Phase 6 · LangGraph Orchestration: Closing the Unification Loop

Goal: express everything from earlier chapters in one unified way with LangGraph. When you see CrewAI crews, AutoGen chats, and single-Agent loops all writable as "state graph + nodes + conditional edges", the project's central claim closes its loop.

Three core LangGraph concepts

1. State: a "shared whiteboard" flowing between nodes, defined with TypedDict or Pydantic. Each node reads and writes it.
2. Node: an ordinary function (state) -> partial state update. It can be an LLM call, a tool, or any logic.
3. Edge: connections between nodes.
   • Normal edge: fixed order, e.g. A → B.
   • Conditional edge add_conditional_edges: a function reads the state and decides where to go next — this is the source of "intelligent routing".

Two reserved endpoints: START (entry) and END (exit).

Example 1: the Phase 5 crew as a graph

File: examples/p6/crew_graph.py

Recall the "Researcher → Writer → Reviewer" pipeline we hand-rolled in Phase 5. In LangGraph:

builder = StateGraph(CrewState)
builder.add_node("researcher", researcher)
builder.add_node("writer", writer)
builder.add_node("reviewer", reviewer)
builder.add_edge(START, "researcher")
builder.add_edge("researcher", "writer")
builder.add_edge("writer", "reviewer")
builder.add_edge("reviewer", END)
graph = builder.compile()

See — a CrewAI crew = a linear graph. Different framework, identical "nodes + edges" structure.

python -m examples.p6.crew_graph

Example 2: coordinator routing as a conditional edge

File: examples/p6/router_graph.py

The Phase 5 "coordinator picks the next expert" logic, expressed with add_conditional_edges:

builder.add_conditional_edges(
    "coordinator",
    route_by_state,                    # reads state["route"] to decide
    {"math": "math", "language": "language", "code": "code"},
)

This is exactly AutoGen's "coordinator decides who speaks" in LangGraph's standard form — a conditional edge.

python -m examples.p6.router_graph

The loop closes: one unified mental model

Tying the whole project together:

What you've seen	Its true graph form
Single-Agent loop (Phase 3)	think node + tool node + conditional edge (to END)
Tool-using Agent (Phase 4)	think node + tool nodes + conditional edge
CrewAI crew (Phase 5)	linear graph: role nodes + sequential edges
AutoGen chat (Phase 5)	role nodes + coordinator conditional edge
This Phase's LangGraph	the standard notation for all of the above

Unification thesis, closed: CrewAI / AutoGen / hand-rolled loops are all fundamentally "state + nodes + conditional edges". LangGraph abstracts that commonality into a general language. Master it and you no longer learn a separate orchestration API per framework — you already understand their underpinnings.

Why a graph instead of a for-loop

• Observable: graph structure can be visualized and traced (with LangSmith / Langfuse).
• Controllable: loop counts, state updates, branching are all explicitly declared, not buried in if/for.
• Composable: subgraphs embed as nodes in larger graphs — natural fit for complex multi-Agent systems.
• Persistable: state can be stored, enabling interruption/resume and human-in-the-loop (later phases).

Acceptance checklist

• [ ] Can state what State / Node / Edge / conditional edge each are
• [ ] Can write a "linear pipeline" and a "conditional-edge router" from memory using StateGraph
• [ ] Have run Example 1 and Example 2
• [ ] Can explain to someone "why CrewAI/AutoGen can both be expressed in LangGraph"

Next

Phase 7 onward moves into engineering and practice: a production-grade multi-Agent system (Capstone), human-in-the-loop (HITL), persistence and resume, and finally Phase 8–9 production deployment. Your "underlying paradigm" foundation is now solid.

Extra Example

Conditional branching graph (examples/p6/branching.py)

Use add_conditional_edges to route a request to different nodes — the graph form of AutoGen's coordinator routing.

python -m examples.p6.branching