@tangle-network/agent-runtime

The engine Tangle's AI agents run on. It runs an agent — a chat turn, a one-shot task, or a team of agents working toward a goal — records every run, and uses those records to measure and improve agents against real pass/fail checks.

One loop, used three ways. Domain behavior (models, tools, knowledge) plugs in as adapters; the scoring statistics and the ship decision come from @tangle-network/agent-eval; sandboxed execution from @tangle-network/sandbox.

pnpm add @tangle-network/agent-runtime @tangle-network/agent-eval @tangle-network/sandbox

What you do with it

You want to…	Call
Run a chat turn — what every product agent does in production	handleChatTurn(...)
Have one agent supervise a team of agents toward a goal	supervise(profile, task, opts)
Improve an agent and prove the gain on fresh tasks	improve(profile, findings, opts)

Run a chat turn

A product agent is one handleChatTurn call inside a route. You give it how to produce the response and how to persist it; it streams, traces, and persists.

import { handleChatTurn } from '@tangle-network/agent-runtime'

const result = handleChatTurn({
  identity: { tenantId, sessionId: threadId, userId, turnIndex: 0 },
  hooks: {
    produce: () => ({ stream: box.streamPrompt(userMessage), finalText: () => box.lastResponse() }),
    persistAssistantMessage: async ({ identity, finalText }) => db.insertMessage(identity, finalText),
  },
  waitUntil,
})
return new Response(result.body, { headers: { 'content-type': result.contentType } })

Supervise a team of agents

One supervisor spawns and steers workers toward a goal. Where the workers run (an in-process loop, or a sandboxed coding harness) is one data value; the budget, journaling, and stopping are handled for you.

import { supervise } from '@tangle-network/agent-runtime/loops'

const result = await supervise(
  { name: 'supervisor', harness: null, systemPrompt: 'Delegate to workers; do not solve the task yourself.' },
  'Implement the feature and make the tests pass.',
  { budget, router, backend }, // backend = where workers run: router-tools | sandbox+harness | bridge
)

Improve an agent

improve optimizes one part of an agent (its prompt, skills, or code) and only ships a change if it beats the current agent on tasks it never practiced on — so registering an agent for self-improvement can never make it worse.

import { improve } from '@tangle-network/agent-runtime'

const { profile, shipped, lift } = await improve(baseProfile, findings, {
  surface: 'prompt',        // what to optimize: prompt | skills | code
  gate: 'holdout',          // certified on a held-back exam, never the practice set
  scenarios, judge, agent,  // how to measure a candidate
})

How it works (the short version)

• One agent, run two ways. The same agent runs at "do the task" speed and at "get better at the task" speed. "Driver", "worker", and "coordinator" aren't separate types — they're roles one agent plays.
• Everything is measured. Every run is a trace: tokens, dollars, time, and a pass/fail score from a real check. "Better" is a number with a denominator, not a vibe — and "equally good but cheaper" is a result you can prove.
• Improvement is gated. A change ships only after it beats the current agent on fresh tasks no tuning step ever saw, with a statistical test — not a single lucky run.
• The grader is honest. Whatever gives feedback never sees the answer key, and scores are recomputed from the attempts actually run — an agent can't fabricate its own win.

Examples

Runnable, grouped by what they show — copy the one nearest your task:

Do this	Example
Run a product chat turn	chat-handler
Drive a team of agents to a goal	supervise · recursive-supervisor
Benchmark strategies on your own domain	coding-benchmark
Benchmark harnesses × models over a real task suite (the real WebCode dataset)	webcode-matrix
Render a multi-profile leaderboard — ranked board + score matrix + SVG/HTML charts, any domain	leaderboard(records) → renderLeaderboardMarkdown / Svg / Html
Trace + bill + effort-gate the WebCode benchmark (the Intelligence SDK)	intelligence-webcode
Self-improve an agent, gated on a held-out set	improve · self-improving-coder
Study coordination vs raw compute	ablation-suite

All 28 live in examples/.

Where to go next

• New here? docs/concepts.md — the mental model in plain terms.
• docs/canonical-api.md — find the primitive: "I want to ___ → use ___".
• docs/architecture.md — the design, end to end.
• bench/HARNESS.md — the experiment harness and how to run a benchmark.