LangChain [Reddit]
Articles about LangChain [Reddit]
Built a Python agent framework where a decision model chooses actions instead of an LLM - looking for feedback
I've been building DecisionLoop, a Python framework for agents where a small decision model handles intent detection, tool selection, arguments, and control flow instead of using an LLM for every decision.
Python or JavaScript for LangChain & LangGraph?
I’m a web developer with good JavaScript knowledge and want to learn LangChain and LangGraph to build AI agents. Should I start with Python or stick with JavaScript/TypeScript? What’s better for real-world projects and industry demand? Would love your suggestions! 🙌 LangChain #LangGraph #Python #JavaScript #AI #AIAgents #WebDevelopment submitted by /u/Glad_Ground5391 [link] [comments]
Lynch asked “why are coding agents so dumb?” After running ticket-to-PR agents with my team, I think the problem is mostly the harness
submitted by /u/Jimbaceo [link] [comments]
How do you handle delayed ground truth in LangSmith evaluations after CI has already passed?
I've been thinking about an evaluation problem that doesn't fit neatly into a normal CI pipeline. Imagine a LangChain or LangGraph workflow where the immediate automated evaluator reports success, but the reliable outcome only becomes available several days later. Examples could include human-reviewed document extraction, customer-confirmed answers, or downstream validation of generated outputs. A passing CI check doesn't necessarily mean the eventual outcome will agree.
The OpenAI Medicare incident makes agent containment feel less theoretical
submitted by /u/TWiT_tv [link] [comments]
[P] OpShield – Open-source safety platform for AI agents (rollback, cost control, chaos testing)
I built OpShield to add a safety layer to AI agents. It intercepts every tool call and provides: - Risk classification (LOW → CRITICAL) with auto-blocking - Budget enforcement with cost forecasting - One-call rollback via state snapshots - Circuit breaker for failing tools - Chaos engineering for resilience testing - Agent scoring (0-100, A-F grades) - OpenTelemetry-compatible distributed tracing - Compliance/audit reports Zero dependencies. Works with OpenAI, Anthropic, LangChain, CrewAI, AutoGen, or any Python function.
Looking for real-world rag project ideas and datasets
hey everyone! i've spent the last week learning about rag, retrieval strategies, and benchmarking. now i want to build a real-world rag project from scratch using data from any domain, with documents in different formats like pdf, html, csv, and markdown. can anyone suggest a good problem to solve, share raw datasets, or point me to projects where i can contribute and learn? i want to experiment with the complete pipeline, from ingestion to generation, and benchmark different retrieval strategies to understand what works best.
We ran an AI red-teamer on 5 OpenRouter setups. One lost 81% of its scorer replies, the cheapest full scan cost $0.14
In July, during the Hugging Face incident, the first models the defenders reached for (Claude Opus and Fable) refused much of the investigation because their guardrails "treated reverse-engineering an exploit the same as launching one." They switched to GLM-5.2 on their own hardware.
Here is how we architected a closed-loop system connecting MQTT (Drones/ESP32/BMS) -> Medallion Streaming -> Hybrid RAG -> LangGraph for autonomous actuation
submitted by /u/Proof_Appeal_5821 [link] [comments]
Looking for Real-World Python Backend + AI Engineering Project Ideas That Can Help Me Stand Out in Interviews and the Job Market
Hi everyone, I’m currently working as a Python Backend Engineer, primarily using Core Python, FastAPI, and AI engineering technologies such as Ollama, RAG (Retrieval-Augmented Generation), and AI agents. I want to build a personal project that goes beyond typical portfolio projects like basic CRUD applications, chatbots, or simple RAG implementations. My goal is to build something that solves a real-world problem, demonstrates strong engineering skills, and helps me stand out during technical interviews and in the current job market.
What do you do when an agent hits its budget mid-run?
I've been thinking about how to handle per-run budget limits for agents. Say a run has a $5 cap. It's already spent $4.90 collecting data and only needs one final step to produce the result. Stopping it right there feels wasteful. But letting it keep going is risky too, especially if it gets stuck retrying the same tool call. I'm leaning toward blocking new work once the limit is hit, but reserving a small budget for saving state or returning a partial result.
Before shipping a RAG retrieval change, do you know which queries might lose evidence?
Imagine your team changes its embedding model, chunking strategy, or reranker. Your overall retrieval metrics improve. But a few important customer queries silently lose the documents they need. Would your existing evaluation suite catch that before release? I've been working on independent RAG retrieval audits focused on exactly this kind of evaluation risk. A real example: RouteMind I independently audited an open-source RAG benchmark and identified 15 scoring discrepancies across 1,400 query/arm results.
I built Betrox AI: A multi-worker agentic system that turns single high-level commands into full OS-level execution
Hey folks, Most AI agents today run in a single linear loop inside an isolated browser tab—meaning as soon as a workflow requires multi-app context, desktop interaction, or OS-level execution, the agent stalls. To solve this, I’ve been building **Betrox AI**—an autonomous agentic system engineered for complete laptop and operating system control. Instead of relying on a single agent trying to do everything sequentially, Betrox AI uses a multi-worker agent architecture.
Would your team actually pay for an independent RAG/AI agent evaluation audit?
I've been investigating retrieval quality, evaluation consistency, and regressions in RAG systems. In a recent independent audit of an open-source RAG benchmark, I identified 15 scoring discrepancies. The project's maintainer independently reproduced the findings and corrected the benchmark. That demonstrated technical value, but it left me with a different question: Is independent RAG evaluation something companies actually pay for, or do most teams prefer handling it internally? Consider this scenario:
How are you guys evaluating document parsers based on your documents? PDF parsing or whatever
Now theres almost a leaderboard for everything and even for parsers which are just a part of a workflow theres a leaderboard as well, also the vendor ones but with a bit biased insight. so I'd rather say for documents wich vary on various type like with or without text layer , either its digital or what they are in
Does scoped session intent + read budgets stop "in-scope" agent abuse better than simple allowlists?
In practice, a lot of agent abuse happens within allowed tools - e.g. looping through sensitive read endpoints until budget depletion, or making an outbound call right after reading sensitive state. Standard static allowlists don't catch this because each individual call looks valid in isolation.
Free-tier LLM APIs kept returning 429s in my multi-agent app, so I built a multi-provider fallback. Here’s what I learned
submitted by /u/Willing_Advisor_9998 [link] [comments]
Autonomous agent workflow for e-commerce listing creation - looking for architecture & GitHub recommendations
Hey everyone, I run a small dropshipping e-commerce store and currently use a semi-automated Python script to generate listing copy and push drafts via marketplace API. However, it still requires manual inputs and URL passing, and for sure - looking for good products on the marketplace (as I use other sellers products for reselling). I want to build an autonomous agent pipeline that handles this end-to-end or at least some of the process. Hardware: NVIDIA GeForce RTX 4070 Ti (with 12GB VRAM) and 32GB DDR5
I independently audited a RAG benchmark. 15 scoring mismatches revealed a flaw in its main comparison — the maintainer confirmed and fixed it
I recently completed an independent retrieval audit of RouteMind, an open-source project exploring document routing as an alternative to traditional RAG retrieval. The interesting part wasn't finding a dramatic regression or proving that one architecture was better. It was discovering that two approaches were being evaluated under different definitions of success. Here's what happened. The project had 700 evaluation questions comparing traditional RAG, RAG + reranking, and document-routing approaches.
Guidence to learn langchain from scratch
Guidence please folks submitted by /u/neaxty558 [link] [comments]
Our AI bill hit $11,400 last month and nobody on the team could tell me where it went
submitted by /u/vigilAPI [link] [comments]
A Harness in a single file: Agent skills as an evolving REPL module
submitted by /u/schmuhblaster_x45 [link] [comments]
For those building AI agents with financial authority: how would you prefer to security-test them?
I'm building AgentPaySec, a security-testing platform for AI agents that can take financial actions like purchases, payments, refunds, or transfers. It tests agents against things like prompt injection, unauthorized tool calls, payment manipulation, duplicate transactions, and approval bypasses. I'm designing the integration for developers to connect their own agents, and I'd like feedback before locking into an approach. Which would be easiest for you to adopt? API endpoint Python/TypeScript SDK MCP integration
How will LangChain continue making money?
Looking at the business case for LangChain, it seems like most of the revenue is tied to LangSmith. However, with tools like AWS Omni and other observability platforms that solve the same problem, I’m failing to see the growth potential. Like, take a mid tier organization that already has a BAA with AWS. They can build all their agents on Agentcore, and connect all their various services. Omni allows them to to do everything LangSmith offers (evals, observability, traces view, annotations, etc).
We built a Slack agent that can buy office supplies with Stripe Link
I’ve been working on Restock, a sample agent built with Managed Deep Agents(MDA). You can ask it something like: “Find me a 12 pack of pens for the office", "Help me restock our fridge with yogurts"
Production-grade Agent Graph Engineering: a LangGraph project and blog series, distilled from 5 years
Hi all. I have been building realtime AI assistants since the ChatGPT beta, first for financial advisers and then as a platform, and I have just published a distilled working version of that architecture along with a series explaining every decision in it.
Is auto-prompt tuning dying?
I have been facing this lately that since day one i wanted my llm to master prompt tuning, i put so much effort into making it learn from its own mistakes and fine tuning its own prompt and recalibration. But now I have started to feel it's not the best, firstly because a lot of tokens are consumed in this back and forth even the it gives good result i cannot overlook the cost right, then the variation keeps increasing so the auto-tuning keeps happening and its again the becomes the first problem, costly.
Analyse massive documents with AI
Hi everyone, I work at a large telecom company in the US. When we win a tender, the client sends us a requirements specification, and we write technical deliverables based on it and on our internal technical documentation (100k+ documents). A single tender can involve thousands of deliverables, some of them up to ~600 pages long. Goal: before delivery, automatically verify each deliverable. That means checking that the facts it contains are accurate against our technical documentation, and detecting anomalies (errors, internal contradictions,
Introducing Managed Deepagents v0.9
Hey r/langchain! We just announced Managed Deepagents v0.9. Here's a quick TLDR: Agent-created schedules: agents can set up reminders and recurring tasks from a conversation. Per-run configuration: one deployment can pick its model, skills, and tools for each run. Slack reactions: agents acknowledge messages before they reply. These updates make it easier to build internal agents that live in Slack or your own channels and work more like teammates.
Where does agent waste actually come from: model calls or orchestration?
I’ve been digging into a problem I think agent observability still misses. Most tools can tell us what an agent called, how many tokens it used, and what it cost. But the more useful question is: Why did those calls happen at all? I started looking at this after AUDR came out and built a small open-source CLI called KORA Doctor to inspect agent traces. Right now it looks for things like: repeated model calls missed cache or reuse deterministic work sent to an LLM expensive models doing simple work
The "agents need memory vs just use documentation" argument keeps going in circles, mostly because "memory" is three different problems
If you follow the agent threads here, you have watched the "do agents need memory or just documentation" argument go in circles. Most of that, we think, is that "memory" covers three separate problems, and each camp is defending a different one.
I built a self-hosted AI agent for GitLab on LangGraph.js. It has reviewed 1,000+ merge requests
I built a GitLab agent on LangGraph.js that reviews merge requests, resolves issues and has a chat. It's been in production a few months (1k+ reviews, 133 releases since June), and about two-thirds of its comments get resolved by the author. Most of the work wasn't the agent logic.
Where should file artifacts live when a LangGraph run can pause and resume?
A checkpoint can preserve graph state, but generated files have a different lifecycle. Reports, HTML bundles, images, and spreadsheets may be too large for messages, may change independently, and may need a stable review link after the run that created them has ended. Storing only a temporary path or signed URL makes a later resume fragile. Storing the bytes inside graph state makes checkpoints heavy and blurs workflow state with durable output.
Agent built with LangGraph ran unattended overnight on a refactor. Redid two completed nodes that had already finished correctly hours earlier
No crash, no error, no retry logged. It just circled back through nodes whose state said done and reprocessed them anyway, once with a slightly different naming convention than the first pass, overwriting the correct output with a worse one.
Aplomb 1: open-weights 5.3B decision model (Qwen3.5-4B Base), 1M context, text/image/video/audio in one request, #1 among 4B models on the Decision Index
We released Aplomb 1 today, a 5.3B decision model with open weights. It reads up to 1M tokens of text, JSON, images, video and audio in a single request, and on our API it makes a decision on a 1M-token document in about 3 seconds, at $0.02 per 1M input tokens with free output and ZDR by default.
I built Forge, an open-source, self-hosted visual builder for AI agents (MIT)
Forge lets you build AI agents and workflows on a canvas. You wire together agents, tools, knowledge (RAG) and routing logic, then test and ship them without writing framework code.
Should I include narrative in openwiki
Hi, I am wondering if including narrative docs like prds, design, strategy and vision etc would help the wiki, or make it worse. I can exclude the docs folder and go just for code, or include the docs with it, not sure if that would be a good or bad thing. Thanks. submitted by /u/gleeber [link] [comments]
How much of your inference stack is actually spent getting models ready?
I was testing an inference server recently and ended up spending more time looking at model loading than inference itself. The server was SIE from Superlinked, running locally. One thing I liked about the API was that /v1/models exposes the model state. For example, after requesting a reranker that wasn't loaded yet, I could see: state: loading The logs then showed the model going through the loading process before the worker became ready. The sequence was roughly: request ↓ model loading ↓ download weights ↓ load / deserialize ↓
I’m trying to learn AI Agents, but I’m getting really confused. How should I approach it?
submitted by /u/Money-Designer-9724 [link] [comments]
Which browser mcp are you running for agents that need to log in and stay logged in
wired a browser mcp into my agent last month so it can hit a couple dashboards after login demo was fine. overnight the session is gone and the next run burns half its tokens just re-authing before it does anything useful. cookie dump between calls helped once, then the site rotated something and i was babysitting again budget is tight so im not trying five tools. what browser mcp is actually keeping logins alive across runs for you submitted by /u/DisciplineIcy7891 [link] [comments]
Title: AI agents in production: what problems are you actually facing?
I'm researching the challenges of deploying AI agents in real businesses, and I'd love to hear from people who are actually building or using agents in production. What has been the biggest problem or pain point you've faced? How are you solving it today, and what still doesn't work well? If you've dealt with this firsthand, I'd really appreciate your experience. submitted by /u/Personal_Ganache_924 [link] [comments]
I made an open-source CLI that finds the LangChain agents in your repo and puts a verified policy in front of every tool call
I kept shipping LangChain agents with tools like refund_order or run_sql and had no real answer to "what stops this tool from doing something it shouldn't?" Prompt rules help most of the time, not every time, and nothing records what happened.
Planning trajectory and attention
submitted by /u/Zestyclose-Raisin-66 [link] [comments]
Help to find high-quality learning resources
Hello everyone! I'm building a RAG prototype for an internal knowledge assistant over mostly PowerPoint decks and some PDFs. Stack so far: - LangChain + Claude as the LLM - Loaders: PyMuPDF4LLM for PDFs, Unstructured / python-pptx for slides - Local embeddings (BGE via sentence-transformers), Chroma as vector store - Hybrid retrieval: BM25 (rank_bm25) + dense, then a cross-encoder reranker (bge-reranker) What I know so far, I learnt it mostly through datacamp and I vibecoded some parts.
How are u validating agent actions in production?
if u are building and deploying agents in prod which takes actions on behalf on consumers … how are u validating agent actions and their execution runtime? submitted by /u/Common_Dream9420 [link] [comments]
[Open Source] Local UI to test MCP tools + prompts and export directly to LangGraph Python
Built a local playground (Moka) to test MCP servers and prompts visually before writing code: Test tool calls, streaming, and raw JSON-RPC traffic in a local chat UI Hit "Export as code" to generate the working LangGraph (Python) script (Optional) Point it at an existing LangGraph agent over AG-UI to use it as a debug UI Runs locally with zero config: GitHub: https://github.com/mokahq/mokalabs Let me know what you think! submitted by /u/mokalabshq [link] [comments]
Solo intern working on a large-scale AI project
Hey everyone, I’m an intern at an IT support company and I’ve been given an AI project to handle mostly by myself.
How do you roll back an agent change when the prompt, tool schema and model version all shipped together?
submitted by /u/No-Age-3362 [link] [comments]
We benchmarked 18 RAG pipelines against an agent loop on Google's FRAMES. The best pipeline hit 78.9%. The agent loop hit 92.7%
Hybrid search, reranking, query decomposition, and query expansion are often treated as must-haves for good RAG. We wanted to see how much each actually helped, so we tested them. Same model, same embeddings, same documents, across all 824 multi-hop questions in FRAMES.
How to fix multi-agent orchestration when agents get stuck waiting on each other
Expected outcome: a planning agent, a coding agent, and a review agent would hand off work in sequence without anyone babysitting the pipeline. Actual outcome: the review agent would sometimes wait forever because the coding agent's "done" signal wasn't structured the same way every run. Root causes were a missing shared schema for completion status, plus delegation logic that assumed synchronous responses when the runtime was actually async.
Langgraph-jev – typed decision node for LangGraph, wrapping TypeSafe's Jev API
I wrapped TypeSafe's Jev API as a LangGraph node and LangChain Runnable. You define typed questions, it returns typed answers with confidence scores attached. No parsing.
I've been experimenting with a RAG architecture that separates retrieval, reranking, filtering, and context construction instead of putting everything into one retrieval step
Current pipeline: BGE-M3 → Dense + Sparse embeddings → Qdrant → Hybrid retrieval / RRF → BGE Reranker → Relevance Gate → Context Quality Filter → Context Builder → Local LLM The LLM is DeepSeek R1 7B running through Ollama. The main idea is to make retrieval failures explicit. If the reranker doesn't find sufficiently relevant chunks, the system can return a "not enough information" response instead of passing weak context to the model. GitHub: https://github.com/Taha2hussein/mini-Rag
Will OpenAI Dots and Grok Bots make frameworks like LangChain/LangGraph obsolete?
With the rollout of OpenAI Dots (running autonomous cloud browsers) and Grok Bots (featuring multi bot "Orgs" and screen recording/show and tell learning), the landscape for autonomous agents is changing rapidly.
I stress-tested an autonomous purchasing agent against 16 transactional attacks. 5 bypassed system guardrails and authorized rogue payments (Traces & Post-Mortem)
Giving an LLM function-calling access to checkout or payment tools creates an entirely different threat surface than simple chatbot injection. The issue isn't whether the model says something bad—it's whether untrusted input can manipulate tool payloads and execute unauthorized transactions.
Agent tools often can't tell you if they actually succeeded - found this testing LangChain
submitted by /u/Internal-Chapter1526 [link] [comments]
A test checker rewarded AI agents for typing the right words. They typed them
submitted by /u/Input-X [link] [comments]
Heads up: GoogleSearchAPIWrapper stops working Jan 1 (Google is shutting down the Custom Search API), plus a workaround
If you use `GoogleSearchAPIWrapper` from langchain-google-community, heads up: it calls Google's Custom Search JSON API, which Google is shutting down on January 1, 2027. It's already closed to new signups.
I built a VS Code extension to visualize and debug LangGraph agents
Building agents with LangGraph or CrewAI and tired of tracing control flow by reading source files? I built the Agentic Engineering Extension, it turns your agent codebase into an interactive graph right inside VS Code (also works in Cursor, Windsurf, Antigravity).
Built an AI assistant that remembers past machine breakdowns. My worst bug was a "saved" message that was lying to me
submitted by /u/Ravi_Repswal [link] [comments]
Reporting agent over HTTP API data: structured analysis tools or LLM-generated SQL?
I’m building an internal reporting agent for operations staff using Python, LangGraph, FastAPI, and React/ECharts.
For anyone whose chains work in dev and fall apart in prod a DSPy/MLflow workshop, Oct 3
Chaining prompts togetjer gets you a demo fast. Geting that same pipeline to hold up once the input distribution shifts, or once a PM asks why the output changed after a "small" prompt edit, is a completely different problem, and it's the one this workshop actually addresses.
How are you testing AI agents before deploying them to real users?
I am researching how teams evaluate customer-facing AI agents in production. Traditional LLM evaluation mostly asks: "Did the model generate a good response?" But production AI agents create bigger questions: Did the agent access the correct data? Did it call the right tool/API? Did it follow authentication and permission rules? Did it take the correct action? Did it know when to escalate to a human? Example: A lending AI agent tells a customer: "Your outstanding amount is ₹18,400."
I’m building security for AI agents - what do you think?
In practice, damage happens when an agent calls a tool: HTTP, email, DB, files, etc. A hidden instruction in a doc can push the agent to do something the user never asked for — and from the system’s point of view it can still look like a normal tool call. Site: [https://aegotrax.com](https://aegotrax.com/)) If you run it, I’d genuinely like feedback: Docker or pip - did install work? Did you get a clear BLOCK in the example? What was confusing or wrong? submitted by /u/GameChacking
[Showcase] Built an open-source Security Gate MCP Server: Sub-3ms deterministic firewall against Prompt Injection & Treasury Drain for Autonomous Agents
submitted by /u/EstablishmentTough18 [link] [comments]
Building a Corrective RAG (CRAG) Compliance Research Assistant
Most RAG chatbots will confidently answer with whatever they retrieved — even if it's irrelevant, even if it's wrong. In compliance, that's not a minor bug, it's real regulatory exposure.
Rag OOM issue to process PDF
Hi.. Im building simple rag. in that i used Docling to process PDF. but Docling is heavy. and i have 8 GB ram.. so OOM issue occurred. pdf failed to process. after that i devided pdf into 2-3 pages. u can think like i create 2 pages batch using py PDF.. and it works. but do u think is this right way ?. i don't know it is right or wrong. and while 2 page batches there is some batches failed to process bcz of OOM.. what should i do ? i wanna deploy it too on AWS. if this is my computer level isuue then there not might be the issue in production.
Clean way to stop a langgraph agent acting on stale memory after source data changed
Running into a staleess problem with persistent memory in langgraphg and curious how do the experts handle it cause the obvious fixes all have a catch. Heres how it looks like-
Early 2024 langchain user coming back around to ask about experience before building something new
I was early on langchain around the gpt-4.1 era, built in python and hosted on AWS. Back then pre Claude code dev was kind of a nightmare, every customization was hidden behind layers of abstractions in langchain and features/changes took a while. Spent the last two years in a custom typescript runtime for agents, no frameworks, all custom handcrafted code, and then of course assisted by Claude/Codex.
Repeated agent evals still left me with a CI problem: when should a PR actually fail?
I've been working on CI for agents and ran into a problem I underestimated: the same agent can produce meaningfully different outcomes with no code change.
How often is the thing that caused ur agent bug not even in the trace?
this one has been fucking with me lately u can have what looks like a really detailed trace and still eventually find out the actual issue was something outside it config changed permissions were different stale cache database state different deployment external api behaved differently some hidden context whatever think abt the last weird agent bug u had was the evidence u needed actually sitting inside the trace the whole time and u just had to find it?
How are people handling state, time and conflicting records in RAG?
submitted by /u/jylusdev [link] [comments]
Looking for AI - agent builders working in finance/blockchain to help us test Agaemon - a governance layer between agents and execution
We're building Agaemon, a governance and decision layer designed for AI agents that need to take real world or onchain actions. We've already integrated Agaemon into our agent/runtime flow and we're now looking for developers or teams building agents in finance, trading, treasury,DeFi or blockchain who would be willing to help us integrate and test it with a real agent workflow. The basics idea is simple:
Anyone moved their LangChain app off direct provider SDKs onto a single gateway endpoint?
Our app calls OpenAI and Anthropic directly, and we want to add Gemini and a couple of open-weight models. Rather than wiring in more SDKs, I'm thinking of pointing everything at one OpenAI-compatible gateway and changing only the model string.
[Showcase / Open Source] I built an MCP Security Gate Server to stop prompt injections, credential leaks, and dangerous tool calls (
submitted by /u/EstablishmentTough18 [link] [comments]
New to Chatbot dev field need to programing languages needed
so i need to know what are the best programing languages for someone who wants to make chatbots using rag and best courses / books submitted by /u/Chance-Clue-9990 [link] [comments]
We red teamed our own support agent with a long slow conversation. No single message was malicious, it still broke
During an internal red teaming exercise on our ai agent, we decided to test how multi turn conversations can make the model drift from its guardrails. We asked nothing that outright went against the policy, justa long thread that inches it there.
A green pipeline that produced nothing for two months — the success flag was measuring the wrong thing
Long-running agent jobs here reported done/success whenever the run finished without raising. Sounds obvious. It's wrong, and it hid a dead pipeline for two months.
A context optimizer needs tests for the evidence it throws away
Reducing a tool set from 100 schemas to four is easy to measure. The difficult case is the request whose only useful tool was number five. The same problem appears when trimming documents: average answer quality can stay steady while a rare but necessary piece of evidence disappears.
Building a RAG based chatbot on a huge codebase
submitted by /u/OkWeird2709 [link] [comments]
My ops agent could open GitOps PRs for anyone who asked it. I built a check that asks GitHub whether the person is allowed first
I run an internal ops agent. One tool call gives it a service's health from every angle in about 15 seconds, so it reads everything on a read-only account and I don't gate reads at all. It changes things one way only: a pull request against the GitOps repo.
I built a local commitment extractor for AI agents. No API key, no cloud. Just pip install
I've been building COGEXT for a while — an accountability layer for AI agents. The core idea: agents make promises ("I'll send the report by Friday") and there's no reliable way to verify they were kept. I just shipped a local, offline version of the extractor. No API key. No cloud. No signup. Just: pip install cogext-primitive from cogext_primitive import extract_commitments commitments = extract_commitments( "I'll send the report to Sarah by Friday EOD." ) for c in commitments:
Agentic SOAR vs SOAR plus a chatbot for incident response, honest opinions?
Agentic SOAR gets described as combining traditional playbooks with agents that reason dynamically and decide in context, positioned as the successor to playbook only SOAR. Skeptical question, is that different in practice from a SOAR platform that just added an LLM layer for summarization and suggestion, or does agentic SOAR actually change how the underlying playbook executes, not just how it's explained to an analyst afterward? submitted by /u/Pristine-Exuspe-4219 [link] [comments]
LangChain September release adds delegated access
LangChain September release adds delegated access LangChain’s September 2026 release introduced several updates across Python and JavaScript packages. Highlights include delegated LangSmith access for sandboxes, improved SDK run tracking with stop_reason capture, and enhanced security patches. These changes strengthen developer control and observability in workflow automation, making LangChain more reliable for orchestrating complex agentic workflows in production environments.
What should I build to become job-ready in Agentic AI?
I’ve learned LangChain, LangGraph, RAG, tool calling, agents, MCP, and memory. With new AI models and agent frameworks coming out constantly, what projects and skills should I focus on now to become internship/job ready? What 2–3 projects would you recommend building for a strong resume? I’m looking for real-world projects, not basic chatbots or simple RAG apps. Would love advice from people working in or hiring for Agentic AI. submitted by /u/Educational_Weird597 [link] [comments]
I built a local runtime check for AI agent tool calls - looking for people to break it
Site: https://aegotrax.com If you run it, I’d genuinely like feedback: Docker or pip - did install work? Did you get a clear BLOCK in the example? What was confusing or wrong? submitted by /u/GameChacking [l
Detecting prompt injection before data exfiltration gets noticed
We’re building an internal LLM app that can search docs and call a few tools. The part I’m stuck on is detecting prompt injection before it turns into data exfiltration.
How do you decide which AI agents are worth keeping in production?
An agent nobody uses can still have credentials, access customer data, and run scheduled jobs. The token bill might be small. The access it retains is what worries me.
Does OpenWiki actually improve output quality?
I find the idea of OpenWiki sound and understand how it can bring some modest cost savings. But I'm more interested in the output quality. Has anyone been using it and has seen an improvement in that regard? submitted by /u/imperius99 [link] [comments]
Open-source PII boundary for LangChain apps using hosted model
Privacy Gateway for apps that send customer data to OpenAI, Anthropic, or another hosted model. It checks text before the request leaves your app. You can remove names and emails, replace values with tokens, or keep a reversible mapping under your control. It also blocks a request when inspection fails instead of sending the original anyway. The project is self-hosted and MIT licensed. It has Python, TypeScript, ASGI, MCP, and OpenAI-compatible proxy support. Repo: https://github.com/csnyder256/privacy-gateway
Claude Code/Codex with harness vs LangGraph for software development flow
Hey all, Our company recently start building LangGraph workflows for web feature & BAU development. The goal is to have every dev onboard to use the predefined LangGraph workflows to complete dev tasks. Here are my findings after doing some research: - LangGraph workflow is mainly for customer facing LLM features, or as a build pipeline for BAU / Maintenance level of work.
A RAG hallucination checker caught zero hallucinations
submitted by /u/recro69 [link] [comments]
LangGraph vs CrewAI for multi-agent handoffs: which handles context and communication better?
I've been comparing LangGraph and CrewAI specifically around handoffs, not general ease of use, since that's where most of my actual pain has been. LangGraph gives me explicit state, nodes, edges, checkpoints, and routing, so I can control exactly what information moves from one stage to the next. That level of control seems especially useful when a handoff needs validation, a retry, or a human approval step in the middle.
I built open-source analytics that separates agent execution success from task success
submitted by /u/Flat_Arrival_6920 [link] [comments]
Highly disappointed by langchain documentation
I'm a beginner to agentic AI and I'm severely disappointed by the LangChain docs. Mainly for 2 reasons (but there are more):
Built an open-source AI document intelligence platform - RAG Q&A over PDFs/DOCX, swap OpenAI/Claude with one setting
Hi all - just wrapped up an open-source document Q&A / RAG system and wanted to share. What it does: - Upload PDF, DOCX, or TXT files - Chunks and embeds the content into Postgres + pgvector - Ask questions about the document in plain language - Switch between OpenAI and Claude as the LLM with a single env var — no vendor lock-in Stack: React + TypeScript, Node/Express, LangChain, Postgres/pgvector, Docker Compose for local dev. GitHub: https://github.com/Srameshgitnow/AI-DocumentIntelligence
I got tired of manually tuning RAG pipelines, so I built an open-source experiment runner
https://reddit.com/link/1wl2r6l/video/nxxm7b2krkqh1/player A while ago I asked here how people actually experiment with different RAG configurations. I kept running into the same problem myself: change chunking, run tests, change retrieval, run again, try another embedding model, reranker, top-k, query transformation... and somehow keep track of whether things actually got better. So I started building Muffakir.
My agent kept acting on an old decision even after I told it we changed course
This drove me nuts for weeks so I need to vent about it somewhere. I run a couple of coding agents (Claude Code, Codex) on the same project, and my memory setup was "retrieval works great, problem solved." Semantic search, vectors, the whole deal. Retrieval was genuinely fine. It found exactly the right memories every time. It found the WRONG one. That's the part nobody warns you about. I told my agents we switched the local cache from sqlite to duckdb back in, like, August. Every agent acknowledged it, saved it, great.
Qwen 3.8 27B running on a single RTX 5090 researches and creates a full animation using only code
The goal of this post is 2 fold. First, I wanted to show what a local model like Qwen 3.8 27B running on a single RTX 5090 GPU in a harness like Row-Bot can do. Second, I wanted to create a full feature video of what Row-Bot can do. So I decided to combine the two with one prompt: "I want to create a single animation showing everything row-bot can do and also emphasizing the open source, local-first, privacy and model agnostic angle. the animation needs to be accurate and have a wow visual impact and should cover everything row-bot can do.
What are good documentations to learn how to build agents?
Looking for documentation that goes from basics to building full agentic systems. Currently familiar with llms , langchain, and rag systems. submitted by /u/Remarkable-Yard4860 [link] [comments]
How do you reduce token consumption when an LLM retry requires sending the entire context again?
I’m working on an internal AI workflow where an XMP file contains source tables, columns, and metadata. We pass this information to an LLM, which generates a dataflow using an internal MCP tool and our internal dataflow notation. The current flow is roughly: "XMP metadata → LLM → MCP tool → Dataflow → Validation" The issue is with failures. If the first attempt fails validation, our current approach sends the whole original text/context again along with the failure/error information and asks the LLM to correct the dataflow.
What I learned while implementing FAISS vector search
I recently started implementing FAISS vector search in one of my projects, and initially I found it difficult to understand how vector search actually works.
Sharing my learnings: Building a Python layout-aware PDF parser for cleaner RAG chunking
Hey everyone, I wanted to share a quick breakthrough and the code logic I’ve been using to deal with the classic RAG bottleneck: parsing complex, multi-column PDFs and embedded tables without scrambling the semantic order. Standard text extractors read left-to-right blindly across a page, which completely ruins multi-column university maps or invoice grids before they hit the vector database. How I built the parsing logic: To fix this, I wrapped a FastAPI backend around pdfplumber.
How do I implement pre llm user prompt via a guardrails?
I’m working on an enterprise GenAI/RAG application and looking into how teams are implementing guardrails in production, especially when the architecture may involve both Azure and AWS. I’m interested in real-world approaches for: \- Prompt/input validation and jailbreak detection \- PII and sensitive-data protection \- Prompt injection protection for RAG \- Output/content filtering \- Hallucination/grounding checks \- Tool/agent access restrictions \- Token, cost, and rate-limit controls \- Logging, monitoring, and auditability
Tickets to Langchain Interrupt
Hey - I just found out about Langchain Interrupt on Sept 24 does anyone know where I can get a last minute ticket? It looks sold out on the website but also is it worth attending? submitted by /u/Main_Ad2424 [link] [comments]
Testing Qwen 3.8 27B running locally on a single 5090
I wanted to test what Qwen 3.8 27B running locally on a single 5090 could do in Row-Bot. I gave it just one prompt:
How I Built My Biggest Autonomous Data Agent to Fix Real-Time Data Integrity at Scale
submitted by /u/husnain_239 [link] [comments]
I maintain the RAG Techniques repo (29k stars). I just open sourced the kit I use to check which lines in my agent's CLAUDE.md are guesses, and which claims in my docs the code disproves
I thought my repos were well documented. Then I made the agent prove it. Before it read any code, I asked Claude five questions about one of my own projects. Which tools are installed. What the linter enforces. Where a new file goes. It marked each answer SURE, GUESS or WOULD-HAVE-TO-LOOK. One SURE answer named two tools that are installed nowhere in that tree. They sit in my spec as a plan, and it read the plan as the stack. Then I let it write a docs layer from the code and check my old docs against the tree.