langstage-core 1.0.3

Universal parser + host layer for LangGraph agents — typed stream events, agent-spec loading, layered config, an AG-UI bridge, and an async task-delegation engine

langstage-core

The shared core behind the LangStage family: a host layer for LangGraph agents (spec-loading + layered config), an in-process AG-UI bridge that streams any CompiledGraph to a frontend, an async task-delegation engine, and interrupt-aware input helpers. Write your agent once — any LangGraph CompiledGraph — and every LangStage surface runs it the same way.

1.0 — renamed from langgraph-stream-parser. The old StreamParser / events / event_to_dict event layer was retired in favor of the AG-UI wire (see Migrating and ADR 0003). The old import name still works via a compat shim.

Every stage for your LangGraph agent

langstage-core is the shared core of the LangStage family: write your agent once — any LangGraph CompiledGraph — and run it on every stage with the same spec string (module:attr or path/to/file.py:attr), the same langstage.toml config file, and the same LANGSTAGE_* environment variables. (The pre-rename deepagents.toml / DEEPAGENT_* vocabulary still resolves as a deprecated fallback.)

Stage	Package	Try it
Web app	langstage	langstage run --agent my_agent.py:graph
JupyterLab	langstage-jupyter	pip install langstage-jupyter, then the chat sidebar in jupyter lab
Terminal	langstage-cli	langstage-cli -a my_agent.py:graph
VS Code	langstage-vscode	chat participant + stdio sidecar
Reference agent	langstage-hermes	LANGSTAGE_AGENT_SPEC=langstage_hermes.agent:graph on any stage
Shared core	langstage-core	you are here

📖 Full documentation: https://dkedar7.github.io/langstage-docs/

Installation

pip install "langstage-core[agui]"

The [agui] extra pulls the AG-UI runtime (ag-ui-langgraph[fastapi] + uvicorn) — needed for the streaming bridge below and by every LangStage surface. The bare pip install langstage-core (only langchain-core) is enough if you just want the host/config/tasks layer without streaming.

No agent of your own yet? The [stub] extra adds a keyless echo graph you can stream:

pip install "langstage-core[agui,stub]"

Quick start

Wrap any compiled graph with build_agent, then stream a turn. Two shared mappings cover the two frontend styles the family uses:

import asyncio
from langstage_core import load_agent_spec
from langstage_core.agui import build_agent, iter_event_frames

# any LangGraph CompiledGraph — here the keyless demo stub
agent = build_agent(load_agent_spec("langstage_core.demo.stub:graph"))

async def main():
    async for frame in iter_event_frames(agent, "hello", thread_id="s1"):
        if frame["type"] == "content":
            print(frame["content"], end="")
        elif frame["type"] == "complete":
            print()

asyncio.run(main())

• iter_event_frames yields rich, typed frames — content, tool_start, tool_end, reasoning, interrupt, extraction, complete, error — used by the web and VS Code surfaces.
• iter_chunk_frames yields terminal-friendly chunk dicts — {"status": "streaming", "chunk": "..."} … {"status": "complete"} — used by the CLI and Jupyter surfaces.

build_agent attaches an in-memory checkpointer if the graph has none, so multi-turn memory and interrupts work out of the box; pass a thread_id per turn to key per-conversation state.

Human-in-the-loop (interrupt → resume)

When the graph calls interrupt(...), you get an interrupt frame; resume by passing the decision back via resume=:

async for frame in iter_event_frames(agent, "run it", thread_id="s1"):
    if frame["type"] == "interrupt":
        # frame["action_requests"], frame["allowed_decisions"]
        ...

# next turn resumes the same thread with the user's decision
async for frame in iter_event_frames(agent, "", thread_id="s1",
                                     resume={"decisions": [{"type": "approve"}]}):
    ...

Decision types: approve, reject, edit, respond (deepagents 0.6+ / LangGraph 1.1+).

What's in the box

Everything is re-exported from the top-level langstage_core package (except the AG-UI helpers under langstage_core.agui):

Area	API	What it does
Host	load_agent_spec, HostConfig, Workspace	Load a graph from a module:attr / file.py:attr spec; resolve layered config (defaults < langstage.toml < LANGSTAGE_* env < overrides).
AG-UI bridge (langstage_core.agui)	build_agent, iter_event_frames, iter_chunk_frames, build_app, serve, add_agui_endpoint	Stream any CompiledGraph in-process (the iter_* mappings) or serve it as an AG-UI HTTP endpoint.
Session adapter (langstage_core.adapters)	SessionAdapter, Session	A session-scoped driver over the AG-UI agent with a typed terminal outcome — the streaming engine behind the web app + task board.
Input helpers	prepare_agent_input, create_resume_input	Build graph input from a message (+ optional context) or a resume decision.
Extractors	ToolExtractor + built-ins (ThinkToolExtractor, TodoExtractor, DisplayInlineExtractor, SkillManageExtractor, MemoryExtractor, …)	Turn a tool's result into a structured extraction frame; pass extractors=[...] to the iter_* mappings.
Task engine	TaskRunner, TaskStore, InMemoryTaskStore, TASK_TOOLS, set_runner, get_runner	Async delegate-and-walk-away worker pool + a persistence-agnostic store Protocol; TASK_TOOLS are the agent-facing delegation tools.

Serve any agent over AG-UI

Any LangGraph agent can be served over the AG-UI protocol — the event-based wire for streaming rich agent interactions (text, tool calls, reasoning, state, interrupts) to frontends (CopilotKit, React/Vue/Angular components, any AG-UI client). The host layer resolves which agent; the official MIT ag-ui-langgraph adapter owns the wire:

langstage-agui --agent my_agent.py:graph     # serve over AG-UI at http://localhost:8050
langstage-agui --demo                          # keyless echo agent, no API key

from langstage_core.agui import build_app
app = build_app(my_compiled_graph)   # an ASGI (FastAPI) app; run with uvicorn

See ADR 0001 for the rationale.

Configuration

The same resolution chain everywhere — defaults < langstage.toml < LANGSTAGE_* env < CLI/overrides (legacy deepagents.toml / DEEPAGENT_* still resolve as a deprecated fallback). Print the resolved value + source of every key:

python -m langstage_core.host      # or each surface's --show-config

Migrating from langgraph-stream-parser

langstage-core 1.0 is the rename of langgraph-stream-parser. Importing the old name still works via a compat shim that re-exports langstage_core (with a DeprecationWarning), so import langgraph_stream_parser and its submodules keep resolving — but update to import langstage_core when convenient.

The event layer was removed in 1.0. If you used it directly, migrate:

Removed (pre-1.0)	Use instead
StreamParser, langstage_core.events, event_to_dict	langstage_core.agui.iter_event_frames / iter_chunk_frames (frame dicts, same vocabulary)
stream_graph_updates, resume_graph_from_interrupt	iter_chunk_frames(agent, msg, thread_id, resume=...)
adapters.CLIAdapter / PrintAdapter / FastAPIAdapter / JupyterDisplay	SessionAdapter (in-process) or build_app / serve (HTTP), both AG-UI

Kept and unchanged: load_agent_spec, HostConfig, prepare_agent_input, create_resume_input, the tasks engine, and the extractors (ToolExtractor + built-ins). Full detail: ADR 0003.

Development

pip install -e ".[dev]"
pytest
pytest --cov=langstage_core

License

MIT