AgentOS · TypeScript AI Agent Framework

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.

Benchmarks * Website * Docs * npm * Discord * Blog

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

• Top open-source memory benchmarks: 85.6% on LongMemEval-S at $0.0090/correct (gpt-4o), and 70.2% on LongMemEval-M, the only open-source library above 65% on M with reproducible methodology.
• 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.
• Persistent cognitive memory with 8 neuroscience-backed mechanisms: Ebbinghaus decay, retrieval-induced forgetting, reconsolidation, source-confidence decay.
• Optional HEXACO personality, 6 orchestration strategies, guardrails, and voice across 11 LLM providers; 100+ extensions and 88 skills auto-load at startup.

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Runtime tool forging + multi-agent collaboration. Reproduce with node examples/emergent-hierarchical-spawning.mjs.

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Install

npm install @framers/agentos

import { agent } from '@framers/agentos';

const tutor = agent({
  provider: 'anthropic',                          // resolves to claude-sonnet-4-6 (provider default)
  // model: 'claude-opus-4-8',                    // pin a specific model to override the default
  instructions: 'You are a patient CS tutor.',
  personality: { openness: 0.9, conscientiousness: 0.95 },
  memory: { types: ['episodic', 'semantic'], working: { enabled: true } },
});

// Provider auto-detected from env when `provider` is omitted.

const session = tutor.session('student-1');
await session.send('Explain recursion with an analogy.');
await session.send('Can you expand on that?'); // remembers context

Full quickstart * Examples cookbook * API reference

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Emergent Design

Three 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.

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 ->

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 ->

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() wires it end to end. Soul Files ->

import { souledAgent } from '@framers/agentos';

const aria = await souledAgent({ provider: 'anthropic', soul: '~/.agentos/agents/aria' });

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Memory Benchmarks

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.

• 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.
• LongMemEval-M: 70.2% (1.5M-token haystacks, 500 sessions): the only open-source library above 65% on M with reproducible methodology.

Full leaderboard -> * Transparency audit -> * LongMemEval paper (Wu et al., ICLR 2025)

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Why AgentOS

vs.	AgentOS differentiator
LangChain / LangGraph	Cognitive memory (8 neuroscience-backed mechanisms), HEXACO personality, runtime tool forging
Vercel AI SDK	Multi-agent teams (6 strategies), 7 vector backends, guardrails, voice/telephony
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

Full framework comparison ->

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Key Features

Category	Highlights
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.
Cognitive Memory	8 mechanisms: reconsolidation, retrieval-induced forgetting, involuntary recall, FOK, gist extraction, schema encoding, source decay, emotion regulation
HEXACO Personality	6 traits modulate memory, retrieval bias, response style
RAG Pipeline	7 vector backends * 4 retrieval strategies * GraphRAG * HyDE * Cohere rerank-v3.5
Multi-Agent Teams	6 coordination strategies * shared memory * inter-agent messaging * HITL gates
Orchestration	workflow() DAGs * AgentGraph cycles * mission() goal-driven planning * checkpointing
Guardrails	5 security tiers * 6 packs (PII, ML classifiers, topicality, code safety, grounding, content policy)
Emergent Capabilities	Runtime tool forging * 4 self-improvement tools * tiered promotion * skill export
Voice & Telephony	ElevenLabs, Deepgram, Whisper * Twilio, Telnyx, Plivo
Channels	37 platform adapters (Telegram, Discord, Slack, WhatsApp, webchat, ...)
Observability	OpenTelemetry * usage ledger * cost guard * circuit breaker

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Multi-Agent in 6 Lines

import { agency } from '@framers/agentos';

const team = agency({
  strategy: 'graph',
  agents: {
    researcher: { provider: 'anthropic', instructions: 'Find relevant facts.' },                            // -> claude-sonnet-4-6
    writer:     { provider: 'openai',    instructions: 'Summarize clearly.', dependsOn: ['researcher'] },   // -> gpt-4o
    reviewer:   { provider: 'gemini',    instructions: 'Check accuracy.',    dependsOn: ['writer'] },       // -> gemini-2.5-flash
  },
});

const result = await team.generate('Compare TCP vs UDP for game networking.');

Strategies: sequential, parallel, debate, review-loop, hierarchical, graph. With hierarchical + emergent: { enabled: true }, the manager forges new sub-agents at runtime. Multi-agent docs ->

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Ecosystem

Package	Role
@framers/agentos	Core runtime: agents, cognitive memory, orchestration, guardrails, voice, 11 LLM providers. Apache-2.0.
@framers/agentos-extensions	100+ first-party extensions: channel adapters, tool packs, integrations, guardrail packs.
@framers/agentos-extensions-registry	Discovery + auto-loader for the extensions catalog.
@framers/agentos-skills	88 curated SKILL.md skills.
[@framers/agentos-skills-registry](h