llmops-agent 0.1.0

Reference Python agent for the LLMOps.Pro sovereign-AI substrate. Consumes the five MCP servers (nwc-mcp, nostr-ops-mcp, marketplace-mcp, albyhub-admin-mcp, paywall-mcp) and demonstrates the agent-pays-paid-MCP loop end-to-end. Ollama-first, API-pluggable. Built on pydantic-ai — no LangChain. MIT.

llmops-agent

Reference Python agent for the LLMOps.Pro sovereign-AI substrate. Consumes the five MCP servers (nwc-mcp, nostr-ops-mcp, marketplace-mcp, albyhub-admin-mcp, paywall-mcp) and demonstrates the agent-pays-paid-MCP loop end-to-end.

Ollama-first, API-pluggable. Built on pydantic-ai — no LangChain. MIT.

v0.1 operational. All six CLI commands wired. Agent layer enforces a budget-and-audit safety pipeline on top of every MCP server's own safety stack. Self-paying research workflow runs end-to-end given a working LLM backend (Ollama / Anthropic / OpenAI). Full design at ../03-python-reference-agent-design.md.

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Status

Layer	State
Design doc	✅ Done — ../03-python-reference-agent-design.md
Package scaffold (uv init, pyproject.toml, ruff/mypy/pytest config)	✅ Done
config.py — env-driven Pydantic Settings	✅ Done
budget.py — persistent rolling-window tracker	✅ Done
audit.py — NDJSON writer	✅ Done
mcp_servers.py — MCPServerStdio launch + doctor probe	✅ Done
agent.py — Pydantic AI agent + process_tool_call middleware	✅ Done
workflows/research.py — self-paying research workflow	✅ Done
cli.py — typer entry-point with ask / doctor / budget / audit / config-print / version	✅ Done
Live LLM smoke test against Ollama / Anthropic	⏳ User-driven (needs the LLM backend running)

Usage

# from this directory, with uv-installed venv
uv run llmops-agent --help
uv run llmops-agent version
uv run llmops-agent budget                # snapshot, no LLM needed
uv run llmops-agent config-print          # effective config, secrets masked
uv run llmops-agent doctor                # ping each configured MCP server
uv run llmops-agent ask "What time is it?"   # the main demo loop
uv run llmops-agent audit --tail 20       # recent audit entries

ask requires a working LLM backend. Default is Ollama at localhost:11434/v1 with model qwen3:8b. Override via LLMOPS_MODEL env var (e.g. anthropic:claude-haiku-4-5-20251001).

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Dev loop

uv sync                 # install deps + dev deps into .venv
uv run ruff check       # lint
uv run mypy             # type check
uv run pytest           # tests

Build wheel for distribution:

uv build

License

MIT — see LICENSE.

Contact

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