harness-runtime 0.1.5

LangGraph Agent Execution Engine — stdio subprocess pattern

harness-runtime

LangGraph agent execution engine designed for the stdio subprocess pattern — spawned as a child process by the Waypoint SDK, communicating via NDJSON over stdin/stdout using the LiteLLM frame protocol.

Overview

Waypoint SDK → spawn → harness-runtime → stdin NDJSON → turn frames → stdout NDJSON → exit

• Execution: LangGraph DAG execution with PostgreSQL checkpointing
• Protocol: LiteLLM-compatible NDJSON frame protocol over stdio
• Persistence: LangGraph PostgresSaver for multi-turn conversations
• No network, no K8s, no Redis: Runs as a local subprocess

Quick Start

# Install
pip install .

# Run (expects NDJSON on stdin)
DATABASE_URL=postgresql://user:pass@localhost:5432/db harness-runtime

Example NDJSON session

echo '{"type":"control_request","request_id":"req_1","request":{"subtype":"initialize","agent_definition":{...},"input_payload":{"messages":[]}}}' | \
DATABASE_URL=postgresql://user:pass@localhost:5432/db harness-runtime

Protocol

See ADR-003 for the full protocol specification.

Frame Types (SDK → Server)

Type	Purpose
control_request {initialize}	Build LangGraph, init PostgresSaver
control_request {interrupt}	Cancel in-flight turn
user {message}	Each turn — runs graph with the input

Frame Types (Server → SDK)

Type	Purpose
control_response {success}	Ack for initialize/interrupt
system {init}	Start of turn — model, tools
assistant {content}	Complete assistant message with text / tool_use blocks
user {content}	Echo of internal tool results (tool_result blocks)
stream_event {content_block_delta}	Streaming LLM token deltas
result	Terminates the turn — success, error_max_turns, or error_during_execution

Architecture

harness-runtime/
├── cli.py                      # CLI entry point — stdin NDJSON loop
├── core/
│   ├── builder.py              # GraphBuilder — builds LangGraph DAG
│   ├── event_publisher.py      # StdioPublisher — LiteLLM frame emitter
│   ├── executor.py             # ExecutionManager — runs graph, maps events → frames
│   ├── session.py              # Session — multi-turn lifecycle
│   ├── model_factory.py        # LLM model creation
│   ├── model_identifier.py     # "provider:model" string builder
│   ├── subagent_builder.py     # Specialist agent compilation
│   ├── tool_loader.py          # Dynamic tool loading from script defs
│   └── state_schema_builder.py # Dynamic AgentState subclass creation
├── models/
│   ├── __init__.py
│   └── frames.py               # LiteLLM frame dataclasses + content blocks
├── migrations/                  # PostgreSQL schema migrations (checkpointer)
├── scripts/ci/run.sh           # Docker entrypoint
├── Dockerfile
└── pyproject.toml

Environment Variables

Variable	Required	Description
DATABASE_URL	Yes	PostgreSQL connection string for LangGraph checkpointer
USE_MOCK_LLM	No	true to use mock LLM (default: false)
LLM_MODEL_NAME	No	Model name for real LLM calls (default: gpt-4o-mini)

LLM provider API keys are read from environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) or from Agent Vault via proxy env vars.

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Code quality
black .
ruff check .
mypy .

# Testing
pytest

References

• ADR-003
• LiteLLM Frame Protocol
• LangGraph Documentation