Build LLM workflows like normal Python while keeping a full audit trail by default.
Visit https://getrepublic.org for concepts, guides, and API reference.
Republic is a tape-first LLM client: messages, tool calls, tool results, errors, and usage are all recorded as structured data. You can make the workflow explicit first, then decide where intelligence should be added.
Quick Start
from __future__ import annotations import os from republic import LLM api_key = os.getenv("LLM_API_KEY") if not api_key: raise RuntimeError("Set LLM_API_KEY before running this example.") llm = LLM(model="openrouter:openrouter/free", api_key=api_key) result = llm.chat("Describe Republic in one sentence.", max_tokens=48) print(result)
Why It Feels Natural
- Plain Python: The main flow is regular functions and branches, no extra DSL.
- Structured error handling: Errors are explicit and typed, so retry and fallback logic stays deterministic.
- Tools without magic: Supports both automatic and manual tool execution with clear debugging and auditing.
- Tape-first memory: Use anchor/handoff to bound context windows and replay full evidence.
- Event streaming: Subscribe to text deltas, tool calls, tool results, usage, and final state.
Authentication
Republic supports:
- static
api_key - provider maps such as
api_key={"openai": "...", "anthropic": "..."} - dynamic
api_key_resolver - provider-specific OAuth resolvers for OpenAI Codex and GitHub Copilot
Keep the README short and move auth setup into the docs:
- Quickstart: docs/quickstart.md
- Authentication guide: docs/guides/authentication.md
- OpenAI Codex example: examples/06_openai_codex_oauth.py
- GitHub Copilot example: examples/07_github_copilot_oauth.py
Development
See CONTRIBUTING.md for local setup, testing, and release guidance.
License
This project is derived from lightning-ai/litai and inspired by pydantic/pydantic-ai; we hope you like them too.