openmake_llm
Eliminates vendor lock-in and data exposure risks by running LLM workloads entirely on-premises, while supporting autonomous agents and research tools that r…
Open-source, self-hosted AI workspace for local and open-weight LLMs with vLLM, LiteLLM, autonomous agents, MCP tools, deep research, artifacts, and BYOK.
- Ask Claude to review code locally without sending data to external servers.
- Generate documentation artifacts from source code using open-weight language models.
- Automate repetitive development tasks with autonomous agents running on your infrastructure.
Eliminates vendor lock-in and data exposure risks by running LLM workloads entirely on-premises, while supporting autonomous agents and research tools that reduce manual code review and investigation cycles.
Enterprise teams requiring isolated LLM infrastructure with IP sensitivity or compliance constraints.
https://github.com/openmake/openmake_llm
By openmake
How to Get It
claude plugins install openmake/openmake_llm
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Review code locally without sending data to external servers
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Hacker News
- Show HN: We Beat MLPerf: Modern Storage for KV Offload and LLM Training — Hacker News · 35 pts
- OpenLake: Fast, Durable Storage for LLM Inference and Training — Hacker News · 2 pts
- Modular LLM framework inspired by Linux – aiming for a one-GPU future — Hacker News · 2 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 43 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 43 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.