105 lines
3.2 KiB
TypeScript
105 lines
3.2 KiB
TypeScript
import { createLLM, toOAITools, LLMMessage } from './llm';
|
|
import { AgentConfig } from './agents';
|
|
import { executeTool, ToolContext } from './tools';
|
|
import { resolvePrompt } from './prompts/loader';
|
|
import { Job, updateJob } from './job-store';
|
|
|
|
const MAX_TURNS = 40;
|
|
|
|
export interface RunResult {
|
|
finalText: string;
|
|
toolCallCount: number;
|
|
turns: number;
|
|
model: string;
|
|
}
|
|
|
|
/**
|
|
* Core agent execution loop — model-agnostic via the unified LLM client.
|
|
*
|
|
* Agents use their configured model tier (A/B/C) or a specific model ID.
|
|
* Tool calling uses OpenAI format throughout.
|
|
*/
|
|
export async function runAgent(
|
|
job: Job,
|
|
config: AgentConfig,
|
|
task: string,
|
|
ctx: ToolContext
|
|
): Promise<RunResult> {
|
|
const llm = createLLM(config.model, { temperature: 0.2 });
|
|
const oaiTools = toOAITools(config.tools);
|
|
|
|
const history: LLMMessage[] = [
|
|
{ role: 'user', content: task }
|
|
];
|
|
|
|
let toolCallCount = 0;
|
|
let turn = 0;
|
|
let finalText = '';
|
|
|
|
updateJob(job.id, { status: 'running', progress: `Starting ${config.name} (${llm.modelId})…` });
|
|
|
|
while (turn < MAX_TURNS) {
|
|
turn++;
|
|
|
|
const systemPrompt = resolvePrompt(config.promptId);
|
|
const messages: LLMMessage[] = [
|
|
{ role: 'system', content: systemPrompt },
|
|
...history
|
|
];
|
|
|
|
const response = await llm.chat(messages, oaiTools, 8192);
|
|
|
|
// Build assistant message for history
|
|
const assistantMsg: LLMMessage = {
|
|
role: 'assistant',
|
|
content: response.content,
|
|
tool_calls: response.tool_calls.length > 0 ? response.tool_calls : undefined
|
|
};
|
|
history.push(assistantMsg);
|
|
|
|
// No tool calls — agent is done
|
|
if (response.tool_calls.length === 0) {
|
|
finalText = response.content ?? '';
|
|
break;
|
|
}
|
|
|
|
// Execute tool calls
|
|
for (const tc of response.tool_calls) {
|
|
const fnName = tc.function.name;
|
|
let fnArgs: Record<string, unknown> = {};
|
|
try { fnArgs = JSON.parse(tc.function.arguments || '{}'); } catch { /* bad JSON */ }
|
|
|
|
toolCallCount++;
|
|
updateJob(job.id, {
|
|
progress: `Turn ${turn}: calling ${fnName}…`,
|
|
toolCalls: [...(job.toolCalls || []), {
|
|
turn,
|
|
tool: fnName,
|
|
args: fnArgs,
|
|
timestamp: new Date().toISOString()
|
|
}]
|
|
});
|
|
|
|
let result: unknown;
|
|
try {
|
|
result = await executeTool(fnName, fnArgs, ctx);
|
|
} catch (err) {
|
|
result = { error: err instanceof Error ? err.message : String(err) };
|
|
}
|
|
|
|
history.push({
|
|
role: 'tool',
|
|
tool_call_id: tc.id,
|
|
name: fnName,
|
|
content: typeof result === 'string' ? result : JSON.stringify(result)
|
|
});
|
|
}
|
|
}
|
|
|
|
if (turn >= MAX_TURNS && !finalText) {
|
|
finalText = `Agent hit the ${MAX_TURNS}-turn safety limit. Tool calls made: ${toolCallCount}.`;
|
|
}
|
|
|
|
return { finalText, toolCallCount, turns: turn, model: llm.modelId };
|
|
}
|