#!/usr/bin/env python3 import argparse from pathlib import Path from agentic_networks.network import load_topology, Network from agentic_networks.agentic_network import AgenticNetwork, MODELS from agentic_networks.experiment import ( DEFAULT_LOG_DIR, chown_to_user, collect_node_logs, collect_route_tables, generate_routes_pdf, setup_logging, setup_node_logs, write_agent_reports, write_final_report, write_timeline_html, ) def parse_args(): parser = argparse.ArgumentParser(description="Simple routing experiment") parser.add_argument("--log-level", "-l", default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"]) parser.add_argument("--model", "-m", default="sonnet", choices=list(MODELS.keys())) parser.add_argument("--report-model", default=None, choices=list(MODELS.keys()), metavar="MODEL", help="Model for final-report generation (default: same as --model)") parser.add_argument("--log-dir", "-d", default=str(DEFAULT_LOG_DIR), metavar="DIR") parser.add_argument("--max-iterations", "-i", type=int, default=50, metavar="N") parser.add_argument("--max-tokens", "-t", type=int, default=16384, metavar="N") parser.add_argument("--window-size", "-w", type=int, default=40, metavar="N", help="Sliding window of messages sent to the model per turn (Claude only). " "Default 40 = last 20 turns, matching half of the default --max-iterations=50. " "Early-iteration discoveries are recoverable from the live routing table.") parser.add_argument("--topology", required=True, metavar="FILE") parser.add_argument("--sequential", "-s", action="store_true", default=False, help="Run agents sequentially round-robin instead of concurrently") parser.add_argument("--vllm-host", default="localhost", metavar="HOST") parser.add_argument("--vllm-port", type=int, default=8000, metavar="PORT") prompt_group = parser.add_mutually_exclusive_group(required=True) prompt_group.add_argument("--prompt", "-p", metavar="FILE", help="Single prompt file given to every agent") prompt_group.add_argument("--prompts-dir", metavar="DIR", help="Directory of per-node prompt files ({node}.txt)") return parser.parse_args() def load_prompts(args) -> tuple[str | dict[str, str], str]: """Return (prompts, stem) where prompts is either a shared string or a per-node dict.""" if args.prompt: p = Path(args.prompt) return p.read_text(), p.stem d = Path(args.prompts_dir) prompts = {f.stem: f.read_text() for f in sorted(d.glob("*.txt")) if f.stem != "final-report"} if not prompts: raise SystemExit(f"No .txt files found in {d}") return prompts, d.name def write_report(network: Network, route_tables: dict[str, str], report_path: Path) -> str: connectivity_str = network.test_all_connectivity() routing_section = "\n".join( f"--- {name} ---\n{route_tables[name] or '(empty)'}" for name in sorted(route_tables) ) report_path.write_text( "=== Connectivity Matrix ===\n" + connectivity_str + "\n\n=== Routing Tables ===\n\n" + routing_section + "\n" ) return connectivity_str def run(seed_stable: bool = False): """Run a routing experiment. seed_stable=False: clear global routes so agents must establish reachability themselves (clean slate — convergence is part of the test). seed_stable=True: pre-install an arbitrary stable routing solution so the network starts already converged (agents focus on the scenario, not bring-up). """ args = parse_args() log_dir = Path(args.log_dir) log_dir.mkdir(parents=True, exist_ok=True) logger = setup_logging(args.log_level) logger.info("Building Mininet network...") network = Network(load_topology(args.topology)) network.start() if seed_stable: logger.info("Seeding an arbitrary stable routing solution (pre-converged start)...") network.seed_stable_routes() else: network.clear_routing_tables() prompts, prompt_stem = load_prompts(args) topology_stem = Path(args.topology).stem run_stem = f"{prompt_stem}-{args.model}-{topology_stem}" setup_node_logs(network.hosts, log_dir, run_stem) logger.info("Writing per-node logs to %s/", log_dir) logger.info("Using model: %s (%s)", args.model, MODELS[args.model]) try: anet = AgenticNetwork( network=network, initial_prompts=prompts, model_key=args.model, max_iterations=args.max_iterations, max_tokens=args.max_tokens, vllm_host=args.vllm_host, vllm_port=args.vllm_port, window_size=args.window_size, ) results = anet.run(concurrent=not args.sequential) route_tables = collect_route_tables(network) connectivity_str = write_report(network, route_tables, log_dir / f"{run_stem}.txt") logger.info("Report written to %s/%s.txt", log_dir, run_stem) logger.info("Gathering agent self-reports...") agent_reports = anet.gather_reports() write_agent_reports(agent_reports, log_dir, run_stem, logger) generate_routes_pdf(network, route_tables, log_dir, run_stem, logger, show_delays=False) write_timeline_html(log_dir, run_stem, network.hosts, logger) final_report_file = Path(args.prompts_dir) / "final-report.txt" if args.prompts_dir else None if final_report_file and final_report_file.exists(): node_logs = collect_node_logs(log_dir, run_stem, network.hosts) write_final_report( model_key=args.report_model or args.model, vllm_host=args.vllm_host, vllm_port=args.vllm_port, max_tokens=args.max_tokens, final_prompt=final_report_file.read_text(), agent_reports=agent_reports, agent_results=results, node_logs=node_logs, connectivity=connectivity_str, route_tables=route_tables, log_dir=log_dir, run_stem=run_stem, logger=logger, ) elif final_report_file: print("No final-report.txt found in prompts directory, skipping final report generation.") network.stop() finally: chown_to_user(log_dir) if __name__ == "__main__": run(seed_stable=False)