OpenJarvis/docs/getting-started/quickstart.md

---
title: Quick Start
description: Get up and running with OpenJarvis in minutes
---

# Quick Start

This guide walks through the core workflows of OpenJarvis: asking questions, using agents with tools, managing memory, running benchmarks, and starting the API server.

!!! info "Prerequisites"
    Make sure you have [installed OpenJarvis](installation.md) and have at least one inference backend running (e.g., `ollama serve`).

## Initialize Configuration

Start by detecting your hardware and generating a configuration file:

```bash
jarvis init
```

This runs hardware auto-detection (GPU vendor, VRAM, CPU, RAM) and writes a config file to `~/.openjarvis/config.toml` with sensible defaults for your system. It also selects the recommended inference engine.

```
Detecting hardware...
  Platform : linux
  CPU      : AMD EPYC 7763 (128 cores)
  RAM      : 512.0 GB
  GPU      : NVIDIA A100 (80.0 GB VRAM, x8)

Config written successfully.
```

To overwrite an existing config:

```bash
jarvis init --force
```

See [Configuration](configuration.md) for the full config reference.

## Your First Question

### Via CLI

The simplest way to interact with OpenJarvis is the `ask` command:

```bash
jarvis ask "What is the capital of France?"
```

OpenJarvis will auto-detect a running engine, select a model using the configured router policy, and return the response.

#### CLI Options

| Option | Description | Example |
|--------|-------------|---------|
| `-m`, `--model` | Override model selection | `jarvis ask -m qwen3:8b "Hello"` |
| `-e`, `--engine` | Force a specific engine | `jarvis ask -e ollama "Hello"` |
| `-t`, `--temperature` | Sampling temperature (default: 0.7) | `jarvis ask -t 0.2 "Hello"` |
| `--max-tokens` | Max tokens to generate (default: 1024) | `jarvis ask --max-tokens 2048 "Hello"` |
| `--json` | Output raw JSON result | `jarvis ask --json "Hello"` |
| `--no-stream` | Disable streaming | `jarvis ask --no-stream "Hello"` |
| `--no-context` | Disable memory context injection | `jarvis ask --no-context "Hello"` |
| `-a`, `--agent` | Use an agent | `jarvis ask -a orchestrator "Hello"` |
| `--tools` | Comma-separated tools | `jarvis ask --tools calculator,think "2+2"` |
| `--router` | Router policy for model selection | `jarvis ask --router heuristic "Hello"` |

### Via Python SDK

The `Jarvis` class provides a high-level Python interface:

```python
from openjarvis import Jarvis

j = Jarvis()
response = j.ask("What is the capital of France?")
print(response)
j.close()
```

For detailed results including token usage and model info:

```python
result = j.ask_full("What is the capital of France?")
print(result["content"])  # The response text
print(result["model"])    # Model that handled the query
print(result["engine"])   # Engine that ran inference
print(result["usage"])    # Token usage statistics
```

#### SDK Constructor Options

```python
# Use default config (auto-detected hardware, ~/.openjarvis/config.toml)
j = Jarvis()

# Override the model
j = Jarvis(model="qwen3:8b")

# Override the engine
j = Jarvis(engine_key="ollama")

# Use a custom config file
j = Jarvis(config_path="/path/to/config.toml")
```

!!! warning "Always call `close()`"
    The `Jarvis` instance holds references to telemetry stores and memory backends. Call `j.close()` when you are done to release resources.

## Using Agents with Tools

Agents add multi-turn reasoning and tool-calling capabilities. The `orchestrator` agent runs a tool-calling loop, invoking tools as needed to answer the query.

### Available Agents

| Agent | Description |
|-------|-------------|
| `simple` | Single-turn, no tools. Sends the query directly to the model. |
| `orchestrator` | Multi-turn tool-calling loop. Invokes tools iteratively until it has an answer. |
| `custom` | Template for user-defined agent logic. |
| `openclaw` | HTTP/subprocess transport to external OpenClaw agent processes. |

### Available Built-in Tools

| Tool | Description |
|------|-------------|
| `calculator` | Safe mathematical expression evaluation (ast-based). |
| `think` | Reasoning scratchpad for chain-of-thought. |
| `retrieval` | Search the memory store for relevant context. |
| `llm` | Make sub-queries to another model. |
| `file_read` | Read files with path validation. |
| `web_search` | Web search via the Tavily API (requires `tools-search` extra). |

### CLI Example

```bash
jarvis ask --agent orchestrator --tools calculator,think "What is 137 * 42?"
```

### SDK Example

```python
from openjarvis import Jarvis

j = Jarvis()
result = j.ask_full(
    "What is the square root of 144?",
    agent="orchestrator",
    tools=["calculator", "think"],
)
print(result["content"])
print(result["tool_results"])  # List of tool invocations and results
print(result["turns"])         # Number of agent turns
j.close()
```

## Memory: Indexing and Search

The memory system lets you index documents and inject relevant context into queries automatically.

### Index Documents

Index a file or directory. OpenJarvis chunks the content and stores it in the configured memory backend (SQLite/FTS5 by default).

=== "CLI"

    ```bash
    # Index a directory
    jarvis memory index ./docs/

    # Index a single file with custom chunk size
    jarvis memory index ./paper.txt --chunk-size 256 --chunk-overlap 32
    ```

=== "Python SDK"

    ```python
    from openjarvis import Jarvis

    j = Jarvis()
    result = j.memory.index("./docs/", chunk_size=512, chunk_overlap=64)
    print(f"Indexed {result['chunks']} chunks")
    j.close()
    ```

### Search Memory

Query the memory store to find relevant chunks:

=== "CLI"

    ```bash
    jarvis memory search "configuration options"
    jarvis memory search -k 10 "how to deploy"
    ```

=== "Python SDK"

    ```python
    results = j.memory.search("configuration options", top_k=5)
    for r in results:
        print(f"[{r['score']:.4f}] {r['source']}: {r['content'][:100]}")
    ```

### Check Memory Statistics

=== "CLI"

    ```bash
    jarvis memory stats
    ```

=== "Python SDK"

    ```python
    stats = j.memory.stats()
    print(f"Backend: {stats['backend']}, Documents: {stats.get('count', 'N/A')}")
    ```

### Automatic Context Injection

When you have indexed documents, OpenJarvis automatically injects relevant context into your queries. The memory system searches for chunks matching your query and prepends them as system context before sending to the model.

To disable this behavior:

=== "CLI"

    ```bash
    jarvis ask --no-context "Hello"
    ```

=== "Python SDK"

    ```python
    response = j.ask("Hello", context=False)
    ```

Context injection is controlled by the `[memory]` config section. See [Configuration](configuration.md) for details on `context_injection`, `context_top_k`, `context_min_score`, and `context_max_tokens`.

## Model Management

### List Available Models

See all models available on running engines:

```bash
jarvis model list
```

This produces a table showing each model, its engine, parameter count, context length, and VRAM requirements.

### Get Model Details

```bash
jarvis model info qwen3:8b
```

### Pull a Model (Ollama)

```bash
jarvis model pull qwen3:8b
```

### SDK Model Listing

```python
from openjarvis import Jarvis

j = Jarvis()
models = j.list_models()
engines = j.list_engines()
print(f"Models: {models}")
print(f"Engines: {engines}")
j.close()
```

## Running Benchmarks

The benchmarking framework measures inference latency and throughput against your engine.

=== "All benchmarks"

    ```bash
    jarvis bench run
    ```

=== "Specific benchmark"

    ```bash
    jarvis bench run -b latency
    jarvis bench run -b throughput
    ```

=== "Custom options"

    ```bash
    # 20 samples, JSON output
    jarvis bench run -n 20 --json

    # Specific model and engine, write to file
    jarvis bench run -m qwen3:8b -e ollama -o results.jsonl
    ```

Example output:

```
Running 2 benchmark(s) on ollama/qwen3:8b (10 samples)...

latency (10 samples, 0 errors)
  mean_ms: 245.3200
  p50_ms: 238.1000
  p95_ms: 312.4500
  min_ms: 201.2000
  max_ms: 345.6000

throughput (10 samples, 0 errors)
  tokens_per_second: 42.1500
  total_tokens: 4215
  total_seconds: 100.0000
```

## Starting the API Server

OpenJarvis provides an OpenAI-compatible API server for integration with existing tools and frontends.

!!! note "Requires the `server` extra"
    ```bash
    pip install 'openjarvis[server]'
    ```

### Start the Server

```bash
jarvis serve --port 8000
```

With custom options:

```bash
jarvis serve --host 0.0.0.0 --port 8000 --engine ollama --model qwen3:8b --agent orchestrator
```

### API Endpoints

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/v1/chat/completions` | `POST` | Chat completions (streaming and non-streaming) |
| `/v1/models` | `GET` | List available models |
| `/health` | `GET` | Health check |

### Use with Any OpenAI-Compatible Client

Once the server is running, point any OpenAI-compatible client at it:

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="qwen3:8b",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
```

Or with `curl`:

```bash
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3:8b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
```

## Telemetry

OpenJarvis records telemetry for every inference call (timing, tokens, cost). View aggregated statistics:

```bash
jarvis telemetry stats
```

Export telemetry data:

```bash
jarvis telemetry export --format json
jarvis telemetry export --format csv -o telemetry.csv
```

Clear all telemetry records:

```bash
jarvis telemetry clear --yes
```

## Complete Working Example

Here is a complete end-to-end session combining multiple features:

```python
from openjarvis import Jarvis

# Initialize with defaults (auto-detect hardware and engine)
j = Jarvis()

# 1. Index some documentation
index_result = j.memory.index("./docs/", chunk_size=512)
print(f"Indexed {index_result['chunks']} chunks from {index_result['path']}")

# 2. Search memory
results = j.memory.search("how to configure engines")
for r in results:
    print(f"  [{r['score']:.3f}] {r['source']}")

# 3. Ask a question (memory context is injected automatically)
answer = j.ask("How do I configure the Ollama engine host?")
print(f"\nAnswer: {answer}")

# 4. Use an agent with tools
calc_result = j.ask_full(
    "Calculate the compound interest on $10,000 at 5% for 10 years",
    agent="orchestrator",
    tools=["calculator", "think"],
)
print(f"\nCalculation: {calc_result['content']}")
print(f"Tools used: {[t['tool_name'] for t in calc_result['tool_results']]}")
print(f"Agent turns: {calc_result['turns']}")

# 5. List available models
models = j.list_models()
print(f"\nAvailable models: {models}")

# 6. Clean up
j.close()
```

## Next Steps

- [Configuration](configuration.md) — Fine-tune engine hosts, model routing, memory settings, and more
- [CLI Reference](../user-guide/cli.md) — Full reference for all CLI commands and options
- [Python SDK](../user-guide/python-sdk.md) — Detailed SDK documentation
- [Architecture Overview](../architecture/overview.md) — Understand the four-pillar design