PurrA

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PurrA is an Agent runtime for Python and TypeScript. It coordinates model calls, tool execution, planning, context, and recoverable Runs inside your application. The application supplies its models, tools, data sources, and access rules.

Install

Python 3.11+:

pip install purra

Node.js 22+ (ESM):

npm install purra

Core has no runtime dependencies. Model SDKs, storage, and memory integrations are available as optional packages.

Python quickstart

Provide a model gateway, a ModelRequest with the selected model's capability snapshot, and its context-window size. The gateway can come from an optional adapter or implement PurrA's ModelGateway port.

from purra.api import AgentCore, AgentPreset, InMemoryAgentAdapters
from purra.contracts import (
    AgentMessage, AgentRunRequest, DomainContext, ModelRequest, RuntimeLimits,
)
from purra.tools import InMemoryToolCatalog

async def ask(gateway, model: ModelRequest, context_window: int):
    storage = InMemoryAgentAdapters()
    core = AgentCore(
        model_gateway=gateway,
        preset=AgentPreset(
            id="assistant",
            revision="1",
            tool_catalog=InMemoryToolCatalog(()),
            runtime_limits=RuntimeLimits(max_run_generation_tokens=8192),
        ),
        run_repository=storage.runs,
        output_repository=storage.outputs,
        output_publisher=storage.publisher,
    )
    try:
        run = await core.submit(AgentRunRequest(
            messages=(AgentMessage(role="user", content="Hello"),),
            model=model,
            domain_context=DomainContext(namespace="example"),
            context_window=context_window,
        ))
        result = await run.wait()
        return result.final_response
    finally:
        await core.close()

This example stores Runs in memory. Use a persistent repository to retain them after restart. For a complete example that runs without API credentials, see local examples. For JavaScript and TypeScript, see the TypeScript guide.

Runtime capabilities

• Runs: submit work, subscribe to committed events, cancel execution, and replay output.
• Tools: validate arguments, enforce access and effect policies, and record results.
• Planning: select Auto, Reactive, or Planned execution when a planner is configured.
• Context: allocate input budgets, retrieve external data, and compress conversation history.
• Agent trees and long tasks: delegate work and persist execution progress through repository ports.

The application chooses models and their limits, authorizes data access and tool side effects, and manages persistent storage. Retrieval sources and tool results are treated as data rather than instructions.

Budgets and recovery

ModelRequest.max_generation_tokens is an optional user ceiling for one Provider call and includes every generated token the Provider counts, including reasoning when the profile declares inclusive accounting. RuntimeLimits.max_run_generation_tokens bounds that quantity cumulatively for a Run. None means no finite cumulative ceiling. A finite budget requires reported model usage. A workflow's optional result_capacity_target_tokens is only a context-sizing and diagnostic target; it does not reduce the Provider allowance or guarantee visible output.

Recovery continues from committed checkpoints. The application must restore the same model and tool configuration and reconcile interrupted external writes before retrying them. Persistent storage alone does not make those writes replayable.

Documentation

• TypeScript guide
• Examples
• Optional packages
• Architecture
• Cross-language conformance
• Changelog

License

MIT