Datasets:

cy0307
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awesome-graph-engineering

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age-0001	Start Here	Architecture guide	Blog	A practical guide to building agents	https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/	OpenAI Guides & Resources	2,025	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.	A useful first check on whether distinct roles justify the coordination cost of a graph.	Practitioner analysis	Roles
age-0002	Start Here	Architecture guide	Blog	Building effective agents	https://www.anthropic.com/engineering/building-effective-agents	Anthropic Engineering	2,024	Erik Schluntz; Barry Zhang	Distinguishes workflows from autonomous agents and presents routing, parallelization, orchestrator-worker, and evaluator-optimizer patterns.	Provides a compact topology vocabulary and a strong case for adding coordination only when the task demands it.	Practitioner analysis	Topology
age-0003	Research Foundations	Agent foundations	Paper	Agent-Oriented Programming	https://doi.org/10.1016/0004-3702(93)90034-9	Artificial Intelligence	1,993	Yoav Shoham	Defines a programming paradigm in which agents are first-class components described through mental state and governed by explicit interaction rules.	Establishes the intellectual lineage for treating an agent role as a programmable organizational unit.	Peer-reviewed research	Roles
age-0004	Research Foundations	Agent foundations	Paper	Intelligent Agents: Theory and Practice	https://doi.org/10.1017/S0269888900008122	The Knowledge Engineering Review	1,995	Michael Wooldridge; Nicholas R. Jennings	Surveys the properties, architectures, and engineering approaches that distinguish autonomous agents from ordinary software modules.	Grounds the agency-at-the-nodes boundary that separates an agent graph from a deterministic workflow.	Peer-reviewed research	Roles
age-0005	Research Foundations	Shared-state architectures	Paper	The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures	https://doi.org/10.1609/aimag.v7i2.537	AI Magazine	1,986	H. Penny Nii	Describes systems in which independent specialists coordinate opportunistically through a shared problem state and a control component.	Supplies a durable model for shared state without requiring every node to exchange its full context directly.	Peer-reviewed research	State
age-0006	Research Foundations	Learned communication	Paper	Learning to Communicate with Deep Multi-Agent Reinforcement Learning	https://proceedings.neurips.cc/paper_files/paper/2016/hash/c7635bfd99248a2cdef8249ef7bfbef4-Abstract.html	NeurIPS	2,016	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.	Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.	Peer-reviewed research	Handoffs
age-0007	Research Foundations	Learned communication	Paper	TarMAC: Targeted Multi-Agent Communication	https://proceedings.mlr.press/v97/das19a.html	ICML	2,019	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.	Motivates selective, recipient-aware handoffs when all-to-all communication is wasteful or distracting.	Peer-reviewed research	Handoffs
age-0008	Research Foundations	LLM multi-agent systems	Paper	AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations	https://openreview.net/forum?id=BAakY1hNKS	COLM	2,024	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.	An early, influential demonstration that agent roles and conversation links can be expressed as an executable topology.	Peer-reviewed research	Topology
age-0009	Research Foundations	Topology optimization	Paper	GPTSwarm: Language Agents as Optimizable Graphs	https://proceedings.mlr.press/v235/zhuge24a.html	ICML	2,024	Mingchen Zhuge et al.	Represents language-agent systems as computational graphs and optimizes graph components from task feedback.	Makes the graph itself an optimization target rather than a fixed orchestration diagram.	Peer-reviewed research	Evolution
age-0010	Research Foundations	Dynamic topology	Paper	A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration	https://openreview.net/forum?id=XII0Wp1XA9	COLM	2,024	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.	Provides evidence for adapting the work graph to the task while keeping the available agent roles reusable.	Peer-reviewed research	Evolution
age-0011	Research Foundations	Communication efficiency	Paper	Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems	https://openreview.net/forum?id=LkzuPorQ5L	ICLR	2,025	Guibin Zhang et al.	Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.	Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.	Peer-reviewed research	Observability & cost
age-0012	Research Foundations	Topology optimization	Paper	G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks	https://proceedings.mlr.press/v267/zhang25cu.html	ICML	2,025	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.	Connects task performance to explicit topology search and exposes communication structure as an engineering variable.	Peer-reviewed research	Evolution
age-0013	Research Foundations	System search	Paper	Automated Design of Agentic Systems	https://openreview.net/forum?id=t9U3LW7JVX	ICLR	2,025	Shengran Hu; Cong Lu; Jeff Clune	Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.	Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.	Peer-reviewed research	Evolution
age-0014	Research Foundations	Workflow search	Paper	AFlow: Automating Agentic Workflow Generation	https://openreview.net/forum?id=z5uVAKwmjf	ICLR	2,025	Jiayi Zhang et al.	Searches over reusable workflow operators to generate task-specific agentic workflows and improve them from evaluation results.	Offers a concrete method for evolving work graphs against measurable objectives rather than intuition alone.	Peer-reviewed research	Evolution
age-0015	Research Foundations	Joint optimization	Paper	Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies	https://openreview.net/forum?id=I05H9RUzHB	ICLR	2,026	Han Zhou et al.	Studies joint optimization of agent prompts and communication topology instead of tuning either component in isolation.	Shows that node behavior and graph structure interact and may need to evolve together.	Peer-reviewed research	Evolution
age-0016	Research Foundations	Debate and councils	Paper	Improving Factuality and Reasoning in Language Models through Multiagent Debate	https://proceedings.mlr.press/v235/du24e.html	ICML	2,024	Yilun Du et al.	Tests rounds of proposal and critique among multiple language-model instances as a way to improve factual and reasoning answers.	Supplies an empirical basis for debate-style gates while leaving room to examine correlated errors and added cost.	Peer-reviewed research	Gates
age-0017	Research Foundations	Debate and councils	Paper	Improving Multi-Agent Debate with Sparse Communication Topology	https://aclanthology.org/2024.findings-emnlp.427/	Findings of EMNLP	2,024	Yunxuan Li et al.	Examines multi-agent debate under sparse communication structures rather than defaulting to full information exchange among every participant.	Isolates topology as a factor in debate quality and communication efficiency.	Peer-reviewed research	Topology
age-0018	Research Foundations	Feedback and memory	Paper	Reflexion: Language Agents with Verbal Reinforcement Learning	https://openreview.net/forum?id=vAElhFcKW6	NeurIPS	2,023	Noah Shinn et al.	Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.	Clarifies how a node loop can persist learning across retries without changing model weights.	Peer-reviewed research	State
age-0019	Research Foundations	Tool-grounded verification	Paper	CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing	https://openreview.net/forum?id=Sx038qxjek	ICLR	2,024	Zhibin Gou et al.	Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.	Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.	Peer-reviewed research	Gates
age-0020	Production Case Studies	Generalist agent team	Paper	Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks	https://www.microsoft.com/en-us/research/publication/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/	Microsoft Research MSR-TR-2024-47	2,024	Adam Fourney et al.	Documents an orchestrator-led team of specialized agents for web, file, coding, and terminal tasks, with progress tracking and replanning.	Provides a concrete generalist topology whose role boundaries and recovery behavior can be examined as a system.	Research preprint	Roles
age-0021	Production Case Studies	Parallel research	Blog	How we built our multi-agent research system	https://www.anthropic.com/engineering/multi-agent-research-system	Anthropic Engineering	2,025	Jeremy Hadfield et al.	Describes a lead research agent that creates parallel subagents, delegates searches, and synthesizes their findings, including operational lessons from production.	A detailed case study of dynamic fan-out and fan-in, context separation, evaluation, and the token cost of an adaptive work graph.	Practitioner analysis	Work graphs
age-0022	Frameworks & SDKs	Role-based teams	Docs	CrewAI Crews	https://docs.crewai.com/en/concepts/crews	CrewAI Documentation	2,026	CrewAI	Documents role-based agent crews, assigned tasks, delegation, and sequential or hierarchical execution processes.	Offers accessible primitives for testing explicit ownership and manager-worker coordination.	Official documentation	Roles
age-0023	Frameworks & SDKs	Agent orchestration	Docs	OpenAI Agents SDK: Agent orchestration	https://openai.github.io/openai-agents-python/multi_agent/	OpenAI Agents SDK Documentation	2,026	OpenAI	Explains manager-style orchestration with agents exposed as tools and decentralized orchestration through handoffs.	Makes the centralized-versus-decentralized topology choice explicit in a production SDK.	Official documentation	Topology
age-0024	Frameworks & SDKs	Pattern catalog	Docs	Strands Agents: Multi-Agent Patterns	https://strandsagents.com/docs/user-guide/concepts/multi-agent/multi-agent-patterns/	Strands Agents Documentation	2,026	Strands Agents; AWS	Documents several coordination shapes for composing agents, including supervisor, swarm, workflow, and graph-oriented patterns.	Lets builders compare topology choices within one SDK instead of treating one pattern as universal.	Official documentation	Topology
age-0025	Frameworks & SDKs	Typed agents	Docs	Pydantic AI: Multi-Agent Applications	https://pydantic.dev/docs/ai/guides/multi-agent-applications/	Pydantic AI Documentation	2,026	Pydantic	Shows delegation, programmatic control flow, and graph-based state machines for composing typed Python agents.	Useful for expressing node inputs, outputs, dependencies, and orchestration boundaries in ordinary application code.	Official documentation	Topology
age-0026	Frameworks & SDKs	Multi-agent orchestration	Docs	LlamaIndex: Multi-Agent Patterns	https://developers.llamaindex.ai/python/framework/understanding/agent/multi_agent/	LlamaIndex Documentation	2,026	LlamaIndex	Covers agent workflow, orchestrator, and planner-oriented approaches for coordinating specialized agents.	Provides implementation patterns for choosing who owns delegation and how results return to the coordinating node.	Official documentation	Handoffs
age-0027	Frameworks & SDKs	Graph workflows	Docs	Google ADK: Graph-based Agent Workflows	https://adk.dev/graphs/	Google ADK Documentation	2,026	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.	Makes the graph load-bearing and inspectable while separating deterministic process control from model reasoning.	Official documentation	Work graphs
age-0028	Frameworks & SDKs	Graph workflows	Docs	Microsoft Agent Framework: Workflows	https://learn.microsoft.com/en-us/agent-framework/workflows/	Microsoft Learn	2,026	Microsoft	Describes workflows built from executors and explicit edges, with support for branching, aggregation, state, and checkpointing.	Exposes the work graph as an inspectable program rather than hiding coordination inside prompts.	Official documentation	Work graphs
age-0029	Frameworks & SDKs	Graph runtime	Docs	LangGraph overview	https://docs.langchain.com/oss/python/langgraph/overview	LangGraph Documentation	2,025	LangChain	Introduces a low-level runtime for stateful agent graphs with durable execution, streaming, memory, and human intervention.	A widely used substrate for implementing explicit nodes, edges, state transitions, and resumable work graphs.	Official documentation	Work graphs
age-0030	Protocols & Handoffs	In-process transfer	Docs	OpenAI Agents SDK: Handoffs	https://openai.github.io/openai-agents-python/handoffs/	OpenAI Agents SDK Documentation	2,026	OpenAI	Documents transfers from one agent to another, including tool-shaped handoff schemas, input filters, and callbacks.	Turns an edge into an explicit contract controlling when ownership moves and what context crosses with it.	Official documentation	Handoffs
age-0031	Protocols & Handoffs	Tool and context protocol	Standard	Model Context Protocol Specification 2025-11-25	https://modelcontextprotocol.io/specification/2025-11-25	MCP Specification 2025-11-25	2,025	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.	Standardizes capability and context edges, while remaining distinct from a protocol for delegating work between autonomous agents.	Industry standard	Handoffs
age-0032	Protocols & Handoffs	Agent interoperability	Standard	Agent2Agent Protocol Specification v1.0.0	https://a2a-protocol.org/v1.0.0/specification/	A2A Specification	2,026	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.	Provides a version-pinned wire contract for cross-system handoffs where agents cannot share an in-process runtime.	Industry standard	Handoffs
age-0033	State, Memory & Artifacts	Checkpointing	Docs	LangGraph Persistence	https://docs.langchain.com/oss/python/langgraph/persistence	LangGraph Documentation	2,025	LangChain	Documents thread-scoped checkpoints, saved state, replay, state inspection, and memory storage for LangGraph runs.	Shows how graph state can become the recoverable system of record instead of living only in model context.	Official documentation	State
age-0034	State, Memory & Artifacts	Conversation state	Docs	OpenAI Agents SDK: Sessions	https://openai.github.io/openai-agents-python/sessions/	OpenAI Agents SDK Documentation	2,026	OpenAI	Documents persistent conversation history that can be loaded and updated across repeated agent runs.	Provides a bounded mechanism for carrying state across nodes and turns without manually rebuilding every prompt.	Official documentation	State
age-0035	Verification & Evals	Workflow evaluation	Docs	Evaluate agent workflows	https://developers.openai.com/api/docs/guides/agent-evals	OpenAI API Documentation	2,026	OpenAI	Explains how to build datasets, graders, trace-based evaluations, and reproducible checks for agent workflows.	Makes gates testable at both the final outcome and the intermediate handoff level.	Official documentation	Gates
age-0036	Verification & Evals	Evaluation practice	Blog	Demystifying evals for AI agents	https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents	Anthropic Engineering	2,026	Mikaela Grace et al.	Presents practical guidance for defining tasks, outcomes, graders, and evaluation suites for agents with non-deterministic trajectories.	Helps turn vague reviewer judgments into evidence gates that can guide graph changes.	Practitioner analysis	Gates
age-0037	Verification & Evals	Evaluation framework	Tool	Inspect AI	https://inspect.aisi.org.uk/	Inspect AI Documentation	2,024	UK AI Security Institute	Provides an open-source evaluation framework with tasks, solvers, scorers, sandboxed tools, and structured logs.	Supports reproducible gate nodes and traceable evidence for both individual agents and composed systems.	Maintained OSS project	Gates
age-0038	Reliability & Durable Execution	Durable actors and workflows	Docs	Dapr Agents introduction	https://docs.dapr.io/developing-ai/dapr-agents/dapr-agents-introduction/	Dapr Documentation	2,026	Dapr; CNCF	Introduces an agent framework built on durable actors and workflows, with state, messaging, and recovery supplied by the Dapr runtime.	Shows how graph nodes can inherit distributed-systems durability instead of implementing recovery only in prompts.	Official documentation	Reliability
age-0039	Reliability & Durable Execution	Durable execution integration	Tool	Temporal integration for OpenAI Agents SDK	https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/openai_agents	Temporal Python SDK	2,025	Temporal	Integrates OpenAI agent runs, model calls, and tools with Temporal workflows and activities for durable execution.	Provides replay, retry, timeout, and recovery semantics beneath an agent graph without asking the model to manage them.	Maintained OSS project	Reliability
age-0040	Reliability & Durable Execution	Database-backed workflows	Docs	DBOS AI Quickstart	https://docs.dbos.dev/ai/ai-quickstart	DBOS Documentation	2,026	DBOS	Shows how to place AI application steps inside durable workflows whose progress is recorded and recoverable after interruption.	Offers a compact path from an agent prototype to resumable execution with explicit step boundaries.	Official documentation	Reliability
age-0041	Reliability & Durable Execution	Durable agent patterns	Docs	Restate Durable Agents	https://docs.restate.dev/ai/patterns/durable-agents	Restate Documentation	2,026	Restate	Documents durable agent patterns using persisted execution, reliable calls, retries, timers, and stateful services.	Maps common mid-graph failures to runtime guarantees rather than fragile application-level retry code.	Official documentation	Reliability
age-0042	Observability & Cost	Tracing	Docs	OpenAI Agents SDK: Tracing	https://openai.github.io/openai-agents-python/tracing/	OpenAI Agents SDK Documentation	2,026	OpenAI	Documents traces and spans for agent runs, model generations, tool calls, handoffs, and guardrails.	Makes node and edge behavior inspectable so latency, failures, and expensive paths can be attributed correctly.	Official documentation	Observability & cost
age-0043	Observability & Cost	Telemetry standard	Standard	OpenTelemetry GenAI Semantic Conventions	https://github.com/open-telemetry/semantic-conventions-genai	OpenTelemetry GenAI repository	2,026	OpenTelemetry GenAI SIG	Develops shared telemetry names and attributes for generative-AI model, tool, and agent operations in traces, metrics, and events.	Helps graph telemetry remain portable across runtimes and observability vendors.	Maintained OSS project	Observability & cost
age-0044	Observability & Cost	Cost attribution	Docs	Arize Phoenix: Cost Tracking	https://arize.com/docs/phoenix/tracing/how-to-tracing/cost-tracking	Arize Phoenix Documentation	2,026	Arize AI	Explains how Phoenix derives and displays token usage and model cost from traced generative-AI calls.	Supports per-node and per-path cost accounting instead of treating a multi-agent run as one opaque bill.	Official documentation	Observability & cost
age-0045	Observability & Cost	Observability platform	Docs	LangSmith Observability	https://docs.langchain.com/langsmith/observability	LangSmith Documentation	2,026	LangChain	Documents tracing, dashboards, alerts, feedback, and evaluation views for language-model and agent applications.	Provides operational views for following execution across nodes and locating failures on the critical path.	Official documentation	Observability & cost
age-0046	Benchmarks & Datasets	Collaboration benchmark	Benchmark	MultiAgentBench: Evaluating Collaboration and Competition of LLM Agents	https://aclanthology.org/2025.acl-long.421/	ACL	2,025	Kunlun Zhu et al.	Benchmarks language-model agents in collaborative and competitive settings while examining coordination processes as well as task outcomes.	Measures properties of the team interaction that single-agent benchmarks cannot expose.	Benchmark/dataset	Observability & cost
age-0047	Benchmarks & Datasets	Failure diagnosis	Benchmark	Why Do Multi-Agent LLM Systems Fail?	https://nips.cc/virtual/2025/poster/121528	NeurIPS Datasets & Benchmarks	2,025	Mert Cemri et al.	Provides a structured taxonomy and evaluation approach for diagnosing coordination failures in multi-agent language-model systems.	Turns reliability incidents into recurring, attributable failure classes that can guide graph redesign.	Benchmark/dataset	Reliability
age-0048	Critiques & Limits	Self-correction limits	Paper	Large Language Models Cannot Self-Correct Reasoning Yet	https://openreview.net/forum?id=IkmD3fKBPQ	ICLR	2,024	Jie Huang et al.	Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.	Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.	Peer-reviewed research	Gates
age-0049	Critiques & Limits	Scaling evidence	Paper	Towards a Science of Scaling Agent Systems	https://arxiv.org/abs/2512.08296	arXiv; Google Research	2,025	Yubin Kim et al.	Studies how agent-system performance changes across tasks, models, coordination structures, and scaling choices under controlled experiments.	Tests the assumption that adding agents reliably helps and frames scaling as an empirical topology decision.	Research preprint	Topology
age-0050	Critiques & Limits	Token-budget comparison	Paper	Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets	https://arxiv.org/abs/2604.02460	arXiv	2,026	Dat Tran; Douwe Kiela	Compares single-agent and multi-agent approaches to multi-hop reasoning while holding the total thinking-token budget constant.	Provides the cost-controlled baseline needed before claiming that coordination, rather than extra inference, caused an improvement.	Research preprint	Topology
age-0051	Start Here	Contemporary framing	Blog	From Loop Engineering to Graph Engineering?	https://x.com/IntuitMachine/status/2078419526354378975	X Articles	2,026	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.	Adds the grounding requirement missing from topology-only accounts: independent counter-metrics, frozen tests or rules, external anchors, and human ownership of root objectives.	Practitioner analysis	Gates
age-0052	Research Foundations	Blackboard coordination	Paper	A Multi-Level Organization for Problem Solving Using Many, Diverse, Cooperating Sources of Knowledge	https://www.ijcai.org/Proceedings/75/Papers/072.pdf	IJCAI	1,975	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.	Provides an early architecture for loosely coupled specialist nodes coordinating through inspectable shared state instead of direct all-to-all calls.	Peer-reviewed research	State
age-0053	Research Foundations	Negotiated delegation	Paper	The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver	https://doi.org/10.1109/TC.1980.1675516	IEEE Transactions on Computers	1,980	Reid G. Smith	Defines a negotiation protocol in which managers announce tasks, potential contractors bid, and awards establish temporary problem-solving relationships.	Supplies the classic contract for capability-aware delegation and auditable assignment edges between autonomous nodes.	Peer-reviewed research	Handoffs
age-0054	Start Here	Communication survey	Paper	The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why – A Survey from MARL to Emergent Language and LLMs	https://openreview.net/pdf?id=LGsed0QQVq	Transactions on Machine Learning Research	2,026	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.	Provides a design-oriented map for engineering edge selection, message timing, payloads, grounding, scalability, and interpretability.	Peer-reviewed research	Handoffs
age-0055	Research Foundations	Collaboration scaling	Paper	Scaling Large Language Model-based Multi-Agent Collaboration	https://openreview.net/forum?id=K3n5jPkrU6	ICLR	2,025	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.	Makes topology a causal scaling variable rather than assuming that larger teams or denser communication automatically help.	Peer-reviewed research	Topology
age-0056	Research Foundations	Holistic orchestration	Paper	MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks	https://openreview.net/forum?id=3fGXBm4c3S	ICML	2,026	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.	Tests when whole-system graph structure helps instead of attributing gains to coordination without controlled task evidence.	Peer-reviewed research	Work graphs
age-0057	Research Foundations	Conditional topology	Paper	CARD: Towards Conditional Design of Multi-agent Topological Structures	https://openreview.net/forum?id=JgvJdICc6P	ICLR	2,026	Tongtong Wu et al.	Generates communication graphs conditioned on agent roles, models, tools, and data sources, and adapts topology as task resources change.	Treats the available capabilities and directed edges as a versionable organizational artifact rather than a fixed team template.	Peer-reviewed research	Evolution
age-0058	Reliability & Durable Execution	Resilient topology	Paper	ResMAS: Resilience Optimization in LLM-based Multi-agent Systems	https://ojs.aaai.org/index.php/AAAI/article/view/40824	AAAI	2,026	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.	Moves resilience from reactive recovery into the design of the graph itself and evaluates transfer to new tasks and models.	Peer-reviewed research	Reliability
age-0059	Research Foundations	System evolution	Paper	EvoMAS: Evolutionary Generation of Multi-Agent Systems	https://openreview.net/forum?id=ic0AGRIkmY	ICML	2,026	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.	Shows how an inspectable team specification can evolve from execution evidence while retaining executability and runtime robustness.	Peer-reviewed research	Evolution
age-0060	Critiques & Limits	Error propagation	Paper	Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems	https://aclanthology.org/2025.emnlp-main.623/	EMNLP	2,025	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.	Provides evidence against defaulting to dense graphs and links topology decisions to measured error amplification.	Peer-reviewed research	Topology
age-0061	Frameworks & SDKs	Graph-centric orchestration	Paper	MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing	https://aclanthology.org/2026.acl-demo.35/	ACL System Demonstrations	2,026	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.	Provides a direct implementation path from an inspectable organizational graph to execution and evaluates it on seven public benchmarks.	Peer-reviewed research	Work graphs
age-0062	Frameworks & SDKs	Directed agent graphs	Docs	AutoGen GraphFlow (Workflows)	https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/graph-flow.html	Microsoft AutoGen Documentation	2,026	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.	Distinguishes the execution graph from the message graph, exposing both who acts next and what context each agent receives.	Official documentation	Work graphs
age-0063	Protocols & Handoffs	Durable remote tasks	Standard	Model Context Protocol: Tasks	https://modelcontextprotocol.io/specification/2025-11-25/basic/utilities/tasks	MCP Specification 2025-11-25	2,025	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.	Turns a remote tool or context edge into a recoverable task contract that can outlive one synchronous request.	Official documentation	Reliability
age-0064	Protocols & Handoffs	Secure agent messaging	Docs	Secure Low-Latency Interactive Messaging (SLIM)	https://datatracker.ietf.org/doc/draft-mpsb-agntcy-slim/	IETF Datatracker; individual Internet-Draft	2,026	Luca Muscariello; Michele Papalini; Mauro Sardara; Sam Betts	Proposes a transport layer for A2A and MCP using gRPC over HTTP/2 and HTTP/3 with stream multiplexing, flow control, group communication, native RPC semantics, and MLS end-to-end encryption. It is an individual informational Internet-Draft with no formal IETF standing.	Adds a concrete secure transport substrate for high-volume graph edges that must cross process and organizational boundaries.	Official documentation	Handoffs
age-0065	Protocols & Handoffs	Agent identity and authorization	Docs	Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization	https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd	NIST NCCoE Initial Public Draft	2,026	Harold Booth; William Fisher; Ryan Galluzzo; Joshua Roberts	Outlines considerations and open questions for standards-based identity, authorization, auditing, and non-repudiation when software and AI agents access enterprise systems and take actions. The concept paper remains an initial public draft under review.	Grounds graph roles and permissions in identity practice so delegation does not silently transfer more authority than an edge contract allows.	Official documentation	Roles
age-0066	State, Memory & Artifacts	Versioned work products	Docs	Google ADK: Artifacts	https://adk.dev/artifacts/	Google ADK Documentation	2,026	Google	Defines named, automatically versioned binary work products that agents and tools can save, load, list, and exchange within session-scoped or persistent user-scoped namespaces.	Provides explicit, inspectable edge artifacts instead of forcing large or structured outputs through conversational context.	Official documentation	State
age-0067	State, Memory & Artifacts	Procedural memory	Paper	LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation	https://www.microsoft.com/en-us/research/publication/legomem-modular-procedural-memory-for-multi-agent-llm-systems-for-workflow-automation/	AAMAS	2,026	Dongge Han; Camille Couturier; Daniel Madrigal; Xuchao Zhang; Victor Ruehle; Saravan Rajmohan	Decomposes execution trajectories into reusable procedural memories and allocates them to an orchestrator and specialist agents for workflow automation.	Shows how durable experience can improve decomposition and delegation without collapsing every node's memory into one undifferentiated store.	Peer-reviewed research	State
age-0068	Verification & Evals	Adversarial agent detection	Paper	When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems	https://openreview.net/forum?id=BnduUW8izq	ICML	2,026	Haowen Xu et al.	Evaluates activation-space detection and restorative steering of compromised agents across five attack scenarios, multiple models, and synchronous and asynchronous multi-agent interactions.	Tests a topology-agnostic gate for locating and repairing a malicious node when its messages appear superficially benign.	Peer-reviewed research	Reliability
age-0069	Reliability & Durable Execution	Intervention-driven debugging	Paper	DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems	https://iclr.cc/virtual/2026/poster/10007537	ICLR	2,026	Ming Ma et al.	Tests failure hypotheses by editing messages or plans and measuring whether each intervention repairs the outcome or advances execution in branching multi-agent traces.	Turns graph debugging into causal repair experiments instead of relying on plausible but unverified post-hoc explanations.	Peer-reviewed research	Reliability
age-0070	Reliability & Durable Execution	Durable graph workflows	Docs	Microsoft Agent Framework: Durable Extension	https://learn.microsoft.com/en-us/agent-framework/integrations/durable-extension	Microsoft Learn	2,026	Microsoft	Adds persisted sessions, checkpointed progress, failure recovery, external-event waits, and distributed hosting to agents, multi-agent orchestrations, and graph workflows. The documented packages remain prerelease.	Shows how an agent graph can resume without losing context or repeating completed work after interruption.	Official documentation	Reliability
age-0071	Critiques & Limits	Control-flow security	Paper	Breaking and Fixing Defenses Against Control Flow Hijacking in Multi-Agent Systems	https://openreview.net/forum?id=PNU9Rj5RDQ	ICLR	2,026	Rishi Dev Jha; Harold Triedman; Justin Wagle; Vitaly Shmatikov	Demonstrates attacks against alignment-check defenses and introduces ControlValve, which generates permitted control-flow graphs and enforces least privilege for each agent invocation.	Makes invocation authority and contextual permissions explicit on every edge instead of trusting a separate checker that can also be hijacked.	Peer-reviewed research	Reliability
age-0072	Observability & Cost	Budget-aware topology	Paper	BAMAS: Structuring Budget-Aware Multi-Agent Systems	https://ojs.aaai.org/index.php/AAAI/article/view/40226	AAAI	2,026	Liming Yang; Junyu Luo; Xuanzhe Liu; Yiling Lou; Zhenpeng Chen	Selects an LLM team with integer programming and then learns its collaboration topology under an explicit budget, reporting comparable performance with cost reductions of up to 86% on three tasks.	Jointly engineers node selection, edge structure, and spend instead of optimizing quality while treating inference cost as an afterthought.	Peer-reviewed research	Observability & cost
age-0073	Observability & Cost	Workflow reconstruction	Paper	AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction	https://arxiv.org/abs/2602.05353	ICML	2,026	Ruijie Shi; Houbin Zhang; Yuecheng Han; Yuheng Wang; Jingru Fan; Runde Yang; Yufan Dang; Huatao Li; Dewen Liu; Yuan Cheng; Chen Qian	Reconstructs an editable explicit stand-in workflow for a black-box agentic system from input-output behavior using iterative search and evaluation.	Offers a path to inspect, compare, and modify systems whose load-bearing workflow is hidden behind an API.	Peer-reviewed research	Observability & cost
age-0074	Benchmarks & Datasets	Distributed coordination	Benchmark	SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems	https://aclanthology.org/2026.acl-long.1354/	ACL	2,026	Yuzhe Zhang et al.	Benchmarks free-form coordination under information silos across 30 exact-answer tasks, three communication protocols, six agent scales, and three models while recording success, tokens, and communication density.	Tests whether more nodes and denser edges overcome distributed information constraints under explicit coordination and cost measures.	Benchmark/dataset	Topology
age-0075	Benchmarks & Datasets	Dynamic asynchronous evaluation	Benchmark	Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments	https://openreview.net/forum?id=9gw03JpKK4	ICLR	2,026	Romain Froger et al.	Evaluates agents in asynchronous simulated environments across execution, search, ambiguity, adaptation, temporal reasoning, noise, and agent-to-agent collaboration, with action-level verifiers and structured traces.	Makes delays, environmental change, peer communication, and externally checked writes first-class work-graph conditions.	Benchmark/dataset	Work graphs
age-0076	Benchmarks & Datasets	Adversarial multi-agent safety	Benchmark	TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems	https://aclanthology.org/2026.acl-long.1442/	ACL	2,026	Ishan Kavathekar; Hemang Jain; Ameya Rathod; Ponnurangam Kumaraguru; Tanuja Ganu	Provides five scenarios with 300 adversarial instances, six attack types, 211 tools, 100 harmless tasks, and multiple AutoGen and CrewAI interaction configurations, plus an Effective Robustness Score.	Evaluates whether safety controls preserve useful work while attacks exploit the distinctive trust and communication surfaces of a multi-agent graph.	Benchmark/dataset	Reliability
age-0077	Benchmarks & Datasets	Failure attribution	Benchmark	Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems	https://aclanthology.org/2026.acl-long.912/	ACL	2,026	Mengzhuo Chen et al.	Introduces TraceElephant with full execution traces and reproducible environments for attributing failures to responsible agents and decisive steps.	Measures causal diagnosis over nodes, messages, plans, and dependencies instead of evaluating only the final team output.	Benchmark/dataset	Observability & cost
age-0078	Production Case Studies	Secure delegated access	Blog	Creating AI agent solutions for warehouse data access and security	https://engineering.fb.com/2025/08/13/data-infrastructure/agentic-solution-for-warehouse-data-access/	Engineering at Meta	2,025	Can Lin; Uday Ramesh Savagaonkar; Iuliu Rus; Komal Mangtani	Describes collaborating data-user and data-owner agents, specialized subagents, triage, permission negotiation, human oversight, access budgets, analytical risk rules, output guardrails, traces, and daily regression evaluation.	Shows plural bounded agency governed by independent rule-based gates rather than relying on model judgment for security decisions.	Practitioner analysis	Gates
age-0079	Production Case Studies	Staged agent swarm	Blog	How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines	https://engineering.fb.com/2026/04/06/developer-tools/how-meta-used-ai-to-map-tribal-knowledge-in-large-scale-data-pipelines/	Engineering at Meta	2,026	Krishna Ganeriwal; Plawan Rath; Ashwini Verma	Reports a staged swarm of more than 50 explorer, analyst, writer, critic, fixer, upgrader, tester, and gap-filling tasks, with repeated review rounds and recurring automated refresh runs.	Provides a concrete fan-out, critique, repair, integration, and recurrence graph with explicit artifacts and quality gates; efficiency figures are company-reported and preliminary.	Practitioner analysis	Work graphs
age-0080	Production Case Studies	Multi-hop identity and provenance	Blog	Solving the Identity Crisis for AI Agents	https://www.uber.com/by/en/blog/solving-the-agent-identity-crisis/	Uber Engineering	2,026	Matt Mathew; Prasad Borole; Meng Huang; Sergey Burykin; Gaurav Goel; Bayard Walsh	Describes an internal agent mesh with registered identities, SPIRE-backed workload attestation, short-lived audience-scoped tokens for every hop, actor-chain provenance, MCP gateway enforcement, and a standardized A2A client.	Treats identity, delegated authority, and provenance as mandatory edge state across a multi-agent graph; adoption and latency metrics are company-reported.	Practitioner analysis	Handoffs
age-0081	Research Foundations	Task-adaptive topology synthesis	Paper	Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models	https://aclanthology.org/2026.acl-long.1764/	ACL	2,026	Eric Hanchen Jiang et al.	Introduces Guided Topology Diffusion, which iteratively synthesizes sparse, task-adaptive communication graphs using a proxy model for objectives such as accuracy, utility, and cost.	Treats topology as a multi-objective, per-task design artifact rather than a static collaboration template.	Peer-reviewed research	Evolution
age-0082	Reliability & Durable Execution	Topology-conditioned memory leakage	Paper	Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs	https://aclanthology.org/2026.findings-acl.1980/	Findings of ACL	2,026	Jinbo Liu et al.	Measures private-information leakage across six communication topologies, agent counts, and attacker-target placements, finding higher leakage with denser connectivity, shorter graph distance, and greater target centrality.	Makes privacy a measurable consequence of edge structure and node placement, motivating topology-aware access controls.	Peer-reviewed research	Reliability
age-0083	Critiques & Limits	Collaboration-induced diversity collapse	Paper	Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation	https://aclanthology.org/2026.findings-acl.13/	Findings of ACL	2,026	Nuo Chen et al.	Studies multi-agent ideation across model, cognition, and system levels, finding diminishing group-size returns, authority effects, and faster premature convergence under dense communication.	Shows that interaction can contract a search space, so graph designs for exploration must preserve independence and meaningful disagreement.	Peer-reviewed research	Topology
age-0084	Research Foundations	Dynamic node and edge elimination	Paper	AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration	https://aclanthology.org/2025.acl-long.1170/	ACL	2,025	Zhexuan Wang; Yutong Wang; Xuebo Liu; Liang Ding; Miao Zhang; Jie Liu; Min Zhang	Optimizes communication-graph adjacency matrices across collaboration rounds to remove redundant agents and messages, reducing both prompt and completion token consumption in its evaluations.	Makes node participation and edge density explicit cost-quality decisions instead of assuming every role should run on every task.	Peer-reviewed research	Evolution
age-0085	Benchmarks & Datasets	Process-level collaboration	Benchmark	Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents	https://aclanthology.org/2025.emnlp-main.249/	EMNLP	2,025	Haochen Sun; Shuwen Zhang; Lujie Niu; Lei Ren; Hao Xu; Hao Fu; Fangkun Zhao; Caixia Yuan; Xiaojie Wang	Provides 30 open-ended cooperative tasks and process-oriented measures for goal interpretation, active collaboration, communication, and continuous adaptation across 13 language models.	Distinguishes reaching an answer from coordinating well, exposing collaboration failures that final-outcome metrics can hide.	Benchmark/dataset	Observability & cost
age-0086	Critiques & Limits	Inter-agent communication attacks	Paper	Red-Teaming LLM Multi-Agent Systems via Communication Attacks	https://aclanthology.org/2025.findings-acl.349/	Findings of ACL	2,025	Pengfei He; Yuping Lin; Shen Dong; Han Xu; Yue Xing; Hui Liu	Introduces an Agent-in-the-Middle attack that intercepts and manipulates inter-agent messages, evaluated across multiple frameworks, communication structures, and applications.	Shows that graph edges can compromise the whole system without altering its nodes, making message integrity and provenance part of the handoff contract.	Peer-reviewed research	Handoffs
age-0087	Research Foundations	Query-dependent architecture search	Paper	Multi-agent Architecture Search via Agentic Supernet	https://proceedings.mlr.press/v267/zhang25bi.html	ICML	2,025	Guibin Zhang; Luyang Niu; Junfeng Fang; Kun Wang; Lei Bai; Xiang Wang	Represents possible agentic architectures as a probabilistic supernet and samples query-dependent systems with tailored language-model calls, tool calls, and token costs across six benchmarks.	Replaces a one-size-fits-all graph with per-query architecture and resource allocation while keeping the design space explicit.	Peer-reviewed research	Evolution
age-0088	Critiques & Limits	Unequal agent contribution	Paper	Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation	https://openreview.net/forum?id=5J6u03ObRZ	ICLR	2,026	Zhiwei Zhang et al.	Identifies lazy-agent behavior in which one role dominates a nominally collaborative system, then measures causal influence and uses verifiable rewards to encourage deliberation and selective reasoning restarts.	Requires builders to verify that each node contributes causal value rather than allowing a multi-agent graph to collapse into one effective agent.	Peer-reviewed research	Roles
age-0089	Benchmarks & Datasets	Multi-party negotiation	Benchmark	Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation	https://openreview.net/forum?id=59E19c6yrN	NeurIPS	2,024	Sahar Abdelnabi; Amr Gomaa; Sarath Sivaprasad; Lea Schönherr; Mario Fritz	Provides scorable, multi-agent, multi-issue negotiation games with metrics for task performance and role alignment, including cooperative, competitive, greedy, and adversarial participants.	Tests communication and collective decisions when graph nodes hold conflicting objectives or attempt manipulation rather than cooperating by default.	Benchmark/dataset	Handoffs
age-0090	Reliability & Durable Execution	Agentic application security risks	Standard	OWASP Top 10 for Agentic Applications for 2026	https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/	OWASP GenAI Security Project	2,025	OWASP GenAI Security Project; Agentic Security Initiative	Provides a globally peer-reviewed operational framework for critical risks in autonomous and agentic applications, developed with contributions from more than 100 security experts, researchers, and practitioners.	Supplies a practical threat checklist for node authority, tool permissions, memory provenance, inter-agent trust, and cascading failures across an agent graph.	Industry standard	Reliability
age-0091	Research Foundations	Task-adaptive graph routing	Paper	AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System	https://aclanthology.org/2025.emnlp-industry.144/	EMNLP Industry Track	2,025	Hui Yi Leong; Yuheng Li; Yuqing Wu; Wenwen Ouyang; Wei Zhu; Jiechao Gao; Wei Han	Introduces a dynamic graph selector that uses lightweight LLM adaptation to choose task-specific communication structures and route each query through a matching agent pathway.	Operationalizes topology as an input-conditioned decision rather than a fixed template; evidence is currently concentrated in question answering, mathematics, and code generation.	Peer-reviewed research	Evolution
age-0092	Research Foundations	Collaboration-mode, role, and model routing	Paper	MasRouter: Learning to Route LLMs for Multi-Agent Systems	https://aclanthology.org/2025.acl-long.757/	ACL	2,025	Yanwei Yue; Guibin Zhang; Boyang Liu; Guancheng Wan; Kun Wang; Dawei Cheng; Yiyan Qi	Defines multi-agent system routing as a joint decision over collaboration mode, role allocation, and language-model selection, implemented with a cascaded controller that progressively constructs a task-specific system.	Makes node roles, model assignment, and whether collaboration is needed explicit routing decisions with measurable cost-quality trade-offs.	Peer-reviewed research	Evolution
age-0093	Research Foundations	Multi-hop evidence propagation	Paper	MOC: Multi-Order Communication in LLM-based Multi-Agent Systems	https://openreview.net/forum?id=wyynWicO5s	ICML	2,026	Yao Guan; Lin Wang; Zhihui Lu; Ziyi Wang; Wenzhu Yan; Qiang Duan	Constructs structured multi-order evidence streams so agents can receive relevant information from multiple upstream hops, then applies semantic-topological merging under token constraints.	Shows that graph engineering includes what evidence survives across paths, not only which nodes and edges exist; it improves communication over a supplied topology rather than selecting that topology.	Peer-reviewed research	Handoffs
age-0094	Research Foundations	Hierarchical node and topology optimization	Paper	HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems	https://openreview.net/forum?id=p7p5foAXaB	ICML	2,026	Tianjun Yao; Zhaoyi Li; Zhiqiang Shen	Models each functional role as a supernode containing heterogeneous language models in a propose-synthesis structure, then uses multi-level reward attribution and graph classification to select the inter-supernode topology.	Jointly engineers node composition, role capability, credit assignment, and communication edges instead of optimizing each dimension independently.	Peer-reviewed research	Evolution
age-0095	State, Memory & Artifacts	Hierarchical graph memory	Paper	GAM: Hierarchical Graph-based Agentic Memory for LLM Agents	https://aclanthology.org/2026.acl-long.1600/	ACL	2,026	Zhaofen Wu; Hanrong Zhang; Fulin Lin; Wujiang Xu; Xinran Xu; Yankai Chen; Henry Peng Zou; Shaowen Chen; Weizhi Zhang; Xue Liu; Philip S. Yu; Hongwei Wang	Separates rapid memory encoding from stable consolidation through an event-progression graph, a topic-associative network, and graph-guided multi-factor retrieval.	Provides a concrete graph contract for encoding, consolidating, connecting, and retrieving state; it does not address shared-memory permissions or concurrent writes among agents.	Peer-reviewed research	State
age-0096	State, Memory & Artifacts	Selective shared memory	Paper	Learning to Share: Selective Memory for Efficient Parallel Agentic Systems	https://openreview.net/forum?id=cCFyY2LmF5	ICML	2,026	Joseph Fioresi; Parth Parag Kulkarni; Ashmal Vayani; Song Wang; Mubarak Shah	Adds a global memory bank to parallel agent teams and trains an admission controller with stepwise reinforcement learning and usage-aware credit assignment to retain reusable intermediate work.	Treats cross-team memory writes as governed graph operations that can reduce duplicate work while limiting indiscriminate context growth.	Peer-reviewed research	State
age-0097	Observability & Cost	Graph-aware cache reuse	Paper	Accelerating Language Model Workflows with Prompt Choreography	https://aclanthology.org/2026.tacl-1.13/	TACL	2,026	TJ Bai; Jason Eisner	Executes multi-agent workflows with a dynamic global key-value cache in which each call can attend to a reordered subset of previously encoded messages, including parallel branches.	Makes workflow dependencies and reusable message state explicit execution concerns; its primary result concerns speed, and cache reuse can alter model behavior.	Peer-reviewed research	Work graphs
age-0098	Benchmarks & Datasets	Enterprise workflow benchmark	Benchmark	Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments	https://aclanthology.org/2025.emnlp-main.466/	EMNLP	2,025	Harsh Vishwakarma; Ankush Agarwal; Ojas Patil; Chaitanya Devaguptapu; Mahesh Chandran	Introduces EnterpriseBench with 500 tasks across software engineering, HR, finance, and administration, including fragmented data, access-control hierarchies, and cross-functional workflows.	Tests work graphs that retrieve, modify, and transfer artifacts across services while respecting authority boundaries; the organization and services are simulated.	Benchmark/dataset	Work graphs
age-0099	Benchmarks & Datasets	Stateful tool-use evaluation	Benchmark	ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities	https://aclanthology.org/2025.findings-naacl.65/	Findings of NAACL	2,025	Jiarui Lu; Thomas Holleis; Yizhe Zhang; Bernhard Aumayer; Feng Nan; Haoping Bai; Shuang Ma; Shen Ma; Mengyu Li; Guoli Yin; Zirui Wang; Ruoming Pang	Evaluates stateful tool execution, implicit dependencies between tools, on-policy user interaction, and intermediate and final milestones over arbitrary trajectories.	Transfers milestone and state-transition testing to graph prerequisites, although the evaluated system is an agent-user-tool loop rather than an agent-agent topology.	Benchmark/dataset	State
age-0100	Benchmarks & Datasets	Trajectory-level evaluation	Benchmark	AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents	https://openreview.net/forum?id=4S8agvKjle	NeurIPS Datasets & Benchmarks	2,024	Chang Ma; Junlei Zhang; Zhihao Zhu; Cheng Yang; Yujiu Yang; Yaohui Jin; Zhenzhong Lan; Lingpeng Kong; Junxian He	Unifies partially observable, multi-round agent environments and adds a fine-grained progress-rate metric plus interactive trajectory analysis beyond final success.	Supplies milestone-level evaluation that can localize incomplete work, while its primarily single-agent tasks do not identify the responsible graph component by themselves.	Benchmark/dataset	Gates

End of preview. Expand in Data Studio

Awesome Graph Engineering Resource Atlas

A versioned collection of research, standards, frameworks, protocols, reliability systems, evaluations, and critiques for graph-structured multi-agent systems and programmable AI-agent organizations.

This dataset mirrors Awesome Graph Engineering. The GitHub JSONL file is canonical; the Hub exposes the same records through Dataset Viewer, direct downloads, datasets, and pandas.

Working definition

Graph engineering is the practice of specifying, executing, observing, and evolving a graph-structured agent system—its roles and runtime instances, the contracts that connect them, the state and artifacts they share, and the evidence by which their collective behavior is judged—so that the system can be controlled, tested, and improved as an engineered whole.

The graph must be load-bearing rather than decorative. Its declared topology, realized run graph, or graph-generating policy must materially constrain execution and remain inspectable enough to version, trace, evaluate, or deliberately change.

Graph engineering is used here as an emerging, non-standard term. The scope synthesizes multi-agent research, historical agent-system foundations, standards, runtime documentation, benchmarks, negative results, and practitioner analysis. The evidence map separates source-backed claims from analytical inferences. Carlos E. Perez’s “graph of loops” essay is one contemporary source, not an authority for the definition.

Dataset structure

The default configuration contains one train split. “Train” is the Hub's conventional single-split label; these rows are a resource atlas, not labeled examples or a model-training benchmark.

Each row has 13 fields:

Field	Type	Meaning
`id`	string	Stable `age-NNNN` resource identifier.
`section`	string	Reader-facing directory section.
`subcategory`	string	Specific discovery label within the section.
`rtype`	string	Resource format, such as Paper, Docs, Standard, or Benchmark.
`title`	string	Canonical resource title.
`url`	string	Canonical source URL.
`venue`	string	Publisher, venue, documentation site, or repository host.
`year`	integer	Publication year or year of the cited major release.
`authors`	string	Authors or maintaining organization.
`description`	string	Original summary of the resource's concrete contribution.
`why`	string	Practitioner rationale for including the resource.
`evidence`	string	Source-type label, not a quality score.
`layer`	string	Primary graph-engineering design layer.

resources.jsonl drives the Dataset Viewer. resources.csv is an equivalent convenience export in the same row and field order. resource.schema.json is the machine-readable JSON Schema for one record.

Interoperable access points:

Load the data

from datasets import load_dataset

dataset = load_dataset("cy0307/awesome-graph-engineering")
resources = dataset["train"]
print(resources.num_rows, resources.column_names)

import pandas as pd

url = (
    "https://huggingface.co/datasets/cy0307/awesome-graph-engineering/"
    "resolve/main/resources.csv"
)
resources = pd.read_csv(url)
print(resources.groupby("layer").size().sort_values(ascending=False))

Intended uses

discover primary sources and implementation references by engineering layer;
seed literature reviews and architecture comparisons, followed by reading the linked originals;
analyze how the collection is distributed across source types, years, sections, and layers;
build educational tools, resource browsers, or retrieval indexes; and
propose corrections and additions through the canonical GitHub repository.

Limitations and responsible use

The collection is selective and versioned; it is not an exhaustive scrape or systematic review.
evidence records publication form. It does not score correctness, replication, maintenance, safety, or endorsement.
Official documentation is authoritative about intended product behavior, not independent proof of reliability.
Practitioner analysis may define or challenge a boundary, but recency does not give it priority over primary evidence.
The field and its vocabulary are changing quickly; verify current documentation, publication status, licenses, and security posture before adoption.
Linked resources are catalogued by URL only; verify each resource’s rights and license before reuse.

Read the full curation methodology, scope boundaries, and dataset contract before drawing aggregate conclusions.

License

The dataset metadata, schema, original summaries, and repository-created assets are dedicated under CC0 1.0 Universal. Linked resources are not included in that dedication and remain subject to 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.

Versioning and synchronization

GitHub’s data/resources.jsonl is canonical. The CSV, README tables, interactive atlas, and Hub mirror are generated from it. Stable IDs are never recycled. When HF_TOKEN is configured, accepted changes to main trigger validation and mirror publication.

Citation

Curated by He Chaoyue.

@misc{he2026awesomegraphengineering,
  author       = {He, Chaoyue},
  title        = {Awesome Graph Engineering: A Field Guide, Dataset, and Interactive Atlas for Programmable AI-Agent Organizations},
  year         = {2026},
  version      = {1.3.0},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/ChaoYue0307/awesome-graph-engineering/releases/tag/v1.3.0}},
  url          = {https://github.com/ChaoYue0307/awesome-graph-engineering/releases/tag/v1.3.0}
}

Machine-readable metadata is available in CITATION.cff. Cite the original linked works for claims derived from them.

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