feat: vendor midstream and sublinear-time-solver libraries (#109)

Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
2026-08-10 20:31:42 +00:00 · 2026-03-02 23:34:05 -05:00
parent 14902e6b4e
commit 407b46b206
1600 changed files with 1852646 additions and 0 deletions
@@ -0,0 +1,121 @@
+/**
+ * Agentic loop implementation for autonomous decision-making
+ */
+
+import {
+  Action,
+  Observation,
+  Plan,
+  Goal,
+  Context,
+} from './types';
+
+export class AgenticLoop {
+  private actionHistory: Action[] = [];
+  private totalReward: number = 0;
+  private actionCount: number = 0;
+
+  /**
+   * Plan phase: Generate a plan based on goals and context
+   */
+  async plan(context: Context, input: string): Promise<Plan> {
+    const goal: Goal = {
+      id: `goal_${this.actionCount}`,
+      description: `Process: ${input}`,
+      priority: 1.0,
+      achieved: false,
+    };
+
+    const actions = await this.generateActionCandidates(input, context);
+    const rankedActions = this.rankActions(actions, context);
+
+    const steps = rankedActions.slice(0, 5).map((action, i) => ({
+      sequence: i,
+      action,
+      preconditions: [],
+      postconditions: [],
+    }));
+
+    return {
+      goal,
+      steps,
+      estimatedReward: rankedActions[0]?.expectedReward || 0,
+      confidence: steps.length > 0 ? 0.8 : 0.0,
+    };
+  }
+
+  /**
+   * Generate candidate actions based on input
+   */
+  private async generateActionCandidates(
+    input: string,
+    context: Context
+  ): Promise<Action[]> {
+    const candidates: Action[] = [];
+    const inputLower = input.toLowerCase();
+
+    if (inputLower.includes('weather')) {
+      candidates.push({
+        actionType: 'get_weather',
+        description: 'Fetch weather information',
+        parameters: { query: input },
+        toolCalls: ['weather_api'],
+        expectedOutcome: 'Weather data',
+        expectedReward: 0.8,
+      });
+    }
+
+    if (inputLower.includes('learn') || inputLower.includes('remember')) {
+      candidates.push({
+        actionType: 'update_knowledge',
+        description: 'Update knowledge graph',
+        parameters: { content: input },
+        toolCalls: [],
+        expectedOutcome: 'Knowledge updated',
+        expectedReward: 0.9,
+      });
+    }
+
+    // Default action
+    candidates.push({
+      actionType: 'process_text',
+      description: `Process: ${input}`,
+      parameters: { text: input },
+      toolCalls: [],
+      expectedOutcome: 'Processed text',
+      expectedReward: 0.5,
+    });
+
+    return candidates;
+  }
+
+  /**
+   * Rank actions by expected reward
+   */
+  private rankActions(actions: Action[], context: Context): Action[] {
+    return actions.sort((a, b) => b.expectedReward - a.expectedReward);
+  }
+
+  /**
+   * Record action execution
+   */
+  recordAction(action: Action, reward: number): void {
+    this.actionHistory.push(action);
+    this.totalReward += reward;
+    this.actionCount++;
+  }
+
+  /**
+   * Get average reward
+   */
+  getAverageReward(): number {
+    return this.actionCount > 0 ? this.totalReward / this.actionCount : 0;
+  }
+
+  /**
+   * Get action count
+   */
+  getActionCount(): number {
+    return this.actionCount;
+  }
+}