How do I share compiled Langgraph subgraphs between containerized microservices?

Below is one approach that many have found effective: rather than trying to pickle the entire compiled subgraph (with its nested functions and closures), you can decouple the graph’s definition from its instantiation. In a microservices setup, this means transmitting a serializable graph configuration (for example, in JSON or YAML) that describes the nodes, edges, and tool references. Then, in the parent container you “re‐compile” or instantiate the subgraph from that configuration.

Key Ideas

• Avoid Nested Function Definitions:
  The pickling error arises because nested functions (or closures) can’t be pickled with the standard serializer. One solution is to refactor these nested functions so that they’re defined at the module level. That way they are importable via a module reference once unpickled.

• Graph as a Configuration:
  Instead of serializing live Python objects, consider designing your system so that the compiled graph can be represented as a declarative configuration (e.g., a JSON document). The parent agent in a separate container can then reconstruct the subgraph by reading this configuration and using a factory method to instantiate nodes and edges accordingly.

• Alternative Serialization Tools:
  If refactoring your nested functions is not an option, you might explore alternative serializers such as dill or cloudpickle. These libraries can sometimes handle more complex objects than the standard pickle—but even they may have issues with deeply nested closures, and the resulting serialized object might be more fragile or harder to version.

Example Workflow

1. Define a Serializable Graph Specification:
   Create a JSON schema that details your subgraph’s nodes, their types, the tools they use, and how they connect.

2. Transmit the Configuration:
   Send this JSON from your microservice with the subgraph (child agent) to the parent agent’s container, perhaps via an API, a message broker, or a shared data store.

3. Reinstantiate the Graph in the Parent Container:
   In the parent agent, load the JSON document and use a factory or builder function to create the same agent nodes (using pre-defined, module-level functions or classes), thereby “compiling” the subgraph locally.

Here’s a brief sketch in code:

PYTHONCopied
# Example: Define a configuration for your subgraph
subgraph_config = {
    "nodes": [
        {
            "name": "search_agent",
            "agent_class": "SearchAgent",  # A class defined at module level
            "tools": ["tavily_tool"]
        },
        {
            "name": "plot_agent",
            "agent_class": "PlotAgent",
            "tools": ["python_repl_tool"]
        }
    ],
    "edges": [
        {"from": "search_agent", "to": "plot_agent", "condition": "if_final_answer"}
    ]
}

# Serialize configuration to JSON
import json
serialized_config = json.dumps(subgraph_config)

# On the parent container, deserialize and compile the graph.
def build_subgraph(config_json):
    config = json.loads(config_json)
    nodes = {}
    # Assume we have a registry mapping class names to actual agent classes
    registry = {
        "SearchAgent": SearchAgent,
        "PlotAgent": PlotAgent,
    }
    for node_config in config["nodes"]:
        agent_class = registry[node_config["agent_class"]]
        # Instantiate the agent with tools retrieved from a tool registry
        tools = [tool_registry[tool_name] for tool_name in node_config["tools"]]
        nodes[node_config["name"]] = agent_class(tools)
    # Process edges as needed to wire the nodes together
    # (e.g., setting routing logic between agents)
    return nodes

# Now, in the parent agent's container:
compiled_subgraph = build_subgraph(serialized_config)

Summary

By transmitting a serializable representation of your subgraph (and ensuring that all agent/tool functions are defined at importable module level), you avoid the pickle limitations caused by nested functions. This method not only sidesteps serialization issues but also offers more flexibility and versioning in a distributed, microservice environment.