mawkone
e29dccf745
Layer 1 — src/agents/ (thin agent definitions, no prompt text)
registry.ts — AgentConfig, registerAgent(), getAgent(), AGENTS proxy, pick()
orchestrator.ts, coder.ts, pm.ts, marketing.ts — one file each, just metadata + tool picks
index.ts — barrel: imports prompts then agents (correct registration order)
Layer 2 — src/prompts/ (prompt text separated from agent logic)
loader.ts — registerPrompt(), resolvePrompt() with {{variable}} substitution
orchestrator.ts, coder.ts, pm.ts, marketing.ts — prompt templates as registered strings
orchestrator.ts now uses resolvePrompt('orchestrator', { knowledge }) instead of
inline SYSTEM_PROMPT const; {{knowledge}} variable injects project memory cleanly.
agent-runner.ts uses resolvePrompt(config.promptId) per agent turn.
Layer 3 — src/tools/skills.ts (new skills capability)
list_skills(repo) — lists .skills/<name>/SKILL.md directories from a Gitea repo
get_skill(repo, name) — reads and returns the markdown body of a skill file
Orchestrator and all agents now have get_skill in their tool sets.
Orchestrator also has list_skills and references skills in its prompt.
Also fixed:
- server.ts now passes history + knowledge_context from request body to orchestratorChat()
(these were being sent by the frontend but silently dropped)
- server.ts imports PROTECTED_GITEA_REPOS from tools/security.ts (no more duplicate)
- Deleted src/agents.ts (replaced by src/agents/ directory)
Made-with: Cursor
83 lines
3.0 KiB
JavaScript
83 lines
3.0 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.runAgent = runAgent;
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const llm_1 = require("./llm");
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const tools_1 = require("./tools");
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const loader_1 = require("./prompts/loader");
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const job_store_1 = require("./job-store");
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const MAX_TURNS = 40;
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/**
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* Core agent execution loop — model-agnostic via the unified LLM client.
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*
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* Agents use their configured model tier (A/B/C) or a specific model ID.
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* Tool calling uses OpenAI format throughout.
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*/
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async function runAgent(job, config, task, ctx) {
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const llm = (0, llm_1.createLLM)(config.model, { temperature: 0.2 });
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const oaiTools = (0, llm_1.toOAITools)(config.tools);
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const history = [
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{ role: 'user', content: task }
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];
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let toolCallCount = 0;
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let turn = 0;
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let finalText = '';
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(0, job_store_1.updateJob)(job.id, { status: 'running', progress: `Starting ${config.name} (${llm.modelId})…` });
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while (turn < MAX_TURNS) {
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turn++;
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const systemPrompt = (0, loader_1.resolvePrompt)(config.promptId);
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const messages = [
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{ role: 'system', content: systemPrompt },
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...history
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];
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const response = await llm.chat(messages, oaiTools, 8192);
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// Build assistant message for history
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const assistantMsg = {
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role: 'assistant',
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content: response.content,
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tool_calls: response.tool_calls.length > 0 ? response.tool_calls : undefined
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};
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history.push(assistantMsg);
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// No tool calls — agent is done
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if (response.tool_calls.length === 0) {
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finalText = response.content ?? '';
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break;
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}
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// Execute tool calls
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for (const tc of response.tool_calls) {
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const fnName = tc.function.name;
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let fnArgs = {};
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try {
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fnArgs = JSON.parse(tc.function.arguments || '{}');
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}
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catch { /* bad JSON */ }
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toolCallCount++;
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(0, job_store_1.updateJob)(job.id, {
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progress: `Turn ${turn}: calling ${fnName}…`,
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toolCalls: [...(job.toolCalls || []), {
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turn,
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tool: fnName,
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args: fnArgs,
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timestamp: new Date().toISOString()
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}]
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});
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let result;
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try {
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result = await (0, tools_1.executeTool)(fnName, fnArgs, ctx);
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}
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catch (err) {
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result = { error: err instanceof Error ? err.message : String(err) };
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}
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history.push({
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role: 'tool',
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tool_call_id: tc.id,
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name: fnName,
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content: typeof result === 'string' ? result : JSON.stringify(result)
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});
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}
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}
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if (turn >= MAX_TURNS && !finalText) {
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finalText = `Agent hit the ${MAX_TURNS}-turn safety limit. Tool calls made: ${toolCallCount}.`;
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}
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return { finalText, toolCallCount, turns: turn, model: llm.modelId };
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}
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