grok-loop-kit

Automatic Grok 4.5 context compaction for xAI Responses API agent loops.

Long-running agents blow past the context window. xAI ships a compaction endpoint (POST /v1/responses/compact) that folds a transcript into a compact record — but you have to call it yourself, on the right cadence. grok-loop-kit is a drop-in client that does it for you: it compacts every N turns or whenever accumulated tokens cross a budget, and hands back the same completion object plus a small bookkeeping field so you can see what it did.

Ships for Node (CJS + ESM + types) and Python, with a LangChain/LangGraph chat-model adapter.

Requires your own xAI API key (BYOK). Grok 4.5 is $2 / $6 per 1M input/output tokens. This repo develops and tests entirely against a local mock — no key or spend needed to build or run the tests.

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Install

npm install grok-loop-kit          # Node
pip install grok-loop-kit          # Python (from ./python)

The LangChain adapter needs @langchain/core (an optional peer, >=0.3 <2 — works on both the 0.3 and 1.x lines). For LangGraph agents also install @langchain/langgraph:

npm install @langchain/core @langchain/langgraph

Feature matrix

	Node	Python
Auto-compaction (turns + token budget)	✅	✅
Verbatim encrypted_content reuse	✅	✅
Tool-calling loop (sendToolOutputs)	✅	✅
Streaming (SSE)	✅	✅
Retries + timeout + custom headers	✅	✅
onCompact hook	✅	✅
State persistence (getState/loadState)	✅	✅
Per-call abort signal / extraBody	✅	✅ (extra_body)
asyncio client	—	✅ (AsyncGrokLoopClient)
LangChain / LangGraph adapter	✅	—

Node usage

import { GrokLoopClient } from 'grok-loop-kit';

const client = new GrokLoopClient(process.env.XAI_API_KEY!, {
  compactEvery: 8,        // compact on every 8th turn (default)
  compactAtTokens: 8000,  // ...or when the CURRENT rendered context exceeds this (default)
  model: 'grok-4.5',      // default
});

for (const turn of userTurns) {
  const res = await client.sendMessage(turn, tools);
  console.log(res.output);              // raw xAI Responses API output
  console.log(res._grokLoopKit);        // { turnsSinceCompact, estimatedTokensSaved, totalCompactions }
}

sendMessage(userContent, tools?):

1. appends the user message to the rolling transcript,
2. calls POST /v1/responses with the current messages (+ any tools),
3. if turnCount % compactEvery === 0 or accumulatedTokens > compactAtTokens (where accumulatedTokens is the current rendered context size, per xAI's "compact when the rendered context is above a threshold" guidance — not a running sum), calls POST /v1/responses/compact, rebuilds the transcript from the compaction result, and resets the counter,
4. returns the completion with an extra _grokLoopKit field.

When does compaction actually save tokens?

Compaction replaces the transcript with one dense encrypted_content record. That's a win once the raw transcript outweighs the record — i.e. long conversations. With very short messages the record can cost more than the messages it replaced, so keep compactAtTokens at a realistic size (thousands, not hundreds). Measured on a live 20-turn run with padded context: 22% fewer cumulative input tokens vs a never-compacting control, with identical recall (see scripts/hardtest-live.mjs).

Correct compaction reuse (the important bit)

The xAI docs require the compaction result to be fed back into the next request's input verbatim — as a { type: 'compaction', id, encrypted_content } item, never modified. grok-loop-kit does exactly that. (A plain-string compaction reply, as some gateways and the bundled mock return, collapses to a single user message.) So this works unchanged against the real Grok 4.5 API, not just the mock.

Tool-calling loops

Assistant output items — including function_calls — are preserved in the transcript verbatim, so tool continuations reference the right call_ids:

let res = await client.sendMessage('weather in Tokyo?', tools);
for (const call of client.getToolCalls(res)) {
  const output = await runTool(call.name, call.arguments);
  res = await client.sendToolOutputs([{ call_id: call.call_id, output }], tools);
}

sendToolOutputs counts as a turn and compacts on the same schedule. See examples/tool-loop.mjs.

Streaming

const res = await client.streamMessage('write a haiku', {
  onToken: (delta) => process.stdout.write(delta),
});
// res is the assembled final completion (+ _grokLoopKit); compaction runs on the
// same schedule as sendMessage. See examples/streaming.mjs.

Resilience & hooks

new GrokLoopClient(key, {
  maxRetries: 2,          // retry 429/5xx/network with exponential backoff (honors Retry-After)
  retryBaseMs: 500,
  timeoutMs: 30_000,      // per-request timeout (AbortSignal)
  headers: { 'x-team': 'agents' },
  onCompact: (e) => log(`compacted #${e.totalCompactions} at turn ${e.atTurn}, dropped ${e.droppedMessageCount}`),
});

Examples & scripts

• examples/basic.mjs — 20-turn loop, context flatlining
• examples/tool-loop.mjs — tool-calling agent loop
• examples/streaming.mjs — streamed tokens with compaction
• scripts/smoke-live.mjs — LIVE smoke against real Grok (needs a funded key; spends < $0.01)
• scripts/hardtest-live.mjs — LIVE adversarial test: needle-retention through 4 real compactions, cost vs a no-compaction control, live tool round-trip, live streaming

State persistence (durable agents)

Snapshot the loop and resume it later — in another process, after a restart, per user:

const state = client.getState();      // JSON-serializable
await kv.set(userId, JSON.stringify(state));
// ...later / elsewhere...
const resumed = new GrokLoopClient(key);
resumed.loadState(JSON.parse(await kv.get(userId)));
await resumed.sendMessage('continue where we left off');
// also: client.reset() to clear state and reuse the client

LangGraph / LangChain

GrokLoopChatModel is a first-class BaseChatModel — use it anywhere a LangChain chat model goes, including LangGraph's createReactAgent. Tool calls are parsed onto the returned AIMessage.tool_calls, and the incoming history is compacted when it's long or over the token budget.

import { GrokLoopChatModel } from 'grok-loop-kit/langgraph';
import { createReactAgent } from '@langchain/langgraph/prebuilt';
import { tool } from '@langchain/core/tools';
import { z } from 'zod';

const model = new GrokLoopChatModel({
  apiKey: process.env.XAI_API_KEY,
  grokLoop: { compactEvery: 8, compactAtTokens: 120_000 },
});

const getWeather = tool(async ({ city }) => `18°C, sunny in ${city}`, {
  name: 'get_weather',
  description: 'Get the weather for a city',
  schema: z.object({ city: z.string() }),
});

const agent = createReactAgent({ llm: model, tools: [getWeather] });
const out = await agent.invoke({ messages: [new HumanMessage('weather in Tokyo?')] });
console.log(model.lastMeta); // { turnsSinceCompact, estimatedTokensSaved, totalCompactions }

Because LangChain invokes a model with the full history each turn, the adapter compacts that history (a pure function of the incoming messages) rather than keeping a parallel transcript — so it never double-counts. Verified end-to-end against a real createReactAgent tool loop (see test/langgraph-agent.test.mjs). Extends BaseChatModel (not ChatOpenAI), so it never touches the OpenAI SDK and survives bindTools/withConfig. Works on @langchain/core 0.3 and 1.x.

Python usage

from grok_loop_kit import GrokLoopClient

client = GrokLoopClient(
    api_key,
    compact_every=8,
    compact_at_tokens=8000,
    model="grok-4.5",
)

res = client.send_message("hello", tools=None)
print(res.output)          # raw output
print(res["_grokLoopKit"]) # GrokLoopKitMeta(turnsSinceCompact=..., estimatedTokensSaved=..., totalCompactions=...)

# Tool loop
res = client.send_message("weather in Tokyo?", tools=tools)
for call in client.get_tool_calls(res.raw):
    out = run_tool(call["name"], call["arguments"])
    res = client.send_tool_outputs([{"call_id": call["call_id"], "output": out}], tools)

Streaming and asyncio too:

# sync streaming
client.stream_message("write a haiku", on_token=lambda d: print(d, end=""))

# asyncio (non-blocking; wraps the sync client in a worker thread, zero deps)
from grok_loop_kit import AsyncGrokLoopClient
aclient = AsyncGrokLoopClient(api_key)
res = await aclient.send_message("hello")
await aclient.stream_message("stream this", on_token=print)

Same feature set as the Node client: verbatim compaction reuse, tool round-trips, streaming, retries (max_retries, retry_base_ms), timeout, custom headers, and an on_compact hook. Zero runtime dependencies (stdlib urllib); pass poster= / stream_poster= to inject a custom transport (used by the tests). Ships py.typed. Live smoke: python python/smoke_live.py (needs a funded key).

What _grokLoopKit reports

field	meaning
turnsSinceCompact	turns elapsed since the last compaction (0 on a turn that just compacted)
estimatedTokensSaved	cumulative estimate of context tokens removed, from each compaction reply's usage (pre-compaction input_tokens − compacted-record output_tokens). An idealized figure — the net on-wire saving also depends on later turns; see the hard-test's measured 22%.
totalCompactions	compactions performed over this client's lifetime

Develop & test

npm install
npm run build       # tsup -> dist/ (CJS + ESM + .d.ts)
npm run typecheck   # tsc --noEmit, strict: true
npm test            # node --test -> 20-turn loop compacts at turns 8 & 16

# Python
cd python && python -m venv .venv && ./.venv/Scripts/python -m pip install -e .
./.venv/Scripts/python test_loop.py

Everything runs against test/mock-server.mjs, a self-contained mock of xAI's POST /v1/responses and POST /v1/responses/compact.

Using it with the grokscope mock

The brief referenced adding a compact endpoint to D:\Repos\ideas\grokscope\test\mock-server.mjs. To keep this package hermetic we ship our own mock instead. If you want the grokscope mock to answer /v1/responses/compact, add this branch before its /responses handler:

if (req.method === 'POST' && req.url?.endsWith('/responses/compact')) {
  let raw = '';
  req.on('data', (c) => (raw += c));
  req.on('end', () => {
    const n = (JSON.parse(raw).input ?? []).length;
    res.writeHead(200, { 'content-type': 'application/json' });
    res.end(JSON.stringify({ output: `[COMPACTED: ${n} turns condensed]`, usage: { input_tokens: Math.floor(n / 4) } }));
  });
  return;
}

First distribution step

Publish to npm (npm publish, package name grok-loop-kit) and post a short before/after on X showing an agent loop's context size flatlining across 20 turns instead of growing linearly — tag the xAI dev account. The one-liner hook: "drop-in Grok 4.5 client that keeps your agent's context flat — compaction on autopilot."

License

MIT