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Prodagent

Prodagent is an MIT-licensed Python agent-framework kernel and runtime that exposes plans, runs, scheduling, event logging, interrupts and external observation as small composable parts. The public repository presents it as a teaching-grade implementation for understanding how agent frameworks work internally, while also providing primitives for LLM-agent, workflow and multi-agent prototypes.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Prodagent — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Prodagent when readability, framework education and a minimal runtime matter more than a large prebuilt ecosystem. It is particularly relevant for studying execution state, routing, fan-out, human pauses and event history in one small codebase.

Who it is for

  • Developers learning how agent frameworks such as LangGraph and Google ADK are structured
  • Python developers prototyping small LLM-agent workflows
  • Engineers studying checkpointing, durable execution and event-sourced runtime design
  • Builders experimenting with inspectable or MCP-connected agent systems

Documented uses

  • Read and understand core agent-runtime mechanisms
  • Compose ReAct, plan-first and multi-agent workflows from shared primitives
  • Record, replay and inspect workflow execution through events and traces
  • Prototype an agent application with zero third-party runtime dependencies
Before you choose

Requirements & limits

The repository is public and uses the MIT license. Its documented setup targets Python 3.11 or later and provides offline examples with zero runtime dependencies. Model-backed use is documented through an OpenAI-compatible model and the OPENAI_API_KEY environment variable.

  • The repository is intentionally small and readable, described as about 1,800 lines across 14 files.
  • Runtime dependencies are documented as zero, while the examples and tests can run offline.
  • Execution is represented through an append-only EventLog, enabling documented replay and time-travel concepts.
  • Human interaction is modeled as an interrupt followed by resume, with both events recorded.
  • The framework exposes routing, fan-out and multi-agent composition as primitives rather than fixed ReAct or execution-mode classes.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run Prodagent’s offline workflow

Set up the repository and inspect a deterministic agent-runtime flow locally.

Before you start

  • Python 3.11 or later
  • A local clone of the public Prodagent repository
  • The repository’s documented installation tools

Steps

  1. Clone the repository and change into its directory.

  2. Install the project in editable mode with the command below.

    Documented command
    pip install -e .

  3. Run prodagent run and answer its approval prompt.

  4. Run prodagent run --trace to view the same flow as parent and child runs.

  5. Use the command below to open the browser playground for traces, events, state, files and the graph.

    Documented command
    make play

Expected outcome

A locally installed Prodagent example can be explored offline through its run and trace interfaces.

Guide limitations

  • The examples use a scripted model and are deterministic; this does not demonstrate a live model integration.
  • The guide documents local exploration, not deployment or production suitability.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
3 October 2026
Original website title
limenagent/prodagent · Official homepage title · retained in its source language
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