Architecture
Internal architecture for contributors and extenders
Camel-Kit is built on a 4-layer architecture designed for AI agent composability, cross-agent portability, and token efficiency. The system is designed around the principle: “The prompt is the product” — Camel-Kit ships instructions, not implementations.
Four Layers
Graph CLI has 14 subcommands:
- Analysis: stats, find, neighbors, path, subgraph
- Camel-specific: route-flow, impact, route-topology, dead-code
- Normalization: project-norms, project-context, route-context
- Output: generate, visualize
Learn more about graph intelligence →
The Prompt Is the Product
Camel-Kit’s architecture embodies a key principle: the prompt is the product. Unlike traditional code generators that ship Java/Python implementations, Camel-Kit ships:
- Markdown guides that instruct AI agents how to generate code
- MCP tool definitions for real-time verification
- Graph parsers for code analysis (optional)
This means:
- No vendor lock-in — Skills work on any agent (Claude, Gemini, Qwen, etc.)
- Easy customization — Edit Markdown files to change behavior
- Version-independent — No recompilation when Camel versions change
- Transparent — Users can read the exact instructions agents follow
Progressive Disclosure
Skills use progressive disclosure to minimize token usage:
- Metadata (always loaded) — Skill name, description, trigger patterns (~50 tokens/skill)
- SKILL.md (on trigger) — Main skill logic, loaded only when invoked (~500-2000 tokens)
- Guides (as needed) — Shared utilities, loaded only when referenced (~100-500 tokens each)
Example flow:
User: "Create a Camel project for order processing"
→ Agent sees "start a new Camel project" in AGENTS.md
→ Agent loads /camel-project/SKILL.md
→ SKILL.md references {{GUIDE:shared/camel-constitution.md}}
→ Agent loads constitution guide
→ Agent generates project requirements
This progressive loading keeps context usage minimal while maintaining full catalog coverage.
Context Efficiency
By combining progressive disclosure and MCP on-demand queries, Camel-Kit avoids loading full component catalogs into the agent’s context. This enables:
- Faster agent responses — less context to process per turn
- Support for smaller models — fits within constrained context windows
- Cost reduction — fewer input tokens per request
- Full coverage — every component is still verifiable via MCP
Next Steps
- Skills System — Deep dive into the 11 skills and 79 guides
- MCP Integration — How catalog verification and knowledge search work
- Graph Intelligence — Property graph analysis with 8 parsers