REVIEW 13 cited by
Agents: An Open-source Framework for Autonomous Language Agents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.
Forward citations
Cited by 13 Pith papers
-
ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control
A self-consistency admission gate that verifies facts against their source context before writing them to LLM agent memory reduces memory contamination on three planted-corruption benchmarks.
-
Heterogeneous Agent Cohorts for Safe Open-Ended Exploration with Runtime Constraint Memory
Role-specialized LLM cohorts with MCTS-compiled Scars and CAS bandwidth control reach remote sandbox targets with zero executed breaches and lower token cost than homogeneous debate.
-
Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges
Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.
-
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
A single LLM is trained with multi-agent distilled trajectories plus agentic RL, and the resulting Chain-of-Agents models set state-of-the-art Pass@1 scores among tool-integrated reasoning methods on GAIA, BrowseComp,...
-
Unleashing Embodied Task Planning Ability in LLMs via Reinforcement Learning
A 7B LLM trained with sparse completion rewards and a GRPO-style algorithm reaches state-of-the-art on ALFWorld and ScienceWorld.
-
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation
A large-scale LLM-driven urban simulator with recursive planning, memory, and belief modules, claimed to reproduce real-world time use, travel, and crowd patterns better than prior agent frameworks.
-
TaskCraft: Automated Generation of Agentic Tasks
TaskCraft automatically generates verifiable, multi-tool agentic tasks with execution trajectories via atomic task construction and depth/width extension, and fine-tuning on the resulting 36,000-task dataset improves ...
-
OAgents: An Empirical Study of Building Effective Agents
OAgents reports a framework and ablation study claiming open-source state-of-the-art on GAIA, but the empirical evidence is weakened by benchmark fitting and missing statistics.
-
Scaling Test-time Compute for LLM Agents
On the GAIA benchmark, Best-of-N sampling with list-wise answer selection gives the largest agent gains, and selective (score-triggered) reflection beats reflection at every step.
-
WebDancer: Towards Autonomous Information Seeking Agency
A four-stage data-and-training pipeline (synthetic QA, trajectory rejection sampling, SFT, DAPO reinforcement learning) produces WebDancer, a web agent that outperforms vanilla ReAct and several open-source agentic fr...
-
EvolveSearch: An Iterative Self-Evolving Search Agent
An iterative loop of RL and filtered SFT on the agent's own rollouts improves a 7B web-search agent by a few accuracy points on multi-hop QA benchmarks.
-
Introspection of Thought Helps AI Agents
INoT wraps prompts in XML-defined pseudo-code so an LLM simulates two debating agents internally, reporting better scores and lower tokens than seven baselines.
-
Get Experience from Practice: LLM Agents with Record & Replay
AgentRR is a proposed paradigm that records agent traces, generalizes them into multi-level experiences, and replays them under safety checks to make LLM agents cheaper, faster, and more reliable.
Discussion (0). Continue with ORCID to comment.