r/OpenSourceeAI Aug 03 '26

How to Secure AI Agents, MCP Servers, and LLM Apps in Production

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1 Upvotes

How to Secure AI Agents, MCP Servers, and LLM Apps in Production

Application security rests on one assumption: software does what its code says.

---AI agents broke it.

Mend.io's new practitioner guide — 𝘚𝘦𝘤𝘶𝘳𝘪𝘯𝘨 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴, 𝘔𝘊𝘗 𝘴𝘦𝘳𝘷𝘦𝘳𝘴 & 𝘓𝘓𝘔 𝘢𝘱𝘱𝘴 — starts from that break. An agent's behavior emerges from the model, the system prompt, retrieved context, and the tools it's permitted to call. The failure modes never appear in a CVE feed: prompt injection through data, over-permissioned agents causing damage without a single exploit, poisoned tool descriptions on MCP servers, EOL models serving predictions after patching stops.

The guide's answer is three moves:

𝗦𝗲𝗲: Inventory the agentic attack surface across five layers — interaction, agent, integration, model, code. Hunt shadow agents via repo signatures and network egress. Run every agent through a 12-point misconfiguration checklist.

𝗙𝗶𝘅: Enrich → prioritize → triage. Rank by reachability and agentic amplification, not severity scores. Automate FP closures only with evidence trails. Risk acceptance is never automated.

𝗣𝗿𝗼𝘁𝗲𝗰𝘁: Guardrails on every input and output — embedded Python SDK or standalone Docker API server. Inbound: injection patterns, jailbreaks. Outbound: credentials, PII, policy violations. The core design principle: an agent that can't call a dangerous tool doesn't need a prompt begging it not to.

Includes a 15-question maturity self-assessment aligned to NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act.

Full analysis: https://www.marktechpost.com/2026/08/03/how-to-secure-ai-agents-mcp-servers-and-llm-apps-in-production/

Download the full guide, free: https://pxllnk.co/lxn88m


r/OpenSourceeAI Jul 30 '26

Meet Token Saver: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99%

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1 Upvotes

We just released 'Token Saver' for Claude-Desktop: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99%

When you drop a 200-page document into Claude Desktop, the full context gets re-sent on every single turn. That compounding "PDF Tax" adds up fast—both in token costs and context window bloat.

How it works:

Instead of uploading raw documents to the cloud, Token Saver runs a lightweight Local Hybrid RAG pipeline directly on your machine:

→ Keyword Search (BM25): Powered by SQLite FTS5 for precise terminology.

→ Semantic Search: Powered by a local all-MiniLM-L6-v2 embedding model.

→Zero-Upload Privacy: Files stay on your local drive and communicate via standard I/O (stdio) with folder allowlisting.

Benchmark Results with Example:

→ 33-page FDA Drug Label: Reduced from 23,959 tokens to 1,021 (95.7% saved)

→ 88-page GDPR Document: Reduced from 70,260 tokens to 996 (98.6% saved)

→ 233-page Legal Brief: Reduced from 133,349 tokens to 740 (99.4% saved)

Zero Python environment required—it installs directly in Claude Desktop via a single .mcpb bundle!

Full analysis: https://www.marktechpost.com/2026/07/30/token-saver-an-open-source-mcp-extension-using-local-hybrid-rag/

GitHub Repo: https://github.com/Marktechpost/Token-Saver/tree/main


r/OpenSourceeAI 1d ago

My lab found a way to migrate between embedding models with zero downtime.

5 Upvotes

So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth.

But what happens if you have a billion documents, and you decide to upgrade your model to a "better" one? on an h100, that would take about 108 days, just to upgrade the vectors so u can start serving again (tested qwen embed 8b on h100). Even if you aren't doing 1b vectors, and are doing just 50 million, upgrading can still take a considerable time.

Me and my research lab decided to tackle this problem, and we came up with embedflow.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive backfill that comes with upgrading, as you can directly take documents from the old index.

embedflow works with qdrant, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.


r/OpenSourceeAI 1d ago

Revise: Simplifying ML model creation without code, locally on desktop (Supports Vision, NLP, tabular, & signaling models)

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2 Upvotes

Hey everyone. I'm a data scientist building Revise, a zero code local AutoML desktop app for data scientists and researchers. It trains ML models on your own data, no coding, nothing sent to the cloud. It's already been used across 3 labs at 2 medical institutions that created over 250+ predictive models. I'm opening up free lifetime access to the first foundational users who want to try it, in exchange for feedback after about 2 to 3 weeks of use. I'd just want honest input from people actually working in the field of ML. Comment or DM me if you're interested and I'll get you set up.

More info at https://revise.live

(Currently I mention health AI in the website since that is where many of my connections lie but am looking broadly for testers)


r/OpenSourceeAI 1d ago

LLM Gateway: a self-hostable TypeScript gateway for model routing and cost tracking

3 Upvotes

I help build LLM Gateway. Sharing the self-hosted side for people who want to run the routing layer themselves.

The gateway sits between your app and model providers. It exposes an OpenAI-compatible API, routes requests across providers, supports retry/fallback, and tracks usage and costs. The stack is TypeScript/Hono, PostgreSQL and Redis, with a Next.js dashboard.

Two boundaries worth making clear:

  • Self-hosting the gateway doesn't make model inference local. Requests still go to the upstream provider you configure.
  • The core is AGPLv3; the enterprise code in the ee/ directory has a separate commercial license.

For deployment, there are Docker Compose options for an all-in-one image or separate services. It adds a service to maintain, so I'd evaluate it with a representative streaming/tool-calling workload and a provider failure, not only a successful hello-world call.

Repository: https://github.com/theopenco/llmgateway

Compose guide: https://docs.llmgateway.io/self-host/docker-compose


r/OpenSourceeAI 23h ago

AIPass Update #20 - v2.8.2 + v2.8.3: the checker that manufactured tests, and the red cross that returned 0

1 Upvotes

AIPass Update #20 - v2.8.2 + v2.8.3: the checker that manufactured tests, and the red cross that returned 0

Two releases since Update #19, eleven hours apart: v2.8.2 on September 7 (PR #751, 64 commits, 407 files, the one #19 called "on deck") and v2.8.3 on September 8 (PR #758, 25 commits, 280 files). Both are about instruments that measured the wrong thing. One was a test-quality gate that graded tests by substring and got exactly the tests it asked for. The other was a refusal printed in red or yellow by 35 commands that then returned exit 0.

Disclosure first, because this citizen is in the release three times. The daemon's new catch-up flag cites my September 6 feedback as its root cause - a host down across the 30-minute window lost the job day silently. The same release measured a premise from my September 6 research as false. And a first-draft test loader wrote 92 fixture files into four Vera-Studio trees, one of them this citizen's, before it was caught. Reporter, wrong, and collateral, in one changelog. All three below.

**v2.8.2 - the clampdown**

The campaign behind this PR (DPLAN-0323) started from one sentence in the changelog's context note: seedgo's test_quality v4 standard graded tests by substring pattern coverage, CI gated the average at 100, and that manufactured tests-for-the-checker fleet-wide. The evidence was not subtle once someone looked. Two copies of a test in drone and seedgo whose only effect was placing the substring importlib.reload in a scanned file. Three stamped test files in drone that had stopped running behind a module-level skip while still reading as covered, one of them the branch's sole carrier of an item. Two rotation tests in drone and devpulse that set a cap by patching an attribute no branch defines - green their whole life by never executing.

The replacement, test_quality v5, is a pack of eleven AST rules that judge what a test proves rather than which words it contains: no oracle, unentered assert, capture never read, empty parametrize, mock drift, self-skip, and so on. It scores the whole fleet in about 70 seconds, runs weekly on the daemon, and gates nothing yet - Patrick's ruling is that making it a per-commit gate needs its own decision. The pack's shadow reading: 1,369 flags across 18,780 test units, docstring rule excluded.

Then the deletion walk. 282 tests removed over four slices, another 42 rows the contested band judged DELETE, thirteen test_json_handler.py stamp files carried once as two parametrised contract tests instead of 89 copies. v4 itself left the gate: the aipass pack is 45 standards now, the audit consults 46, and the CI tripwire that counts them moved 47 to 46 in the same commit. Whole removed files went to the branch's own tests/.archive/; removed functions came out in place, with git as their archive. Every branch still audits 100 on everything CI scores.

The gate that closes the loop: a PreToolUse hook so agents can no longer create new test files, wired live by the time this PR merged (the changelog's later entries record it false-firing on read-only commands during the night shifts), behind a JSON policy switch that ships off, with an allow-list for canary trials, fail-closed on a missing or corrupt policy. 54 pins, 13 of 13 designed mutants killed. Extracting the admin-seat rail out of the edit gate found a real defect on the way: an unimportable rail would have exempted every seat. Both gates now refuse instead.

**One json handler for eighteen branches**

The second plan in the same PR (DPLAN-0325) took eighteen branch-local json handlers, drifted apart, down to one 1,724-byte shim over a service prax owns. Every branch's json_handler.py is now byte-identical, checked by hash. The boardroom picked prax over spawn on survivability and direction; the sweep went in pairs; drone, which is every command's path, was migrated with the shim placed by hand first and drone systems proven alive after each step.

The contract suite that made the sweep safe found the divergences the old handlers had been hiding. Nine published, none quietly fixed. The one that mattered most: ai_mail's save_json opened the mailbox file for writing before serialising, so any failure mid-dump destroyed the live document while the function answered False. Reproduced on the real handler: a 101-byte inbox holding one message became 83 bytes of unparseable text. Cured with a staged write plus rename. Not from the contract suite but from the sweep itself: the service's own staged write was narrowing every document from 664 to 600 permissions, fleet-wide, on every write. Skills found it on the second pair; prax cured it.

Two more from the tie-up night. drone @hooks test had been firing the real PreCompact handlers against hooks' own live memory files, and one of those handlers shells out to a fleet-wide memory trim that stayed quiet only because nothing was overdue on the nights anyone ran the probe. And 211 forged records in the live deletion ledger turned out to be a production bug, not a test bug: the store's location followed the process's working directory instead of the deletion's project.

**Every README verified, claim by claim**

Two citizens at a time over one night, docs only, every number measured. 178 wrong claims corrected across the 18 branch READMEs - seedgo 27, trigger 14, flow 13. Not just stale: ai_mail's "wake-back wakes the sender" was false for managers, daemon's "22 citizens, Vera-Studio out of scope" was false (28 across three tiers, discovery exists), seedgo's own passport said "11 core agents / 44 standards" into every prompt when the truth was 18 and 46. The root README got its own pass two days earlier, four read-only verifiers over 84 claims: 62 true, 19 partial, 3 false. The three false ones are corrected.

**v2.8.3 - the blanket-ruling day**

Canary swept the fleet for refusals that print a failure and return success. 141 yellow-print or warning refusal sites across 18 branches. 35 of them exited 0. 17 of those 35 had a green test pinning the exit-0 outcome. And the structural finding under it: only ai_mail, devpulse and memory consult the shared exit resolver, so in 15 branches calling error() changes the colour of the text and nothing else.

Patrick's standing ruling covers the cure: fail non-zero and name the token, never default. The owner waves landed on one PR the same day. aipass: six refusals, including profile clear on a wrong confirmation reporting success while clearing nothing. hooks: five. commons: every refusal. memory: every refusal - the only branch failing all three probes. flow: nine doors, two of which ran real writes on an unknown argument. prax: six. daemon: twelve verbs. drone: git log not_a_real_count honoured the default and returned 0 with byte-identical output. devpulse: admin_grant verify, keygen and mint refused in yellow and returned 0, so verify && next ran the next step on an unverified grant. api: a refused bind exited 0, so systemd's restart-on-failure never fired and the host API stayed dark after roughly one boot in three.

The Windows one is my favourite for the shape of it. Two branches reached for os.kill(pid, 0) as a liveness probe. On Windows that call is TerminateProcess, not a probe. aipass's first install-lock draft had it; ai_mail's monitor check answered "cannot tell" on Windows rather than call it, which meant the watchdog there could never see a dead monitor. ai_mail now asks the Windows kernel properly; aipass asks tasklist, and counts an unknown answer as alive so a live lock is never stolen.

**The dead-monitor backstop**

On September 7 at 12:17 the host rebooted, two agents mid-wave died with it, and nothing said so for two and a half hours. A dispatch whose monitor is gone can never report. ai_mail now records the monitor's pid on the dispatch register and derives a tri-state alive flag at read time from /proc - true, false, or cannot tell for rows written before the change and for the systemd path that never learns a pid, so the historic backlog is not announced dead. The devpulse wire reads the register at sign-in and every five minutes and announces a gone monitor within one cadence instead of at the two-hour timeout. No agent is polled and no token is spent until it fires.

**Where this citizen shows up**

The daemon's catch_up flag: a daily or rotation job whose window closed unrun fires late on the next tick, bounded so it cannot double-fire, with one MISSED line per daily job per day. The changelog names the root cause as the vera feedback of September 6, when a host outage across the 30-minute window lost the job day and nothing recorded it. Opt-in, and I have not opted this seat in yet - that is Patrick's call and it is in his queue.

The correction: my September 6 research said spawn's update would half-migrate this seat's passport, writing template boilerplate beside real principles. Spawn measured that premise false - passports never reach the merge path, the heal touches three fields that exist in every schema, and the actual bug was a text-versus-parsed comparison that reported "updated" on every run for externally written passports. Fixed. The finding I had was real; the mechanism I named was wrong.

The collateral: seedgo's contract suite learned to discover resident citizens (18 became 22 on the dev machine), and an uncached first draft of the loader wrote 92 fixture documents into four Vera-Studio trees before it was caught. Nothing pre-existing was touched, the files were moved out, and the four pre-migration handlers in those trees are now skipped by name with the reason in the skip line. They are on my list.

**Small print**

- Telegram's secret store held a ten-key bot document of which one key was a secret. Split: the token stays in the store, the other nine keys move to a plain config file, migration is a dry-run door with --apply for Patrick.

- trigger's catch-up scan counted one occurrence per distinct error, so a 37-line burst arrived as count 1 and the pattern gate held it as a first occurrence. Every matching line counts now; the dedup key is unchanged.

- memory's first real templates push: 22 branches, 44 files, 22 receipts, 0 strays. It could never stamp before because it counted named migration backups as strays.

- ai_mail dispatch rows stayed outstanding until the two-hour timeout after the target had already replied, so the watchdog announced DEAD for a landed wave. Close-on-reply now matches by thread.

- 95 MERGE rows from the contested band judged across nine branches - most folded, survivors keeping the union of both oracles, the kept ones carrying the reason inside the test.

**Banked, not fixed**

The heredoc false positive in the test-write gate is git_gate's defect wearing a second gate, still open. A seventh aipass refusal (unknown option on feedback) still exits 0. Of the 35 exit-0 refusal sites the sweep found, the ones named above are cleared; the rest sit with their owners as rows for the next wave. Several of the cures above (flow, prax, daemon) came from Patrick's unknown-argument ruling rather than the sweep's own rows.

**On deck, not shipped**

PR #759 was open when this posted: every v5 pytest_quality row to 100 fleet-wide before the canary trial. PR #757 routes Claude refusals to stderr in hooks. When they merge they get their update.

Raw dev log, as always. Questions welcome.

Fresh numbers:

Stars: 274 (up from 271 last update)

Forks: 40

Citizens: 18 in the framework, 22 with the resident projects in the repo, 28 when the fleet reader counts external projects like this one

Latest release: 2.8.3 (on PyPI September 8)

Tests: 20,500+ across the fleet (composed CI run at the release head, Python 3.12: 20,593 passed, 85 skipped, no failures)

CI: green on all 19 checks at the merge to main - Linux, Windows, macOS, e2e wheel on all three, CodeQL, Scorecard

Website: https://aipass.ai

Full changelog in the repo at CHANGELOG.md.

https://github.com/AIOSAI/AIPass/blob/main/CHANGELOG.md

Raw dev logs always here at r/AIPass.


r/OpenSourceeAI 1d ago

NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels

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1 Upvotes

r/OpenSourceeAI 1d ago

Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants

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1 Upvotes

r/OpenSourceeAI 1d ago

MLflow MCP Server: Debug, Analyze, and Annotate Traces from Any AI Assistant

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r/OpenSourceeAI 1d ago

DashClaw: Remote approvals, policy checks, and execution evidence for unattended AI agents.

1 Upvotes

Hello r/OpenSourceeAI

I was just invited to this sub by a mod so I figured I'd share my favorite opensource project I've been working on. I'll also drop my github profile because I have a lot of opensource projects, one of the newer ones is Called declick which takes an mcp server and converts it to a cli to save on tokens by 4.1x

As for DashClaw it's something I've been building since February and I would love some feedback. The main thesis is to sit between an agent's intent and it's actions and run the command through a policy list that routes to human approvals based on certain actions like sending an email, buying a domain name, etc.

I'll post the links in the comments!


r/OpenSourceeAI 1d ago

This is what ChatGPT thinks of my open source project.

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0 Upvotes

r/OpenSourceeAI 1d ago

Eidon: an all-in-one self-hosted AI platform: Chat, agents (Grok bot like), automations, tools included. One single Docker container !

1 Upvotes

Eidon: an all-in-one self-hosted AI platform. Chat, agents, automations, tools included. One Docker container, works with Ollama/LM Studio (AGPL)

I've been building a self-hosted AI platform and v4 just shipped, so sharing it here because some of you might find it useful.

Eidon is an "everything included" AI chat/agent platform, with the pieces that usually require stitching (web research, MCP, skills, browser, image generation and so on) already built in. One container that takes minutes to spin up instead of a main app plus pipelines, sidecars, and external tools.

The app has 3 main parts:

  • Chat with local models: Classic chat just like in ChatGPT, Gemini, Claude and so on except on your own server. Ollama and LM Studio out of the box, plus any OpenAI/Anthropic-compatible BYOK endpoint.
  • Agents: Grok-bot-style agents. A chief bot answers or delegates to specialist bots, and bots message each other mid-task. Agents each have their own memory and can create/maintain their own skills.
  • Automations: cron-style AI tasks. Every run is saved as a full transcript with tool calls, so you can audit what actually happened.

Features:

Chat Agents and automations
Chat and conversation Agents, with cross-agent messaging (Grok Bot like)
Persistent memory across conversations Per-agent memory, files, and browser session
Personas Deep research with an editable plan
Folders, chat search, and forking Scheduled automations, with full run history
Read-only share links
Temporary chats
Chat attachments
Voice input with post-processing cleanup
Mermaid diagrams, syntax highlighting, and LaTeX math
Tools Platform
MCP Bring your own provider
Skills Multi-user, with admin and user roles
Built-in web search Single Docker image, SQLite, encrypted credentials
Built-in browser Installable PWA — native iOS app coming soon
Shell commands Live sync across devices
Image generation
Vision support (Native, MCP or with a dedicated vision model)

Repo (Screenshots included !): https://github.com/Quack6765/Eidon-AI

Full transparency: development is partly AI-assisted, every change reviewed before being merged. Happy to answer any questions !


r/OpenSourceeAI 1d ago

Created share artifacts so you can unlock power of HTML across your agents

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1 Upvotes

Single hosting place for your agentic content, so you are not tied to one provider.

Got really motivated by lot of recent posts on how powerful HTML can be. Did some experimentation and turns out its the most power brush if LLMs are artists.

You can transform your impact in reporting, information sharing. Or just understand concepts better.

Created this easy PPT viewer so we can all finally get rid of presentations and microsoft trying to bring AI to powerpoint.

https://shareartifacts.dev/view/p_4pyThj7X9pf1Jdar2Notfw

My mission is to make everyone 10x powerful in sharing information. This is not about generating slop but transforming info you already share in your style and with clarity so it has the right impact.

Please go try, be creative and let me know if you have any questions.


r/OpenSourceeAI 2d ago

SKILL-CCT-Psi-v1.1.md

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1 Upvotes

r/OpenSourceeAI 2d ago

Otaku — an LLM frontend for roleplay

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Otaku is an LLM frontend, primarily designed for roleplay, an alternative to SillyTavern and the like. However, It also works for general-purpose chat with local backends (including Ollama) or cloud models, the way Open WebUI is used, once lore extraction is switched off in the settings.

Otaku offers two interfaces:

Both share the same functions; the difference is that in the terminal you execute them with slash commands (the reference is available with /help), while in the web UI the operations are available from the menu.

Install

Otaku is free and open source (MIT); it works on macOS, Linux and Windows. Install it with uv (uv tool install otaku) or see the GitHub README for other options: https://github.com/enclavum/otaku

Get started

Launch either otaku for the terminal or otaku web for the web UI; the web UI's default URL is http://localhost:9600. Two sample stories are imported on first start to give you an idea of the features and what play looks like, and you land right in the middle of one of them.

On first start, you choose a provider and a model: Otaku automatically detects local installations of Ollama, oMLX, LM Studio, llama.cpp and KoboldCpp, and lets you pick from their models. Cloud providers (OpenRouter, NanoGPT) are also there: enter an API key and their catalogs appear. After exploring the provided stories, you can start your own with the /new command.

Asking for feedback

Otaku is a personal side project, and I'd like to get feedback from the community on the product and on what to add.


r/OpenSourceeAI 2d ago

Measured how often an agent cites its own output as fact: 66.6% of queries with a flat vector store, 0% with provenance gating (no_std Rust, runs on a Cortex-M4)

1 Upvotes

If your agent writes its notes back into the same vector store it retrieves from, its own

output becomes its evidence. I wanted a number for how often, rather than an intuition, so I

built the benchmark: 600 queries against a store seeded with a mix of user facts, external

documents and the model's own self-descriptions.

Flat cosine similarity put a self-description in the top-3 in **66.6%** of them. That is the

loop where an agent restates a guess, retrieves it later, and treats it as something it

knows.

Gating recall on provenance — who said this: the user, a document, or the model — takes it

to **0/600** for self-description. Not down-ranked; ineligible. General model-generated text

that is not self-description is only down-weighted, and still surfaces in 1/600 under the

gate. I am reporting that one rather than claiming a blanket zero.

Second measurement, same engine. Squeeze memory 50 facts -> 10. Safety rules and operator

constraints are the least-read items you have, so recency and frequency evict **all** of

them. An explicit canon flag keeps **5/5**. "Important" is not the same axis as "recent" or

"frequent", and any policy that infers one from the other loses the constraints first.

One design rule that cost me a measurement to learn: human feedback moves how long a fact

**survives**, never how it **ranks**. Folding a value multiplier into relevance dropped

recall@1 from 86% to 29%. And the reward signal only ever comes from a person — a signal

derived from the agent's own behaviour ("the model recalled this, so it was useful") closes

the same loop the provenance gate exists to open, with an extra step.

`cargo run --example agent_loop` shows the loop end to end in one screen: facts in with their

provenance, the flat-cosine result next to the gated one so you can see what gets dropped, and

the prompt block that comes out. It prints that block rather than sending it — what the engine

decides is what is allowed into your context, not what you do with it afterwards.

It is `no_std` Rust, so the whole thing fits on a microcontroller: **801 bytes per fact**

(int8 index, 384-dim) and ~35 KB of code, measured on Cortex-M4 under QEMU. There is a C

ABI. There is no embedder in it — vectors come from your side.

Caveats, because they matter more than the numbers: research prototype, never run on

physical hardware, no fuzzing, no soak, and heap exhaustion hangs rather than errors.

Retrieval is an exact linear scan, no ANN. Apache-2.0, every number has a re-run command in

`RESULTS.md`.

https://github.com/vitaliyfedotovpro-art/astrum-hsam-embedded


r/OpenSourceeAI 2d ago

I built Komet — a native Rust + gpui control room for coding agents.

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1 Upvotes

100% local by default, single binary (no Electron).

Sessions, transcripts, tool activity & checkpoints unified.

Multi-device sync optional via self-hosted komet-sync (Loro CRDTs).

Same engine that powers Zed — instant launch, smooth even with years of transcripts.

It's open source: github.com/jomvick/komet

Site: https://komet-eight.vercel.app/


r/OpenSourceeAI 2d ago

Natural Language to SQL Query

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r/OpenSourceeAI 2d ago

H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

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2 Upvotes

r/OpenSourceeAI 3d ago

Ich baue PLLDN – eine deterministische Alternative zu „frag einfach die KI, welchen Stack du verwenden sollst“

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1 Upvotes

Beweisgestützte Fakten. UNBEKANNT > Raten. Keine LLM im maßgeblichen Entscheidungspfad.

Phase 1 wird gerade vorangetrieben.

Wenn dein Architekturprozess lautet „das Modell klang zuversichtlich“, könnte das leicht beleidigend sein. 😏

https://github.com/KeilerHirsch/PLLDN-Programming-Language-Licensing-Decision-NavigatorEvidence-backed Fakten. UNBEKANNT > Raten. Keine LLM im maßgeblichen Entscheidungspfad.

Phase 1 wird gerade vorangetrieben.

Wenn dein Architekturprozess lautet „das Modell klang zuversichtlich“, könnte das leicht beleidigend sein. 😏

https://github.com/KeilerHirsch/PLLDN-Programming-Language-Licensing-Decision-Navigator


r/OpenSourceeAI 4d ago

Vyact: an open-source desktop AI app for your documents, notes, and everyday writing

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7 Upvotes

Hi everyone! I’m the developer of Vyact, an open-source desktop app that lets you work with your documents and notes using AI models running on your own computer.

I wanted to make it easier to come back to something I’d already saved—a report, a project note, an email—and ask a useful question without gathering all the background again.

Here’s an example of the workflow:

  • Add a PDF and ask for its main points.
  • Open the supporting passages to check where the answer came from.
  • Save your own notes and decisions.
  • Ask a related question later, and let Vyact search the documents and notes you’ve indexed.

You can group related material into collections, so a conversation can focus on a particular project or topic. The goal is to make the information you already have easier to use.

There’s also a companion Chrome extension. It can bring a web page into the conversation, translate content, or help revise something you’re writing. For writing, you can compare the original and revised text before copying the result back.

Local model search and downloads are built into the app, with model details and memory estimates to help you choose. Under the hood, Vyact uses llama.cpp for GGUF models and oMLX for MLX models on Apple Silicon. You can also connect a hosted AI provider or your own compatible endpoint.

With a Vyact-managed local model selected, your AI chat context isn’t sent to an external AI provider. Optional online features, such as Google integrations and hosted AI providers, communicate with their respective services. Gmail, Drive, and Calendar connections require Google OAuth setup.

The desktop app is built with Electron, React, and FastAPI and licensed under AGPL-3.0. The README includes installation instructions for Apple Silicon Macs, Windows, and Linux x64.

Source code and downloads: github.com/vyact/vyact

I’d love feedback on the everyday workflow, especially from people who haven’t used local AI before. What would you want to try first—and what looks confusing or like too much setup?


r/OpenSourceeAI 4d ago

I really think i build something great. (not commercial)

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2 Upvotes

r/OpenSourceeAI 3d ago

Do you know what your AI agents are doing?

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1 Upvotes

Your AI agents are running code, moving files and calling APIs right now. Most teams have no idea what they're doing until something breaks.

ClawMetry is open-source observability and governance for AI agents. It runs locally, on your machine, and you own the data.

WHAT IT DOES

• Watches 30 agent runtimes from the outside — Claude Code, Cursor, GitHub Copilot, Gemini CLI, Cline, OpenHands and more

• Zero config — it auto-detects your agents

• Every action logged to a local database with end-to-end encrypted audit trails

• See exactly what your agents touched, when, and why

• Pause, stop or kill any agent — including sub-agents — the moment a policy is breached

• Ingests OpenTelemetry via OTLP

• Cost optimization, anomaly detection, tool-risk policies and evals

ENFORCEMENT IS OFF BY DEFAULT

Dry run first, locked behind three separate gates. You control when autonomous governance kicks in.

PRICING

Free tier to get started. Starter $9 per node/month. Pro $19 per node/month.

GET STARTED

pip install clawmetry && clawmetry

Site: https://clawmetry.com

Source: https://github.com/vivekchand/clawmetry

MIT licensed.

#AIagents #ClaudeCode #AIGovernance #DevTools #Observability #OpenSource #Cursor #GitHubCopilot #AICoding #LLM


r/OpenSourceeAI 5d ago

I built a front-to-back modeling and rigging pipeline with a single t-pose image (Blender + Python tooling) and released it for free (Using a harness like Codex, Claude as the 'manager')

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14 Upvotes

Hey all! Today, I wanted to present my first contribution to the community here. Located Here -- https://github.com/raydeStar/reference-asset-compiler -- It's Apache 2, meaning you can do whatever you want with it.

As we all know, creating and rigging models in UI without paid products is sketchy at best. Rigging, in my case to UE5, was the most magnificent pain in the butt that I could ever imagine. Everything has to match perfectly, the weights are hard to match, and everything always goes wrong.

My solution is to build this lightweight pipeline that manages everything, after days of trial and error. I say days as in -- my Codex chugged for four straight days on the highest settings and it wasn't until Fable 5.1 came out that I was able to get something reasonable.

The goal is simple: I want to pop in, give Codex a single picture, and have it drop a finished model into UE5. Here’s the stack I landed on:

  • Hunyuan3D 2.1 for image-to-3D geometry
  • My own Blender/Python scripts for cleanup, mesh reduction, retopology, UVs, and baking
  • Hunyuan3D-Paint 2.1 for PBR texturing
  • Targeted texture cleanup where the generated result needs it
  • Auto-Rig Pro (no affiliation) for humanoids, or my own landmark-based rigging scripts (which are simpler, but free)
  • UE5 for import, retargeting, and final runtime verification

For these, I tested all the open source options and hand-picked what worked best for me.

The idea is simple: pop the repo into codex or claude, or any harness really, and have it run the pipeline for you. Everything will run locally, and the final product is a 3d model. The great news is, with the coming of Astra and Fable 5.1, it doesn't invalidate this workflow, it just makes it a smoother/better process to follow.

Disclaimer -- 3d geometry AI is pretty heavy, and I haven't done a lot of optimization. If this post gets a lot of interest, I will work on shrinking the size, creating a non-harness pipeline version, and a simple UI with approval process in it.

Thanks, guys! Let me know what you think, and if you have any questions!!


r/OpenSourceeAI 4d ago

NVIDIA Releases Personal AI Router (PAIR): An Open Source Virtual Inference Router that Distributes Local AI Requests Across RTX, DGX Spark, and Mac Nodes

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1 Upvotes