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Arg2P

Arg2P

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tuprolog.github.ioAI & Agents14Updated 1d ago
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About Arg2P

Arg2P implements the ASPIC+ structured argumentation framework and also reads assumption-based and abstract frameworks. It is for researchers and developers building explainable reasoning systems, with a shared API across JVM, JavaScript, and Python and solver access in browser, VS Code, and AI assistants.

Key features

  1. 01Solve ASPIC+ argumentation theories
  2. 02Read assumption-based and abstract frameworks
  3. 03Shared API across JVM, JavaScript, and Python
  4. 04Run solver in browser and VS Code
  5. 05Use through AI assistant integrations

Use cases

  1. 01Explore structured argumentation results
  2. 02Build explainable reasoning systems
  3. 03Use argumentation in legal reasoning research

MCP support

The project provides an MCP server for AI assistants, but the README does not describe its tools or exposed capabilities.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "tuprolog-arg2p": {
      "command": "uvx",
      "args": [
        "arg2p"
      ]
    }
  }
}
Run commanduvx arg2p

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypiarg2p0.23.0

Tools

5

Saved tool list · updated 2d ago

ParameterTypeRequiredDescription
flagsstringNoThe flags, as Prolog facts: argumentLabellingMode(grounded|complete|preferred|stable|semistable|ideal|eager|naive|stage|cf2|stage2|...), statementLabellingMode(statement), queryMode, graphBuildMode(...), ... Default: grounded semantics, standard statement labelling, query mode.
querystringNoA Prolog goal on the theory (default: arg2p::solve(In, Out, Und))
theorystringYesThe theory, in the language of arg2p: rules `r1 : bird(X) => flies(X).` (defeasible) and `r2 : a -> b.` (strict), premises `f1 :=> bird(tweety).`, axioms `f2 :-> a.`, preferences `sup(r1, r2).`, negation `-flies(X)`, Prolog clauses. Its profile, `graphBuildMode(aspic_plus).` (the default), `aspic`, `aspic_defeasible`, `meta`, `aba` (assumption/1, contrary/2, strict rules), `aba_plus`, `abstract` (arguments `a.`, attacks `a -> b.`, `a <-> b.`) or `free`, tells the framework it is read in.
maxSolutionsintegerNoHow many solutions at most (default 5, at most 100)
timeoutSecondsintegerNoHow long to wait, in seconds (default 60, at most 600)

Details

From the project README

Arg2P logo

Arg2P

CI/CD Maven Central npm PyPI Kotlin coverage Prolog coverage Documentation License

Arg2P is an implementation of the ASPIC+ framework for structured argumentation, which reads assumption-based and abstract frameworks as well. Built on top of the tuProlog engine, it runs on the JVM, in JavaScript (Node.js and browsers) and in Python, with the same API; its solver runs in the browser, in VS Code, and for AI assistants.

The documentation starts from Getting Started.

The playground

Installing

JVM (Gradle, Maven Central)implementation("it.unibo.tuprolog.argumentation:arg2p-jvm:<VERSION>")
JavaScript (npm)npm install @tuprolog/arg2p
Python (PyPI)pip install arg2p (the wheel bundles a Java runtime)
Browserthe Web Playground: nothing to install
VS Codethe extension
Claude Code/plugin install arg2p --marketplace tuProlog/arg2p-kt
Codexcodex plugin marketplace add tuProlog/arg2p-kt, then install arg2p from /plugins

<VERSION> is the latest release, shown by the Maven Central badge above. See Execution for a first program on each platform, the playground, the extension of VS Code, and the plugin and MCP server of Arg2P for AI assistants (the other clients, and the server alone).

Arg2P in VS Code

Issue tracking

If you encounter any issues, please report them on the GitHub Issues page.
Your feedback helps improve the project!

References

Arg2P is described in:

  • Roberta Calegari, Andrea Omicini, Giuseppe Pisano, Giovanni Sartor. Arg2P: an argumentation framework for explainable intelligent systems. Journal of Logic and Computation, 32(2):369–401, 2022. doi:10.1093/logcom/exab089
  • Giuseppe Pisano. Argumentation for legal reasoning: meta-models, technology and beyond. PhD thesis, Alma Mater Studiorum — Università di Bologna, 2024. doi:10.48676/unibo/amsdottorato/11671

Individual features — burden of persuasion, defeasible preferences, modularity, causality, distributed reasoning — have their own papers, listed on the References page of the wiki.

Cite me

If you use Arg2P in academic work, please cite:

@article{arg2p,
  author  = {Calegari, Roberta and Omicini, Andrea and Pisano, Giuseppe and Sartor, Giovanni},
  title   = {{Arg2P}: an argumentation framework for explainable intelligent systems},
  journal = {Journal of Logic and Computation},
  volume  = {32},
  number  = {2},
  pages   = {369--401},
  year    = {2022},
  doi     = {10.1093/logcom/exab089}
}

@phdthesis{pisano2024argumentation,
  author  = {Pisano, Giuseppe},
  title   = {Argumentation for legal reasoning: meta-models, technology and beyond},
  school  = {Alma Mater Studiorum --- Universit\`a di Bologna},
  year    = {2024},
  doi     = {10.48676/unibo/amsdottorato/11671}
}

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