dynamic-langgraph 0.1.1

Build LangGraph graphs from JSON configuration

dynamic-langgraph — DynamicLangGraph Engine

Build LangGraph graphs from JSON configuration — no hardcoded topologies.

dynamic-langgraph is a Python library that converts declarative JSON config documents into fully compiled, executable LangGraph graphs. Define your nodes, edges, state schema, and execution settings in JSON. The engine validates, compiles, and hands you back an async-ready graph.

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Features

• JSON-driven graph construction — declare nodes, edges, and state in config; skip the boilerplate
• Pre-compilation validation — catches unreachable nodes, static cycles, bad module paths, and signature mismatches before any node runs
• Async-first — sync handlers are auto-wrapped in run_in_executor; no event loop blocking
• Human-in-the-loop — interrupt_before/interrupt_after breakpoints with Command(resume=...) support
• Map-reduce fan-out — Send API for parallel branch execution and aggregation
• Nested subgraphs — register compiled CompiledStateGraph instances as nodes in a parent graph
• Streaming — all 5 LangGraph stream modes (values, updates, debug, messages, custom)
• Persistence — InMemorySaver, SqliteSaver, or any custom BaseCheckpointSaver
• Per-node caching — CachePolicy with configurable TTL
• LangSmith telemetry — structured trace attributes emitted automatically
• Graph visualization — export to Mermaid (.mmd) and images (PNG, SVG, JPG, PDF)
• Strictly typed — fully annotated public API; passes mypy --strict

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Requirements

• Python 3.11+
• langgraph >= 1.0.6
• jsonschema >= 4.23

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Installation

pip install dynamic-langgraph

From source (development):

git clone https://github.com/your-org/dynamic-langgraph.git
cd dynamic-langgraph
uv sync --dev

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Quickstart

1. Write a JSON config

{
  "graph_id": "echo-graph",
  "version": "1.0.0",
  "state_definition": {
    "type": "typed_dict",
    "module_path": "myapp.state",
    "class_name": "EchoState"
  },
  "nodes": [
    {
      "name": "echo",
      "handler": {
        "module_path": "myapp.nodes",
        "function_name": "echo_node"
      }
    }
  ],
  "edges": {
    "entry_point": "echo",
    "static_edges": [
      {"from": "echo", "to": "__end__"}
    ]
  }
}

2. Compile and run

import asyncio
from dlg import DLGEngine

graph = DLGEngine.from_file("graph_config.json")
result = asyncio.run(graph.ainvoke({"message": "hello"}))
print(result)

Or validate and compile separately:

from dlg import DLGValidator, DLGCompiler

config = {"graph_id": "echo-graph", "version": "1.0.0", ...}

DLGValidator(config).validate()
graph = DLGCompiler(config).compile()

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Human-in-the-Loop

Add "interrupt_before": true to a node and provide a checkpointer:

{
  "nodes": [
    {
      "name": "review",
      "handler": {"module_path": "myapp.nodes", "function_name": "review_node"},
      "interrupt_before": true
    }
  ],
  "checkpointer": {"type": "memory"}
}

import asyncio
from langgraph.types import Command
from dlg import DLGEngine

graph = DLGEngine.from_file("graph_config.json")
thread = {"configurable": {"thread_id": "session-1"}}

# Pauses at the interrupt_before node
await graph.ainvoke({"task": "review this"}, config=thread)

# Resume after human decision
result = await graph.ainvoke(Command(resume="approved"), config=thread)

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Map-Reduce Fan-Out

{
  "edges": {
    "entry_point": "fan_out",
    "conditional_edges": [
      {
        "from": "fan_out",
        "send_router": {
          "module_path": "myapp.routers",
          "function_name": "fan_out_router"
        }
      }
    ]
  }
}

from langgraph.types import Send

def fan_out_router(state):
    return [Send("process_item", {"item": x}) for x in state["items"]]

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Streaming

async for event in graph.astream(inputs, stream_mode="updates"):
    print(event)

Supported modes: "values", "updates", "debug", "messages", "custom".

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Graph Visualization

Python API

from dlg.mermaid import json_to_mermaid, convert_json_to_mmd, convert_mmd_to_image

# Get Mermaid diagram text from a config dict
config = {"graph_id": "my-graph", "version": "1.0.0", ...}
print(json_to_mermaid(config))
# flowchart TD
#     __start__((__start__)) --> echo
#     echo --> __end__((__end__))

# Write .mmd beside the JSON file
mmd_path = convert_json_to_mmd("graph_config.json")
# -> graph_config.mmd

# Convert .mmd to PNG (requires mmdc or npx)
img_path = convert_mmd_to_image(mmd_path, "png")
# -> graph_config.png

# Write image to a specific output directory
img_path = convert_mmd_to_image(mmd_path, "png", output_dir="graph-previews/")
# -> graph-previews/graph_config.png

CLI

# Generate .mmd only
python -m dlg graph_config.json

# Generate .mmd and a PNG
python -m dlg graph_config.json --format png

# Generate .mmd, PNG and SVG into a target folder
python -m dlg graph_config.json -f png -f svg --output-dir graph-previews/

# After pip install dynamic-langgraph:
dlg graph_config.json -f png --output-dir graph-previews/

mmdc from @mermaid-js/mermaid-cli is required for image export. Install globally with npm i -g @mermaid-js/mermaid-cli, or ensure npx is available (falls back automatically).

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Error Hierarchy

All validation errors are raised before any node executes.

Exception	Trigger
GraphValidationError	Schema violation, missing field, bad module path
UnreachableNodeException	Node with no path from entry point
StaticLoopException	Cycle in static-only edges
MixedRoutingConflict	Node has both static edge and Command(goto=...)
SendTargetError	Send targets an undeclared node
SubgraphCompatibilityError	Subgraph state schema conflicts with parent
CheckpointerMissingError	Interrupt/resume attempted without checkpointer
SchemaVersionMismatch	Checkpoint config version differs from current config

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Testing Utilities

from dlg.testing import DLGTestHarness

harness = DLGTestHarness(config)
result = await harness.invoke_node("echo", state={"message": "hi"})
assert result["message"] == "hi"

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Configuration Reference

A full JSON config supports the following top-level keys:

Key	Required	Description
graph_id	Yes	Unique graph identifier
version	Yes	Semantic version string (e.g. "1.0.0")
state_definition	Yes	State schema — type (typed_dict, pydantic, dataclass), module_path, class_name
nodes	Yes	List of node descriptors (each with name and handler or subgraph)
edges	Yes	entry_point, static_edges, conditional_edges
checkpointer	No	{"type": "memory"}, {"type": "sqlite", "db_path": "..."}, or custom
settings	No	recursion_limit, max_concurrency
global_defaults	No	Default retry_policy, timeout_seconds
cache	No	Global cache policy
security	No	allowed_module_prefixes allowlist

See docs/concepts/json-schema.md for the full schema reference.

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Project Structure

dlg/
├── __init__.py        # Public API exports
├── __main__.py        # CLI entry point (python -m dlg / dlg command)
├── compiler.py        # DLGCompiler — StateGraph assembly
├── mermaid.py         # Mermaid/image export (json_to_mermaid, convert_*)
├── validator.py       # DLGValidator — all pre-compilation checks
├── exceptions.py      # Typed exception hierarchy
├── testing.py         # DLGTestHarness utility
├── _config.py         # Pydantic config models (GraphConfig, NodeConfig, …)
├── _schema.py         # JSON metaschema
├── _wrappers.py       # Async node wrapper + sync adapter
└── _telemetry.py      # LangSmith integration

tests/
├── unit/              # DLGValidator, DLGCompiler, mermaid, models
└── integration/       # Full graph execution, streaming, checkpointing

docs/                  # MkDocs site (concepts, API reference, examples)

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Development

# Install dev dependencies
uv sync --dev

# Run tests
uv run python -m pytest

# Run tests with coverage
uv run python -m pytest --cov=dlg --cov-report=term-missing

# Type check
uv run mypy dlg --strict

# Build docs locally
uv run mkdocs serve

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Contributing

1. Fork the repo and create a feature branch
2. Add tests for new behavior (coverage target: 90%)
3. Ensure mypy --strict passes
4. Open a pull request — CI runs on Python 3.11, 3.12, and 3.13

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License

Apache License 2.0 — see LICENSE.

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Acknowledgements

Built on top of LangGraph by LangChain, Inc.