Async Context Engine

Plug-and-play async context re-entry for LangGraph agents.

Dispatch long-running tasks to external systems, let the user keep chatting, and deliver results back into the conversation automatically — without blocking.

The Problem

LangGraph agents are synchronous. When a task takes 10 seconds or 5 minutes, you have two bad options: block the user, or lose the result. This library gives you a third option: dispatch, continue, re-enter.

How It Works

1. User asks for something slow     →  Your graph dispatches the task
2. Library tracks it in a TaskStore  →  User keeps chatting normally
3. External system finishes work     →  Calls update_task_result()
4. Built-in poller detects it        →  Re-enters the graph automatically
5. Result appears in conversation    →  Without the user asking again

Install

pip install async-context-engine

# Or with uv (recommended):
uv add async-context-engine

Quick Start

1. Extend your state:

from async_context_engine import AsyncTaskState

class MyState(AsyncTaskState):
    messages: Annotated[list[dict], operator.add]

2. Dispatch tasks in your graph:

from async_context_engine import dispatch_task

def dispatcher(state, config):
    task = dispatch_task(store, config["configurable"]["thread_id"], "calculate total")
    my_system.run(task.task_id, "calculate total")  # Send to your worker
    return {"messages": [...], "task_records": {task.task_id: task}}

3. Report results from your external system:

from async_context_engine import update_task_result

update_task_result(store, task_id="abc-123", result="$1,250")

4. Start the poller:

from async_context_engine import AsyncPoller

poller = AsyncPoller(store=store, graph=graph, config=config, interval=5, on_result=display)
poller.start()

That's it. Results flow back into the conversation automatically.

What You Build vs. What the Library Handles

Documentation

Developer Guide — Comprehensive walkthrough of every concept, the full API reference, architecture diagrams, and step-by-step integration instructions.

Shipped Components

Running the Example

git clone https://github.com/sagarmainkar/async-context-engine.git
cd async-context-engine

# Install everything (library + example dependencies)
uv sync --extra examples

# Start Ollama (required for the example LLM)
ollama serve

# Run the demo
cd examples/basic
uv run runner.py

No venv creation needed. uv sync creates the .venv automatically, resolves all dependencies, and installs the library in editable mode — one command, everything works.

License

MIT