Agent Workflow

Move from open-source intent to an evidence-backed decision.

Use this workflow to move from trend context to shortlist, graph inspection, alternatives, score explanation, and final comparison for "evaluate Claude Code alternatives". 5 candidate(s) were shortlisted. Focus project: claude-code.

Open Workflow JSON Agent Map MCP Docs
Claude Code RAG
Workflow Steps7
Shortlist5
Trend Projects1159
Data Sourced1

Recommended Sequence

What to call, inspect, and cite

JSON
1

REST

Check data trust

GET

Verify that production recommendations can rely on D1-backed knowledge.

GET/api/health?require_d1=true
2

get_trends

Read trend context

GET

Understand corpus-level category, deployment, language, and rising-project signals before picking candidates.

GET/api/trends?limit=5MCP get_trends
3

recommend_project

Generate shortlist

GET

Get ranked candidates with fit profile, adoption plan, risk flags, confidence, and next actions.

GET/api/recommend?use_case=evaluate+Claude+Code+alternatives&deployment=local&limit=5MCP recommend_project
4

get_project_graph

Inspect project graph

GET

Read alternatives, related projects, dependencies, deployment targets, and graph edges for the leading candidate.

GET/api/graph/claude-code?limit=24MCP get_project_graph
5

get_alternatives

Find alternatives

GET

Separate direct substitutes from adjacent options with similarity score, match signals, adoption notes, and replacement risk.

GET/api/alternatives/claude-code?limit=5MCP get_alternatives
6

get_quality_score

Explain score

GET

Inspect Git.Top Score dimensions, score confidence, evidence, risk flags, and adoption guidance.

GET/api/score/claude-codeMCP get_quality_score
7

compare_projects

Compare final candidates

GET

Turn the shortlist into a decision matrix with winner reasoning and tradeoffs.

GET/api/compare?repos=OpenHands%2FOpenHands%2Cheadroomlabs-ai%2Fheadroom%2CiOfficeAI%2FAionUi%2Comnigent-ai%2Fomnigent%2Clanggenius%2FdifyMCP compare_projects