CodeTether breaks the context window barrier with infinite memory. Watch it read your workspace context, understand requirements, and ship production deliverables autonomously.
No credit card required · MIT Licensed · Self-host or use cloud
And 145,000 developers starred it on GitHub.
I'm not going to pretend you haven't heard of OpenClaw. It's everywhere right now. Your Twitter feed. Your Discord servers. Your coworker's Slack status. "The future of AI agents" they're calling it.
And I'll give credit where it's due: Peter Steinberger proved the market. He showed the world that people want an AI agent that actually does things — not just another chatbot you talk to in a browser tab. That took vision. That took execution. And he shipped it at exactly the right moment.
But here's what nobody in those breathless Twitter threads is talking about.
The thing shipped with authentication disabled by default.
Not "weak authentication." Not "basic auth." No authentication. auth: none. The gateway — the thing that controls your email, your files, your shell, your entire digital life — was wide open on the public internet for anyone who found the port.
When the project renamed itself from Clawdbot to Moltbot, crypto scammers hijacked the old Twitter handle and GitHub namespace in ten seconds flat. A malicious VS Code extension called "ClawdBot Agent" appeared on the marketplace the same day. People deployed it on DigitalOcean droplets with the port exposed and no auth, because that's what the YouTube tutorials told them to do.
There is a difference between a demo and infrastructure. And the entire world just confused the two.
Steinberger said in an interview that he thinks 80% of software goes away and people don't really care where their data is stored.
That's the kind of thing that sounds profound on a podcast and falls apart the second you think about it for more than thirty seconds.
Your company cares where its data is stored. Your customers care. Your users care. Anyone who's ever dealt with a breach, a leaked database, a compromised API key, or an angry email from a customer whose data ended up somewhere it shouldn't — they care.
"People don't care where their data is stored" is the worldview of someone who's never been responsible for anyone's data but their own.
And that's the gap I built CodeTether to fill.
Here's the thing nobody else in the "AI agent" space seems to understand:
An autonomous agent with system-level access IS infrastructure.
Full stop. It's not an app. It's not a productivity hack. It's not a weekend project you install from npm. It's a piece of software that can read your files, execute commands, make decisions, and take actions — on its own, without asking you first.
That means it needs to be built like infrastructure.
Not Node.js, not Python. When your agent has elevated privileges on your system, memory safety isn't a nice-to-have. It's the bare minimum.
CodeTether manages its own pods and recovers from failures automatically. Because if your "AI assistant" crashes, you have a liability.
It's not a flag you set. You cannot turn it off. Because when you let developers opt out of security, they opt out. Every single time.
Open Policy Agent with Rego policies for authz, tenant isolation, and API key scoping. Fine-grained access control — not just "allow all."
Most AI agents work like this: you send a message, they think, they respond. Request-response. Just like every chatbot since the 1960s, except now there's an LLM in the middle.
CodeTether doesn't work like that.
CodeTether runs continuous thought loops that persist across restarts, survive node failures, and scale horizontally. Your agents aren't waiting for you to type something. They're reasoning. They're planning. They're acting. Right now, while you're reading this.
You don't get one bot. You get a coordinated team. A monitoring persona that watches your systems. A deployment persona that manages your staging environment. A review persona that reads pull requests. Each with its own scoped permissions. Each in its own security boundary.
A compromised monitor can't escalate to deploy access. That's not paranoia — that's basic engineering.
Your agents can write their own code, open their own PRs, and evolve their own capabilities. But every modification goes through an auditable gate that you define. Every decision is logged. Every action is traceable. The agents get smarter. You stay in control.
I had CodeTether running for 48 hours straight — self-deploying on Kubernetes, making its own pull requests, managing its own lifecycle. Not because I was watching it. Because that's what it does.
The first wave of AI agents — OpenClaw included — proved something important: people are ready for this. Developers want agents that go beyond chat. They want agents that act. That open PRs. That manage infrastructure. That handle the work you don't want to do at 11 PM on a Tuesday.
But the first wave also proved something else: most of these tools were built for the demo, not for the day after the demo.
They work great in the tweet. They work great in the screen recording. They work great when you're showing your friends on Discord. But then you try to run one in an environment that matters — where your team depends on it, where your client data flows through it, where a failure means more than restarting a process — and the cracks show up fast.
CodeTether exists because I got tired of watching smart developers plug security holes with duct tape and ship it as "the future."
Same category. Different engineering.
| Feature | OpenClaw | CodeTether |
|---|---|---|
| Language | Node.js | Rust (memory-safe) |
| Auth default | auth: none | Mandatory HMAC-SHA256 — cannot disable |
| Plugin isolation | None — shared process | Sandboxed + Ed25519 code-signed |
| Authorization | None — ambient authority | Open Policy Agent (OPA) Rego policies |
| Audit trail | None | Append-only JSON Lines — every action |
| Cognition model | Request/response | Perpetual thought loops |
| Agent coordination | Single bot | Persona swarms w/ scoped permissions |
| Self-modification | Not supported | Auditable gates + logged decisions |
| Deployment | Manual setup | Self-deploys on Kubernetes |
You've got two paths in front of you right now.
You install the thing with 145,000 GitHub stars. The one that shipped with no auth. The one built in Node.js by a guy who thinks people don't care where their data is stored. You connect it to your email and your shell and your git repos, and you hope that the plugin you downloaded from a Discord marketplace doesn't have a supply chain attack baked in.
And for a while, it's cool. It texts you a morning briefing. It manages your to-do list. And then one day something goes sideways, and you realize you handed the keys to your system to software that was designed for convenience, not for consequences.
You deploy an agent runtime built by someone who runs production systems for a living. Written in Rust. Deployed on Kubernetes. Auth mandatory. Plugins sandboxed and signed. Every autonomous decision audit-logged.
Not because those things are trendy — because when you've been the person who gets paged when things break, you build differently.
CodeTether is open source. It's free to self-host. And it's built for people who understand that the most dangerous thing in technology is a powerful tool built by someone who's never had to clean up the mess when it breaks.
Go to codetether.run, pull the repo, and deploy it on your own infrastructure. Read the code. Audit the architecture. Compare it to anything else on the market.
If you're the kind of developer who actually thinks about what happens after the demo — who cares about what your code does when you're not watching, who understands that giving an AI agent root access to your machine is a decision that deserves more than a one-liner install script — you'll see the difference in the first ten minutes.
And if you just want something to manage your calendar and text you the weather? The other guys have you covered. Seriously. No shade.
But if you build things that need to keep running, keep working, and keep your data where it belongs —
CodeTether was built for you. By someone who builds the same kind of things you do.
Because your infrastructure shouldn't depend on someone else's business model.
Most AI degrades as context grows. RLM processes large inputs recursively—breaking work into chunks, verifying results, and producing deliverables.
RLM treats input as an environment variable, not direct context. The AI writes code to peek, decompose, and recursively call itself on chunks.
Your data becomes a variable in a Python REPL, not direct LLM input
AI writes code to chunk, filter, and navigate your data programmatically
Sub-LLMs process chunks, verify results, and report back
Results combined into real files: CSV, PDF, code, reports
RLM is based on peer-reviewed research from MIT CSAIL.
Read the Paper: Recursive Language ModelsZhang, Kraska, Khattab. "Recursive Language Models." MIT CSAIL, 2025.
See how CodeTether processes 847K tokens using recursive decomposition
Task: Analyze 12,847 customer records and find top revenue drivers
From trigger to delivery—it's that simple.
Use dashboard, iOS app, webhook API, Zapier, n8n, or Make.
The AI decomposes the job, runs recursive work, verifies outputs, and stitches results—5-60 minutes unattended.
Get results via email (with attachments), webhook callback (JSON), or dashboard download.
RLM (MIT research) processes 10M+ tokens without degradation. Handles datasets and workflows normal AI can't.
ChatGPT is great for quick answers. CodeTether is for tasks you'd normally spend 30–60 minutes doing yourself—or pay someone else to do.
Trigger once → walk away → get deliverables.
ChatGPT gives you text. You copy it, paste it, format it… again and again.
CodeTether delivers real files ready to use.
Complex tasks take multiple prompts and constant steering.
CodeTether runs in the background and finishes the job.
Zapier "AI steps" fail when tasks take longer than 30 seconds.
CodeTether runs for up to 60 minutes with checkpointed progress.
Normal LLMs break down on big datasets and long context.
CodeTether uses RLM (MIT research) to process 10M+ tokens without degradation.
| Capability | ChatGPT | CodeTether |
|---|---|---|
| Task Duration | Seconds (single response) | Up to 60 min (background) |
| Output Format | Text in chat window | Real files (CSV, PDF, code) |
| Automation | Manual copy/paste | Webhook trigger + callback |
| Authorization | All-or-nothing access | OPA Rego fine-grained policies |
| Plugin Security | Shared process, no isolation | Sandboxed + Ed25519 signed |
| Audit Trail | None | Every action logged (append-only) |
| Context Handling | Degrades on long inputs | RLM - unlimited context |
Ralph runs autonomous dev loops: PRD → implement → test → commit → repeat. When context grows too large, RLM compresses it so the loop keeps going.
Project
TaskApp
Branch
ralph/task-status
User Stories
RLM Status
Click "Start Ralph" to watch autonomous development
Watch how RLM compresses context when it gets too large
Based on Geoffrey Huntley's Ralph pattern
Ralph implements each story, runs tests, commits code, and moves to the next without stopping.
When memory exceeds threshold, RLM compresses progress while preserving key learnings and file paths.
Turns PRD into production code without context degradation. This is how you ship without losing quality.
*RLM compression triggers automatically when context exceeds threshold (default: 80K tokens)
Create a PRD → Convert to prd.json → Let Ralph + RLM build it
We benchmark CodeTether using real-world PRDs — not synthetic puzzles. Every story runs through four quality gates. Every token is counted. Every dollar is tracked.
cargo checkcargo clippycargo testcargo build --releaseSame agent, same PRDs, different models. Ranked by weighted score (50% accuracy, 25% speed, 25% cost).
| # | Model | Pass Rate | Speed | $/Story | Score |
|---|---|---|---|---|---|
| 1 | Kimi K2.5 Moonshot AI | 100% | 40.7/hr | $0.19 | 98 |
| 2 | Claude Sonnet 4 Anthropic | 100% | 32.1/hr | $0.84 | 91 |
| 3 | GPT-4.1 OpenAI | 95% | 28.5/hr | $1.12 | 82 |
| 4 | DeepSeek R1 DeepSeek | 90% | 35.2/hr | $0.22 | 85 |
20 stories × 4 hrs/story = 80 hrs manual. CodeTether: 29.5 min, $3.75.
Same model (Kimi K2.5). Native Rust vs Bun runtime = fewer tokens, less overhead.
Not synthetic benchmarks. Not cherry-picked examples. These are complete PRD executions with production quality gates.
| PRD Run | Stories | Duration | Speed |
|---|---|---|---|
| LSP PRD (run 1) | 5/10 | 8.5 min | 1.7 min/story |
| LSP PRD (run 2) | 5/10 | 6.5 min | 1.3 min/story |
| Missing Features PRD | 10/10 | 14.5 min | 1.45 min/story |
| Total | 20/30 | 29.5 min | 1.48 min/story |
Methodology: Each benchmark uses Ralph's PRD-driven autonomous loop. Stories define acceptance criteria. Quality gates (typecheck, lint, test, build) run after every story. No human touches the keyboard. Token counts and cost come from actual API usage, not estimates.
No 30-second timeouts. No context limits. Real deliverables.
| Your Need | Zapier AI | CodeTether |
|---|---|---|
| Process 500 leads with AI analysis | Rate limits, timeouts, expensive | Webhook in, all 500 processed, callback when done |
| AI step that runs 20+ minutes | 30-second timeout. Task fails. | Up to 60 min with recursive decomposition. |
| Handle large datasets or context | Limited context window. | 10M+ tokens with RLM processing. |
| Audit trail for compliance | Basic run history. | Every action append-only logged with timestamps. |
| Plugin security and isolation | Shared runtime, trust-based. | Sandboxed with Ed25519 code signing. |
| Refine the output after seeing it | Run the whole Zap again. | Reply to email to continue. |
Recursive Processing
Tasks are decomposed, processed in parallel, verified, and stitched back together. No 30-second wall.
Input as Environment
Your data becomes the context. The model navigates and processes it like a workspace, not a fixed prompt.
Cost Efficient
Median RLM run costs less than a single base model call. Smarter routing, not brute force.
POST /v1/tasks
{
"prompt": "Analyze leads.csv",
"email_results": true,
"callback_url": "your-webhook"
}Email arrives:
Your task is complete.
Analyzed 847 leads. Found 142 high-value.
lead_scores.csv attached
Reply to refine the analysis
For repeatable tasks, AI workers are faster, cheaper, and more consistent.
| Aspect | Virtual Assistant | CodeTether |
|---|---|---|
| Cost (300 tasks/mo) | $1,500-2,500/month | $297/month |
| Availability | 8 hours/day | 24/7 |
| Speed | 2-4 hours | 5-30 minutes |
| Scalability | 1 task at a time | 10+ concurrent |
| Consistency | Varies | Same every time |
| Data processing | Limited by human memory | 10M+ tokens with RLM |
RLM technology ensures consistent quality—no context rot, same thorough analysis every time.
Trigger from dashboard, API, or automation platform. Kick back. Get real deliverables in your inbox.
Write a PRD, run Ralph. Wake up to implemented features with tests passing and commits pushed. RLM ensures infinite context so your full workspace is understood, not just single files.
PRD → Ralph implements → Review PR in morning
Stop spending hours on landing pages. Tell the agent "build me a sales page for my new course" and get production-ready HTML in minutes. It reads your existing content, matches your brand, and writes converting copy.
New course idea → Agent builds funnel → You review & launch
Your clients need reports, audits, and content. RLM agents analyze entire websites, generate SEO reports, and write blog posts—all triggered from your project management tool via webhook.
Client request → n8n triggers agent → Deliverable ready
Product descriptions that convert. Feed the agent your inventory spreadsheet and get optimized listings for Amazon, Shopify, and Etsy. Bulk processing hundreds of SKUs overnight.
CSV upload → Agent writes listings → Export to platforms
18 Zapier components connect CodeTether to 6,000+ apps. Trigger autonomous tasks from Airtable, Slack, or Gmail. No code required—just connect and configure.
Zap trigger → Agent runs task → Result to destination
Agents that monitor your systems, manage deployments, and self-heal on Kubernetes. Ed25519-signed plugins ensure only trusted tools touch production. Every action audit-logged for compliance.
Alert fires → Agent diagnoses → Auto-remediation + audit log
Your product workspace is growing. RLM agents can analyze your full source context, find bugs, write tests, and generate documentation across teams and tools.
Push to GitHub → Agent reviews → Issues created
Research, outline, write, optimize. Agents that read your competitors' content, analyze what ranks, and produce SEO-optimized articles. Triggered from your editorial calendar.
Calendar event → Agent researches & writes → Ready for review
Client intake forms trigger personalized analysis. The agent reads their questionnaire, analyzes their situation, and drafts a custom proposal—before your first call.
Form submission → Agent analyzes → Proposal in inbox
Start free. Upgrade when you're hooked. Cancel anytime.
$0
Get started free. Perfect for testing workflows.
Best for: Testing & experimentation
$5 token credit included
$49/mo
For builders who need serious throughput.
Best for: Solopreneurs & automation builders
$50 token credit / month included
$199/mo
Unlimited tasks, workers, and workspaces.
Best for: Teams & agencies
$150 token credit / month included
Every plan includes prepaid token credits. You only pay for what your agents actually use, tracked per-request with full model-level cost breakdowns.
| Model | Input | Output | Cache Read |
|---|---|---|---|
| Claude Opus 4.6 | $15.00 | $75.00 | $1.50 |
| Claude Opus 4.5 | $5.00 | $25.00 | $0.50 |
| Claude Sonnet 4 | $3.00 | $15.00 | $0.30 |
| GPT-5.2 | $3.00 | $12.00 | — |
| GPT-4.1 | $2.00 | $8.00 | — |
| Gemini 3 Pro | $1.50 | $12.00 | — |
| Gemini 2.5 Flash | $0.15 | $0.60 | — |
Send a POST request or start from the web interface
RLM processes your task with unlimited context
Get files in your inbox or webhook callback
Deploy CodeTether on your own terms. Read the code. Audit the architecture. Own your data.
OpenClaw is a Node.js gateway that shipped with auth: none by default. CodeTether is a Rust-based perpetual cognition runtime with mandatory HMAC-SHA256 authentication, Ed25519 code-signed sandboxed plugins, persona swarms with scoped permissions, and append-only audit logging. It self-deploys on Kubernetes. These are shipped features in v1.1.0, not a roadmap.
An autonomous agent with system-level access IS infrastructure. Memory safety isn't optional when your agent can read files, execute commands, and take actions on its own. You wouldn't write a database in JavaScript — you shouldn't write an autonomous agent in it either.
Instead of one bot, you get a coordinated team of agents — monitoring, deployment, code review — each with its own scoped permissions and security boundary. A compromised monitor can't escalate to deploy access. That's not paranoia, that's basic engineering.
Most AI agents are request-response: you ask, they answer. CodeTether runs continuous thought loops that persist across restarts, survive node failures, and scale horizontally. Your agents reason, plan, and act autonomously — not just when you type something.
OPA is a CNCF-graduated policy engine that CodeTether uses for fine-grained authorization. We ship Rego policies for API authorization, tenant isolation, and API key scoping — all declarative, testable, and auditable. You can write custom Rego rules to control exactly what each agent, user, or API key is allowed to do.
Yes. CodeTether agents can write code, open PRs, and evolve their capabilities. But every modification goes through an auditable gate you define. Every decision is logged, every action is traceable. The agents get smarter while you stay in control.
RLM (Recursive Language Models) is MIT research that treats input as an environment variable, not direct LLM context. Instead of stuffing everything into one prompt, RLM recursively decomposes tasks, processes chunks independently, verifies results, and stitches them together. CodeTether uses RLM to handle 10M+ tokens.
Yes. CodeTether is open source under the MIT License. Run it on your own servers with full control. Docker images and Helm charts provided. Your infrastructure shouldn't depend on someone else's business model.
Authentication uses HMAC-SHA256 bearer tokens and is mandatory — there is no flag to disable it. All plugins are sandboxed with Ed25519 code signing and SHA-256 integrity checks. Every action is logged to an append-only JSON Lines audit trail with timestamps, categories, and metadata. Persona swarms enforce least-privilege — each agent only has the permissions it needs. Enterprise plans can add custom OPA Rego policies.
Yes. Self-host for free, or use our managed platform with tiered pricing: Free (100 tasks/mo + $5 token credit), Pro ($49/mo, 5,000 tasks + $50/mo token credit), and Enterprise ($199/mo, unlimited tasks + $150/mo token credit). All plans include the same security guarantees.
Every AI request consumes tokens — input tokens (your prompt), output tokens (the response), and sometimes cache or reasoning tokens. We track usage per-request at the model level and deduct from your prepaid credit balance. You can see exactly what each model costs per million tokens, set monthly spending limits, and add credits any time. No surprises.
Tell us about your infrastructure. We'll help you deploy CodeTether.