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LangChain launches Managed Deep Agents v0.8, adding features like user-owned credentials, user-level memory, HTTP channels, and a web search tool to improve agent deployment in production.
The paper introduces JAZ, a minimalist agent framework that uses a single invoke primitive to enable LLM agents to handle long-horizon tasks and self-improvement without external harnesses, outperforming existing systems like MemGPT and ACE in evaluations.
AX is a declarative orchestrator from Google for running autonomous agent workloads at scale, providing sandboxing, workspace management, and gateway controls for secure execution.
Smart Ralph is a tool that structures AI coding by first defining requirements as specifications, then having agents implement them step by step, with support for Claude Code and Codex to improve engineering processes.
EvolveTrade introduces a self-evolving framework for LLM trading agents that refines tool-use policies using decision traces and portfolio feedback, improving Sharpe Ratio and cumulative returns across market regimes.
The changelog for fx v0.0.9 introduces the ability to use frontier AI models for steering while delegating implementation to faster models, with new features for subagent customization, mid-task feedback, and improved Markdown rendering.
The article explains OpenAI's new Agents API, detailing how it fits with existing products like ChatGPT and Codex, and provides examples of its use in building agent-driven applications.
SkillSpec is a Hoare-style framework that uses intent-masked specification reasoning to detect defects in agent skills by aligning descriptions, instructions, and code, achieving 61.2% precision on real-world skills from SkillsBench and other repositories.
Raft provides Maka with an unlimited version to support the maintainer community's review system, while Maka has entered Apache with growing activity and over 100 contributors focused on Agent design and benchmarks.
Discovered an open-source Deepseek memory plugin called OpenContext, which improves the long-term memory functionality of Agent frameworks through designs like Temporal Context Graphs and Trust Models.
Headlong is an open-source agent microharness featuring persistent agency and recursive language models, implemented in Bash for autonomous agent operation and human interaction across platforms.
AgentDecarbonizer is a carbon optimizer for AI agents that reduces carbon emissions by scheduling tasks to low-carbon periods or grids, leveraging deadline flexibility.
ANOLISA is an agentic operating system layer for AI agent workloads, featuring eBPF-based observability via AgentSight, token optimization, and runtime management without code changes.
Release of AWiki identity and messaging tool for DeepSeek Harness, featuring stable identity handles and local-first messaging to address attribution and silent-failure issues in AI agent systems.
This release introduces an identity and messaging plugin for DeepSeek Harness, enabling agents to manage identities via DID and handle messages and emails through a Web UI with secure features.
Agent Lightning v1.0 is a lightweight framework from Microsoft that connects agent harnesses to reinforcement learning training, improving Qwen3.5-9B's performance on SWE-bench Verified using modest compute.
Microsoft has open-sourced the Agent Framework, a production-ready tool for building AI agents with features like tool calling, memory, and multi-language support.
Aeon is an open-source autonomous agent framework designed for unattended operation, allowing users to configure AI agents once and run them in the background on tasks like monitoring or code reviews using GitHub Actions, with skills defined in Markdown files and features such as self-healing and persistent memory.
Introduces the Chinese tutorial *Hands-On Pi*, covering the step-by-step implementation of a Pi-style coding agent from scratch through 15 runnable checkpoints, including protocols, tool loops, conversation tree storage, context compression, and evaluation. Each chapter comes with the main text, a corresponding commit, tests, and failure experiments.
A tinkerer shares custom quality-of-life upgrades for local AI agents, including an MCP broker to reduce context overhead, temporal awareness, context warnings, auto-swapping models, and hybrid memory search with Postgres.