jarvis-runtime 0.1.0a5

Jarvis Agent Runtime for executing ARP-style flows with planning support.

JARVIS Runtime

This repo contains the Jarvis Agent Runtime: a Python runtime for executing Agent Runtime Protocol-style agent flows using the 3-role loop:

Planner → Tool Executor (arg-gen + invoke) → Chat

It is designed to run against the Tool_Registry service and share contracts via jarvis-model.

Quickstart

See:

• docs/quickstart.md
• docs/trace.md

Install

From PyPI (once published):

pipx install jarvis-runtime

Pre-release (e.g. 0.1.0a1):

pipx install --pip-args="--pre" jarvis-runtime

Or in a virtualenv:

pip install jarvis-runtime

For --tool-registry inproc, install Tool Registry too:

pip install jarvis-tool-registry

Or install the extra:

pip install "jarvis-runtime[inproc]"

Run against a real Tool Registry service

Terminal A (Tool Registry):

tool-registry

Terminal B (Runtime):

jarvis-runtime demo --tool-registry http --tool-registry-url http://127.0.0.1:8000

OpenAI mode (optional)

This runtime uses the OpenAI Python SDK for Responses parsing + structured outputs. To enable it:

pip install -e ".[openai]"
export OPENAI_API_KEY=...
jarvis-runtime demo --mode openai --tool-registry inproc

Optional model overrides:

• JARVIS_MODEL_PLANNER
• JARVIS_MODEL_TOOL_ARGS
• JARVIS_MODEL_CHAT
• JARVIS_MODEL_DEFAULT

Validation

Unit tests:

python -m unittest discover -v

Or (if you have pytest installed):

pytest -q

Typecheck (pyright):

pyright -p pyrightconfig.json

Design docs

• docs/intro.md
• docs/design/overview.md

Repo boundaries

• This repo: flow execution, LLM role orchestration, runtime packaging.
• Tool_Registry (separate repo): tool discovery + schemas + invocation routing (+ MCP aggregation).
• JARVIS/Model (separate repo): shared schemas / data model (and eventually the ARP wire protocol spec).

MVP capabilities + known gaps

Capabilities:

• Stub-mode 3-role loop (Planner → Tool → Chat) with trace JSONL.
• Tool Registry integration via HTTP (and inproc for tests/sandboxed environments).
• Trace replay: rerun Chat from recorded tool results.

Known gaps:

• No production hardening (auth, multi-tenancy, concurrency controls, streaming, persistence).
• Prompt packs and planning heuristics are MVP-grade; no memory/scheduler/control plane yet.