AI Agents

Reviews & Perspectives

Published

• 2024-04: LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models (code)
• 2024-08: From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
• 2024-09: Towards a Science Exocortex
• 2024-09: Large Language Model-Based Agents for Software Engineering: A Survey
• 2024-09: Agents in Software Engineering: Survey, Landscape, and Vision

Continually updating

• OpenThought - System 2 Research Links
• Awesome LLM Strawberry (OpenAI o1): Collection of research papers & blogs for OpenAI Strawberry(o1) and Reasoning

Analysis/Opinions

• LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
• Cutting AI Assistant Costs by Up to 77.8%: The Power of Enhancing LLMs with Business Logic

AI Assistants

Components of AI Assistants

Information Retrieval

• See also RAG.
• 2024-10: Agentic Information Retrieval

Open-source

• Khoj (code): self-hostable AI assistant
• RAGapp: Agentic RAG for enterprise
• STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking
  • Can write (e.g.) Wikipedia-style articles
  • code
  • Preprint: Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Personalities/Personas

• 2023-10: Generative Agents: Interactive Simulacra of Human Behavior
• 2024-11: Microsoft TinyTroupe 🤠🤓🥸🧐: LLM-powered multiagent persona simulation for imagination enhancement and business insights
• 2024-11: Generative Agent Simulations of 1,000 People (code)

Specific Uses for AI Assistants

Computer Use

• 2024-11: The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use (code)

Science Agents

See Science Agents.

LLM-as-judge

• List of papers.
• LLM Evaluation doesn't need to be complicated
• Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)
• 2024-10: Agent-as-a-Judge: Evaluate Agents with Agents
• 2024-11: A Survey on LLM-as-a-Judge

Advanced Workflows

• Salesforce DEI: meta-system that leverages a diversity of SWE agents
  • Preprint: Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents
• Sakana AI: AI Scientist
  • The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
  • code
• SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
  • code

Software Development Workflows

Several paradigms of AI-assisted coding have arisen:

1. Manual, human driven
2. AI-aided through chat/dialogue, where the human asks for code and then copies it into the project
   1. OpenAI ChatGPT
   2. Anthropic Claude
3. API calls to an LLM, which generates code and inserts the file into the project
4. LLM-integration into the IDE
   1. Copilot
   2. Qodo (Codium) & AlphaCodium (preprint, code)
   3. Cursor
   4. Codeium Windsurf (with "Cascade" AI Agent)
5. AI-assisted IDE, where the AI generates and manages the dev environment
   1. Replit
   2. Aider (code): Pair programming on commandline
   3. Pythagora
   4. StackBlitz bolt.new
   5. Cline (formerly Claude Dev)
6. Prompt-to-product
   1. Github Spark (demo video)
7. Semi-autonomous software engineer agents
   1. Devin (Cognition AI)
   2. Amazon Q
   3. Honeycomb

For a review of the current state of software-engineering agentic approaches, see:

• 2024-08: From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
• 2024-09: Large Language Model-Based Agents for Software Engineering: A Survey
• 2024-09: Agents in Software Engineering: Survey, Landscape, and Vision

Corporate AI Agent Ventures

Mundane Workflows and Capabilities

• Payman AI: AI to Human platform that allows AI to pay people for what it needs
• VoiceFlow: Build customer experiences with AI
• Mistral AI: genAI applications
• Taskade: Task/milestone software with AI agent workflows
• Covalent: Building a Multi-Agent Prompt Refining Application

Inference-compute Reasoning

• Nous Research: Forge Reasoning API Beta

Agentic Systems

• Topology AI
• Cognition AI: Devin software engineer (14% SWE-Agent)
• Honeycomb (22% SWE-Agent)

Increasing AI Agent Intelligence

Proactive Search

Compute expended after training, but before inference.

Training Data (Data Refinement, Synthetic Data)

• C.f. image datasets:
  • 2023-06: StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
  • 2023-11: DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
• 2024-09: Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
• 2024-10: Data Cleaning Using Large Language Models
• Updating list of links: Synthetic Data of LLMs, by LLMs, for LLMs

Generate consistent plans/thoughts

• 2024-08: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers (code)
  • (Microsoft) rStar is a self-play mutual reasoning approach. A small model adds to MCTS using some defined reasoning heuristics. Mutually consistent trajectories can be emphasized.
• 2024-09: Self-Harmonized Chain of Thought
  • Produce refined chain-of-thought style solutions/prompts for diverse problems. Given a large set of problems/questions, first aggregated semantically, then apply zero-shot chain-of-thought to each problem. Then cross-pollinate between proposed solutions to similar problems, looking for refined and generalize solutions.

Sampling

• 2024-11: Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding (code)

Automated prompt generation

• 2024-09: Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts (

Distill inference-time-compute into model

• 2023-10: Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning (U. Maryland, Adobe)
• 2023-11: Implicit Chain of Thought Reasoning via Knowledge Distillation (Harvard, Microsoft, Hopkins)
• 2024-02: Grandmaster-Level Chess Without Search (Google DeepMind)
• 2024-07: Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
• 2024-07: BOND: Aligning LLMs with Best-of-N Distillation
• 2024-09: Training Language Models to Self-Correct via Reinforcement Learning (Google DeepMind)
• 2024-10: Thinking LLMs: General Instruction Following with Thought Generation
• 2024-10: Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces

CoT reasoning model

• 2024-09: OpenAI o1
• 2024-10: O1 Replication Journey: A Strategic Progress Report – Part 1 (code): Attempt by Walnut Plan to reproduce o1-like in-context reasoning
• 2024-11: DeepSeek-R1-Lite-Preview reasoning model
• 2024-11: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
• 2024-11: O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

Scaling

• 2024-08: Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling (Google DeepMind)
• 2024-11: Scaling Laws for Pre-training Agents and World Models

Inference Time Compute

Methods

• 2024-03: Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

In context learning (ICL), search, and other inference-time methods

• 2023-03: Reflexion: Language Agents with Verbal Reinforcement Learning
• 2023-05: VOYAGER: An Open-Ended Embodied Agent with Large Language Models
• 2024-04: Many-Shot In-Context Learning
• 2024-08: Automated Design of Agentic Systems
• 2024-09: Planning In Natural Language Improves LLM Search For Code Generation

Inference-time Sampling

• 2024-10: entropix: Entropy Based Sampling and Parallel CoT Decoding
• 2024-10: TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
• 2024-11: Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs

Inference-time Gradient

• 2024-11: The Surprising Effectiveness of Test-Time Training for Abstract Reasoning (code)

Self-prompting

• 2023-05: Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling
• 2023-11: Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves

In-context thought

• 2022-01: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Google Brain)
• 2023-05: Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Google DeepMind)
• 2024-05: Faithful Logical Reasoning via Symbolic Chain-of-Thought
• 2024-06: A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages
• 2024-09: To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
• 2024-09: Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning (Agnostiq, Toronto)
• 2024-09: Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
• 2024-10: A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration (failed reasoning traces can improve CoT)
• 2024-10: Tree of Problems: Improving structured problem solving with compositionality
• 2023-01/2024-10: A Survey on In-context Learning

Naive multi-LLM (verification, majority voting, best-of-N, etc.)

• 2023-06: LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (code)
• 2023-12: Dynamic Voting for Efficient Reasoning in Large Language Models
• 2024-04: Regularized Best-of-N Sampling to Mitigate Reward Hacking for Language Model Alignment
• 2024-08: Dynamic Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling
• 2024-11: Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models

Multi-LLM (multiple comparisons, branching, etc.)

• 2024-10: Thinking LLMs: General Instruction Following with Thought Generation
• 2024-11: Mixtures of In-Context Learners: Multiple "experts", each with a different set of in-context examples; combine outputs at the level of next-token-prediction
• 2024-11: LLaVA-o1: Let Vision Language Models Reason Step-by-Step (code)

Iteration (e.g. neural-like layered blocks)

• 2024-06: Mixture-of-Agents Enhances Large Language Model Capabilities

Iterative reasoning via graphs

• 2023-08: Graph of Thoughts: Solving Elaborate Problems with Large Language Models
• 2023-10: Amortizing intractable inference in large language models (code)
• 2024-09: On the Diagram of Thought: Iterative reasoning as a directed acyclic graph (DAG)

Monte Carlo Tree Search (MCTS)

• 2024-05: AlphaMath Almost Zero: process Supervision without process
• 2024-06: ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
• 2024-06: Improve Mathematical Reasoning in Language Models by Automated Process Supervision
• 2024-06: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
• 2024-07: Tree Search for Language Model Agents
• 2024-10: Interpretable Contrastive Monte Carlo Tree Search Reasoning

Other Search

• 2024-11: Scattered Forest Search: Smarter Code Space Exploration with LLMs

Scaling

• 2021-04: Scaling Scaling Laws with Board Games
• 2024-03: Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
• 2024-04: The Larger the Better? Improved LLM Code-Generation via Budget Reallocation
• 2024-07: Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
• 2024-08: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
• 2024-08: Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• 2024-10: (comparing fine-tuning to in-context learning) Is In-Context Learning Sufficient for Instruction Following in LLMs?

Theory

• 2024-02: Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Expending compute works

• 2024-06-10: Blog post (opinion): AI Search: The Bitter-er Lesson
• 2024-07-17: Blog post (test): Getting 50% (SoTA) on ARC-AGI with GPT-4o
• 2024-09-12: OpenAI o1: Learning to Reason with LLMs

• 2024-09-16: Scaling: The State of Play in AI

Code for Inference-time Compute

• optillm: Inference proxy which implements state-of-the-art techniques to improve accuracy and performance of LLMs (improve reasoning over coding, logical and mathematical queries)

Memory

• 2024-10: Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation

Tool Use

• 2024-11: DynaSaur: Large Language Agents Beyond Predefined Actions: writes functions/code to increase capabilities

Multi-agent Effort (and Emergent Intelligence)

• 2024-10: Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
• 2024-10: Agent-as-a-Judge: Evaluate Agents with Agents
• 2024-11: Project Sid: Many-agent simulations toward AI civilization

ML-like Optimization of LLM Setup

• 2023-03: DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines (code: Programming—not prompting—Foundation Models)
• 2024-05: Automatic Prompt Optimization with "Gradient Descent" and Beam Search
• 2024-06: TextGrad: Automatic "Differentiation" via Text (gradient backpropagation through text)
• 2024-06: Symbolic Learning Enables Self-Evolving Agents (optimize LLM frameworks)

Multi-agent orchestration

Research demos

• Camel
• LoopGPT
• JARVIS
• OpenAGI
• AutoGen
  • preprint: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
  • Agent-E: Browser (eventually computer) automation (code, preprint, demo video)
  • AutoGen Studio: GUI for agent workflows (code)
  • Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• AG2 (previously AutoGen) (code, docs, Discord)
• TaskWeaver
• MetaGPT
• AutoGPT (code); and AutoGPT Platform
• Optima
  • preprint: Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System
  • code
• 2024-04: LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models (code)
• 2024-06: MASAI: Modular Architecture for Software-engineering AI Agents
• 2024-10: Agent S: An Open Agentic Framework that Uses Computers Like a Human (code)

Related work

• 2024-07: PersonaGym: Evaluating Persona Agents and LLMs

Inter-agent communications

• 2024-10: Agora: A Scalable Communication Protocol for Networks of Large Language Models (preprint): disparate agents auto-negotiate communication protocol
• 2024-11: DroidSpeak: Enhancing Cross-LLM Communication: Exploits caches of embeddings and key-values, to allow context to be more easily transferred between AIs (without consuming context window)
• 2024-11: Anthropic describes Model Context Protocol: an open standard for secure, two-way connections between data sources and AI (intro, quickstart, code)

Architectures

• Mixture-of-Agents Enhances Large Language Model Capabilities
• Motleycrew.ai (code)

Open Source Frameworks

• LangChain
• ell (code, docs)
• AgentOps AI AgentStack
• Agent UI
• kyegomez swarms
• OpenAI Swarm (cookbook)
• Amazon AWS Multi-Agent Orchestrator
• KaibanJS: Kanban for AI Agents? (Takes inspiration from Kanban visual work management.)

Open Source Systems

• ControlFlow
  • documentation
  • code
• OpenHands (formerly OpenDevin)
  • code: platform for autonomous software engineers, powered by AI and LLMs
  • Report: OpenDevin: An Open Platform for AI Software Developers as Generalist Agents

Commercial Automation Frameworks

• Lutra: Automation and integration with various web systems.
• Gumloop
• TextQL: Enterprise Virtual Data Analyst
• Athena intelligence: Analytics platform
• Nexus GPT: Business co-pilot
• Multi-On: AI agent that acts on your behalf
• Firecrawl: Turn websites into LLM-ready data
• Reworkd: End-to-end data extraction
• Lindy: Custom AI Assistants to automate business workflows
  • E.g. use Slack
• Bardeen: Automate workflows
• Abacus: AI Agents
• LlamaIndex: (𝕏, code, docs, Discord)
• MultiOn AI: Agent Q (paper) automated planning and execution

Spreadsheet

• V7 Go
• Otto Grid
• Paradigm

Cloud solutions

• Numbers Station Meadow: agentic framework for data workflows (code).
• CrewAI says they provide multi-agent automations (code).
• LangChain introduced LangGraph to help build agents, and LangGraph Cloud as a service for running those agents.
  • LangGraph Studio is an IDE for agent workflows
• C3 AI enterprise platform
• Deepset AI Haystack (docs, code)

Frameworks

• Google Project Oscar
  • Agent: Gaby (for "Go AI bot") (code, documentation) helps with issue tracking.
• OpenPlexity-Pages: Data-aggregator implementation (like Perplexity) based on CrewAI

Optimization

Metrics, Benchmarks

• 2022-06: PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
• 2024-04: AutoRace (code): LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
• 2024-04: OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments (github)
• 2024-07: AI Agents That Matter
• 2024-09: CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark (leaderboard)
• 2024-09: LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
• 2024-09: On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
• 2024-10: MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• 2024-10: WorFBench: Benchmarking Agentic Workflow Generation
• 2024-10: VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models
• 2024-10: SimpleAQ: Measuring short-form factuality in large language models (announcement, code)
• 2024-11: RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts (blog, code)

Agent Challenges

• Aidan-Bench: Test creativity by having a particular LLM generate long sequence of outputs (meant to be different), and measuring how long it can go before duplications appear.
• Pictionary: LLM suggests prompt, multiple LLMs generate outputs, LLM judges; allows raking of the generation abilities.
• MC-bench: Request LLMs to build an elaborate structure in Minecraft; outputs can be A/B tested by human judges.

Automated Improvement

• 2024-06: EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms
• 2024-06: Symbolic Learning Enables Self-Evolving Agents
• 2024-08: Automated Design of Agentic Systems (ADAS code)
• 2024-08: Self-Taught Evaluators: Iterative self-improvement through generation of synthetic data and evaluation

See Also

• Science Agents
• AI tools
• AI understanding
• Robots
• Exocortex