Local LLM Delegator

Local LLM Delegator is an installable Codex and Claude Code plugin that shifts small coding implementations to a cheaper model while the primary agent keeps architecture, scoping, review, and final acceptance.

The bundle contains:

• A dependency-free Python MCP server with propose_patch and apply_patch.

• A delegation skill that triggers before the primary model reads broad source context.

• Templates for Hugging Face GPT-OSS, llama.cpp, and OpenRouter.

• Exact-patch SHA-256 approval, path limits, stale-source checks, validation, partial-failure restoration, and one bounded repair.

Requirements

• Python 3.11+

• POSIX sh and the patch utility

• Codex with plugin support or Claude Code

• An OpenAI-compatible model endpoint

No Python package installation is required.

Related MCP server: delegations-mcp

Install in Codex

Clone this repository, add it as a local marketplace, and install the plugin:

git clone https://github.com/tushrv/local-llm-delegator.git
codex plugin marketplace add ./local-llm-delegator
codex plugin add local-llm-delegator@local-llm-delegator

Restart Codex and begin a new thread so the skill and MCP tools are discovered.

Install in Claude Code

Add the cloned repository as a marketplace and install the plugin:

/plugin marketplace add /absolute/path/to/local-llm-delegator
/plugin install local-llm-delegator@local-llm-delegator

Restart Claude Code after installation.

Configure a project

From the target repository, create a policy:

python3 /absolute/path/to/local-llm-delegator/plugins/local-llm-delegator/scripts/init_project.py \
  --provider huggingface

Supported provider values are huggingface, llamacpp, and openrouter. Review the generated local-llm-worker.toml; narrow allowed_paths and replace the example validation command with the project's actual checks.

For Hugging Face, export the credential before launching Codex or Claude:

export HF_INFERENCE_TOKEN=your-token

For OpenRouter, export OPENROUTER_API_KEY. llama.cpp needs no token by default. The plugin inherits these variables but never stores or returns them.

The skill passes the target repository as workspace_root on each proposal, so one installed MCP server can work across projects without re-registration. LOCAL_LLM_CONFIG may optionally force a policy file outside each project.

Use it

In Codex, ask:

Use $delegate-local-worker to implement this focused bug fix.

In Claude Code, invoke:

/local-llm-delegator:delegate-local-worker

The skill is explicit-invocation-only on both hosts. Codex sets allow_implicit_invocation: false. Claude uses a separate mirrored skill with disable-model-invocation: true, which also keeps its description out of Claude's context until manual invocation.

The full SKILL.md is therefore not loaded on every prompt. While the plugin is enabled, the host may still advertise or discover the two MCP tool schemas; that small, host-dependent overhead exists even when the skill is idle. Invoke the skill only when the source context and implementation work being offloaded is larger than that overhead.

The primary model scopes the task and calls propose_patch. The worker reads only allowed files, asks the cheaper model for a diff, validates it without mutating the project, and returns the patch plus its digest. After full review, the primary model calls apply_patch, which applies those exact bytes and runs configured checks.

The MCP reduces primary-model context only when delegation happens before the primary model reads and solves the implementation itself. Returned patches and validation results still consume review context. The plugin does not persist Codex or Claude conversation memory.

Security

• Never place tokens in TOML, plugin JSON, prompts, or supporting context.

• Treat project validation commands as trusted code.

• Review the complete patch before approving its digest.

• Hosted models receive the source files inside the delegated scope.

• Proposal state is process-memory-only and expires after 15 minutes by default.

• The worker does not commit, push, or execute commands proposed by the model.

Development

cd plugins/local-llm-delegator/mcp
PYTHONPATH=src python3 -m unittest discover -s tests -v
ruff check .
mypy src

Validate the skill and plugin with the validation scripts from Codex's built-in skill-creator and plugin-creator skills.