Awesome Graph Engineering · Open field guide

Engineer the organization, not just the agent.

A curated field guide to graph-structured multi-agent systems — programmable AI-agent organizations with explicit roles, topologies, handoffs, work graphs, verification, reliability, and observability.

Explore the atlas View on GitHub
9design layers —curated resources HFopen dataset ✓schema validated

Here, graph nodes are AI agents — not data entities. Looking for graph databases, GNNs, knowledge graphs, or GraphRAG? See the boundary guide.

A graph of specialized AI agents connected by explicit handoffs and an evidence gate.

Focus the graph, then use arrow keys to orbit, plus or minus to zoom, and zero to reset. Hold Alt while scrolling to zoom without trapping normal page scrolling.

Working definition · emerging practice

Graph engineering, in 30 seconds

Graph engineering is the practice of specifying, executing, observing, and evolving a graph-structured agent system—its roles and runtime instances, connecting contracts, shared state and artifacts, and the evidence by which collective behavior is judged. Each agent may contain a local loop; tests, audit loops, humans, and real-world anchors may be non-agent controls. The graph must materially constrain execution and remain inspectable.

  1. 01

    Node

    A separately accountable agent role or runtime instance with bounded authority.

  2. 02

    Edge

    A designed dependency: what crosses, in what shape, under which condition.

  3. 03

    Org graph

    The stable topology of roles, ownership, and standing relationships.

  4. 04

    Work graph

    The task-specific structure created, routed, and revised for one objective.

  5. 05

    Gate

    An independent test, audit, human decision, or real-world anchor that controls an edge.

The scope is deliberately narrow because the term is emerging and overloaded. It describes a practical engineering layer across agent orchestration, multi-agent systems, and durable workflows—not an industry standard or a claim of invention.

Read the full definition and scope

The core model

Two graphs. One system.

A stable organization of agents executes ephemeral work. These are complementary analytical views—not proposed universal standards or necessarily separate runtime objects.

Drag or use arrows · Alt-scroll or +/− to zoom
Switch between a stable organization graph and an ephemeral work graph.

Use the Org graph and Work graph buttons to change the model. Focus the graph, then use arrow keys to orbit, plus or minus to zoom, and zero to reset.

Agent persistent role Edge contract explicit handoff Evidence gate quality or policy check

A practical taxonomy

Nine layers from roles to evolution

Start with roles, topology, and handoffs. Add state, gates, and operations only as real failure modes earn the complexity.

Before you add a second agent

Does this job need a graph?

Graphs add coordination cost, latency, and new failure modes. They earn that overhead only when at least one condition is real.

  1. 01

    Parallelism

    Can meaningful work proceed concurrently without constant coordination?

  2. 02

    Isolation

    Must contexts, permissions, or failure domains remain separated?

  3. 03

    Specialization

    Do distinct skills, tools, models, or durable contexts pay for separate roles?

  4. 04

    Verification

    Must a producer’s output pass an independent evidence gate before it advances?

None applyBuild a better loop.

Keep the free context-sharing and lower coordination cost of one agent.

One or more applyDesign the smallest graph that earns its overhead.

Start minimal, define the edges, and require evidence before you evolve.

Guided entry points

Start where you are

Choose the question closest to the one blocking you now.

Explore the interactive model
01Loop builderI have one agent that works. Now what?

Decide whether a second role earns its place and draw the first edge.

 02ArchitectWhich topology and framework?

Match the shape of the graph to the shape of the task.

 03OperatorMake the graph survivable.

Design retries, evidence gates, budgets, tracing, and escalation.

 04ResearcherWhat does the evidence say?

Read the foundations, benchmarks, topology studies, and critiques.

Searchable resource directory

The graph engineering atlas

Search the field by design layer, resource type, and source/evidence label. Every result maps back to the README and open dataset.

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Open data

Use the field guide as data.

Every resource ships as CSV and JSONL, mapped to a design layer and checked against the README.

Download CSV Download JSONL View JSON Schema Explore on Hugging Face
Python
import pandas as pd

url = "https://raw.githubusercontent.com/ChaoYue0307/awesome-graph-engineering/main/data/resources.csv"
resources = pd.read_csv(url)

resources.query("layer == 'Gates'").head()
Schemalayer stringrtype stringvenue stringyear integerdescription stringevidence string

License

Repository-created metadata, schema, summaries, documentation, code, and visual assets are dedicated to the public domain under CC0 1.0 Universal.

Linked papers, software, names, logos, and other third-party materials retain their own rights and licenses. CC0 does not waive trademark or patent rights and provides the work without warranties. Citation is appreciated for scholarly traceability but is not required by CC0.

Read the full license