feat: add quickstarts to examples dir

Author: carenthomas

examples/quickstart.py

@@ -0,0 +1,37 @@
+"""
+Quickstart - Agentic Learning SDK
+
+Get started with persistent memory in 30 seconds.
+
+Prerequisites:
+    pip install agentic-learning anthropic
+    export ANTHROPIC_API_KEY="your-api-key"
+    export LETTA_API_KEY="your-api-key"
+
+Usage:
+    python3 quickstart.py
+"""
+
+from anthropic import Anthropic
+from agentic_learning import learning
+
+# Initialize LLM client
+client = Anthropic()
+
+def ask_claude(message: str):
+    """Send a message to Claude and print the response."""
+    print(f"User: {message}\n")
+
+    # That's it - wrap your API calls to enable persistent memory
+    with learning(agent="quickstart-demo"):
+        response = client.messages.create(
+            model="claude-sonnet-4-20250514",
+            max_tokens=1024,
+            messages=[{"role": "user", "content": message}]
+        )
+        print(f"Assistant: {response.content[0].text}\n")
+
+# Memory automatically persists across LLM API calls
+ask_claude("My name is Alice and I love Python.")
+
+ask_claude("What's my name and favorite language?")

examples/quickstart.ts

@@ -0,0 +1,44 @@
+/**
+ * Quickstart - Agentic Learning SDK (TypeScript)
+ *
+ * Get started with persistent memory in 30 seconds.
+ *
+ * Prerequisites:
+ *     npm install @letta-ai/agentic-learning @anthropic-ai/sdk
+ *     export ANTHROPIC_API_KEY="your-api-key"
+ *     export LETTA_API_KEY="your-api-key"
+ *
+ * Usage:
+ *     npx tsx quickstart.ts
+ */
+
+import Anthropic from "@anthropic-ai/sdk";
+import { withLearning } from "@letta-ai/agentic-learning";
+
+// Initialize LLM client
+const client = new Anthropic();
+
+async function askClaude(message: string) {
+  console.log(`User: ${message}\n`);
+
+  // That's it - wrap your API calls to enable persistent memory
+  await withLearning({ agent: 'quickstart-ts-demo' }, async () => {
+    const response = await client.messages.create({
+      model: "claude-sonnet-4-20250514",
+      max_tokens: 1024,
+      messages: [{ role: "user", content: message }],
+    });
+
+    const text = response.content[0].type === "text" ? response.content[0].text : "";
+    console.log(`Assistant: ${text}\n`);
+  });
+}
+
+async function main() {
+  // Memory automatically persists across LLM API calls
+  await askClaude("My name is Bob and I love TypeScript.");
+
+  await askClaude("What's my name and favorite language?");
+}
+
+main();

examples/quickstart_async.py

@@ -0,0 +1,42 @@
+"""
+Quickstart Async - Agentic Learning SDK
+
+Get started with persistent memory in async Python.
+
+Prerequisites:
+    pip install agentic-learning anthropic
+    export ANTHROPIC_API_KEY="your-api-key"
+    export LETTA_API_KEY="your-api-key"
+
+Usage:
+    python3 quickstart_async.py
+"""
+
+import asyncio
+from anthropic import AsyncAnthropic
+from agentic_learning import learning_async
+
+# Initialize LLM client
+client = AsyncAnthropic()
+
+async def ask_claude(message: str):
+    """Send a message to Claude and print the response."""
+    print(f"User: {message}\n")
+
+    # That's it - wrap your API calls to enable persistent memory
+    async with learning_async(agent="quickstart-demo"):
+        response = await client.messages.create(
+            model="claude-sonnet-4-20250514",
+            max_tokens=1024,
+            messages=[{"role": "user", "content": message}]
+        )
+        print(f"Assistant: {response.content[0].text}\n")
+
+async def main():
+    # Memory automatically persists across LLM API calls
+    await ask_claude("My name is Alice and I love Python.")
+
+    await ask_claude("What's my name and favorite language?")
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "agentic-learning"
-version = "0.2.1"
+version = "0.2.2"
 description = "Drop-in SDK for adding persistent memory and learning to any agent."
 readme = "../README.md"
 requires-python = ">=3.10"

typescript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@letta-ai/agentic-learning",
-  "version": "0.2.1",
+  "version": "0.2.2",
   "description": "Agentic Learning SDK for TypeScript - Automatic memory integration with Letta",
   "main": "./dist/index.js",
   "types": "./dist/index.d.ts",
@@ -67,10 +67,9 @@
     "typescript": "^5.3.0"
   },
   "peerDependencies": {
-    "@anthropic-ai/sdk": "^0.30.1",
-    "@google/generative-ai": "^0.21.0",
-    "claude-agent-sdk": "^0.1.0",
-    "openai": "^4.104.0"
+    "@anthropic-ai/sdk": ">=0.30.1",
+    "@google/generative-ai": ">=0.21.0",
+    "openai": ">=4.104.0"
   },
   "peerDependenciesMeta": {
     "openai": {
@@ -81,9 +80,6 @@
     },
     "@google/generative-ai": {
       "optional": true
-    },
-    "claude-agent-sdk": {
-      "optional": true
     }
   },
   "engines": {

typescript/src/core.ts

@@ -62,7 +62,7 @@ export function getCurrentConfig(): LearningConfig | null {
  */
 export function learning(options: {
   agent: string;
-  client: AgenticLearning;
+  client?: AgenticLearning;
   captureOnly?: boolean;
   memory?: string[];
   model?: string;
@@ -79,9 +79,12 @@ export function learning(options: {
   // Save the previous context (for nested contexts)
   const previousStore = learningContext.getStore();
 
+  // Create default client if not provided
+  const client = options.client || new (require('./client').AgenticLearning)();
+
   const config: LearningConfig = {
     agentName: options.agent,
-    client: options.client,
+    client: client,
     captureOnly: options.captureOnly || false,
     memory: options.memory || [],
     model: options.model,
@@ -127,7 +130,7 @@ export function learning(options: {
 export async function withLearning<T>(
   options: {
     agent: string;
-    client: AgenticLearning;
+    client?: AgenticLearning;
     captureOnly?: boolean;
     memory?: string[];
     model?: string;
@@ -141,9 +144,12 @@ export async function withLearning<T>(
     interceptorsInstalled = true;
   }
 
+  // Create default client if not provided
+  const client = options.client || new (require('./client').AgenticLearning)();
+
   const config: LearningConfig = {
     agentName: options.agent,
-    client: options.client,
+    client: client,
     captureOnly: options.captureOnly || false,
     memory: options.memory || [],
     model: options.model,