> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentium.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance

> Agentium performance benchmarks, optimizations, and comparison with LangChain and Agno.

# Performance

Agentium is built for minimal overhead — fewer tokens, faster responses, lower cost. This page covers the key optimizations and benchmark results.

***

## Benchmark Results

All benchmarks use `gpt-4o-mini`, identical prompts, and 5 runs per scenario. Agentium and LangChain run on Node.js; Agno runs on Python.

### Simple Completion

| Metric            | Agentium       | LangChain      | Agno           |
| ----------------- | -------------- | -------------- | -------------- |
| Startup (ms)      | **171**        | 301            | 2730           |
| Avg Response (ms) | 769            | **737**        | 2077           |
| Avg Prompt Tokens | **28**         | **28**         | **28**         |
| Avg Total Tokens  | **35**         | **35**         | **35**         |
| Avg Cost / Run    | **\$0.000008** | **\$0.000008** | **\$0.000008** |

### Tool Calling

| Metric            | Agentium       | LangChain      | Agno       |
| ----------------- | -------------- | -------------- | ---------- |
| Avg Response (ms) | **1617**       | 1678           | 3064       |
| Avg Prompt Tokens | **167**        | **167**        | 173        |
| Avg Total Tokens  | **196**        | **196**        | 202        |
| Avg Cost / Run    | **\$0.000042** | **\$0.000042** | \$0.000043 |

Agentium and LangChain produce identical tool schemas (167 prompt tokens). Agentium strips verbose JSON Schema metadata (`$schema`, `additionalProperties`) to keep schemas compact.

### Multi-turn Memory

| Metric                | Agentium       | LangChain  | Agno       |
| --------------------- | -------------- | ---------- | ---------- |
| Avg Response (ms)     | 2408           | **2324**   | 5892       |
| Avg Prompt Tokens     | 189            | 309        | **94**     |
| Avg Completion Tokens | **30**         | 57         | 66         |
| Avg Total Tokens      | 219            | 366        | **160**    |
| Avg Cost / Run        | **\$0.000046** | \$0.000081 | \$0.000054 |

Agentium uses 39% fewer prompt tokens and 43% less cost than LangChain for multi-turn conversations. LangChain injects heavier system prompts and history formatting overhead.

### Summary

| Scenario          | Fastest               | Fewest Tokens      | Cheapest                  |
| ----------------- | --------------------- | ------------------ | ------------------------- |
| Simple Completion | LangChain (737ms)     | Tied (35)          | Tied                      |
| Tool Calling      | **Agentium** (1617ms) | **Agentium** (196) | Tied                      |
| Multi-turn Memory | LangChain (2324ms)    | Agno (160)         | **Agentium** (\$0.000046) |

Agentium is the fastest for tool calling, the cheapest for multi-turn conversations, and matches LangChain on tool schema efficiency. Response latency is within noise across simple completions.

***

## Optimizations

### Tool Schema Caching & Optimization

Tool definitions (Zod-to-JSON Schema conversion) are computed once at agent construction and cached. Verbose JSON Schema metadata (`$schema`, `additionalProperties`, `description` on the root object) is stripped automatically — reducing token overhead without losing semantic information.

```typescript theme={null}
const agent = new Agent({
  name: "bot",
  model: openai("gpt-4o"),
  tools: [weatherTool, searchTool],
});
```

For OpenAI models, tools can opt into **strict mode** for guaranteed valid JSON output:

```typescript theme={null}
const weatherTool = defineTool({
  name: "getWeather",
  description: "Get weather for a city",
  parameters: z.object({ city: z.string() }),
  execute: async ({ city }) => `Sunny in ${city}`,
  strict: true, // enables OpenAI Structured Outputs on this tool
});
```

### Automatic Retry

Transient LLM API failures are automatically retried with exponential backoff + jitter. Retryable errors include HTTP 429, 5xx, and network errors.

```typescript theme={null}
const agent = new Agent({
  name: "reliable-bot",
  model: openai("gpt-4o"),
  retry: {
    maxRetries: 5,
    initialDelayMs: 1000,
    maxDelayMs: 30000,
  },
});
```

Default: 3 retries, 500ms initial delay, 10s max delay.

### Token-Based History Trimming

Set `maxContextTokens` to automatically trim conversation history (oldest messages first) to fit within a token budget:

```typescript theme={null}
const agent = new Agent({
  name: "bot",
  model: openai("gpt-4o"),
  maxContextTokens: 8000,
});
```

### Non-Blocking User Memory

When `userMemory` is configured, fact extraction runs asynchronously in the background after the response is returned. This eliminates 500-1000ms+ of latency per request.

### Smart Context Deduplication

When `userMemory.asTool()` is registered in the agent's tools, user facts are **not** also injected into the system prompt. The agent retrieves facts on demand via the tool, saving tokens.

### Streaming Usage Tracking

Token usage (`promptTokens`, `completionTokens`, `totalTokens`, `reasoningTokens`) is accurately tracked in both `run()` and `stream()` modes. Stream usage is accumulated from provider `finish` chunks.

***

## Methodology

* All benchmarks use `gpt-4o-mini` with identical prompts
* Each scenario runs 5 times; results are averaged
* Startup time measures framework import + agent initialization
* Cost uses gpt-4o-mini pricing: $0.15/1M input, $0.6/1M output
* Network latency to OpenAI is shared across all frameworks
* Full benchmark scripts are in `benchmarks/` in the repository