In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style message schema that allows agents to communicate via a shared state rather than calling each other directly, enabling modularity, traceability, and production-grade orchestration. We implement three specialized agents,…

As the industry moves from simple Large Language Model (LLM) inference toward autonomous agentic systems, the challenge for devs have shifted. It is no longer just about the model; it is about the environment in which that model operates. A team of researchers from Alibaba released CoPaw, an open-source framework designed to address this…

best_C = best[“params”][“C”] best_solver = best[“params”][“solver”] final_pipe = Pipeline([ (“scaler”, StandardScaler()), (“clf”, LogisticRegression( C=best_C, solver=best_solver, penalty=”l2″, max_iter=2000, random_state=42 )) ]) with mlflow.start_run(run_name=”final_model_run”) as final_run: final_pipe.fit(X_train, y_train) proba = final_pipe.predict_proba(X_test)[:, 1] pred = (proba >= 0.5).astype(int) metrics = { “test_auc”: float(roc_auc_score(y_test, proba)), “test_accuracy”: float(accuracy_score(y_test, pred)), “test_precision”: float(precision_score(y_test, pred, zero_division=0)), “test_recall”: float(recall_score(y_test, pred, zero_division=0)), “test_f1”: float(f1_score(y_test,…

Generative AI’s current trajectory relies heavily on Latent Diffusion Models (LDMs) to manage the computational cost of high-resolution synthesis. By compressing data into a lower-dimensional latent space, models can scale effectively. However, a fundamental trade-off persists: lower information density makes latents easier to learn but sacrifices reconstruction quality, while higher density enables near-perfect reconstruction…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”: context.get(“goal”), “assumptions”: context.get(“assumptions”, []), “prior_results”: [ {“step_id”: r.step_id, “title”: r.title, “tool”: r.tool, “output”: r.output[:1500]} for r in context.get(“results”, []) ], } if tool == “python”: code = llm_chat(…

def create_marker_cluster_map(): “””Create a map with marker clustering for large datasets””” np.random.seed(123) n_locations = 5000 lats = np.random.uniform(25, 49, n_locations) lons = np.random.uniform(-125, -65, n_locations) values = np.random.randint(1, 100, n_locations) df_markers = pd.DataFrame({ ‘lat’: lats, ‘lon’: lons, ‘value’: values }) m = folium.Map(location=[37.8, -96], zoom_start=4) marker_cluster = MarkerCluster( name=”Location Cluster”, overlay=True, control=True ).add_to(m) for…