feat: Day 13 - root README, example agent scripts, and demo seed script

2026-02-10 01:48:47 +00:00
parent e0f13cdaa6
commit 98bfa968ce
7 changed files with 2189 additions and 2 deletions
--- a/examples/basic_agent.py
+++ b/examples/basic_agent.py
@@ -0,0 +1,92 @@
+"""
+AgentLens Basic Example — Simplest possible usage.
+
+Demonstrates:
+  - Initializing the SDK
+  - Creating a trace with tags
+  - Logging decision points (TOOL_SELECTION, PLANNING)
+  - Graceful shutdown
+
+Usage:
+    pip install vectry-agentlens
+    python basic_agent.py
+"""
+
+import agentlens
+import time
+
+# 1. Initialize AgentLens
+agentlens.init(
+    api_key="your-api-key-here",
+    endpoint="http://localhost:4200",
+)
+
+# 2. Run an agent task inside a trace context
+with agentlens.trace("research-task", tags=["demo", "basic"]):
+    # Simulate: agent decides which tool to use for research
+    agentlens.log_decision(
+        type="TOOL_SELECTION",
+        chosen={
+            "name": "search_web",
+            "confidence": 0.85,
+            "params": {"query": "latest AI research papers 2025"},
+        },
+        alternatives=[
+            {
+                "name": "search_docs",
+                "confidence": 0.6,
+                "reason_rejected": "Internal docs unlikely to have latest papers",
+            },
+            {
+                "name": "search_arxiv",
+                "confidence": 0.78,
+                "reason_rejected": "Web search covers arXiv plus other sources",
+            },
+        ],
+        reasoning="Web search gives the broadest coverage for recent AI papers.",
+    )
+
+    time.sleep(0.3)  # Simulate tool execution time
+
+    # Simulate: agent plans next steps after getting search results
+    agentlens.log_decision(
+        type="PLANNING",
+        chosen={
+            "name": "summarize_top_3",
+            "confidence": 0.92,
+            "params": {"max_papers": 3, "format": "bullet_points"},
+        },
+        alternatives=[
+            {
+                "name": "summarize_all",
+                "confidence": 0.5,
+                "reason_rejected": "Too many results, would dilute quality",
+            },
+        ],
+        reasoning="Focusing on top 3 papers gives concise, high-value summary.",
+    )
+
+    time.sleep(0.2)  # Simulate summarization
+
+    # Simulate: decide whether to retry with refined query
+    agentlens.log_decision(
+        type="CUSTOM",
+        chosen={
+            "name": "return_results",
+            "confidence": 0.95,
+            "params": {"result_count": 3},
+        },
+        alternatives=[
+            {
+                "name": "refine_and_retry",
+                "confidence": 0.3,
+                "reason_rejected": "Current results are already high quality",
+            },
+        ],
+        reasoning="Results are comprehensive enough; no need to retry.",
+    )
+
+# 3. Shutdown — flush any pending data
+agentlens.shutdown()
+
+print("Done! Check your AgentLens dashboard for the 'research-task' trace.")