F
licenseNot graded
qualityA
maintenanceGuardrails service for AI agents that evaluates every tool call for safety and alignment before execution, providing default-deny policy, LLM safety evaluation, and audit trail.
39 PyPI
22
-
fusion
by Katawwr
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@fusionrun an eval on my agent and grade it, fail below a B"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Use
pip install "fusion-safety[serve,mcp]"
fusion doctor
fusion run start --prompt agent.txt --target ollama:llama3.2:1b --grader claude-cli
fusion run finalize --min-grade B # report card; exit 1 below the bar
fusion serveAny model or agent can be the target: ollama:<model>, openai-compat:<url>#<model>, claude-cli:<model>, a
hosted API with your own key (openai:, anthropic:, hf:, ...; needs FUSION_ALLOW_API_SPEND=1), or any
program via cmd:<command>.
Runtime guardrail in your agent loop:
from fusion_first.guardrail.guard import Guardrail
from fusion_first.guardrail.policy import GuardConfig
guard = Guardrail(GuardConfig(allowlisted_domains=["your-co.com"], require_authorization=True))
outcome = guard.guard_tool_call(tool_name, tool_args, user_request=user_message,
untrusted_context=True, untrusted_text=tool_results)
if outcome.blocked: ...Claude Code: claude plugin marketplace add "$(fusion plugin-dir)" && claude plugin install fusion@fusion-first.
MCP: { "mcpServers": { "fusion": { "command": "fusion-mcp" } } } exposes the run engine and the guard's
guardrail_snippet / check_tool_call tools.
Related MCP server: evalmine
License
MIT
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
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Runtime permission, approval, and audit layer for AI agent tool execution.
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