All Products items under Agents on AIFIRST News, newest first. 34 items.
Lattice is a TypeScript package collection for building isometric games designed to be played by AI agents. The zero-dependency core package is under 80 KB gzipped, with all rendering handled on an HTML canvas. It ships with agentic skills and plugins aimed at simplifying development and testing of agent-driven game environments.
dripy-wechat is an open-source project that distills WeChat chat history into a digital twin capable of taking over WeChat conversations autonomously, responding in the original user's style. The system is described as 100% AI-generated in its own codebase. It targets users who want an AI to respond on their behalf using a conversation style extracted from their historical messages.
Hermes is an Electron desktop application that functions as a unified control center for managing interactions across multiple LLM providers. The app provides a local interface for switching between providers and managing AI-assisted workflows without a browser.
Munder Difflin is a hosted multi-agent harness that instantiates multiple AI model instances playing different office roles such as manager, developer, and reviewer in a coordinated workflow. The service is publicly accessible and targets teams wanting to experiment with structured multi-agent collaboration.
Autolith is a terminal programming agent for Common Lisp repositories that runs as a single integrated process with filesystem, shell, and search tools. It embeds a live SBCL 2.6.6 runtime that the agent can inspect and modify, with mutations either saved as private image commits or discarded. It handles corpora larger than the model context window through recursive inference, and keeps portable session state across restarts including memories, agendas, checkpoints, and recovery artifacts.
OpenClaw v2026.8.1-beta.2 binds shared-store secrets to exact HTTPS destination hosts to prevent credential exfiltration, adds GPT-5.6 Ultra runtime switching, macOS profile isolation, and plugin install provenance.
OpenClaw introduces a monthly extended-stable channel (v2026.6.34) backporting only security and reliability fixes, plus a public maturity taxonomy of feature readiness, targeting enterprise adoption concerns about.
OpenClaw v2026.7.2-beta.7 enables fast active-memory recall across separate conversations by default, renames cron to Automations, adds a /loop command, durable message ingress recovery, Claude Opus 5 support and a.
OpenClaw v2026.7.2 betas add remote coding sessions on cloud workers, Android voice wake, a plugin install trust gate requiring --force for untrusted sources, autonomous Skill Workshop session scanning, and SQLite.
OpenClaw v2026.7.1 (2,018 PRs, 532 contributors) ships a Control UI overhaul with task management and cost visibility, gateway crash-loop recovery, broad security hardening, and support for GPT-5.6 and other new models.
OpenClaw v2026.6.10 adds automatic fast mode for short conversations, per-DM model overrides and a Slack relay, but v2026.6.11 carries a tool output regression, memory search races and a missing reentrancy guard.
OpenClaw v2026.6.8 (192 merged PRs) is the recommended stable while v2026.6.9 (422 PRs) ships with 39 known regressions and a community skip recommendation.
OpenClaw v2026.6.5-6.6 tighten transcript boundaries, harden sandbox binds, make exec approvals fail closed on timeout, isolate unauthorized messages from cache and context, and close deleted-agent ACP bypasses.
Prompt Engineering = "what do I tell the agent?" Loop Engineering = "what should happen next, discovered by the system itself." The bottleneck in most agent projects isn't the model - it's that the system has no mecha...
On brownfield codebases the code itself outweighs system prompts: an agent saw a deprecated pattern in 40+ files versus the correct one in 6, followed the majority and shipped working features that perpetuate old.
A screenshot shows an AI deleting production data without confirmation. Community consensus calls it user error: the AI had the key and no guardrails.
A personal AI OS structured around three hierarchy levels, each with rules, facts and history files. Strict separation of rules from facts keeps it reliable; plain-text routing triggers skill sequences automatically.
The same research task run single-agent versus a 4-agent parallel pipeline: 4x faster but quality fell from 8/10 to 5/10 at higher cost.
numasec is an open-source agent that wires an LLM directly into Kali Linux, giving it awareness of installed tools and full context across recon, exploitation, and OSINT phases - no more explaining context from scratc...
The case for agents over chatbots: memory, persistence and acting on schedules instead of waiting for input. A top commenter dismantles each point with technical precision, making the thread a useful steelman of both.
OpenClaw v2026.5.4-5.7 add authorization-gated skill dispatch, admin-scoped memory writes, skill snapshot invalidation on session reset, stale-context purging, and compaction summary token clamping after a rough.
Power user running OpenClaw for a month notices the core problem: all agent output lives in the chat thread. Finding previous work = scrolling. Charts rebuilt slightly differently each time due to model dri...
After a bad stretch of slow gateways, plugin dependency loops, dropped chat connections and CPU spikes, OpenClaw 2026.5.4 lands as the verified stable release with faster cold starts and cron state split into.
OpenClaw v2026.4.27 through v2026.5.3 focus on hardening, streaming UX and CLI backend maturity rather than new features. Highlights: the /steer command, progress-draft streaming, and incremental Dreaming memory.
OpenAI's production-readiness update brings controlled sandboxes across seven compute providers, an inspectable open-source execution loop, and two-tier memory separating session history from distilled lessons.
Ralph Loop is a pattern, not a library: the agent writes steps to a tasks file, updates it as it works, a monitor restarts stalled processes, and a QA step validates output at the end.
A CLI skill lets an agent decide who to call, run the conversation and return a structured summary. Use cases that landed: comparing quotes, booking appointments and checking store hours.
In the fictional "OpenClaw" universe, v2026.4.12 shipped an Active Memory plugin that runs a memory sub-agent before every reply - automatically pulling relevant preferences and past context without needing explicit "...
ChatPRD founder Claire Vo describes running 9 OpenClaw agents that operate her businesses, write code and close sales deals. The stack: a chat channel, a local machine, identity files, cron heartbeats and skills.
Honest community thread on agent adoption: most people who install autonomous agents have nothing to automate yet. The practical wins come from automating processes that are already breaking, not inventing new ones.
After 30 days of an autonomous agent with 7 cron jobs: a LEARNINGS.md file must be explicitly loaded in cron prompts or rules get ignored, and the phrase 'think step by step' makes cheaper models self-escalate to.
Five agent businesses with $1M-$5M ARR follow an identical pattern: index client data in Nia for memory and RAG, wire SaaS actions via Composio, and fall back to Browserbase scraping when no API exists.
A multi-agent operator built a 3-layer memory system on OpenClaw: always-injected root files (max 7k tokens), semantic search over a memory directory, and on-demand reference files.
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