A headless, multi-provider AI agent harness — embed it as a library, script it from the CLI, or run it as a stateful agent server.
A headless, multi-provider AI agent harness. One install gives you the `cortex` CLI and HTTP server: npm i -g nexus-cortex.
2026-11-14
ดาว
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เครื่องมือ
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อัปไทม์
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Nexus Cortex is the engine underneath the agent — not another interactive terminal app, but the harness you build on. One orchestrator drives many models across many providers through a pluggable adapter layer, with sub-agents, MCP, sandboxed artifacts, a permission engine, context management, and append-only session history built in.
Run it three ways: as a library (import { CortexOrchestrator }), as a headless CLI (cortex "…"), or as a stateful HTTP agent server.
Release 1 is the headless harness — core, executors, server, cli. The interactive React/Ink terminal UIs (@nexus-cortex/tui) land in Release 2.
Driving it from your own agent harness? Cortex is built to run as an autonomous sub-agent that another agent configures and prompts headlessly — often more precisely than a human at a UI can. The bundled cortex-subagent skill is the operational formula: the three headless entry points, the config levers worth setting, how to write a self-contained autonomous brief, budgets, and orchestrator-side verification.
Quick Start
npm install -g nexus-cortex
cortex setup
cortex setup is the first-run wizard: pick the providers you intend to use, paste their API keys, then choose the action
model (does the work), a cheap helper (compaction, summaries), the stronger mentor (plans and judges finishes), the vision
hand-off and the web-tools backend. It writes everything to ~/.cortex/.env, then sends one tiny request to each chosen model so
you know it works before you start. One DeepSeek key is enough: the defaults run every role on deepseek-flash.
Running cortex before setup in a terminal starts the wizard for you. Prefer to script it? One provider key is all it takes:
cortex config set DEEPSEEK_API_KEY sk-... # writes ~/.cortex/.env (or export it — the environment wins)
Then just talk to it — the server auto-starts on first use, and headless runs auto-approve tools:
cortex "What is this project?" # multi-turn; the session persists across calls
cortex agent --cwd ./my-project "add a --version flag and run the tests" # one-shot autonomous run (alias: cortex run)
What you get out of the box: every role on deepseek-flash (served by DeepSeek-V4.1-Flash-1017), the standard lever profile
(the harness's proven failure-path and steering levers, interactive and headless; CORTEX_PROFILE=off restores the bare defaults),
and web page reads via WebFetch. Web search and browse turn on when a search-capable key is present (Gemini, Anthropic or xAI)
— cortex setup offers the choice. See how everything resolved with cortex config get or GET /health/config on the server.
The server the CLI auto-starts persists in the background and self-stops after 60s idle. To run a long-lived, interactive server yourself, use cortex-server — it stays up until you stop it, and (being a real terminal) prompts for tool approval there.
Customize it — your agents, skills, and commands
Cortex ships a library of agents, skills, and commands. Browse the bundled set in ~/.cortex/builtin/ (a read-only reference that re-syncs to your installed version automatically). To add your own, drop files into the matching folder in your home dir — they load alongside (and override) the builtins:
~/.cortex/agents/ your agent profiles (.md with YAML front-matter)
~/.cortex/skills/ your skills
~/.cortex/commands/ your slash commands
~/.cortex/system-messages/ your custom system prompts
~/.cortex/builtin/ read-only copy of everything that ships — look here for examples
These folders are created for you on first run, each with a short README. Agent profiles should use model: inherit (the default) — the agent then runs on whatever model the orchestrator is using, and falls back gracefully if a pinned model's provider key isn't configured.
What the harness does between the model and the task
Most of Nexus Cortex is the layer that steers a cheap model through hard, long tasks — the part a bare API loop does not have:
Lift plan — before the first edit, a mentor call (thinking on) turns the task into a plan with the real acceptance criteria and an
edit scope; the action model follows it.
EndTurn judge + resolver — a finish is not accepted on the model's say-so: a judge reads the evidence (checks run, outputs, the
task's own rules) and returns MEETS or a GAP with a fix plan; the resolver carries the loop instead of the action model.
Exit planners — a detected tool loop, a reasoning-exhaustion wall, or a looming deadline each route to a bounded planner call rather
than letting the model spin.
Second attempt — when a finish is accepted with a gap and budget remains, a fresh attempt is opened and the better one is selected.
Lever profiles — every one of these is a lever with a ledger line. CORTEX_PROFILE=standard (shipped) turns on the proven safe set;
bench-tb4 reproduces our benchmark bundle; off is the bare frame. cortex config get shows every lever's resolved value and source.
Every major provider, one harness. The five major labs — Anthropic, OpenAI, Google/Gemini, xAI, and DeepSeek — are proven end-to-end. A dozen more (Cloudflare Workers AI, Zhipu/GLM, Qwen, Moonshot/Kimi, MiniMax, Mercury) are wired through the same adapter layer. 122 models across 12 providers in all — switch mid-session, route from benchmark history, or mix providers across sub-agents. Run cortex models list for the live set.
Headless and scriptable by design. No UI required. Pipe JSON, resume sessions by ID, and chain multi-turn agent workflows — the server is a stateful agent, not a stateless endpoint. Drop it in a container with secrets in the environment and it's a true one-shot: npm i -g nexus-cortex → cortex "…".
An embeddable engine, not a closed app. The orchestrator, adapters, 52 built-in tools, and middleware are a clean TypeScript library you build on.
A real harness, batteries included. Parallel sub-agents (Task) with per-agent permissions, MCP tool integration (incl. a zero-config headless browser), a sandboxed-artifact toolset (run + inspect real web apps), git/PR tooling, a policy-based permission engine, token-budget + prompt-cache context management, and append-only JSONL sessions with file checkpoints.
Portable agent memory (canon). Sessions live in a provider-neutral canonical JSONL store — the pattern the field now calls portable agent memory / cross-harness handoff. History is written once, lossless and append-only, and translated per provider at the edge: switch models mid-session, keep prompt caches warm, and carry context across harnesses. A git-backed backend (GitHistoryStore) makes that store a repo you own, and it keeps itself current: cortex config set CANON_AUTO_SYNC true syncs after every turn (debounced), cortex canon watch captures sessions any other harness writes on disk. See Canon.
A built-in improvement loop (opt-in). Build a baseline and a candidate, benchmark both on a graded task set, and gate a keep/discard decision with real statistics — driving the harness's own self-improvement (inspired by karpathy/autoresearch).
Run cortex headlessly as an autonomous sub-agent from your own agent harness — entry points, config levers, autonomous prompting, budgets, verification
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ตรวจสอบล่าสุดเมื่อ 2026-11-14 A headless, multi-provider AI agent harness. One install gives you the `cortex` CLI and HTTP server: npm i -g nexus-cortex.
