AI Agents

AgentBrain is a local-first, long-term memory tool for AI agents that stores everything as a plain Markdown vault. Instead of locking your agent’s memory into a proprietary database or cloud API, it gives you an append-only Markdown folder you can read, edit, grep, and version with Git. It uses index-first retrieval with CJK-aware BM25 to stay token-efficient, and enforces human-approved consolidation to prevent multi-agent write conflicts. What Is AgentBrain? A Local-First Markdown Memory Vault for AI Agents AgentBrain is an open-source Python tool (Python 3.10+) that gives AI agents a durable, local-first memory layer stored as plain Markdown files. The core idea is simple: an agent’s long-term knowledge should live in a human-readable folder on your own machine, not inside a black-box database or a third-party cloud service. ...

An agentic PostgreSQL investigation is an AI-driven workflow that explores a database to answer operational questions, and OpsGraph makes it evidence-first: it discovers an approved read-only schema, validates every SELECT against the PostgreSQL AST and policy bounds, and records a tamper-evident audit chain so no conclusion can be asserted without opening its exact evidence. Instead of giving a model direct credentials and “asking it anything,” OpsGraph constrains the investigation to bounded, read-only, auditable queries that fail closed on unsafe SQL. ...

An AI company framework is a system that assigns distinct roles to multiple agents so they collaborate like a real company to complete complex tasks. Instead of one assistant doing everything, a PM, architect, engineer, and QA agent each own a slice of the work, coordinated by encoded standard operating procedures. This guide explains how these frameworks work, the leading open-source options, and how to adopt them. What Is an AI Company Framework? (One-Sentence Definition) An AI company framework is a multi-agent orchestration system that treats an organization chart as an agent architecture — assigning distinct roles to multiple AI agents so they collaborate through encoded standard operating procedures (SOPs) to complete complex tasks that a single agent cannot reliably handle. ...

An agent trajectory monitor watches the full sequence of steps, tool calls, decisions, and token spend an AI agent makes in real time — not just the final answer. Because AI agents consume 5-30x more tokens per task than standard chatbots, real-time trajectory monitoring has moved from a nice-to-have to a budget requirement. It lets you see whether your agent is doing the right work or just getting the right answer by chance. ...

DeepSeek Harness observability usually means standing up an OpenTelemetry collector, a tracing backend, and code instrumentation. The TencentCloud AgentObs SDK for DSH eliminates all of that: it is a DeepSeek Harness plugin that observes the harness’s native session, agent-loop, LLM-stream, and tool lifecycles and uploads GenAI trace spans directly to Tencent Cloud Log Service (CLS) using Protobuf. There is no OTLP collector, no sidecar, and no extra service to deploy. If you already run agents on Tencent Cloud, this is the fastest path from “where did my agent spend its tokens?” to a dashboards-and-alerts answer. ...

An AI agent architecture is the structural design that lets an LLM observe its environment, reason about a goal, and act on it by calling tools in a repeating loop. In this 20-chapter tutorial you will move from the fundamentals—what an agent is and how the Observe-Reason-Act loop works—through the core building blocks, the five workflow patterns and three agent patterns, hands-on Python code, memory, tool integration, framework comparisons, and production concerns such as evaluation, security, and emerging standards like MCP. By the end you will know exactly when to use a workflow, when to use a full agent, and how to design and ship a reliable agentic system. ...

Agent sandboxes get expensive because most platforms size them for the resources an agent requests, not the resources it actually uses. Snapshots and copy-on-write flip that equation: instead of paying for a full 128 GiB disk and 16 GiB of RAM per session, you pay only for the base image plus the small set of divergences each agent creates. The result is a cost model that scales with real usage, letting a single host run dozens of isolated agents instead of a handful. ...

What Is HashAgent? A Private AI Agent You Share as a URL HashAgent is an open-source web application that lets you build, run, and share an AI agent as a single self-contained URL that executes entirely in your browser via WebGPU. Instead of sending your prompts to a cloud inference server, HashAgent packages an agent’s behavior — its system prompt, tool wiring, and a runtime model profile — into one compressed #agent= URL. When someone opens that link, their own device downloads a small model and runs the agent locally, with no inference server, no account, and no tracking. It launched on Hacker News on August 14, 2026 (story 49298088) and hit the front page with 38 points and 4 comments at the time of writing. ...

AgentProof is a free, local-first proof harness that verifies AI-generated code and diffs by detecting the project type, running safe local checks, launching the app, crawling routes with Playwright, and scoring launch readiness — all without uploading your source, using an API key, or trusting a cloud vendor. For AI code CI verification, it answers one question the chat transcript never can: did this app actually work, or did it just look good in the transcript? This review covers how AgentProof works, its GitHub Action score gate, its pricing, and how it compares to ProofPack and the broader “proof beats diffs” movement. ...

A knowledge inbox for AI agents and Obsidian is a local-first ingestion pipeline that turns raw inputs — links, PDFs, videos, screenshots, and plain text — into structured Markdown knowledge cards inside your own vault. Instead of dumping every snippet into cloud read-it-later apps, it normalizes all sources through a single pipeline, enriches them with OCR, transcription, and auto-tagging, then stores everything as human-readable files plus a queryable SQLite index. Your AI agents read the same local store your notes live in. ...