Home/Compare/caveman vs mcpproxy-go

Comparison

caveman vs mcpproxy-go

Verdict

Pick caveman when caveman is primarily JavaScript; mcpproxy-go is Go; pick mcpproxy-go when mcpproxy-go is primarily Go; caveman is JavaScript.

Markdown twin · caveman alternatives · mcpproxy-go alternatives

GraphCanon updated today

caveman logo

caveman

JuliusBrussee/caveman

88kpushed Jul 3, 2026
vs
mcpproxy-go logo

mcpproxy-go

smart-mcp-proxy/mcpproxy-go

285pushed Jul 11, 2026

Trust & integrity

Signalcavemanmcpproxy-go
Maintenance
Active (7d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No criticals
As of today · osv@v1

Tagline

caveman
Reduce token usage with concise 'caveman'-style prompts.
mcpproxy-go
Supercharge AI Agents, Safely

Stars

caveman
88k
mcpproxy-go
285

Forks

caveman
5.1k
mcpproxy-go
36

Open issues

caveman
392
mcpproxy-go
14

Language

caveman
JavaScript
mcpproxy-go
Go

Adopt for

caveman
The **caveman** tool is designed for developers and AI users who aim to optimize their token usage through the generation of more concise prompts, thereby potentially reducing costs and improving efficiency. However, it犺
mcpproxy-go
-

Persona

caveman
-
mcpproxy-go
-

Runtime

caveman
-
mcpproxy-go
-

License

caveman
MIT
mcpproxy-go
MIT

Last pushed

caveman
Jul 3, 2026
mcpproxy-go
Jul 11, 2026

Categories

caveman
Developer Tools, LLM Frameworks
mcpproxy-go
AI Agents, Developer Tools, LLM Frameworks

Trust and health

Maintenance

caveman
Active (82%)
mcpproxy-go
Very active (96%)

Days since push

caveman
7d
mcpproxy-go
0d

Open issues (now)

caveman
392
mcpproxy-go
14

Owner type

caveman
User
mcpproxy-go
Organization

Security scan

caveman
No lockfile
mcpproxy-go
No criticals

Full report

mcpproxy-go
Trust report

Choose caveman if…

  • caveman is primarily JavaScript; mcpproxy-go is Go.
  • Tags unique to caveman: anthropic, caveman, claude-code, prompt-engineering.
  • When you need to significantly cut down on token usage in AI interactions, up to 65%, without losing essential information content.

When NOT to use caveman

  • When requiring complex and detailed prompts that necessitate more nuanced expression beyond simple, 'caveman'-style sentences.
  • For situations where adherence to formal or specific linguistic structures is mandatory for the task's success.

Choose mcpproxy-go if…

  • mcpproxy-go is primarily Go; caveman is JavaScript.
  • Tags unique to mcpproxy-go: ai-agents, audit-logging, bm25, cli.
  • Also covers AI Agents.
  • mcpproxy-go ships Docker support for self-hosted deployment.

When NOT to use mcpproxy-go

  • AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
  • Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: caveman 88k · mcpproxy-go 285 (synced Jul 11, 2026).

Common questions

What is the difference between caveman and mcpproxy-go?
caveman: Reduce token usage with concise 'caveman'-style prompts.. mcpproxy-go: Supercharge AI Agents, Safely. See the comparison table for live GitHub stats and shared categories.
When should I choose caveman over mcpproxy-go?
Choose caveman over mcpproxy-go when caveman is primarily JavaScript; mcpproxy-go is Go; Tags unique to caveman: anthropic, caveman, claude-code, prompt-engineering; When you need to significantly cut down on token usage in AI interactions, up to 65%, without losing essential information content.
When should I choose mcpproxy-go over caveman?
Choose mcpproxy-go over caveman when mcpproxy-go is primarily Go; caveman is JavaScript; Tags unique to mcpproxy-go: ai-agents, audit-logging, bm25, cli; Also covers AI Agents; mcpproxy-go ships Docker support for self-hosted deployment.
When should I avoid caveman?
When requiring complex and detailed prompts that necessitate more nuanced expression beyond simple, 'caveman'-style sentences. For situations where adherence to formal or specific linguistic structures is mandatory for the task's success.
When should I avoid mcpproxy-go?
AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Is caveman or mcpproxy-go more popular on GitHub?
caveman has more GitHub stars (87,950 vs 285). Stars measure visibility, not whether either tool fits your constraints.
Are caveman and mcpproxy-go open source?
Yes - both are open-source projects on GitHub (caveman: MIT, mcpproxy-go: MIT).
Where can I find alternatives to caveman or mcpproxy-go?
GraphCanon lists graph-backed alternatives at caveman alternatives and mcpproxy-go alternatives (caveman markdown twin, mcpproxy-go markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, caveman or mcpproxy-go?
caveman: Active. mcpproxy-go: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for caveman and mcpproxy-go?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: caveman trust report; mcpproxy-go trust report.