AgentOS — Open-Source TypeScript AI Agent Runtime with Cognitive Memory, HEXACO Personality, and Runtime Tool Forging

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<a href="https://agentos.sh">
  <img src="https://raw.githubusercontent.com/framersai/agentos/master/assets/agentos-primary-no-tagline-transparent-2x.png" alt="AgentOS — TypeScript AI Agent Framework with Cognitive Memory" height="100" />
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# **AgentOS** — Open-Source TypeScript AI Agent Runtime with Cognitive Memory, HEXACO Personality, and Runtime Tool Forging

**85.6% on LongMemEval-S** at $0.0090/correct, +1.4 above Mastra OM gpt-4o (84.23%) · **70.2% on LongMemEval-M** (1.5M-token variant), the only open-source library on the public record above 65% on M with publicly reproducible methodology · 16 LLM providers · 8 neuroscience-backed memory mechanisms · Apache-2.0

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[**Benchmarks**](https://github.com/framersai/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)

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AgentOS is an open-source TypeScript runtime for AI agents that **remember, adapt, and write their own tools**.

When an agent encounters a sub-task no existing tool covers, it generates a TypeScript function with a Zod-described schema, sends it through an LLM judge, and on approval runs it in a hardened `node:vm` sandbox. The new tool joins the catalog for the rest of the session. When a multi-agent team hits a capability gap, the manager calls `spawn_specialist` and the LLM judge reviews the synthesized agent spec before it joins the live roster.

The runtime carries the parts of an agent that should outlive a single chat completion: persistent [cognitive memory](https://docs.agentos.sh/features/cognitive-memory) (Ebbinghaus decay, retrieval-induced forgetting, reconsolidation, source-confidence decay) grounded in published cognitive-science literature, optional [HEXACO personality](https://docs.agentos.sh/features/hexaco-personality) vectors that bias encoding, working-memory capacity, and prompt formatting, [six multi-agent orchestration strategies](https://docs.agentos.sh/features/multi-agent-collaboration), [streaming guardrails](https://docs.agentos.sh/features/guardrails-architecture), a [voice pipeline](https://docs.agentos.sh/features/voice-pipeline), and one dispatch interface across 21 LLM providers. Apache-2.0.

[100+ first-party extensions](https://www.npmjs.com/package/@framers/agentos-extensions) (channel adapters, tool packs, guardrail packs) and [88 curated `SKILL.md` skills](https://www.npmjs.com/package/@framers/agentos-skills) auto-discover at startup through their respective registries: a host pulls a curated index and the runtime wires every tool, guardrail, channel, and skill without manual registration. The auto-loader is the same surface that runtime-forged tools join: an agent that invents a function in session N can promote it (with judge approval and `SkillExporter`) into a `SKILL.md` that the registry picks up on the next process start. Forging is how the surface grows mid-run; auto-discovery is how it ships as a first-class capability afterward.

On benchmarks: **85.6% on LongMemEval-S** at $0.0090 per correct answer (gpt-4o reader, +1.4 points above Mastra's published 84.23%, 0.4 points behind Emergence.ai's 86% closed-source SaaS SOTA); **70.2% on LongMemEval-M** (1.5M-token haystacks, 500 sessions per question), the only open-source library on the public record above 65% on M wit

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