->](https://docs.agentos.sh/features/agency-api)\n\n---\n\n## Ecosystem\n\n| Package | Role |\n|---|---|\n| [`@framers/agentos`](https://www.npmjs.com/package/@framers/agentos) | Core runtime: agents, cognitive memory, orchestration, guardrails, voice, 11 LLM providers. Apache-2.0. |\n| [`@framers/agentos-extensions`](https://www.npmjs.com/package/@framers/agentos-extensions) | 100+ first-party extensions: channel adapters, tool packs, integrations, guardrail packs. |\n| [`@framers/agentos-extensions-registry`](https://www.npmjs.com/package/@framers/agentos-extensions-registry) | Discovery + auto-loader for the extensions catalog. |\n| [`@framers/agentos-skills`](https://www.npmjs.com/package/@framers/agentos-skills) | 88 curated `SKILL.md` skills. |\n| [`@framers/agentos-skills-registry`](https://www.npmjs.com/package/@framers/agentos-skills-registry) | Discovery + auto-loader for skills; where promoted forged tools land. |\n| [`@framers/agentos-bench`](https://github.com/framerslab/agentos-bench) | Open benchmark harness: bootstrap 95% CIs, judge-FPR probes, per-case run JSONs. MIT. |\n| [`@framers/sql-storage-adapter`](https://www.npmjs.com/package/@framers/sql-storage-adapter) | Cross-platform SQL persistence: SQLite, Postgres, IndexedDB, Capacitor SQLite. |\n| [`paracosm`](https://www.npmjs.com/package/paracosm) | AI agent swarm simulation on AgentOS. [Live demo](https://paracosm.agentos.sh/sim). |\n| [`wunderland`](https://www.npmjs.com/package/wunderland) | Batteries-included CLI + daemon over the AgentOS registries (preview). Apache-2.0. |\n\nExtensions and skills auto-load at startup. [Extensions architecture ->](https://docs.agentos.sh/architecture/extension-loading)\n\n---\n\n## Configure API Keys\n\nThree layers, highest priority first: inline `apiKey` on the call, a module-level `setDefaultProvider()` at boot, or environment-variable auto-detection (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, and the rest, resolved in priority order and reorderable with `setProviderPriority([...])`). Comma-separated keys auto-rotate on quota.\n\n[Full credential resolution + default models per provider ->](https://docs.agentos.sh/architecture/llm-providers)\n\n---\n\n## API Surfaces\n\n- **`agent()`**: lightweight stateful agent. Prompts, sessions, personality, hooks, tools, memory.\n- **`agency()`**: multi-agent teams built from `agent()` members, with emergent tooling, guardrails, RAG, voice and HITL. It wires no channels; channel adapters run on the full runtime or with `ChannelRouter`.\n- **`generateText()` / `streamText()` / `generateObject()` / `generateImage()` / `generateVideo()` / `generateMusic()` / `performOCR()` / `embedText()`**: low-level multi-modal helpers with native tool calling.\n- **`workflow()` / `AgentGraph` / `mission()`**: three orchestration authoring APIs over one graph runtime.\n\nProvider fallback is on by default for `generateText()`, `streamText()`, `agent()` and `agency()`: when a call fails with a retryable error, it is retried on the other providers whose keys are in the environment. Pass `fallbackProviders: []` to turn it off, or a list to set the chain yourself. The GMIs of the full runtime (`processRequest()`) call their provider without a fallback chain.\n\n[Full API reference ->](https://docs.agentos.sh/api) * [High-Level API guide ->](https://docs.agentos.sh/getting-started/high-level-api)\n\n---\n\n## Documentation & Community\n\n- **[Benchmarks](https://github.com/framerslab/agentos-bench/blob/master/results/LEADERBOARD.md)**: benchmark tables, 95% confidence intervals, methodology audit\n- **[Architecture](https://docs.agentos.sh/architecture/system-architecture)**: system design, layer breakdown\n- **[Cognitive Memory](https://docs.agentos.sh/features/cognitive-memory)**: 8 mechanisms with 30+ APA citations\n- **[RAG Configuration](https://docs.agentos.sh/features/rag-memory)**: vector stores, embeddings, sources\n- **[Guardrails](https://docs.agentos.sh/features/guardrails-architecture)**: 5 tiers, 6 packs\n- **[Voice Pipeline](https://docs.agentos.sh/features/voice-pipeline)**: TTS, STT, telephony\n- **[Blog](https://docs.agentos.sh/blog)**: engineering posts, benchmark publications, transparency audits\n- **[Discord](https://wilds.ai/discord)** * **[GitHub Issues](https://github.com/framerslab/agentos/issues)** * **[Wilds.ai](https://wilds.ai)** (AI game worlds powered by AgentOS)\n\n---\n\n## Contributing\n\n```bash\ngit clone https://github.com/framerslab/agentos.git && cd agentos\npnpm install && pnpm build && pnpm test\n```\n\nWe use [Conventional Commits](https://www.conventionalcommits.org/). Project guides:\n\n| Guide | What |\n|---|---|\n| [Contributing](https://github.com/framerslab/agentos/blob/master/CONTRIBUTING.md) | Development setup, commit and pull request rules, review threads, contribution licensing |\n| [Adding an LLM provider](https://github.com/framerslab/agentos/blob/master/docs/contributing/new-provider.md) | Provider interface, acceptance checklist, sponsorship and disclosure |\n| [Release guide](https://github.com/framerslab/agentos/blob/master/docs/getting-started/RELEASING.md) | How a merge to master becomes an npm release |\n| [Agent instructions](https://github.com/framerslab/agentos/blob/master/AGENTS.md) | Commands and conventions for coding agents |\n| [Maintainers](https://github.com/framerslab/agentos/blob/master/MAINTAINERS.md) | Who reviews and merges changes |\n| [Code of Conduct](https://github.com/framerslab/agentos/blob/master/.github/CODE_OF_CONDUCT.md) | Community standards |\n| [Security Policy](https://github.com/framerslab/agentos/blob/master/.github/SECURITY.md) | Reporting vulnerabilities privately |\n| [Support](https://github.com/framerslab/agentos/blob/master/SUPPORT.md) | Where to get help |\n| [Sponsors](https://github.com/framerslab/agentos/blob/master/SPONSORS.md) | Funding, sponsor placement and disclosure |\n\n---\n\n## Startups & Partnerships\n\nAgentOS is Apache-2.0 and free. We integrate any quality provider on technical merit, and partners and sponsors are featured in the README and docs, labeled as such. Companies engage through partner startup programs, sponsorship, or a provider integration. See [SPONSORS.md](./SPONSORS.md).\n\n### Programs & partners\n\n| Partner | Type | Provides | Since |\n|:-:|:--|:--|:-:|\n| [![Deepgram](https://img.shields.io/badge/Deepgram-13EF93?style=for-the-badge&logo=deepgram&logoColor=000000)](https://deepgram.com/startups) | Startup Program | Speech-to-text + text-to-speech credits, go-to-market | 2026 |\n\n### Ways to engage\n\n| Track | What it is | Where |\n|:--|:--|:--|\n| **Sponsor** | Fund development. Disclosed logo placement + release-notes credit. | [SPONSORS.md](./SPONSORS.md) |\n| **Provider integration** | Ship your model or API as a supported provider. Free, on technical merit. | [Provider guide](./docs/contributing/new-provider.md) |\n\nInterested? Email team@frame.dev.\n\n---\n\n## License\n\n[Apache 2.0](./LICENSE)\n\n<div align=\"center\">\n\n<a href=\"https://agentos.sh\">\n  <img src=\"https://raw.githubusercontent.com/framerslab/agentos/master/assets/agentos-primary-transparent-2x.png\" alt=\"AgentOS\" height=\"40\" />\n</a>\n&nbsp;&nbsp;&nbsp;\n<a href=\"https://frame.dev\">\n  <img src=\"https://raw.githubusercontent.com/framerslab/agentos/master/assets/frame-logo-green-no-tagline.svg\" alt=\"Frame.dev\" height=\"40\" />\n</a>\n\n**Built by [Frame](https://frame.dev) * [Wilds.ai](https://wilds.ai)**\n\n</div>\n","readmeFilename":"README.md"}