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/openai_agent.py
+++ b/examples/openai_agent.py
@@ -0,0 +1,113 @@
+"""
+AgentLens OpenAI Integration Example — Wrap the OpenAI client for automatic tracing.
+
+Demonstrates:
+  - Wrapping openai.OpenAI() so all LLM calls are traced as spans
+  - Combining automatic LLM tracing with manual decision logging
+  - Using trace tags and metadata
+
+Usage:
+    pip install vectry-agentlens openai
+    export OPENAI_API_KEY="sk-..."
+    python openai_agent.py
+"""
+
+import agentlens
+from agentlens.integrations.openai import wrap_openai
+import openai  # pip install openai
+
+# 1. Initialize AgentLens
+agentlens.init(
+    api_key="your-api-key-here",
+    endpoint="http://localhost:4200",
+)
+
+# 2. Create and wrap the OpenAI client — all completions are now auto-traced
+client = openai.OpenAI()
+wrap_openai(client)
+
+# 3. Use the wrapped client inside a trace
+with agentlens.trace("email-drafting-agent", tags=["openai", "email", "demo"]):
+    # Decision: which model to use for this task
+    agentlens.log_decision(
+        type="TOOL_SELECTION",
+        chosen={
+            "name": "gpt-4o",
+            "confidence": 0.9,
+            "params": {"temperature": 0.7, "max_tokens": 512},
+        },
+        alternatives=[
+            {
+                "name": "gpt-4o-mini",
+                "confidence": 0.7,
+                "reason_rejected": "Task needs higher quality reasoning for tone",
+            },
+        ],
+        reasoning="Email drafting requires nuanced tone — use the larger model.",
+    )
+
+    # This call is automatically captured as an LLM_CALL span
+    classification = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": "Classify the intent of this email request."},
+            {
+                "role": "user",
+                "content": "Write a professional follow-up email to a client "
+                "who hasn't responded to our proposal in 2 weeks.",
+            },
+        ],
+        temperature=0.3,
+        max_tokens=100,
+    )
+
+    intent = classification.choices[0].message.content
+    print(f"Classified intent: {intent}")
+
+    # Decision: choose email style based on classification
+    agentlens.log_decision(
+        type="ROUTING",
+        chosen={
+            "name": "polite_follow_up",
+            "confidence": 0.88,
+            "params": {"tone": "professional-warm", "urgency": "medium"},
+        },
+        alternatives=[
+            {
+                "name": "formal_reminder",
+                "confidence": 0.65,
+                "reason_rejected": "Too stiff for a 2-week follow-up",
+            },
+            {
+                "name": "casual_check_in",
+                "confidence": 0.4,
+                "reason_rejected": "Client relationship is still formal",
+            },
+        ],
+        reasoning="Professional-warm tone balances urgency with courtesy.",
+    )
+
+    # Second LLM call — also auto-captured
+    draft = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[
+            {
+                "role": "system",
+                "content": "You draft professional emails. Tone: warm but professional.",
+            },
+            {
+                "role": "user",
+                "content": f"Draft a polite follow-up email. Context: {intent}",
+            },
+        ],
+        temperature=0.7,
+        max_tokens=512,
+    )
+
+    email_body = draft.choices[0].message.content
+    print(f"\nDrafted email:\n{email_body}")
+
+# 4. Shutdown
+agentlens.shutdown()
+
+print("\nDone! Check AgentLens dashboard for the 'email-drafting-agent' trace.")