Interaction modes

ravenclaws
ravenclaws --mode single

ravenclaws --mode swarm
ravenclaws --mode swarm --swarm-topology mesh --swarm-max-workers 50

ravenclaws --mode supervisor

ravenclaws --mode orchestrate
ravenclaws --mode orchestrate --swarm-dynamic-roles --swarm-communication

ravenclaws --mode debate
ravenclaws --mode debate --pattern-max-rounds 5 --pattern-verbose

ravenclaws --mode review-loop
ravenclaws --mode review-loop --pattern-max-review 5

ravenclaws --mode research-synthesize
ravenclaws --mode research-synthesize --pattern-research-agents 5

ravenclaws --mode voting
ravenclaws --mode voting --pattern-voters 5

ravenclaws --exec "What is the capital of France?"
echo "Summarize this text" | ravenclaws --exec
ravenclaws --exec "Analyze this image" --image photo.jpg

ravenclaws --repl
ravenclaws --repl --image diagram.png

ravenclaws --serve
ravenclaws --serve --server-host 0.0.0.0 --server-port 8080

# Chat
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello!"}'

# Background task
curl -X POST http://localhost:8080/execute \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Analyze this data"}'

# List tools
curl http://localhost:8080/tools

# Execute a tool
curl -X POST http://localhost:8080/tools/web_fetch \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com"}'

# Health & metrics
curl http://localhost:8080/health
curl http://localhost:8080/ready
curl http://localhost:8080/metrics

# Reload config
curl -X POST http://localhost:8080/reload

ravenclaws --mcp-server

ravenclaws --mcp-sse-server
ravenclaws --mcp-sse-server --mcp-sse-host 0.0.0.0 --mcp-sse-port 8081

ravenclaws --mcp-command "npx -y @modelcontextprotocol/server-filesystem /tmp"
ravenclaws --mcp-command "python mcp-server.py" --mcp-args "--port 9000"
ravenclaws --mcp-command "my-server" --mcp-env "API_KEY=sk-..."

# Submit a task
TASK_ID=$(ravenclaws --background --exec "Analyze this data")
echo "Task ID: $TASK_ID"

# Check status
ravenclaws --task-status "$TASK_ID"

# List all tasks
ravenclaws --task-list

# Cancel a task
ravenclaws --task-cancel "$TASK_ID"

# Resume incomplete tasks on restart
ravenclaws --task-resume

ravenclaws --scheduler
ravenclaws --scheduler --webhook-port 9090

[scheduler]
triggers = [
  { type = "cron", schedule = "0 */6 * * *", prompt = "Daily system health check" },
  { type = "webhook", endpoint = "/webhook/github", prompt = "Process GitHub event" },
  { type = "file_watch", path = "/data/incoming", prompt = "Process new file" },
]

ravenclaws --heartbeat --heartbeat-goal "Monitor system health and report anomalies"
ravenclaws --heartbeat \
  --heartbeat-goal "Watch for security threats" \
  --heartbeat-tick-interval 60 \
  --heartbeat-max-ticks 100

# Resume a previous session
ravenclaws --heartbeat --heartbeat-session "session-uuid"

ravenclaws --eval tests/eval/basic-suite.toml
ravenclaws --eval tests/eval/security-suite.toml --eval-json

ravenclaws -c /etc/ravenclaws/config.toml

export RAVENCLAWS__LLM__PROVIDER=openai
export RAVENCLAWS__LLM__ENDPOINT=https://api.openai.com
export RAVENCLAWS__LLM__API_KEY=sk-...
export RAVENCLAWS__LLM__MODEL=gpt-4o
export RAVENCLAWS__SECURITY__REQUIRE_TLS=false
export RAVENCLAWS__RUNTIME__HOST=0.0.0.0
export RAVENCLAWS__RUNTIME__PORT=8080

docker build -t ravenclaws:latest .
docker run --rm ravenclaws:latest --version
docker run --rm -p 8080:8080 ravenclaws:latest --serve

docker compose up
# RavenClaws at http://localhost:8080
# LiteLLM at http://localhost:4000

docker build -f Dockerfile.slim -t ravenclaws:slim .
docker run --rm ravenclaws:slim --mcp-command "npx -y @modelcontextprotocol/server-filesystem /tmp"

kubectl apply -f k8s/deployment.yaml

helm install ravenclaws charts/ravenclaws/

[dependencies]
ravenclaws = "1.1"

use ravenclaws::config::Config;
use ravenclaws::llm::{create_client, ChatMessage, LLMProviderTrait};

let config = Config::load(None)?;
let llm = create_client(&config.llm)?;
let response = llm.chat(vec![
    ChatMessage::new("user", "Hello!"),
]).await?;
println!("{}", response.choices[0].message.content);

use ravenclaws::config::Config;
use ravenclaws::llm::create_client;
use ravenclaws::agent::{run_agent_loop, AgentLoopConfig};
use ravenclaws::tools::ToolRegistry;

let config = Config::load(None)?;
let llm = create_client(&config.llm)?;
let tool_registry = ToolRegistry::with_config(&config);
let loop_config = AgentLoopConfig::default();
let response = run_agent_loop(
    llm,
    "Analyze this data",
    &config.llm.system_prompt,
    loop_config,
    None,
    Some(tool_registry),
).await?;
println!("{}", response);

cargo run --example basic_chat
cargo run --example agent_loop
cargo run --example swarm
cargo run --example mcp_client -- "npx @modelcontextprotocol/server-filesystem /tmp"
cargo run --example heartbeat

# Uses all configured providers in rotation
ravenclaws --mode single
ravenclaws --mode debate

ravenclaws --exec "Describe this image" --image photo.jpg
ravenclaws --repl --image diagram.png --image chart.png
ravenclaws --exec "Compare these images" -I img1.jpg -I img2.jpg

# One-shot
ravenclaws --exec "Hello"

# Interactive
ravenclaws --repl

# HTTP server
ravenclaws --serve

# MCP server
ravenclaws --mcp-server

# Background task
ravenclaws --background --exec "Long task"

# Autonomous agent
ravenclaws --heartbeat --heartbeat-goal "Monitor system"

# Swarm
ravenclaws --mode swarm

# Multi-agent patterns
ravenclaws --mode debate
ravenclaws --mode review-loop
ravenclaws --mode research-synthesize
ravenclaws --mode voting

# Eval
ravenclaws --eval tests/eval/basic-suite.toml

# Scheduler
ravenclaws --scheduler

# With images
ravenclaws --exec "Describe" --image photo.jpg

# Docker
docker run --rm ravenclaws:latest --exec "Hello"

# Kubernetes
kubectl apply -f k8s/deployment.yaml