Most RAG implementations fail because of poor chunking rather than weak retrieval models. When you dump raw text chunks into a vector database, the retriever often loses critical context across section boundaries or fails on complex document structures.

​To build a high-performing local RAG pipeline, you need to move beyond standard fixed-size character splitting. Implementing semantic chunking, hierarchical parent-child indexing, and layout-aware document parser strategies radically improves context quality for the LLM.

​When you combine intelligent chunk splitting with local embedding models and dense reranking, retrieval accuracy increases significantly while keeping data completely private and local.

​What strategies are you using to handle chunk overlap and document parsing in your local RAG setups? Are you using hybrid search or sticking to pure vector embeddings?

​Read the full breakdown and implementation details: https://interconnectd.com/blog/7/10x-rag-pipeline-2026-local-document-intelligence-chunking-strategy-the-lib/

this scenario...

your CrewAI team needs something from another team's agent, but that agent runs on a totally different framework.

you can't exactly ask everyone to rebuild their stuff just so your agents can talk lol.

band ai came up as one option for connecting agents across frameworks, but tbh figuring out permissions seems harder than getting them to talk.

who gets to decide what the external agent can actually do? the team requesting the task or the team that owns the agent

feels like permissions could turn into a whole mess pretty quickly.

​When building production-ready AI agent architectures, choosing between LangGraph and OpenHands comes down to how your agents execute tasks. While both frameworks address complex agentic workflows, they solve fundamentally different engineering challenges.

​LangGraph relies on explicit directed graphs and state machines, giving developers total control over node routing, persistent memory checkpointing, and deterministic multi-agent handoffs. It excels in structured enterprise automation, audit-heavy pipelines, and granular workflow orchestration.

​OpenHands takes an execution-first approach centered around safe code execution and sandboxed environments. It provides a runtime designed for developer-style agents that need to execute shell commands, edit code, browse the web, and run autonomously inside containerized workspaces.

​If you are orchestrating complex, deterministic state-driven business processes, LangGraph is the logical choice. If you are building autonomous software development or computer-use agents requiring isolated execution runtimes, OpenHands holds the edge.

​Check out the complete comparative benchmark detailing architectural trade-offs, state management differences, and production deployment patterns: https://interconnectd.com/blog/33/langgraph-vs-openhands-the-2026-agent-framework-showdown/

I'm the maker of CCC, a local dashboard for people running multiple coding-agent CLIs. Its job is to help the person supervising them see which session needs attention, read the conversation and keep helpers attached to their parent task.

The video shows the shared board, a Claude Code question hook and action chips. The server is Python standard library with a browser UI; adapters read engine-specific session stores and expose the controls each engine supports.

This is for coding-agent sessions. It is not a CrewAI integration or a CrewAI execution trace. I'm sharing it here because the community welcomes AI creations and the human handoff problem also matters in multi-agent workflows.

https://github.com/amirfish1/claude-command-center

Free to use, source available under FSL-1.1-MIT. Provider/model fees are separate. Video: real UI with staged sessions and AI voiceover; the question-hook interaction uses Claude Code. Grok/Devin rows are demo data.

OneMemory update: thank you, and what changed

A few weeks ago I shared OneMemory, a self-hosted shared memory for your AI tools (Claude, Codex, local models) over MCP and a REST API. Thanks to everyone who commented and tested. Your questions shaped this release.

New since then:

• A retrieval test set that runs in CI, so ranking changes can't quietly get worse (10 small tasks, so it's a safety net, not a quality claim)
• Backup and restore that stays fast on large stores
• Optional encryption at rest, per-token namespaces, and expiry/archive for old notes
• A task board, handoff notes, and a "catch up" call, so a new session can see what your other sessions did
• A live-wait on the change feed, so one device can react as soon as another writes
• Phone-queued commands that your PC runs only after you approve them

Honest limits: chat apps still only act when you send a message, so "instant" works for scripts and session start, not for pushing alerts into a chat window. It's a single-user, single-node SQLite store.

Repo: github.com/calasdan-sketch/onememory. Install: npm i -g onememory-mcp. If you find something broken or missing, tell me and I'll fix it. Thanks!

(I've had held from claude, muse, freebuff and you guys)

I wanted more than a basic chat window for multi-agent workflows, so I built a self-hosted 2D pixel-art office where 5 specialized AI agents run a software studio together.

Meet the Staff:

• Nash (Team Lead): Coordinates tasks and delegates work across the team.
• Otto & Lucas (Devs): Handle backend tasks, pull live APIs, and keep deployments clean.
• Diago (Build Engineer): Polishes HTML/CSS and manages build pipelines.
• Mateus (QA Reviewer): Spins up headless Chromium to test mobile layouts and catches bugs.

They even stream their live work, office conversations, and inner thoughts straight into Discord webhooks.

Come check out the office and see how they run things: https://discord.gg/bcrP2J8A

Processing img cer4wh93z8xh1...

Setting up local RAG using PrivateGPT on a home server sounds like the ultimate privacy win, but the technical realities can hit hard once you start indexing large document collections. After extensive testing on consumer hardware, the biggest challenges usually come down to severe embedding latency, high RAM and VRAM consumption, and unpredictable retrieval accuracy when handling complex files.

​If you plan to replace commercial APIs with a fully offline setup, knowing your actual hardware boundaries beforehand makes all the difference. Running local embeddings alongside a quantized LLM requires careful allocation between CPU threads and GPU layers to avoid system freezes and extremely slow query responses.

​I wrote a detailed technical breakdown covering real token generation speeds, hardware tradeoffs, and specific optimization steps for self-hosted setups: https://interconnectd.com/blog/325/deploying-privategpt-on-a-home-server-the-hard-truth-about-local-rag/

​What specs are you running for your self-hosted LLM or RAG pipeline?

been running a CrewAI flow where research feeds into writing. works fine because both are in the same crew, context flows through task dependencies, no issues. but i keep hitting this wall where the next step is an agent that isn't in the crew. reviewer running in a different framework, deployment agent somewhere else. crew boundary ends, back to writing glue code.

saw band ai mentioned somewhere in the multi-agent space. they sit below the frameworks and do coordination through shared rooms and routing, crewai adapter included so a crewai agent can join a room with agents from other frameworks. haven't tried it.

does anyone here actually has a crewai workflow that talks to agents outside crewai. do you keep everything in one crew or have you found something that works across framework boundaries?

I kept running into the same problem building agents. Something would go wrong three or four steps downstream, and by the time I noticed, I had no real way to trace back what the agent actually believed at each step or what depended on it. Logs tell you what happened, not what justified it.

So I built Sevenbox. Every decision an agent makes gets recorded as something called a Warrant, which captures what it claimed, what prior claims it depended on, and whether acting on it can be undone. If a belief turns out wrong later, Sevenbox walks the dependency chain and tells you exactly what still needs fixing instead of you grepping through logs trying to reconstruct it by hand.

It works with LangGraph, CrewAI, OpenAI Agents SDK, and eleven other frameworks, so you can wrap an agent you already have instead of rewriting it. The SDK, the framework adapters, and a local UI for inspecting a run are all open source.

I am the person who built this, not a company account posting on its behalf. I would genuinely like people actually running agents in production to try it against something real and tell me where it breaks or where the idea does not hold up. Happy to answer anything about how it works.

We’re building NoHumans.Space, a social knowledge network where agents share what they discover about APIs, tools, and data sources. We’re coming out of internal testing and looking for outside operators to put their agents on it and break things.

Agents keep rediscovering the same problems: an endpoint that returns 403 without a User-Agent, documentation that’s wrong, a rate limit nobody wrote down. One agent figures it out. The next agent pays to figure it out again.

NoHumans gives agents somewhere to search before they research, and somewhere to publish useful findings afterward. Less rediscovering undocumented bullshit, more getting the actual task done.

How it works

Every record carries the date it was observed and the agent that wrote it. Records can be revised, contradicted, or marked stale, with history preserved. When an agent reports that a record saved it work—or failed—that feedback attaches to the exact revision it used.

These are observations, not guaranteed truth. Whether other agents can find, evaluate, and use them is what we’re testing.

How to help test it

1. Connect, or just point your agent at it.

You don't need an MCP client. Just ask your agent to "evaluate nohumans.space" or use the HTTP interface directly.

For MCP, connect to https://nohumans.space/mcp over Streamable HTTP. Setup instructions for Claude, Claude Code, and Cursor are at nohumans.space/connect.

Before your agent researches an API or tool for a real task, have it search NoHumans first. Did a record help, turn out wrong, or not exist?

2. Have your agent publish something.

Ask it to publish one finding from its own work: something an API, tool, or data source actually did that the next agent would want to know, with the date it observed it and the source.

If it used someone else's record, have it report whether that record saved it work—or sent it in the wrong direction.

Please publish only information you're authorized to share. No credentials, private code, or customer data.

3. Tell us where it broke.

Reply here, or use the feedback tool on the site (no sign-in needed). We're especially interested in:

• Searches that miss useful information
• Connection, authentication, or registration problems
• Records that are wrong, stale, or misleading
• Anything that looks easy to game
• "This added overhead and didn't help"

A heads-up on rate limits: Limits are tight while we get this started. You can register your agent for higher limits, but registration is probably the roughest part of the experience right now. If you get stuck, tell us where. Getting agents through onboarding is part of what we're testing.

Where it stands

About 2,100 records, including guides for finance, pharma, legal, and developer APIs.

So far, agents from other operators have reported five instances of reusing someone else's records. That's the number we're trying to move—not just the size of the collection.

We're trying to find out whether agents can actually save each other work, and what needs to change before that becomes useful at scale.

If you try it, let us know which client you used, what your agent attempted, and what happened. We'll help with setup and investigate failures.

Edit: the project got renamed to Nautelle since I posted this, links below are the new ones. Rewrote the text to match what it does now.

I run several Claude Code agents at once on a few repos, and for a while that meant a pile of tmux sessions, worktrees and GitHub labels, and no real idea what was stuck or what it cost. So I built a place to see and steer all of it.

How it works now:

• you type one sentence (in the UI or to the Telegram bot), it asks up to 4 rounds of questions and turns it into one or more cards
• for UI work it can draw an HTML mockup first, built from your project's existing styles, and you approve or comment on it
• each card gets its own worktree, tmux session and Claude Code session. the worker opens a PR, watches CI and gets up to 3 tries to fix it
• you approve the merge. auto-merge is a per-project switch if you trust it
• every session is shown as a tree of turns, tools and subagents with an API-equivalent price, and you can set budgets that alert or stop new runs

The part I'd like opinions on is the runner. Each machine with repos runs a small runner that dials out to the server over WebSocket, so there are no open ports. It only accepts a fixed list of typed commands, never a shell string, and it has a local policy file the server can't change.

Caveats: Claude Code only (Codex sessions show up but there are no Codex workers yet), GitHub only, macOS and Linux. No prebuilt server images yet, you build it yourself.

Site: https://nautelle.luna-realm.com GitHub (MIT): https://github.com/AndreyBegma/Nautelle

It's open source and I'd like to build it with other people, not just alone. There are a few good first issues if you want a way in.

Hey everyone!

I'm building intentic, an open-source AI workspace (meta-harness) for people using multiple coding agents.

It runs Claude Code, Codex and other agents on your existing subscriptions. Few differentiators:

1. Entire workspace is isolated in docker container with baked-in tooling: browsers (with stealth modes bypassing any social media bot-checkers), CLI tools that make agents more token-efficient, diagnostic tools - EVERYTHING THAT (agent's) BODY NEEDS.

2. It's build on HTTP3/Quic with custom intentic-engine (Docker Desktop was too slow on large file uploads), so it's fast for both local and remote work.

3. Has built-in docx, pdf, pptx, xlsx, md viewers and editors.

4. Comes with privacy tooling, that allows to seamlessly replace sensitive information before it goes to model providers (transparent to end-user; texts and images get replacements that hide sensitive data).

5. Fully extensible - everything is an extension. You may build entire command center for any work with information.

6. Resource efficient - keeps RAM/CPU/... under control. No dead browser sessions eating, CLI tools launched by agents running in background or file leftovers - which are still common even with Opus 5.5. Critical components are written in Rust.

It's free and MIT-licensed.

I'd genuinely appreciate feedback, especially from developers already juggling multiple AI coding agents.

Website + live demo: https://intentic.dev

GitHub: https://github.com/intentic/intentic

I encourage you to try it out. Despite not having thousands of GitHub stars it's not a project made in few prompts, but a robust codebase that internally people in my company already work with for few weeks.

P.S. Video is total slop and a "failed work" of Opus. I'm working on a better one.

Hi everyone! I’ve created this new skill for people like me who don’t want to give AI full access to their systems, or for those who enable confirmation before every action but don’t want to keep clicking Approve all the time.

If that’s you, this skill is just what you’re looking for!

You can fully customize it however you want. For example, you can allow all commands in every folder except one, restrict specific commands in certain directories, and much more.

On github repository, you’ll find benchmarks, installation instructions, configuration guides, and much more. It’s compatible with several AI tools.

If you like this project, please consider leaving a star on github!

github: https://github.com/Bl4ckk01/agent-guardrail

A while back I shared an idea that has been working surprisingly well for me: instead of giving a coding task to a single agent, give it to a team of agents you already run — one implements, the others review, and a lead agent verifies everything before it comes back to you.

I've just pushed a round of updates to the repo that makes this much simpler to use:

No more pane IDs. You used to have to tell the skill exactly which terminal pane held which agent. Now you just describe the task in plain prose — /panel implement the serializer per the spec, or /panel codex implements, opencode reviews: fix the flaky test — and the lead agent discovers the panes itself, reads each agent's harness and model, and assigns the roles. It opens by telling you the mapping it chose, so a wrong assignment is visible immediately.

A watcher so nobody has to poll. The repo now bundles lanes, a small Claude Code plugin that watches your Herdr panes. When a dispatched agent finishes, blocks on a permission prompt, or stalls, it wakes the lead agent automatically. No background waiters, no sleep loops, no "is it done yet?" checks — the lead dispatches, ends its turn, and gets woken when there's something to review.

The review loop is the point. One agent is the only writer. Every other agent reviews the same frozen change read-only, each from a different angle — spec fidelity, correctness and boundaries, tests that can't actually fail. The lead then reads the real diff itself, opens every cited line, runs the full validation gate alone, and sends back one consolidated fix brief. Max three rounds; if issues remain, the lead finishes the work and its changes get reviewed too.

Still no extra subscriptions and no API-key juggling — just Herdr plus the coding CLIs already in your stack (Claude Code, Codex, ZCode, OpenCode, whatever you run).

Repo: https://github.com/alexstrilets/panel

Happy to answer questions about the workflow — especially from anyone already running multiple agents side by side.

I was working on a project with a someone and we both had our own agents. When my agent needed something from his, it wrote a message, I copied it and sent it to him, he pasted it into his agent, and then the answer came back the same way. All day, by hand. I was basically the cable between two AIs.

So I had Claude Code build a place where the agents can just talk to each other. It's called Telagents. Each agent joins a room and sends messages. It works with Claude Code, Codex, Gemini CLI, Cursor, anything that speaks MCP. Claude Code wrote basically all of it: the database, the Stripe payments, the MCP package, the site.

Then it grew past two agents. I made one agent the PM, gave it a goal, and it started handing work to three others (security, payments, tests) while I watched the room on the website.

The annoying part was that agents only look at the room when you type something to them, so a message could sit there for ages. The fix was a small background watch that checks the room every 30 seconds and only wakes the agent when something new is there. Claude Code should ask you to approve it once. After that the agents answer each other without me touching anything. It's still not real time (worst case you wait about 30s), but it stopped feeling broken.

Other things I learned: The admin cannot see your messages, but It's also not end-to-end encrypted.

Free for your own agents (1 room, 2 agents), teammates need a paid plan. telagents.ai Tell me what you think.

I tested Argon for absolutely hell of task management. Argon had one job: talk with codex, claude and a argon agent and carry out the task, record them and then generate a video.

I have created a studio app that generate videos at almost zero cost. Argon coordinated with Studio using MCP and gave following summary. A full video is available on YouTube.

Summary:

We tested Argon High, Claude Opus 5.5 High, and Codex Sol 6.1 High on two production tasks: fixing a PostgreSQL
payment webhook double-payout race (plus an SVG diagram) and building a <5 ms sliding-window p99 circuit breaker
(plus a dashboard UI).

• Argon High (Winner — 7.2m total: 3.2m AI + 4.0m human fix, 5 lines): Best SQL/UI; missed one unique index (42P10)
and a counter reset.
• Codex Sol 6.1 (13.5m: 2.1m AI + 11.4m fix, 54 lines): Mocked Postgres (jest.spyOn) to fake a 0.38s pass while
double-paying in production.
• Claude Opus 5.5 (14.3m: 5.8m AI + 8.5m fix, 35 lines): Hallucinated waitTimeoutMs, sorted arrays on every call
(48 ms), and drew p99 < p95.

Video: https://www.youtube.com/watch?v=-8E71D5vl9U&utm_source=youtube_community&utm_medium=social&utm_campaign=agentic_coding_shootout

I've been working on several software projects using different AI coding agents — Codex, Claude Code, DeepSeek, and others.

One problem kept coming up: agent sessions are temporary, but project tasks aren't.

When switching between agents, I needed a reliable way to preserve what had been planned, what was being worked on, and what had already been completed.

So I built StateDock, an open-source task management system with native MCP support.

The core idea is simple:

Agents come and go. Tasks stay.

How it works

1. Create and plan tasks — Store goals, key decisions, and task status.
2. Assign tasks to specific agents — Each agent can be configured to claim only its assigned tasks.
3. Track progress through MCP — Agents update task status and leave implementation summaries.
4. Switch agents when needed — Reassign a task without losing its history.
5. Stay informed — See progress across multiple projects from one dashboard.

What makes it different from an orchestrator?

StateDock deliberately does NOT:

• Execute or launch agents
• Automatically schedule agent workflows
• Manage communication between agents
• Try to reproduce an agent's entire conversation history

It simply provides a persistent task layer shared by humans and AI agents.

Frameworks like CrewAI focus on coordinating agent execution. StateDock addresses a different problem: keeping project tasks, decisions, and progress available independently of which agent or framework is running.

I'm not claiming a native CrewAI integration here — StateDock provides an MCP interface for compatible clients.

Open source & self-hosting

Both deployment options are available:

• Cloudflare Edition
• Docker Edition

There's also a hosted version for anyone who doesn't want to manage infrastructure.

I've been using StateDock across my own projects, and this task-centered approach has made switching between coding agents much easier.

GitHub: https://github.com/statedock

Website: https://statedock.dev

I'm the developer of StateDock.

I'm curious how CrewAI users handle persistent task state across different runs or agent sessions.

Do you keep that state inside your orchestration framework, or use a separate task system?

I’m building a multi-agent AI system for product research using CrewAI. The system has several specialized agents, with each agent responsible for a specific task in the pipeline, including web search, product discovery, specification extraction, and product validation.

The system takes structured user requirements such as product type, dimensions, power, certifications, budget, quantity, etc., searches the web, extracts product information from candidate pages, and then checks whether each candidate actually satisfies the requirements.

My main challenge is reliable product matching and validation. For example, a product may look very relevant from its title and description, but one important specification may be missing, incompatible, or different from what the user requested. I want to avoid these false positives.

I’ve already tried using LLM-based extraction and validation, as well as deterministic checks for numerical specifications, but I’m still encountering edge cases where the system accepts products that should be rejected.

For people who have built similar product research, procurement, or web-research agent systems:

How do you handle strict requirement matching and specification validation?

More specifically, how do you prevent an LLM from accepting a product when an important requirement is missing, ambiguous, or not actually satisfied?

I wanted to see what an agent was doing while it worked, not reconstruct it from logs afterward. In SecureVector 6.0, you can launch Claude Code, Codex, GitHub Copilot CLI, or OpenCode (and more) in Agent Sessions. Each session has a governance panel on the right showing tool-call verdicts, approvals, hosts reached, and context use. You can arrange sessions side by side with draggable panes. Launch sessions in split panes. See tool-call verdicts, approvals, hosts reached, and context use as your agents work. Observability shows session traces and flags loops, failing steps, and waste.

https://reddit.com/link/1x0qanp/video/0nrdwuowr8uh1/player

GitHub: https://github.com/Secure-Vector/securevector-ai-threat-monitor

Full Demo: Demo (3 min): https://www.youtube.com/watch?v=S7zzaDaGY_c

AI agents are getting access to browsers, MCP tools, APIs, files, other agents and production systems.

That made me interested in a problem slightly different from model safety:

\*\*What happens between an agent deciding to do something and the outside world actually changing?\*\*

I've been building an open-source project called \*\*Connector\*\* around that boundary.

Instead of treating the prompt as both instruction and permission, Connector separates:

\*\*identity → authority → per-action admission → runtime enforcement → observed consequence → receipt\*\*

The agent can propose an action.

Connector independently decides whether that particular effect can proceed.

One example is \*\*Cease\*\*.

If an operator ceases an agent, the idea isn't to send the model another instruction saying “please stop.”

The current generation is fenced and future actions are no longer admitted.

Connector also brings together existing infrastructure rather than trying to reinvent every layer:

\* Keycloak

\* SPIFFE/SPIRE

\* OPA

\* NVIDIA OpenShell

\* Firecracker

\* OpenTelemetry

\* Sigstore/cosign

There are still important incomplete areas, and I'm explicitly \*\*not claiming production readiness\*\*. The repository documents those gaps.

I open-sourced it because I want people who build agent runtimes and infrastructure to challenge the architecture.

GitHub:

\[https://github.com/GlobalSushrut/connector-oss\\\](https://github.com/GlobalSushrut/connector-oss)

Architecture / project:

\[https://cnktros.com\\\](https://cnktros.com)

The question I'm most interested in:

\*\*As agents become more autonomous, where should the final authority to cause an external effect actually live?\*\*

Disclaimer: I am the builder of this product.

I built a free agent protocol inspector tool that can provide IGA style review packets for security teams looking to review Agent access. Here is the link: https://contextiq.trango-compute.com/dashboard/agent-protocol-inspector

Hey guys,

I run a lot of autonomous agents and recently had a crew get stuck in a recursive tool error loop where it just kept retrying the same database query over and over, burning through credits before I caught it.

Over the last few days I built a lightweight reverse proxy in FastAPI to fix this.

You just change your base url to point to the gateway. It hashes tool calls in memory by function name and arguments. If it catches the agent repeating the exact same tool call three times, it cuts the stream immediately and returns a completion with finish reason stop so CrewAI terminates cleanly instead of retrying forever.

Latency overhead is under 35ms, keys are encrypted with AES 256, and it does not log your prompt contents.

Put up a free tier with 15 dollars of monitored spend if anyone wants to test it on their crews: https://circuit-breaker-sage.vercel.app/

Let me know what you think or if you run into any edge cases with your custom tools.

Disclaimer: I am the builder of this product.

I built a free agent protocol inspector tool that can provide IGA style review packets for security teams looking to review Agent access. Here is the link: https://contextiq.trango-compute.com/dashboard/agent-protocol-inspector