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Use convertToLlm before serializing, include thinking, remove truncation

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- serializeConversation now takes Message[] (after convertToLlm)
- Handles all custom message types via convertToLlm
- Includes thinking blocks as [Assistant thinking]
- Removes truncation of tool args and results (already token-budgeted)
This commit is contained in:
Mario Zechner 2025-12-30 00:01:43 +01:00
parent 2add465fbe
commit 17ce3814a8
1 changed files with 21 additions and 15 deletions
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36
packages/coding-agent/src/core/compaction/compaction.ts
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@ -6,7 +6,7 @@
*/
import type { AgentMessage } from "@mariozechner/pi-agent-core";
import type { AssistantMessage, Model, Usage, UserMessage } from "@mariozechner/pi-ai";
import type { AssistantMessage, Message, Model, Usage } from "@mariozechner/pi-ai";
import { complete, completeSimple } from "@mariozechner/pi-ai";
import { convertToLlm, createBranchSummaryMessage, createHookMessage } from "../messages.js";
import type { CompactionEntry, SessionEntry } from "../session-manager.js";
@ -121,10 +121,11 @@ function formatFileOperations(readFiles: string[], modifiedFiles: string[]): str
}
/**
* Serialize conversation messages to text for summarization.
* Serialize LLM messages to text for summarization.
* This prevents the model from treating it as a conversation to continue.
* Call convertToLlm() first to handle custom message types.
*/
function serializeConversation(messages: AgentMessage[]): string {
function serializeConversation(messages: Message[]): string {
const parts: string[] = [];
for (const msg of messages) {
@ -137,38 +138,41 @@ function serializeConversation(messages: AgentMessage[]): string {
.map((c) => c.text)
.join("");
if (content) parts.push(`[User]: ${content}`);
} else if (msg.role === "assistant" && "content" in msg && Array.isArray(msg.content)) {
} else if (msg.role === "assistant") {
const textParts: string[] = [];
const thinkingParts: string[] = [];
const toolCalls: string[] = [];
for (const block of msg.content) {
if (block.type === "text") {
textParts.push(block.text);
} else if (block.type === "thinking") {
thinkingParts.push(block.thinking);
} else if (block.type === "toolCall") {
const args = block.arguments as Record<string, unknown>;
const argsStr = Object.entries(args)
.map(([k, v]) => `${k}=${JSON.stringify(v).slice(0, 100)}`)
.map(([k, v]) => `${k}=${JSON.stringify(v)}`)
.join(", ");
toolCalls.push(`${block.name}(${argsStr})`);
}
}
if (thinkingParts.length > 0) {
parts.push(`[Assistant thinking]: ${thinkingParts.join("\n")}`);
}
if (textParts.length > 0) {
parts.push(`[Assistant]: ${textParts.join("\n")}`);
}
if (toolCalls.length > 0) {
parts.push(`[Assistant tool calls]: ${toolCalls.join("; ")}`);
}
} else if (msg.role === "toolResult" && "content" in msg) {
// Summarize tool results briefly
const content = Array.isArray(msg.content)
? msg.content
.filter((c): c is { type: "text"; text: string } => c.type === "text")
.map((c) => c.text.slice(0, 500))
.join("")
: "";
} else if (msg.role === "toolResult") {
const content = msg.content
.filter((c): c is { type: "text"; text: string } => c.type === "text")
.map((c) => c.text)
.join("");
if (content) {
parts.push(`[Tool result]: ${content.slice(0, 1000)}${content.length > 1000 ? "..." : ""}`);
parts.push(`[Tool result]: ${content}`);
}
}
}
@ -595,7 +599,9 @@ export async function generateSummary(
}
// Serialize conversation to text so model doesn't try to continue it
const conversationText = serializeConversation(currentMessages);
// Convert to LLM messages first (handles custom types like bashExecution, hookMessage, etc.)
const llmMessages = convertToLlm(currentMessages);
const conversationText = serializeConversation(llmMessages);
// Build the prompt with conversation wrapped in tags
let promptText = `<conversation>\n${conversationText}\n</conversation>\n\n`;