mirror of
https://github.com/openclaw/openclaw.git
synced 2026-06-02 18:05:18 +00:00
* refactor: extract agent core package Introduce packages/agent-core as the OpenClaw-owned home for reusable agent loop, harness, session, prompt, and runtime dependency contracts. * refactor: extract shared llm runtime Move provider model registries, stream wrappers, OAuth helpers, and LLM utilities into src/llm with plugin-sdk barrels instead of depending on the old embedded runtime layout. * refactor: remove pi runtime internals Rename remaining Pi-shaped agent surfaces to OpenClaw agent runtime names, delete obsolete Pi docs and package graph checks, and add the third-party notice for incorporated code. * refactor: tighten agent session runtime Make agent-core/runtime dependencies explicit, consolidate compaction and session transcript helpers, and move model/session helpers behind OpenClaw-owned contracts. * refactor: remove static model and pi auth paths Drop static model catalogs and Pi auth bridges, move model/provider facts to manifest-owned runtime contracts, and harden internal embedded-agent utilities. * refactor: remove legacy provider compat paths * docs: remove agent parity notes * fix: skip provider wildcard metadata parsing * refactor: share session extension sdk loading * refactor: inline acpx proxy error formatter * refactor: fold edit recovery into edit tool * fix: accept extension batch separator * test: align startup provider plugin expectations * fix: restore provider-scoped release discovery * test: align static asset packaging expectations * fix: run static provider catalogs during scoped discovery * fix: add provider entry catalogs for scoped live discovery * fix: load lightweight provider catalog entries * fix: refresh provider-scoped plugin metadata * fix: keep provider catalog entries on release live path * fix: keep static manifest models in release live checks * fix: harden release model discovery * fix: reduce OpenAI live cache probe reasoning * fix: disable OpenAI cache probe reasoning * ci: extend OpenAI gateway live timeout * fix: extend live gateway model budget * fix: stabilize release validation regressions * fix: honor provider aliases in model rows * fix: stabilize release validation lanes * fix: stabilize release memory qa * ci: stabilize release validation lanes * ci: prefer ipv4 for live docker node calls * fix: restore shared tool-call stream wrapper * ci: remove legacy pi test shard alias * fix: clean up embedded agent test drift * fix: stabilize runtime alias status * fix: clean up embedded agent ci drift * fix: restore release ci invariants * fix: clean up post-rebase runtime drift * fix: restore release ci checks * fix: restore release ci after rebase * fix: remove stale pi runtime path * test: align compaction runtime expectations * test: update plugin prerelease expectations * fix: handle claude live tool approvals * fix: stabilize release validation gates * fix: finish agent runtime import * test: finish post-rebase agent runtime mocks * fix: keep codex compaction native * fix: stabilize codex app-server hook tests * test: isolate codex diagnostic active run * test: remove codex diagnostic completion race # Conflicts: # extensions/codex/src/app-server/run-attempt.test.ts * ci: fix full release manifest performance run id * refactor: narrow llm plugin sdk boundary * chore: drop generated google boundary stamps * fix: repair rebase fallout * fix: clean up rebased runtime references * fix: decode codex jwt payloads as base64url * fix: preserve shipped pi runtime alias * fix: add scoped sdk virtual modules * fix: decode llm codex oauth jwt as base64url * fix: avoid stale vertex adc negative cache * fix: harden tool arg decoding and codeql path * fix: keep vertex adc negative checks live * refactor: consolidate codex jwt and edit helpers * fix: await codex oauth node runtime imports * fix: preserve sdk tool and notice contracts * fix: preserve shipped compat config boundaries * fix: align codex oauth callback host * fix: terminate agent-core loop streams on failure * fix: keep codex oauth callback alive during fallback * ci: include session tools in critical codeql scans * fix: keep Cloudflare Anthropic provider auth header * docs: redirect legacy pi runtime pages * fix: honor bundled web provider compat discovery * fix: protect session output spill files * fix: keep legacy agent dir env blocked * fix: contain auto-discovered skill symlinks * fix: harden agent core sdk proxy surfaces * fix: restore approval reaction sdk compat * fix: keep live docker runs bounded * fix: keep codex oauth redirect host aligned * fix: resolve post-rebase agent runtime drift * fix: redact anthropic oauth parse failures * fix: preserve responses strict tool shaping * fix: repair agent runtime rebase cleanup * docs: redirect retired parity pages * fix: bound auto-discovered resources to roots * fix: repair post-rebase agent test drift * fix: preserve bundled provider allowlist migration * fix: preserve manifest-owned provider aliases * fix: declare photon image dependency * fix: keep provider headers out of proxy body * fix: preserve shipped env aliases * fix: refresh control ui i18n generated state * fix: quote read fallback paths * fix: preview edits through configured backend * test: satisfy core test typecheck * fix: preserve ZAI usage auth fallback * test: repair codex diagnostic test * fix: repair agent runtime rebase drift * test: finish embedded runner import rename * fix: repair agent runtime rebase integrations * test: align compaction oauth fallback expectations * fix: allow sdk-auth session models * fix: update doctor tool schema import * fix: preserve bedrock plugin region * fix: stream harmony-like prose immediately * ci: include session runtime in codeql shards * fix: repair latest rebase integrations * fix: honor explicit codex websocket transport * fix: keep openai-compatible credentials provider-scoped * fix: refresh sdk api baseline after rebase * fix: route cli runtime aliases through openclaw harness * test: rename stale harness mock expectation * test: rename embedded agent overflow calls * test: clean embedded auth test wording * test: use openclaw stream types in deepinfra cache test * fix: refresh sdk api baseline on latest main * fix: honor bundled discovery compat allowlists * fix: refresh sdk api baseline after latest rebase * fix: remove stale rebase imports * test: rename stale model catalog mock * test: mock renamed doctor runtime modules * fix: map canonical kimi env auth * fix: use internal model registry in bench script * fix: migrate deepinfra provider catalog entry * fix: enforce builtin tool suppression * fix: route compaction auth and proxy payloads safely * refactor: prune unused llm registry leftovers * test: update codex hooks session import * test: fix model picker ci coverage * test: align model picker auth mock types
372 lines
12 KiB
TypeScript
372 lines
12 KiB
TypeScript
import type { AgentMessage } from "openclaw/plugin-sdk/agent-core";
|
|
import type { AssistantMessage, ToolResultMessage } from "openclaw/plugin-sdk/llm";
|
|
import { beforeAll, describe, expect, it, vi } from "vitest";
|
|
import { makeAgentAssistantMessage } from "./test-helpers/agent-message-fixtures.js";
|
|
import "./test-helpers/agent-session-token-mock.js";
|
|
|
|
let estimateMessagesTokens: typeof import("./compaction.js").estimateMessagesTokens;
|
|
let pruneHistoryForContextShare: typeof import("./compaction.js").pruneHistoryForContextShare;
|
|
let splitMessagesByTokenShare: typeof import("./compaction.js").splitMessagesByTokenShare;
|
|
|
|
beforeAll(async () => {
|
|
vi.resetModules();
|
|
({ estimateMessagesTokens, pruneHistoryForContextShare, splitMessagesByTokenShare } =
|
|
await import("./compaction.js"));
|
|
});
|
|
|
|
function makeMessage(id: number, size: number): AgentMessage {
|
|
return {
|
|
role: "user",
|
|
content: "x".repeat(size),
|
|
timestamp: id,
|
|
};
|
|
}
|
|
|
|
function makeMessages(count: number, size: number): AgentMessage[] {
|
|
return Array.from({ length: count }, (_, index) => makeMessage(index + 1, size));
|
|
}
|
|
|
|
function compareTimestampIds(left: AgentMessage["timestamp"], right: AgentMessage["timestamp"]) {
|
|
return left < right ? -1 : left > right ? 1 : 0;
|
|
}
|
|
|
|
function makeAssistantToolCall(
|
|
timestamp: number,
|
|
toolCallId: string,
|
|
text = "x".repeat(4000),
|
|
stopReason: AssistantMessage["stopReason"] = "stop",
|
|
): AssistantMessage {
|
|
return makeAgentAssistantMessage({
|
|
content: [
|
|
{ type: "text", text },
|
|
{ type: "toolCall", id: toolCallId, name: "test_tool", arguments: {} },
|
|
],
|
|
model: "gpt-5.4",
|
|
stopReason,
|
|
timestamp,
|
|
});
|
|
}
|
|
|
|
function makeToolResult(timestamp: number, toolCallId: string, text: string): ToolResultMessage {
|
|
return {
|
|
role: "toolResult",
|
|
toolCallId,
|
|
toolName: "test_tool",
|
|
content: [{ type: "text", text }],
|
|
isError: false,
|
|
timestamp,
|
|
};
|
|
}
|
|
|
|
function pruneLargeSimpleHistory() {
|
|
const messages = makeMessages(4, 4000);
|
|
const maxContextTokens = 2000; // budget is 1000 tokens (50%)
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
return { messages, pruned, maxContextTokens };
|
|
}
|
|
|
|
function requireChunkContainingTimestamp(
|
|
parts: AgentMessage[][],
|
|
role: AgentMessage["role"],
|
|
timestamp: number,
|
|
): AgentMessage[] {
|
|
const chunk = parts.find((candidate) =>
|
|
candidate.some((message) => message.role === role && message.timestamp === timestamp),
|
|
);
|
|
if (!chunk) {
|
|
throw new Error(`expected ${role} message with timestamp ${timestamp} in a chunk`);
|
|
}
|
|
return chunk;
|
|
}
|
|
|
|
describe("splitMessagesByTokenShare", () => {
|
|
it("splits messages into two non-empty parts", () => {
|
|
const messages = makeMessages(4, 4000);
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
expect(parts.map((chunk) => chunk.map((msg) => msg.timestamp))).toEqual([
|
|
[1, 2],
|
|
[3, 4],
|
|
]);
|
|
});
|
|
|
|
it("preserves message order across parts", () => {
|
|
const messages = makeMessages(6, 4000);
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 3);
|
|
expect(parts.flat().map((msg) => msg.timestamp)).toEqual(messages.map((msg) => msg.timestamp));
|
|
});
|
|
|
|
it("keeps tool_use and matching toolResult in the same chunk", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeMessage(1, 4000),
|
|
makeAssistantToolCall(2, "call_split"),
|
|
makeToolResult(3, "call_split", "r".repeat(800)),
|
|
makeMessage(4, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
const chunkWithToolUse = requireChunkContainingTimestamp(parts, "assistant", 2);
|
|
const chunkWithToolResult = requireChunkContainingTimestamp(parts, "toolResult", 3);
|
|
expect(chunkWithToolUse).toBe(chunkWithToolResult);
|
|
expect(parts.flat().length).toBe(messages.length);
|
|
});
|
|
|
|
it("keeps multiple toolResults with their assistant in the same chunk", () => {
|
|
const assistant = makeAgentAssistantMessage({
|
|
content: [
|
|
{ type: "text", text: "x".repeat(4000) },
|
|
{ type: "toolCall", id: "call_a", name: "tool_a", arguments: {} },
|
|
{ type: "toolCall", id: "call_b", name: "tool_b", arguments: {} },
|
|
],
|
|
model: "gpt-5.2",
|
|
stopReason: "stop",
|
|
timestamp: 2,
|
|
});
|
|
|
|
const messages: AgentMessage[] = [
|
|
makeMessage(1, 4000),
|
|
assistant,
|
|
makeToolResult(3, "call_a", "result_a".repeat(200)),
|
|
makeToolResult(4, "call_b", "result_b".repeat(200)),
|
|
makeMessage(5, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
const chunkWithAssistant = parts.find((chunk) =>
|
|
chunk.some((m) => m.role === "assistant" && m.timestamp === 2),
|
|
)!;
|
|
const resultTimestamps = chunkWithAssistant
|
|
.filter((m) => m.role === "toolResult")
|
|
.map((m) => m.timestamp);
|
|
expect(resultTimestamps).toEqual([3, 4]);
|
|
expect(parts.flat().length).toBe(messages.length);
|
|
});
|
|
|
|
it("keeps displaced toolResults with their assistant chunk", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeMessage(1, 4000),
|
|
makeAssistantToolCall(2, "call_split"),
|
|
makeMessage(3, 800),
|
|
makeToolResult(4, "call_split", "r".repeat(800)),
|
|
makeMessage(5, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
const chunkWithToolUse = requireChunkContainingTimestamp(parts, "assistant", 2);
|
|
const chunkWithToolResult = requireChunkContainingTimestamp(parts, "toolResult", 4);
|
|
|
|
expect(chunkWithToolUse).toBe(chunkWithToolResult);
|
|
});
|
|
|
|
it("splits after a completed tool_call/result pair when over budget", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeAssistantToolCall(1, "call_x", "y".repeat(4000)),
|
|
makeToolResult(2, "call_x", "r".repeat(4000)),
|
|
makeMessage(3, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
expect(parts.map((chunk) => chunk.map((msg) => msg.timestamp))).toEqual([[1, 2], [3]]);
|
|
});
|
|
|
|
it("splits before a trailing completed tool-call pair", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeMessage(1, 4000),
|
|
makeAssistantToolCall(2, "call_tail", "y".repeat(200)),
|
|
makeToolResult(3, "call_tail", "r".repeat(4000)),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
expect(parts.length).toBe(2);
|
|
expect(parts[0]?.map((m) => m.timestamp)).toEqual([1]);
|
|
expect(parts[1]?.map((m) => m.timestamp)).toEqual([2, 3]);
|
|
});
|
|
|
|
it("does not block splits after aborted tool-call assistants", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeAssistantToolCall(1, "call_abort", "y".repeat(4000), "aborted"),
|
|
makeMessage(2, 4000),
|
|
makeMessage(3, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
expect(parts.map((chunk) => chunk.map((msg) => msg.timestamp))).toEqual([[1], [2, 3]]);
|
|
});
|
|
|
|
it("splits before unfinished tool-call turns that never get a result", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeMessage(1, 4000),
|
|
makeAssistantToolCall(2, "call_missing"),
|
|
makeMessage(3, 4000),
|
|
];
|
|
|
|
const parts = splitMessagesByTokenShare(messages, 2);
|
|
|
|
expect(parts.length).toBe(2);
|
|
expect(parts[0]?.map((m) => m.timestamp)).toEqual([1]);
|
|
expect(parts[1]?.map((m) => m.timestamp)).toEqual([2, 3]);
|
|
});
|
|
});
|
|
|
|
describe("pruneHistoryForContextShare", () => {
|
|
it("drops older chunks until the history budget is met", () => {
|
|
const { pruned, maxContextTokens } = pruneLargeSimpleHistory();
|
|
|
|
expect(pruned.droppedChunks).toBe(2);
|
|
expect(pruned.keptTokens).toBeLessThanOrEqual(Math.floor(maxContextTokens * 0.5));
|
|
expect(pruned.messages.map((msg) => msg.timestamp)).toEqual([4]);
|
|
});
|
|
|
|
it("keeps the newest messages when pruning", () => {
|
|
const messages = makeMessages(6, 4000);
|
|
const totalTokens = estimateMessagesTokens(messages);
|
|
const maxContextTokens = Math.max(1, Math.floor(totalTokens * 0.5)); // budget = 25%
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
const keptIds = pruned.messages.map((msg) => msg.timestamp);
|
|
const expectedSuffix = messages.slice(-keptIds.length).map((msg) => msg.timestamp);
|
|
expect(keptIds).toEqual(expectedSuffix);
|
|
});
|
|
|
|
it("keeps history when already within budget", () => {
|
|
const messages: AgentMessage[] = [makeMessage(1, 1000)];
|
|
const maxContextTokens = 2000;
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
expect(pruned.droppedChunks).toBe(0);
|
|
expect(pruned.messages.length).toBe(messages.length);
|
|
expect(pruned.keptTokens).toBe(estimateMessagesTokens(messages));
|
|
expect(pruned.droppedMessagesList).toStrictEqual([]);
|
|
});
|
|
|
|
it("returns droppedMessagesList containing dropped messages", () => {
|
|
const { messages, pruned } = pruneLargeSimpleHistory();
|
|
|
|
expect(pruned.droppedChunks).toBe(2);
|
|
expect(pruned.droppedMessagesList.map((msg) => msg.timestamp)).toEqual([1, 2, 3]);
|
|
expect(pruned.droppedMessagesList.length).toBe(pruned.droppedMessages);
|
|
|
|
const allIds = [
|
|
...pruned.droppedMessagesList.map((m) => m.timestamp),
|
|
...pruned.messages.map((m) => m.timestamp),
|
|
].toSorted(compareTimestampIds);
|
|
const originalIds = messages.map((m) => m.timestamp).toSorted(compareTimestampIds);
|
|
expect(allIds).toEqual(originalIds);
|
|
});
|
|
|
|
it("returns empty droppedMessagesList when no pruning needed", () => {
|
|
const messages: AgentMessage[] = [makeMessage(1, 100)];
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens: 100_000,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
expect(pruned.droppedChunks).toBe(0);
|
|
expect(pruned.droppedMessagesList).toStrictEqual([]);
|
|
expect(pruned.messages.length).toBe(1);
|
|
});
|
|
|
|
it("removes orphaned tool_result messages when tool_use is dropped", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeAssistantToolCall(1, "call_123"),
|
|
makeToolResult(2, "call_123", "result".repeat(500)),
|
|
{
|
|
role: "user",
|
|
content: "x".repeat(500),
|
|
timestamp: 3,
|
|
},
|
|
];
|
|
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens: 2000,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
const keptRoles = pruned.messages.map((m) => m.role);
|
|
expect(keptRoles).not.toContain("toolResult");
|
|
expect(pruned.droppedMessages).toBe(pruned.droppedMessagesList.length);
|
|
});
|
|
|
|
it("keeps tool_result when its tool_use is also kept", () => {
|
|
const messages: AgentMessage[] = [
|
|
{
|
|
role: "user",
|
|
content: "x".repeat(4000),
|
|
timestamp: 1,
|
|
},
|
|
makeAssistantToolCall(2, "call_456", "y".repeat(500)),
|
|
makeToolResult(3, "call_456", "result"),
|
|
];
|
|
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens: 2000,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
const keptRoles = pruned.messages.map((m) => m.role);
|
|
expect(keptRoles).toContain("assistant");
|
|
expect(keptRoles).toContain("toolResult");
|
|
});
|
|
|
|
it("removes multiple orphaned tool_results from the same dropped tool_use", () => {
|
|
const messages: AgentMessage[] = [
|
|
makeAgentAssistantMessage({
|
|
content: [
|
|
{ type: "text", text: "x".repeat(4000) },
|
|
{ type: "toolCall", id: "call_a", name: "tool_a", arguments: {} },
|
|
{ type: "toolCall", id: "call_b", name: "tool_b", arguments: {} },
|
|
],
|
|
model: "gpt-5.4",
|
|
stopReason: "stop",
|
|
timestamp: 1,
|
|
}),
|
|
makeToolResult(2, "call_a", "result_a"),
|
|
makeToolResult(3, "call_b", "result_b"),
|
|
{
|
|
role: "user",
|
|
content: "x".repeat(500),
|
|
timestamp: 4,
|
|
},
|
|
];
|
|
|
|
const pruned = pruneHistoryForContextShare({
|
|
messages,
|
|
maxContextTokens: 2000,
|
|
maxHistoryShare: 0.5,
|
|
parts: 2,
|
|
});
|
|
|
|
const keptToolResults = pruned.messages.filter((m) => m.role === "toolResult");
|
|
expect(keptToolResults).toHaveLength(0);
|
|
expect(pruned.droppedMessages).toBe(pruned.droppedMessagesList.length);
|
|
});
|
|
});
|