OpenJarvis/docs/getting-started/quickstart.md

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Quick Start	Get up and running with OpenJarvis in minutes	boost 3

Quick Start

What You Can Build

OpenJarvis is a modular AI assistant framework. Here's what developers build with it:

=== "Chat with Any Model"

```bash
jarvis ask "Explain quantum entanglement" -m qwen3:8b
```

=== "Agent + Tools"

```bash
jarvis ask --agent orchestrator --tools calculator,web_search "What is the GDP of France in USD?"
```

=== "Index Docs & Ask"

```bash
jarvis memory index ./docs/
jarvis ask "How do I configure the engine?"
```

=== "5-Line Python SDK"

```python
from openjarvis import Jarvis
with Jarvis() as j:
    print(j.ask("Hello!"))
```

=== "API Server"

```bash
jarvis serve --port 8000
# Now use any OpenAI-compatible client
```

=== "Morning Digest"

```bash
cp configs/openjarvis/examples/morning-digest-mac.toml ~/.openjarvis/config.toml
jarvis connect gdrive       # one OAuth flow for Gmail, Calendar, Tasks
CARTESIA_API_KEY="..." jarvis digest --fresh
# Plays a spoken daily briefing with your email, calendar, health, and news
```

=== "Deep Research"

```bash
jarvis init --preset deep-research
jarvis memory index ~/Documents/papers/
jarvis ask "Summarize all documents about transformer architectures"
# Multi-hop search across your indexed docs with citations
```

=== "Code Assistant"

```bash
jarvis init --preset code-assistant
jarvis ask "Write a Python script that parses CSV files"
# Orchestrator agent with code execution, file I/O, and shell access
```

=== "Scheduled Monitor"

```bash
jarvis init --preset scheduled-monitor
jarvis memory index ~/Documents/
jarvis scheduler start
jarvis scheduler create \
  --prompt "Check for new emails about Project X" \
  --schedule "0 9 * * 1-5" --agent operative
# Persistent agent that runs on a cron schedule
```

For complete copy-paste patterns, see Code Snippets.

Starter Configs

Copy one of these to ~/.openjarvis/config.toml to get a pre-configured setup:

Config	For	What it does
chat-simple.toml	Any machine	Lightweight chat, no tools -- simplest setup
code-assistant.toml	Any machine	Orchestrator agent with code execution, file I/O, shell
deep-research.toml	Any machine	Multi-hop research across indexed documents with citations
scheduled-monitor.toml	Any machine	Persistent operative agent on a cron schedule
morning-digest-mac.toml	Mac (Apple Silicon)	Daily spoken briefing from email, calendar, health, news
morning-digest-linux.toml	Linux / GPU server	Same, with vLLM support
morning-digest-minimal.toml	Any machine	Just Gmail + Calendar

Or generate a config with digest included:

jarvis init --digest

This guide walks through the core workflows of OpenJarvis: the browser app, CLI, Python SDK, agents with tools, memory, benchmarks, and the API server.

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

Browser App

The quickest way to experience OpenJarvis is the full chat UI running in your browser:

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
./scripts/quickstart.sh

This launches the backend API server and a React frontend at http://localhost:5173. You get a ChatGPT-like interface with streaming responses, tool use, energy monitoring, and a telemetry dashboard — all running locally on your hardware.

To stop all services, press ++ctrl+c++ in the terminal.

!!! tip "Environment variable" Set OPENJARVIS_MODEL to change the default model: OPENJARVIS_MODEL=deepseek-r1:14b ./scripts/quickstart.sh

Initialize Configuration

Start by detecting your hardware and generating a configuration file:

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:

jarvis init --force

See Configuration for the full config reference.

Your First Question

Via CLI

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

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:

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:

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

# 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.
operative	Task-oriented agent with structured planning and execution.

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

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

SDK Example

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 agent.context_from_memory in config.toml. The retrieval parameters (context_top_k, context_min_score, context_max_tokens) live under [tools.storage]. See Configuration for details.

Model Management

List Available Models

See all models available on running engines:

jarvis model list

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

Get Model Details

jarvis model info qwen3:8b

Pull a Model (Ollama)

jarvis model pull qwen3:8b

SDK Model Listing

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 uv sync --extra server

Start the Server

jarvis serve --port 8000

With custom options:

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:

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:

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:

jarvis telemetry stats

Export telemetry data:

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

Clear all telemetry records:

jarvis telemetry clear --yes

Complete Working Example

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

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 — Fine-tune engine hosts, model routing, memory settings, and more
• CLI Reference — Full reference for all CLI commands and options
• Python SDK — Detailed SDK documentation
• Architecture Overview — Understand the five-primitive design