Camel CLI - AI Tools

The Camel CLI includes AI-powered commands that use large language models (LLMs) to help you understand, troubleshoot, and secure your integrations.

All three commands work with local models (Ollama) or cloud APIs (OpenAI, Anthropic). The CLI auto-detects the LLM provider from environment variables or a locally running Ollama instance.

Ask — chat with a running integration

The camel ask command is an AI agent that can inspect and interact with a live Camel process. It has access to routes, health checks, metrics, tracing, endpoints, and more — and can answer questions about what your integration is doing right now.

camel ask "what routes are running?"
camel ask "why is my route failing?" --name=myApp
camel ask "are there any blocked exchanges?"

Start an interactive chat session by running without a question:

camel ask

This opens a ask> prompt where you can have a multi-turn conversation, with the AI maintaining context across questions.

What the AI can do

The agent has access to tools that let it:

Inspect the running process — routes, health, endpoints, consumers, properties, inflight/blocked exchanges
Read route source code and dump route definitions as YAML or XML
Show route structure as a processor tree
Find the slowest processors with top statistics
Enable, disable, and dump message tracing
Start, stop, suspend, and resume individual routes
Search the Camel component catalog and read component documentation
List and read built-in CLI examples
Discover and run any CLI command
Read and write files in the current directory

Use --show-tools to see tool calls and results as they happen:

camel ask "show me the route structure" --show-tools

Connecting to a specific process

When multiple Camel processes are running, specify which one to inspect:

camel ask "check health" --name=myApp
camel ask "check health" --name=12345

Without --name, the CLI auto-detects when exactly one Camel process is running.

The catalog, example, file, and CLI tools work even without a running Camel process.

Explain — understand a route

The camel explain command reads route files from disk and uses an LLM to explain what they do — step by step.

camel explain hello.yaml
camel explain my-routes.java --format=markdown
camel explain route1.yaml route2.xml

Use --verbose for detailed technical information, or --catalog-context to enrich the prompt with Camel component and EIP documentation:

camel explain hello.yaml --verbose --catalog-context

Use --format=markdown for structured output with headers, lists, and code blocks.

Harden — security analysis

The camel harden command analyzes route files for security concerns and suggests hardening measures. It covers authentication, encryption, secrets management, input validation, secure configuration, and logging.

camel harden hello.yaml
camel harden my-routes.java --verbose
camel harden *.yaml --format=markdown

Findings are prioritized by severity (Critical, High, Medium, Low). Use --catalog-context to include security-specific notes for each component detected in your routes:

camel harden my-routes.yaml --catalog-context

camel harden currently supports Ollama and OpenAI-compatible APIs only (no Anthropic).

Choosing an LLM provider

All commands auto-detect the LLM provider. The detection order is:

ANTHROPIC_API_KEY environment variable → Anthropic API (ask and explain only)
CLOUD_ML_REGION + ANTHROPIC_VERTEX_PROJECT_ID → Vertex AI (ask and explain only)
AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT → Azure OpenAI (uses the api-key header; optional AZURE_OPENAI_DEPLOYMENT_NAME and AZURE_OPENAI_API_VERSION)
GEMINI_API_KEY environment variable → Google Gemini native API (generativelanguage.googleapis.com). With --api-type=gemini, GOOGLE_API_KEY is also accepted.
OPENAI_API_KEY → OpenAI API (api.openai.com)
LLM_API_KEY + optional LLM_BASE_URL (or OPENAI_BASE_URL) → any OpenAI-compatible API
Ollama running via camel infra → local Ollama
Ollama at localhost:11434 → local Ollama

Override with explicit options:

camel ask "check health" --api-type=anthropic --api-key=sk-...
camel ask "check health" --api-type=openai --model=gpt-4
camel ask "check health" --api-type=gemini --model=gemini-2.0-flash
camel ask "check health" --api-type=ollama --model=llama3.1

Using a local model with Ollama

Install Ollama natively for the best performance — the native binary uses GPU acceleration (Metal on macOS, CUDA/ROCm on Linux). Running Ollama through Docker (camel infra run ollama) bypasses the GPU and makes inference much slower.

# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model and start asking
ollama pull qwen2.5:32b
camel ask "what routes are running?"

Ollama at localhost:11434 is auto-detected — no environment variable needed. The CLI checks what models are available and auto-selects a suitable one.

Model requirements

camel ask and the TUI F8 panel rely on tool calling to inspect your running Camel process. Models smaller than ~14B do not reliably invoke tools and answer from training knowledge instead. Use at least a 14B model; 32B is recommended.

Model RAM (Q4) Notes

Model	RAM (Q4)	Notes
`qwen2.5:14b`	~9 GB	Minimum recommended
`qwen2.5:32b`	~20 GB	Best balance of speed and quality
`deepseek-r1:32b`	~20 GB	Strong reasoning
`hermes3:70b`	~43 GB	Excellent tool calling, needs 64 GB+
`llama3.3:70b`	~43 GB	Best open model, needs 64 GB+

qwen2.5:14b

~9 GB

Minimum recommended

qwen2.5:32b

~20 GB

Best balance of speed and quality

deepseek-r1:32b

~20 GB

Strong reasoning

hermes3:70b

~43 GB

Excellent tool calling, needs 64 GB+

llama3.3:70b

~43 GB

Best open model, needs 64 GB+

On Apple Silicon, all RAM is unified — a 64 GB M-series Mac can run llama3.3:70b comfortably alongside the OS and other dev tools.

Using an OpenAI-compatible local server

Many local LLM servers expose an OpenAI-compatible API. Use LLM_API_KEY and LLM_BASE_URL to point the CLI at any of them:

export LLM_API_KEY=any-value      # required but can be any non-empty string
export LLM_BASE_URL=http://localhost:1234   # your server's base URL
camel ask "what routes are running?"

OPENAI_BASE_URL is accepted as an alternative to LLM_BASE_URL (common in other tools).

Common OpenAI-compatible servers: