""" AgentMesh HuggingFace Space — Interactive AI Agent Cost Savings Calculator. Deployed at: https://huggingface.co/spaces/anilatambharii/agentmesh Run locally: pip install gradio python spaces/app.py """ import gradio as gr # ── Cost model ────────────────────────────────────────────────────────────── MODEL_COSTS = { "claude-haiku-4-5": 0.80, "claude-sonnet-4-6": 3.00, "claude-opus-4-8": 15.00, "gpt-4o-mini": 0.15, "gpt-4o": 2.50, "gemini-1.5-flash": 0.075, "gemini-1.5-pro": 1.25, "meta/llama-3.1-8b (NIM)": 0.20, "meta/llama-3.1-70b (NIM)": 0.99, } def calculate_savings( monthly_tokens_m: float, current_model: str, team_size: int, avg_iterations: int, enable_caching: bool, enable_routing: bool, enable_compression: bool, enable_circuit_breaker: bool, ) -> tuple: """Calculate estimated cost savings with AgentMesh.""" cost_per_1m = MODEL_COSTS.get(current_model, 3.0) monthly_tokens = monthly_tokens_m * 1_000_000 # Baseline cost baseline_cost = (monthly_tokens / 1_000_000) * cost_per_1m # Calculate savings from each feature savings_breakdown = {} remaining_tokens = monthly_tokens if enable_caching: cache_savings_pct = 0.20 # 20% of calls are near-duplicates saved_tokens = remaining_tokens * cache_savings_pct savings_breakdown["Semantic Caching"] = (saved_tokens / 1_000_000) * cost_per_1m remaining_tokens -= saved_tokens if enable_routing: # Route ~70% of calls to haiku, 30% to chosen model haiku_cost = MODEL_COSTS["claude-haiku-4-5"] blended_cost = haiku_cost * 0.70 + cost_per_1m * 0.30 routing_savings_per_1m = cost_per_1m - blended_cost savings_breakdown["Dynamic Model Routing"] = (remaining_tokens / 1_000_000) * routing_savings_per_1m remaining_tokens = remaining_tokens # tokens same, cost drops if enable_compression: # O(n²) context growth: compression saves ~30% of tokens in long chains compression_pct = min(0.30, 0.05 * avg_iterations) saved_tokens = remaining_tokens * compression_pct savings_breakdown["Prompt Compression"] = (saved_tokens / 1_000_000) * cost_per_1m remaining_tokens -= saved_tokens if enable_circuit_breaker: # ~5% of runs hit runaway loops; circuit breaker prevents 100% of that waste runaway_pct = 0.05 saved_tokens = remaining_tokens * runaway_pct savings_breakdown["Circuit Breaker"] = (saved_tokens / 1_000_000) * cost_per_1m total_savings = sum(savings_breakdown.values()) new_cost = max(baseline_cost - total_savings, baseline_cost * 0.10) actual_savings = baseline_cost - new_cost savings_pct = (actual_savings / baseline_cost * 100) if baseline_cost > 0 else 0 # Per-team estimate per_team_baseline = baseline_cost / team_size per_team_new = new_cost / team_size # Breakdown text breakdown_lines = ["**Savings Breakdown:**\n"] for feature, saving in savings_breakdown.items(): pct = saving / baseline_cost * 100 breakdown_lines.append(f"- {feature}: **${saving:,.0f}/mo** ({pct:.0f}% reduction)") breakdown_text = "\n".join(breakdown_lines) summary = f""" ## 💰 AgentMesh Cost Savings Analysis | | Without AgentMesh | With AgentMesh | |---|---|---| | **Monthly Cost** | **${baseline_cost:,.0f}** | **${new_cost:,.0f}** | | **Per-Engineer** | ${per_team_baseline:,.0f}/mo | ${per_team_new:,.0f}/mo | | **Annual Cost** | ${baseline_cost * 12:,.0f} | ${new_cost * 12:,.0f} | | **Annual Savings** | — | **${actual_savings * 12:,.0f}** | ### Total Savings: {savings_pct:.0f}% (${actual_savings:,.0f}/month) {breakdown_text} --- *Based on {monthly_tokens_m:.1f}M tokens/month, {team_size} engineers, {avg_iterations} avg iterations/run.* """.strip() chart_data = { "labels": list(savings_breakdown.keys()), "values": [round(v, 2) for v in savings_breakdown.values()], } return summary, f"${actual_savings:,.0f}/month saved ({savings_pct:.0f}% reduction)" # ── Gradio UI ───────────────────────────────────────────────────────────── with gr.Blocks(title="AgentMesh — AI Agent Cost Calculator") as demo: gr.HTML("""

🕸️ AgentMesh Cost Savings Calculator

The governance plane for AI agents. See how much you'd save.

GitHub · PyPI · pip install agentmesh-proxy

""") with gr.Row(): with gr.Column(scale=1): gr.Markdown("### Your Current Setup") monthly_tokens = gr.Slider( minimum=0.1, maximum=500, step=0.1, value=10.0, label="Monthly Token Usage (millions)", info="Total input + output tokens per month across all agents", ) current_model = gr.Dropdown( choices=list(MODEL_COSTS.keys()), value="claude-sonnet-4-6", label="Primary Model", ) team_size = gr.Slider( minimum=1, maximum=500, step=1, value=50, label="Team Size (engineers)", info="Number of engineers using AI agents", ) avg_iterations = gr.Slider( minimum=1, maximum=50, step=1, value=10, label="Avg Iterations per Agent Run", info="Typical ReAct steps per agent invocation", ) gr.Markdown("### AgentMesh Features to Enable") enable_caching = gr.Checkbox(value=True, label="Semantic Caching (10–30% savings)") enable_routing = gr.Checkbox(value=True, label="Dynamic Model Routing (15–40% savings)") enable_compression = gr.Checkbox(value=True, label="Prompt Compression (5–20% savings)") enable_circuit_breaker = gr.Checkbox(value=True, label="Circuit Breaker (prevents runaway loops)") calc_btn = gr.Button("Calculate Savings", variant="primary", size="lg") with gr.Column(scale=1): gr.Markdown("### Results") savings_headline = gr.Markdown("*Configure your setup and click Calculate.*") result_md = gr.Markdown(elem_classes=["result-box"]) calc_btn.click( fn=calculate_savings, inputs=[ monthly_tokens, current_model, team_size, avg_iterations, enable_caching, enable_routing, enable_compression, enable_circuit_breaker, ], outputs=[result_md, savings_headline], ) gr.Markdown(""" --- ### How AgentMesh Works ```python from agentmesh import AgentMesh from agentmesh.policy.engine import Policy mesh = AgentMesh(policy=Policy.from_yaml("policy.yaml")) # Wrap any framework — zero changes to your existing agent governed_graph = mesh.wrap_langgraph(your_graph) # LangGraph governed_crew = mesh.wrap_crewai(your_crew) # CrewAI governed_agent = mesh.wrap_openai_agent(your_agent) # OpenAI Agents governed_autogen = mesh.wrap_autogen(your_agent) # AutoGen v2 print(mesh.stats) # {'tokens_used': 45231, 'cost_usd': 0.054, 'cache': {'hit_rate': 0.31}} ``` ### Quick Start ```bash pip install agentmesh-proxy agentmesh validate my-policy.yaml agentmesh compliance report --framework eu-ai-act --policy my-policy.yaml ``` Built by [Anil Prasad](https://github.com/anilatambharii) · [Apache 2.0 License](https://github.com/anilatambharii/agentmesh/blob/main/LICENSE) """) if __name__ == "__main__": demo.launch( theme=gr.themes.Soft(primary_hue="indigo"), css=".header { text-align: center; padding: 20px 0; } .result-box { font-size: 1.1em; }", )