cy0307/awesome-graph-engineering huggingface dataset:
A practical guide to building agents (Blog, OpenAI Guides & Resources, 2025)
Authors: OpenAI
Explains when agents are appropriate, how to define their tools and instructions, and how to move from one agent to manager and handoff-based multi-agent patterns.
Why it matters: A useful first check on whether distinct roles justify the coordination cost of a graph.
Building effective agents (Blog, Anthropic Engineering, 2024)
Authors: Erik Schluntz; Barry Zhang
Distinguishes workflows from autonomous agents and presents routing, parallelization, orchestrator-worker, and evaluator-optimizer patterns.
Why it matters: Provides a compact topology vocabulary and a strong case for adding coordination only when the task demands it.
Agent-Oriented Programming (Paper, Artificial Intelligence, 1993)
Authors: Yoav Shoham
Defines a programming paradigm in which agents are first-class components described through mental state and governed by explicit interaction rules.
Why it matters: Establishes the intellectual lineage for treating an agent role as a programmable organizational unit.
Intelligent Agents: Theory and Practice (Paper, The Knowledge Engineering Review, 1995)
Authors: Michael Wooldridge; Nicholas R. Jennings
Surveys the properties, architectures, and engineering approaches that distinguish autonomous agents from ordinary software modules.
Why it matters: Grounds the agency-at-the-nodes boundary that separates an agent graph from a deterministic workflow.
The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures (Paper, AI Magazine, 1986)
Authors: H. Penny Nii
Describes systems in which independent specialists coordinate opportunistically through a shared problem state and a control component.
Why it matters: Supplies a durable model for shared state without requiring every node to exchange its full context directly.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning (Paper, NeurIPS, 2016)
Authors: Jakob Foerster; Ioannis Alexandros Assael; Nando de Freitas; Shimon Whiteson
Introduces reinforcement-learning methods that let agents learn communication protocols alongside their task policies, including discrete messages for execution.
Why it matters: Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.
TarMAC: Targeted Multi-Agent Communication (Paper, ICML, 2019)
Authors: Abhishek Das et al.
Uses attention to let agents address different messages to selected recipients instead of broadcasting the same information to the whole team.
Why it matters: Motivates selective, recipient-aware handoffs when all-to-all communication is wasteful or distracting.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations (Paper, COLM, 2024)
Authors: Qingyun Wu; Gagan Bansal; Jieyu Zhang; Yiran Wu; Beibin Li; Erkang Zhu; Li Jiang; Xiaoyun Zhang; Shaokun Zhang; Jiale Liu; Ahmed Hassan Awadallah; Ryen W. White; Doug Burger; Chi Wang
Presents a framework for composing customizable conversational agents that can combine language models, tools, code execution, and human input.
Why it matters: An early, influential demonstration that agent roles and conversation links can be expressed as an executable topology.
GPTSwarm: Language Agents as Optimizable Graphs (Paper, ICML, 2024)
Authors: Mingchen Zhuge et al.
Represents language-agent systems as computational graphs and optimizes graph components from task feedback.
Why it matters: Makes the graph itself an optimization target rather than a fixed orchestration diagram.
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration (Paper, COLM, 2024)
Authors: Zijun Liu et al.
Constructs task-specific collaboration networks that can vary which agents participate and how they communicate instead of relying on one fixed team.
Why it matters: Provides evidence for adapting the work graph to the task while keeping the available agent roles reusable.
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems (Paper, ICLR, 2025)
Authors: Guibin Zhang et al.
Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.
Why it matters: Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks (Paper, ICML, 2025)
Authors: Guibin Zhang et al.
Uses graph neural networks to design communication structures for multi-agent systems instead of assuming a complete or manually chosen graph.
Why it matters: Connects task performance to explicit topology search and exposes communication structure as an engineering variable.
Automated Design of Agentic Systems (Paper, ICLR, 2025)
Authors: Shengran Hu; Cong Lu; Jeff Clune
Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.
Why it matters: Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.
AFlow: Automating Agentic Workflow Generation (Paper, ICLR, 2025)
Authors: Jiayi Zhang et al.
Searches over reusable workflow operators to generate task-specific agentic workflows and improve them from evaluation results.
Why it matters: Offers a concrete method for evolving work graphs against measurable objectives rather than intuition alone.
Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies (Paper, ICLR, 2026)
Authors: Han Zhou et al.
Studies joint optimization of agent prompts and communication topology instead of tuning either component in isolation.
Why it matters: Shows that node behavior and graph structure interact and may need to evolve together.
Improving Factuality and Reasoning in Language Models through Multiagent Debate (Paper, ICML, 2024)
Authors: Yilun Du et al.
Tests rounds of proposal and critique among multiple language-model instances as a way to improve factual and reasoning answers.
Why it matters: Supplies an empirical basis for debate-style gates while leaving room to examine correlated errors and added cost.
Improving Multi-Agent Debate with Sparse Communication Topology (Paper, Findings of EMNLP, 2024)
Authors: Yunxuan Li et al.
Examines multi-agent debate under sparse communication structures rather than defaulting to full information exchange among every participant.
Why it matters: Isolates topology as a factor in debate quality and communication efficiency.
Reflexion: Language Agents with Verbal Reinforcement Learning (Paper, NeurIPS, 2023)
Authors: Noah Shinn et al.
Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.
Why it matters: Clarifies how a node loop can persist learning across retries without changing model weights.
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing (Paper, ICLR, 2024)
Authors: Zhibin Gou et al.
Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.
Why it matters: Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks (Paper, Microsoft Research MSR-TR-2024-47, 2024)
Authors: Adam Fourney et al.
Documents an orchestrator-led team of specialized agents for web, file, coding, and terminal tasks, with progress tracking and replanning.
Why it matters: Provides a concrete generalist topology whose role boundaries and recovery behavior can be examined as a system.
How we built our multi-agent research system (Blog, Anthropic Engineering, 2025)
Authors: Jeremy Hadfield et al.
Describes a lead research agent that creates parallel subagents, delegates searches, and synthesizes their findings, including operational lessons from production.
Why it matters: A detailed case study of dynamic fan-out and fan-in, context separation, evaluation, and the token cost of an adaptive work graph.
CrewAI Crews (Docs, CrewAI Documentation, 2026)
Authors: CrewAI
Documents role-based agent crews, assigned tasks, delegation, and sequential or hierarchical execution processes.
Why it matters: Offers accessible primitives for testing explicit ownership and manager-worker coordination.
OpenAI Agents SDK: Agent orchestration (Docs, OpenAI Agents SDK Documentation, 2026)
Authors: OpenAI
Explains manager-style orchestration with agents exposed as tools and decentralized orchestration through handoffs.
Why it matters: Makes the centralized-versus-decentralized topology choice explicit in a production SDK.
Strands Agents: Multi-Agent Patterns (Docs, Strands Agents Documentation, 2026)
Authors: Strands Agents; AWS
Documents several coordination shapes for composing agents, including supervisor, swarm, workflow, and graph-oriented patterns.
Why it matters: Lets builders compare topology choices within one SDK instead of treating one pattern as universal.
Pydantic AI: Multi-Agent Applications (Docs, Pydantic AI Documentation, 2026)
Authors: Pydantic
Shows delegation, programmatic control flow, and graph-based state machines for composing typed Python agents.
Why it matters: Useful for expressing node inputs, outputs, dependencies, and orchestration boundaries in ordinary application code.
LlamaIndex: Multi-Agent Patterns (Docs, LlamaIndex Documentation, 2026)
Authors: LlamaIndex
Covers agent workflow, orchestrator, and planner-oriented approaches for coordinating specialized agents.
Why it matters: Provides implementation patterns for choosing who owns delegation and how results return to the coordinating node.
Google ADK: Graph-based Agent Workflows (Docs, Google ADK Documentation, 2026)
Authors: Google
Documents declarative workflows whose nodes combine agents, tools, functions, and human input through explicit edges, typed data passing, routing, branching, state, fan-out and join, loops, escalation, and nesting.
Why it matters: Makes the graph load-bearing and inspectable while separating deterministic process control from model reasoning.
Microsoft Agent Framework: Workflows (Docs, Microsoft Learn, 2026)
Authors: Microsoft
Describes workflows built from executors and explicit edges, with support for branching, aggregation, state, and checkpointing.
Why it matters: Exposes the work graph as an inspectable program rather than hiding coordination inside prompts.
LangGraph overview (Docs, LangGraph Documentation, 2025)
Authors: LangChain
Introduces a low-level runtime for stateful agent graphs with durable execution, streaming, memory, and human intervention.
Why it matters: A widely used substrate for implementing explicit nodes, edges, state transitions, and resumable work graphs.
OpenAI Agents SDK: Handoffs (Docs, OpenAI Agents SDK Documentation, 2026)
Authors: OpenAI
Documents transfers from one agent to another, including tool-shaped handoff schemas, input filters, and callbacks.
Why it matters: Turns an edge into an explicit contract controlling when ownership moves and what context crosses with it.
Model Context Protocol Specification 2025-11-25 (Standard, MCP Specification 2025-11-25, 2025)
Authors: Model Context Protocol project; Agentic AI Foundation
Specifies a client-server protocol through which AI applications discover and use tools, resources, prompts, and contextual data.
Why it matters: Standardizes capability and context edges, while remaining distinct from a protocol for delegating work between autonomous agents.
Agent2Agent Protocol Specification v1.0.0 (Standard, A2A Specification, 2026)
Authors: A2A Protocol Working Group; Linux Foundation
Defines interoperable agent discovery, task lifecycle, messages, artifacts, streaming, asynchronous updates, version negotiation, and multiple protocol bindings across service boundaries.
Why it matters: Provides a version-pinned wire contract for cross-system handoffs where agents cannot share an in-process runtime.
LangGraph Persistence (Docs, LangGraph Documentation, 2025)
Authors: LangChain
Documents thread-scoped checkpoints, saved state, replay, state inspection, and memory storage for LangGraph runs.
Why it matters: Shows how graph state can become the recoverable system of record instead of living only in model context.
OpenAI Agents SDK: Sessions (Docs, OpenAI Agents SDK Documentation, 2026)
Authors: OpenAI
Documents persistent conversation history that can be loaded and updated across repeated agent runs.
Why it matters: Provides a bounded mechanism for carrying state across nodes and turns without manually rebuilding every prompt.
Evaluate agent workflows (Docs, OpenAI API Documentation, 2026)
Authors: OpenAI
Explains how to build datasets, graders, trace-based evaluations, and reproducible checks for agent workflows.
Why it matters: Makes gates testable at both the final outcome and the intermediate handoff level.
Demystifying evals for AI agents (Blog, Anthropic Engineering, 2026)
Authors: Mikaela Grace et al.
Presents practical guidance for defining tasks, outcomes, graders, and evaluation suites for agents with non-deterministic trajectories.
Why it matters: Helps turn vague reviewer judgments into evidence gates that can guide graph changes.
Inspect AI (Tool, Inspect AI Documentation, 2024)
Authors: UK AI Security Institute
Provides an open-source evaluation framework with tasks, solvers, scorers, sandboxed tools, and structured logs.
Why it matters: Supports reproducible gate nodes and traceable evidence for both individual agents and composed systems.
Dapr Agents introduction (Docs, Dapr Documentation, 2026)
Authors: Dapr; CNCF
Introduces an agent framework built on durable actors and workflows, with state, messaging, and recovery supplied by the Dapr runtime.
Why it matters: Shows how graph nodes can inherit distributed-systems durability instead of implementing recovery only in prompts.
Temporal integration for OpenAI Agents SDK (Tool, Temporal Python SDK, 2025)
Authors: Temporal
Integrates OpenAI agent runs, model calls, and tools with Temporal workflows and activities for durable execution.
Why it matters: Provides replay, retry, timeout, and recovery semantics beneath an agent graph without asking the model to manage them.
DBOS AI Quickstart (Docs, DBOS Documentation, 2026)
Authors: DBOS
Shows how to place AI application steps inside durable workflows whose progress is recorded and recoverable after interruption.
Why it matters: Offers a compact path from an agent prototype to resumable execution with explicit step boundaries.
Restate Durable Agents (Docs, Restate Documentation, 2026)
Authors: Restate
Documents durable agent patterns using persisted execution, reliable calls, retries, timers, and stateful services.
Why it matters: Maps common mid-graph failures to runtime guarantees rather than fragile application-level retry code.
OpenAI Agents SDK: Tracing (Docs, OpenAI Agents SDK Documentation, 2026)
Authors: OpenAI
Documents traces and spans for agent runs, model generations, tool calls, handoffs, and guardrails.
Why it matters: Makes node and edge behavior inspectable so latency, failures, and expensive paths can be attributed correctly.
OpenTelemetry GenAI Semantic Conventions (Standard, OpenTelemetry GenAI repository, 2026)
Authors: OpenTelemetry GenAI SIG
Develops shared telemetry names and attributes for generative-AI model, tool, and agent operations in traces, metrics, and events.
Why it matters: Helps graph telemetry remain portable across runtimes and observability vendors.
Arize Phoenix: Cost Tracking (Docs, Arize Phoenix Documentation, 2026)
Authors: Arize AI
Explains how Phoenix derives and displays token usage and model cost from traced generative-AI calls.
Why it matters: Supports per-node and per-path cost accounting instead of treating a multi-agent run as one opaque bill.
LangSmith Observability (Docs, LangSmith Documentation, 2026)
Authors: LangChain
Documents tracing, dashboards, alerts, feedback, and evaluation views for language-model and agent applications.
Why it matters: Provides operational views for following execution across nodes and locating failures on the critical path.
MultiAgentBench: Evaluating Collaboration and Competition of LLM Agents (Benchmark, ACL, 2025)
Authors: Kunlun Zhu et al.
Benchmarks language-model agents in collaborative and competitive settings while examining coordination processes as well as task outcomes.
Why it matters: Measures properties of the team interaction that single-agent benchmarks cannot expose.
Why Do Multi-Agent LLM Systems Fail? (Benchmark, NeurIPS Datasets & Benchmarks, 2025)
Authors: Mert Cemri et al.
Provides a structured taxonomy and evaluation approach for diagnosing coordination failures in multi-agent language-model systems.
Why it matters: Turns reliability incidents into recurring, attributable failure classes that can guide graph redesign.
Large Language Models Cannot Self-Correct Reasoning Yet (Paper, ICLR, 2024)
Authors: Jie Huang et al.
Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.
Why it matters: Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.
Towards a Science of Scaling Agent Systems (Paper, arXiv; Google Research, 2025)
Authors: Yubin Kim et al.
Studies how agent-system performance changes across tasks, models, coordination structures, and scaling choices under controlled experiments.
Why it matters: Tests the assumption that adding agents reliably helps and frames scaling as an empirical topology decision.
Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets (Paper, arXiv, 2026)
Authors: Dat Tran; Douwe Kiela
Compares single-agent and multi-agent approaches to multi-hop reasoning while holding the total thinking-token budget constant.
Why it matters: Provides the cost-controlled baseline needed before claiming that coordination, rather than extra inference, caused an improvement.
From Loop Engineering to Graph Engineering? (Blog, X Articles, 2026)
Authors: Carlos E. Perez
Frames the shift as loop architecture: networks of improvement cycles that monitor, feed, constrain, and correct one another, with reliability located in their edges.
Why it matters: Adds the grounding requirement missing from topology-only accounts: independent counter-metrics, frozen tests or rules, external anchors, and human ownership of root objectives.
A Multi-Level Organization for Problem Solving Using Many, Diverse, Cooperating Sources of Knowledge (Paper, IJCAI, 1975)
Authors: Lee D. Erman; Victor R. Lesser
Presents the Hearsay-II multi-level blackboard, where independent knowledge sources react to shared hypotheses, create explicit structural dependencies, and verify or revise one another's contributions.
Why it matters: Provides an early architecture for loosely coupled specialist nodes coordinating through inspectable shared state instead of direct all-to-all calls.
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver (Paper, IEEE Transactions on Computers, 1980)
Authors: Reid G. Smith
Defines a negotiation protocol in which managers announce tasks, potential contractors bid, and awards establish temporary problem-solving relationships.
Why it matters: Supplies the classic contract for capability-aware delegation and auditable assignment edges between autonomous nodes.
The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why – A Survey from MARL to Emergent Language and LLMs (Paper, Transactions on Machine Learning Research, 2026)
Authors: Jingdi Chen; Hanqing Yang; Zongjun Liu; Carlee Joe-Wong
Synthesizes multi-agent communication across reinforcement learning, emergent language, and LLM systems through sender, recipient, timing, content, and purpose decisions.
Why it matters: Provides a design-oriented map for engineering edge selection, message timing, payloads, grounding, scalability, and interpretability.
Scaling Large Language Model-based Multi-Agent Collaboration (Paper, ICLR, 2025)
Authors: Chen Qian; Zihao Xie; YiFei Wang; Wei Liu; Kunlun Zhu; Hanchen Xia; Yufan Dang; Zhuoyun Du; Weize Chen; Cheng Yang; Zhiyuan Liu; Maosong Sun
Introduces MacNet, a DAG-based collaboration architecture executed in topological order, and studies communication structure while scaling experiments beyond 1,000 agents.
Why it matters: Makes topology a causal scaling variable rather than assuming that larger teams or denser communication automatically help.
MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks (Paper, ICML, 2026)
Authors: Zixuan Ke et al.
Formulates orchestration as reinforcement-learned generation of a complete multi-agent program and evaluates it across controlled dimensions including depth, horizon, breadth, parallelism, and robustness.
Why it matters: Tests when whole-system graph structure helps instead of attributing gains to coordination without controlled task evidence.
CARD: Towards Conditional Design of Multi-agent Topological Structures (Paper, ICLR, 2026)
Authors: Tongtong Wu et al.
Generates communication graphs conditioned on agent roles, models, tools, and data sources, and adapts topology as task resources change.
Why it matters: Treats the available capabilities and directed edges as a versionable organizational artifact rather than a fixed team template.
ResMAS: Resilience Optimization in LLM-based Multi-agent Systems (Paper, AAAI, 2026)
Authors: Zhilun Zhou et al.
Learns task-specific resilient communication topologies and topology-aware prompts after measuring how graph structure and node instructions affect performance under agent failures and other perturbations.
Why it matters: Moves resilience from reactive recovery into the design of the graph itself and evaluates transfer to new tasks and models.
EvoMAS: Evolutionary Generation of Multi-Agent Systems (Paper, ICML, 2026)
Authors: Yuntong Hu et al.
Evolves structured multi-agent configurations through trace-guided mutation, crossover, selection, and an experience memory across reasoning, coding, and tool-use tasks.
Why it matters: Shows how an inspectable team specification can evolve from execution evidence while retaining executability and runtime robustness.
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (Paper, EMNLP, 2025)
Authors: Xu Shen et al.
Causally studies correct and erroneous information propagation across communication densities and finds that moderately sparse structures can preserve useful diffusion while suppressing errors.
Why it matters: Provides evidence against defaulting to dense graphs and links topology decisions to measured error amplification.
MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (Paper, ACL System Demonstrations, 2026)
Authors: Yang Liu et al.
Compiles natural-language intent into an editable workflow specification and executable directed graph, with reusable components, topology preview, runtime tracing, multimodal messages, and human interaction.
Why it matters: Provides a direct implementation path from an inspectable organizational graph to execution and evaluates it on seven public benchmarks.
AutoGen GraphFlow (Workflows) (Docs, Microsoft AutoGen Documentation, 2026)
Authors: Microsoft
Implements directed multi-agent execution graphs with sequential, parallel, conditional, fan-in, and cyclic paths, edge conditions, activation groups, safe loop exits, and separately configurable message filtering. The feature is explicitly experimental.
Why it matters: Distinguishes the execution graph from the message graph, exposing both who acts next and what context each agent receives.
Model Context Protocol: Tasks (Standard, MCP Specification 2025-11-25, 2025)
Authors: Model Context Protocol project; Agentic AI Foundation
Specifies experimental durable asynchronous request state machines with capability negotiation, polling, deferred results, progress, input-required states, cancellation, TTLs, and task-message correlation.
Why it matters: Turns a remote tool or context edge into a recoverable task contract that can outlive one synchronous request.