LangGraph

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

LangGraph AI

I built an MCP server + Claude Code skill that hands Claude a token-budgeted context bundle instead of making it grep

Anthropic's own harness guidance steers agents toward grep/glob to find code. That works, but it's token-hungry: grep gets near-perfect recall and basically zero token efficiency because it pulls in everything around the match. archex is a local MCP server that does the retrieval step properly and hands Claude back a finished bundle — ranked, deduped, dependency-closed, with a token budget you set. Then Claude spends its context on reasoning, not on crawling imports. What's wired up for Claude Code specifically: MCP server (stdio, 14 tools): query, scout, analyze, compare, symbol lookup, graph neighbors/path/stats. Optional --watch mode keeps the index fresh as you edit. In-repo skill + /archex command so you can query/scout from inside a session without dropping to a terminal. scout protocol: a two-phase map → fetch flow. Capped at a token budget, it returns a structural map plus exact fetch handles, so the agent grabs only the bodies it actually needs. archex doctor: checks index health, grammar support, model cache, and MCP registration when results look stale. Fully local, deterministic, no API key, Apache 2.0. Head-to-head vs the nearest OSS tool: the agent needed 922 extra tokens to complete a task from an archex bundle vs 11,188 from theirs (~12x). submitted by /u/tom_mathews [link] [comments]

You asked for DeepLearning.ai-style notebooks for AgentSwarms—so we built 67 of them (TypeScript/LangChain/LangGraph/LlamaIndex/OpenAI-AgentsSDK/VercelAI).

Hey everyone, A few months ago, We shared the visual canvas we built for AgentSwarms. The response was incredible, but the most common piece of feedback was: "The visual canvas is great for architecture, but I need to see the actual code to really understand how to deploy this." You wanted deep-dive, code-first labs—the kind you see on DeepLearning.ai—but for multi-agent systems, faster and with more flexibility. We’ve spent the last few weeks heads-down engineering a completely new Interactive Notebooks section. As of today, we have 67 TypeScript-based notebooks live on the site (with more dropping soon). What’s in the library: We’ve covered everything from basic LangChain fundamentals to complex enterprise-level multi-agent workflows. Everything runs entirely in your browser using TypeScript—no Docker, no Python venv, no local dependencies. A personal favorite: I’m particularly excited about the "Failure Mode & Error Handling" notebook. We’ve all seen agents that work perfectly in a demo but crash in production the moment a tool times out or an LLM returns garbage. This notebook walks through: How to build deterministic validation gates between nodes. How to force an orchestrator to "catch" a worker failure and dynamically re-route or re-prompt. How to handle state recovery when a multi-agent loop gets stuck in a hallucination cycle. Why we built this: I’m tired of seeing AI "tutorials" that are just static blog posts. To master Agentic AI, you need to be able to tweak a system prompt, break the code, watch the error trace, and fix the routing logic in real-time. The entire library of 67 labs is 100% free to use. If you’re currently wrestling with how to make your agents production-grade, I’d love for you to check them out and let me know if there’s a specific "failure mode" or architecture pattern you’d like us to add to the next batch of notebooks. Try it out here: agentswarms.fyi submitted by /u/Outside-Risk-8912 [link] [comments]

Fable 5 was shockingly token-efficient for a full frontend overhaul

EDIT: I updated all the screenshots. I was checked out on a branch that wasn't fully featured to do some A/B testing for Fable vs Opus. Current screenshots show what Fable produced. So I have a webapp that is basically my OS. It has my Daily Overview, Mail, Calendar, Todoist Tasks, Dev Projects, News Curator, Karpathy knowledge base, etc...all in one. On top of that, I have a chat agent orchestrated by LangGraph on the backend, so it can query and act across the different parts of my life. For example: "look at project xyz. I had an email with George from xyz company and he wanted a feature implemented. Look at the repo and tell me what is left on that feature branch. Break the deliverable down into 30-minute working blocks and add it to my calendar during my focus hours on wednesday." I love what I built, but visually it still felt too much like a functional dashboard and not enough like a living personal operating system that was fun to use and visually stunning. So I set Fable to xHigh and ran it with some scoping guardrails I’ll leave out here: ---------------------------- You are a world-class product designer + front-end engineer. I'm testing what you can do. I want you to make my personal dashboard app, "BlaineOS," look absolutely badass — more visually awesome, more alive, more fun to use every day. Be ambitious. Surprise me. A bold reimagining is welcome. What BlaineOS is A single-user personal command center. Modules: Overview, Mail, Calendar, Tasks, Projects, News, Knowledge — plus an AI assistant named "Alfred." Today it's a calm, keyboard-driven, monochrome-leaning dashboard (each module has a single monochrome glyph icon; there's a g-then-letter hotkey scheme). It's deployed and in real daily use. Stack Next.js 16 (App Router, React Server Components, Server Actions) React 19, TypeScript Tailwind CSS v4 Plain components + Tailwind (no component-library lock-in) The brief You have free rein over the look, feel, motion, and interaction design. Massive overhauls are on the table — new design language, new layout, new visual identity, ambitious motion, the works. Treat this as your portfolio piece. -------------------------- Fable (on Xhigh) was able to completely overhaul my front end, make it absolutely stunning, and let's just say it - more BADASS - across the entire project. The wild part is that it did the whole thing start to finish in less than 70% of my 5-hour window on the Pro Max 5x plan. A drop in the bucket. Based on my experience using Opus 4.6-4.8 for this kind of work, I’m pretty confident the same overhaul would have taken multiple 5-hour windows there. Fable just feels unusually efficient for ambitious frontend transformation work. I'm actually blown away right now. Anyone else have similar experience so far? https://preview.redd.it/34flqiuzul6h1.png?width=1920&format=png&auto=webp&s=9621bba61f345f048a0bc4518735f564f9f37377 https://preview.redd.it/xj2pthuzul6h1.png?width=1920&format=png&auto=webp&s=85152ec901402615623bb582e2c29bd546bc6a9a https://preview.redd.it/zghbnhuzul6h1.png?width=1920&format=png&auto=webp&s=9bea4e6a1b1f1e06ccbb5026ce562804abcf88ed https://preview.redd.it/eqomahuzul6h1.png?width=1920&format=png&auto=webp&s=4708f09d2d26901f5147559eb78cf58efa618e4e https://preview.redd.it/9cds4iuzul6h1.png?width=1920&format=png&auto=webp&s=9dca3069e61fee9394b083dcbdbaca3ed4fa468b https://preview.redd.it/p7015iuzul6h1.png?width=1920&format=png&auto=webp&s=51f417c459ab79ce51a6076dc290cc2925a5642e https://preview.redd.it/h64xniuzul6h1.png?width=1920&format=png&auto=webp&s=d322cd3f80f152d4de14d3bd32efbdcf5d1b4ec2 submitted by /u/Optimal_Foundation46 [link] [comments]

I built notmemory — auditable, reversible memory for AI agents. v0.1.0 on PyPI. Looking for contributors.

After too many debugging sessions where I had no idea what my agent remembered or why it made a decision — I got frustrated and built something. notmemory is an open-source Python SDK that gives AI agents auditable, reversible memory. Not magic. Just a tamper-proof record of what your agent knew, when it knew it, and the ability to undo the moment it got something wrong. The problem I kept hitting My agent would do something wrong. I'd dig into it. I could see what was currently in memory — but not what it believed at step 47 when it made the bad decision three days ago. Every debugging session felt like archaeology. I got tired of it. What notmemory does Cryptographic audit trail Every write is SHA-256 hash-chained. Like Git commits, but for memory. You always know what changed, when, and in what order. Git-like rollback await memory.rollback(transaction_id) One line. Bad write gone. Hash chain stays valid. GDPR tombstoning await memory.forget(bank_id) Proven deletion with a forensic trail. Not just "deleted from index." Conflict detection Catches duplicate or contradicting beliefs before they cause problems. Health score 0–100. Confidence decay c(t) = c₀ · 2^(−t/30) — stale memories lose weight automatically. No more old beliefs quietly poisoning recall. LangGraph drop-in from notmemory.adapters.langchain import NotMemoryCheckpointer checkpointer = NotMemoryCheckpointer() graph = builder.compile(checkpointer=checkpointer) # that's it — every checkpoint is now auditable MCP server Works with Claude Desktop, Cursor, Windsurf out of the box. Mem0 + SuperMemory sidecars SQLite is the source of truth. Semantic search layers on top. If the sidecar goes down, your data is fine. Multi-agent sync READ / WRITE / ADMIN permissions per memory bank per agent. Install pip install notmemory # with LangChain / LangGraph pip install "notmemory[langchain]" # with MCP pip install "notmemory[mcp]" Quick example import asyncio from notmemory import AgentMemory async def main(): async with AgentMemory() as memory: # store something entry = await memory.retain( bank_id="facts", content={"fact": "Paris is the capital of France"}, source="user", ) # search it result = await memory.recall(bank_id="facts", query="Paris") # undo it await memory.rollback(entry.transaction_id) # delete it with proof await memory.forget("facts") asyncio.run(main()) Where it is today (v0.1.0) 113 tests passing across Python 3.11, 3.12, 3.13 SQLite + FTS5 full-text search LangChain, LangGraph, Mem0, SuperMemory, MCP adapters Confidence decay, Git backup, multi-agent sync MIT license, CI/CD, full README What's coming in v0.2.0 Feature What it does memory.state_at(timestamp) Read memory as it was at any point in time Crypto-shredding Encrypt-on-write + key destruction for real GDPR compliance memory.export_state() Clean JSON snapshot of any memory bank memory.diff(from_ts, to_ts) Human-readable before/after between two timestamps Belief lineage Which downstream writes were caused by a bad early assumption Honest take This is v0.1.0. The core is solid but it's early. SQLite only for now — Postgres is planned. The adapters are sync-layer wrappers, not full replacements for Mem0 or SuperMemory. If you're running a hobby project with one agent — you probably don't need this yet. If you're running multiple long-lived agents, working in a regulated industry, or have already had a production incident you couldn't properly debug — this is for you. Looking for contributors The codebase is around 2000 lines. Every adapter follows the same BaseAdapter pattern so it's easy to get oriented. Good first issues are tagged on GitHub. Things I'd love help with: Postgres backend Crypto-shredding implementation memory.state_at(timestamp) Dashboard UI (FastAPI + SSE already in optional deps) Docs and examples Feedback Would love to hear from: Anyone running agents in healthcare / finance / legal Fleet operators with 5+ concurrent agents Anyone who's already built their own memory audit system and had to solve things I haven't thought of yet Brutal feedback welcome. That's the only way this gets better. GitHub: https://github.com/notmemory/notmemory PyPI: https://pypi.org/project/notmemory/ submitted by /u/imsuryya [link] [comments]

Switching from React Native + Node.js (4 YOE) to Agentic AI — need roadmap advice

I have 4 years of experience as a React Native and Node.js developer. I am comfortable with REST APIs, async/await, JSON, MongoDB, authentication, and shipping production apps. I am based in India. What I have learned so far: I recently completed an AI/LLM course that covered: • Pydantic (validation, models, serialization) • LLM theory (transformers, embeddings, attention, tokenization) • OpenAI and Gemini API integration • Prompt engineering (zero-shot, few-shot, CoT, persona prompting) • Prompt formats (ChatML, Alpaca, INST) • Ollama for local LLMs • FastAPI basics • Hugging Face model deployment • Agentic AI fundamentals — built a basic CLI coding agent What I understand conceptually: I understand that an AI agent = LLM brain + tools (Python functions) + agent loop + memory (messages list). I understand RAG, vector databases, the difference between fine-tuning and RAG, and how to structure a backend with Node.js calling a Python AI agent service when needed. What I want to do: I want to transition into Agentic AI / AI Engineer roles in India. I am not looking to become an ML researcher or train models. I want to build production AI agent systems — connecting LLMs to real business data, building tools, RAG pipelines, and shipping real products. My specific questions: 1. Is my current foundation strong enough to start building real agent projects or do I have gaps I am missing? 2. What should my learning roadmap look like for the next 3–6 months given my background? 3. Which frameworks should I prioritise — raw OpenAI API first, then LangChain/LangGraph, or jump straight to frameworks? 4. What kind of projects should I build for a strong portfolio targeting ₹20–35 LPA roles in India? 5. Any specific subreddits, communities, or resources beyond YouTube that helped you in this transition? My planned first 3 projects: • Simple agent with web search + calculator tool (no DB) • Agent connected to MongoDB with RAG • Full FastAPI backend wrapping the agent with a React frontend Any advice from people who have made a similar switch or are hiring in this space would be really helpful. Thanks. submitted by /u/rohitrai0101rm [link] [comments]

Learn Agentic AI with quick, easy to run hands on labs, visual canvases and notebooks for free!

If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems. That’s why we builtAgentSwarms.fyi—the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows. 🚀 The Core AgentSwarms Ecosystem: Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments. Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics. Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments. 💣 The New Drop: 60+ Browser-Native TypeScript Notebooks We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction. Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem: 🟢 LangChain.js (Fundamentals & Middleware Guardrails) 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration) 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals) ⚡ Vercel AI SDK (Streaming UI Integration) 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops) Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms. 👉 Dive in for free: agentswarms.fyi/learn submitted by /u/Outside-Risk-8912 [link] [comments]

Learn Agentic AI with quick, easy to run hands on labs, visual canvases and notebooks for free!

If you’re a full-stack engineer or technical architect willing to learn production-grade enterprise agents, you need architecture, security, and type-safe systems. That’s why we builtAgentSwarms.fyi—the ultimate hands-on educational platform for teaching agentic AI and multi-agent workflows. 🚀 The Core AgentSwarms Ecosystem: Real-World Architectures: Skip the generic hello-world loops. Learn production-grade systems like human-in-the-loop validation, automated multi-platform content multiplexers, and secure code-sandbox environments. Deterministic Cloud Guardrails: Deep dives into multi-cloud token economics, dynamic cost-optimized routing, and model evaluation metrics. Grassroots Engineering Focus: No corporate marketing fluff. Just raw, practical code patterns designed to bridge the gap between fragile prototypes and stable cloud deployments. 💣 The New Drop: 60+ Browser-Native TypeScript Notebooks We just completely re-engineered our learning workspace. We’ve added 60+ fully interactive TypeScript Notebooks running 100% natively in your browser. No pip install dependency hell, no local Docker setup, and zero environment friction. Read the architecture, tweak the system prompts or Zod schemas, hit play, and watch the streaming terminal execute live across the five absolute best frameworks in the ecosystem: 🟢 LangChain.js (Fundamentals & Middleware Guardrails) 🔀 LangGraph.js (Cyclic Graphs & Stateful Orchestration) 💾 LlamaIndex.ts (Sentence-Window Retrieval & RAG Triad Evals) ⚡ Vercel AI SDK (Streaming UI Integration) 🤖 OpenAI Agents SDK (Lightweight, low-boilerplate loops) Stop passively scrolling through video courses. Open a canvas, break the graph nodes, and start compiling real multi-agent swarms. 👉 Dive in for free: agentswarms.fyi/learn submitted by /u/Outside-Risk-8912 [link] [comments]

Looking to work on my master's practicum regarding MCP security/privacy and need some ideas

Hi, I'm a master's in security student looking to work on my practicum and need some pointers. I want to secure sensitive PII transfer between an LLM agent and third party apps using MCP. I want to work with Claude, but need a third party app to work with on this. I want to solve problems like prompt injection via cascading agents exploitation. Deliverable wise, I'm thinking it should be some sort of application that can red-team the architectural set-up and ensure no data is being leaked or can be prompt injected. Some questions for you: What third party app do you recommend where I can really strengthen an MCP server and the transfer of sensitive data between Claude and the third party app? What other tools will I need to work with to set the agents up? I've heard of Langchain and Langgraph. How exactly do I work with MCPs in this context? Again I'm very new to all this! Thank you for your help! submitted by /u/ExcellentComment6615 [link] [comments]

Multi-agent loop failures might be org-design failures, not prompt failures

Repo: https://github.com/jeongmk522-netizen/agentlas\_org\_chart Almost every multi-agent setup I have shipped or tested eventually hits the same wall. Agents bouncing between each other, reviewers asking for one more polish pass forever, research workers spawning indefinite subtopics, tool calls spiraling until the recursion limit kicks in. The framework docs usually call these "loops" and offer a max-iteration knob. I started suspecting the knob is treating a symptom, and the real issue is closer to how the agents are organized to begin with. The pattern that kept reappearing: when agents are designed as peers (researcher talks to analyst, analyst talks to writer, writer hands back to reviewer), nobody clearly owns the outcome. Every agent can keep asking another agent for more work. The graph has stop conditions on paper, but no single agent has the authority to declare "this is done, stop the run." That authority is implicit at best and gets diluted across the peer network. The hypothesis I am testing is that loop failures are organization-design failures more than prompt failures. The fix is to treat the agent network as an org chart with explicit reporting lines, not a chat room of peers. One accountable mission owner. One owner per workstream. Finite delegation depth. A typed return contract per worker (status, evidence, output, blockers, next action). Manager-only authority to reopen or terminate. Memory lives at the authority layers, specialists get scoped context only. The layers I have been working with are roughly chair, strategy office, division manager, team lead, and specialist worker, with QA and policy as separate staff offices that can reject and escalate but cannot themselves spawn unbounded new work. The reviewer-recursion failure mode in particular gets killed when verifiers are structurally allowed one reject pass, then must escalate. Frameworks already have most of the primitives. CrewAI has a hierarchical process where a manager validates worker output. LangGraph has supervisors, subagents, and an explicit recursion limit. OpenAI Agents SDK has manager-style orchestration distinct from peer handoffs. AutoGen has GroupChatManager. Anthropic's published research system is orchestrator-worker. What I think is underused is treating the manager not as a moderator for an open group chat but as a formal reporting line with authority to terminate. Two things I am unsure about. First, hierarchy can become its own bottleneck. If every decision routes upward, the chair agent becomes a single point of latency and a single point of failure. Second, escalation-as-feature only works if the top of the org chart has real stop authority. If the chair just calls another LLM that calls more LLMs, the loop just moved one floor up. submitted by /u/Hot-Leadership-6431 [link] [comments]

Chat based form filler in natural language

Hi folks, I am building an AI chat based system whose eventual goal is to get answers to all the questions I want to have answered from user in plain language conversation. It’s quite similar to filling out a form, but instead of boxes, it happens through a chatbot. I want to design and build it end-to-end for maximum scalability. I also want to make it feature-rich — for example, the bot should be able to use tools like search in the middle of conversations, read uploaded files /images. If users diverge into different topics, I want to allow that and let bot helps it, but eventually bring things back to where we want to lead them. The system should generate questions based on the user's input and intelligently decide what to ask next. I’m confused about how to build it. I previously built a state machine, but it didn’t perform as expected because out-of-order data coming from users breaks it. I want to explore other tools like LangGraph, but I’m not really sure how to design the overall architecture. I need help designing it in a way that it can be plugged into different systems and reused across products. The data I want to gather is stored in a Pydantic model. I also have a couple of helper functions like web search, DB update functions, and utility functions to extract data from user input, which I can probably wrap into tools. Would love some help figuring out the right architecture and approach for this. submitted by /u/sagar12sagar [link] [comments]

Why I added a governance layer on top of my Claude agents (and why it made a huge difference)

Hey r/ClaudeAI, I’ve been heavily using Claude 3.5 Sonnet and Opus through the Anthropic API to build agents and workflows. Claude is honestly one of the best models right now for complex reasoning and tool calling. But here’s what I kept running into: even though Claude is smart, when I put it into longer-running agent loops (CrewAI, LangGraph style setups), it still does the classic agent things occasional silent failures, burning through tokens in loops, or just going off in directions I didn’t expect. The worst part wasn’t even the cost. It was the constant checking. I couldn’t fully trust the agent to run for hours without me babysitting it. So I started using a lightweight governance/observability layer that sits below the agent (not inside the system prompt). It basically adds: Hard safety boundaries and fail-closed behavior Real-time live traces so I can actually see what Claude is doing step by step Human-in-the-loop control (I can pause, resume or stop the agent from Telegram/phone) Automatic checkpointing Proper runtime budget caps (not just “please don’t spend too much” in the prompt) The difference is night and day. I can now let my Claude agents run for long periods and actually feel safe ignoring them. Curious if other people building with Claude have run into the same trust/cost/monitoring issues. Have you tried any governance tools or patterns that made your Claude agents feel truly production-ready? Or are you still manually monitoring them? Would love to hear what’s working for you. submitted by /u/Necessary_Drag_8031 [link] [comments]

Cavemen skill questions

Caveman looks amazing for reducing output tokens! Has anyone tried applying the Caveman skill to a headless, automated backend application? I have a Python/LangGraph pipeline making direct API calls to Claude to validate telecom engineering drawings, and I'd love to get these token savings. Can the MCP proxy be wrapped around standard API calls, or should I just manually inject the Caveman prompts into my backend logic submitted by /u/Special_Spring4602 [link] [comments]

The open-source AI agent config repo the community has been building just hit 888 stars — asking for feedback & feature ideas

Over the past year our team and community have been building an open-source collection of AI agent configs: production-ready system prompts, tool-calling schemas, RAG setups, multi-agent orchestration patterns, and model-specific tuning files. Repo: https://github.com/caliber-ai-org/ai-setup This week it crossed 888 GitHub stars and nearly 100 forks. All free, no paywall, no product to sell. What's in there: - System prompt templates across GPT-4o, Claude 3.5/3.7, Gemini 2.5 Pro - Tool-use and function calling schemas for agentic workflows - LangChain / LangGraph agent setup configs - RAG pipeline configurations with different retrieval strategies - Ollama and local model setups - CLAUDE.md / AGENTS.md templates for coding agent contexts - Multi-agent orchestration patterns We'd love to hear from this community: What AI agent patterns are you using that you'd want to see in the repo? What's missing that would make this genuinely useful to you? What setups have you found work well in production? All feedback and contributions are welcome. submitted by /u/Substantial-Cost-429 [link] [comments]

Today I learned about this

submitted by /u/YogurtWild [link] [comments]

I run a team of Claude agents that ships PRs to production — open source

I've been running a multi-agent system in production for a few months — a co-CTO agent + specialist agents (PM, dev, ops) that handle real engineering work end-to-end: design specs, code review, PR implementation, deploys, monitoring. The architecture: Each agent is a Docker container running claude -p (with optional Codex fallback) wrapped in .NET 10. A central orchestrator coordinates them via Temporal workflows + RabbitMQ. Agents talk to me over Telegram (DMs + group chat for the whole team). Memory is Qdrant + Ollama embeddings — agents recall past decisions across sessions. A web dashboard shows live agent status and in-flight workflows. What it does day-to-day: I drop a one-line request in Telegram. PM writes the spec, two reviewers run consensus, dev implements the PR, CI ships to staging, PM verifies, I approve the merge gate, prod deploy. Same pattern handles infra: deploy verifications, health checks, daily digests, incident triage. Agents have access to fleet-memory (semantic memory MCP) — they search before acting, write learnings after. 5-min demo of an actual production PR being shipped: https://youtu.be/DIx7Y3GfmGc Why I built it instead of using crewai/autogen/langgraph: I wanted Temporal-backed durability (workflows survive restarts, retries are deterministic) and ops-grade observability (every workflow visible in the temporal UI, every signal auditable). The agents themselves are just claude -p — the magic is in the orchestration layer. Open source: https://github.com/anurmatov/phleet Side note for those who recognize me — this runs on the Mac Studio I documented in mac-studio-server. The dogfooding is real. Happy to dig into prompts, system architecture, memory strategy, or how the agents handle PR reviews — AMA. submitted by /u/_ggsa [link] [comments]