Skip to content

Roadmap

This project is intentionally complete for its limited scope. Future work should focus on release hardening, examples and small usability improvements rather than expanding into orchestration.

Completed Milestones

  • Package skeleton and typed public API.
  • Agent facade.
  • Runtime execution loop.
  • Prompt builder.
  • Conversation history.
  • Memory protocol and in-memory implementation.
  • Tool registry and executor.
  • Callback system.
  • Streaming responses.
  • Runtime configuration.
  • Official python-rag-framework integration through RAG.retrieve(...).
  • Callable and OpenAI LLM adapters.
  • Examples for basic usage, tools, memory, RAG and OpenAI.
  • MkDocs documentation site.
  • GitHub Actions CI.
  • Package build verification.

Current Status

The runtime is suitable as a small portfolio-quality Python library. It can execute single agents, use tools, include optional memory, and consume retrieval context from python-rag-framework.

The preferred next work is conservative:

  • Keep documentation aligned with the public API.
  • Add small examples based on real usage.
  • Improve release notes and packaging checks.
  • Fix bugs and typing issues as they appear.

Non Goals

  • Multi-agent systems.
  • Workflow graphs or DAG execution.
  • Scheduling.
  • Long-running services.
  • Distributed execution.
  • Cloud deployment or Kubernetes.
  • Web servers, REST APIs, frontends or dashboards.