Ship AI systems
20× faster than Claude Code
The platform for teams building AI agents, workflows, and automations
A purpose-built language that AI writes naturally and humans review visually
press enter to start building
Our founder stress-tested AI for
OpenAI
Anthropic
METR
Amazon AGIProof
Your team ships in minutes, not hours
Same AI model builds the same system. WeaveMind finishes in 4 minutes, Claude Code takes 1h 30
Both videos play in sync on a shared timeline
WeaveMind4 min
Time
600
Lines of code
0
Errors
Claude Code1h 30
Time
2.2k
Lines
1
Errors
Built for
Teams that build AI for a living
Whether you're an AI consultancy delivering to enterprise clients or an internal team shipping AI products, WeaveMind is the platform you build on
AI Consultancies & Agencies
Your solutions engineers customize per client in hours, not weeks
White-label WeaveMind under your brand. Let your team focus on the client's problem, ship the next engagement while competitors are still writing boilerplate
Internal AI Teams
Your CEO wants AI. Your team of 5 needs to actually deliver it
Human approval flows, audit trails, stakeholder review gates. Not another engineering project on top of the AI project. It's already in the language
Or you could
Use n8n
AI builder can't handle real complexity. Your team ends up building around it anyway
Build from scratch
3 weeks to build each system, then maintain every single one of them forever
Hire 5 more engineers
$800k/year to reinvent state management and approval flows
Why a new language
AI systems need a language designed for them
LLM calls, databases, APIs, human approvals, browser agents. In Python, each one is a library, a config file, and 50 lines of boilerplate. In Weft, each one is a primitive.
Compact, uniform, type-safe. AI writes it naturally (no hallucinated imports), humans review it visually (the code IS a graph), and the runtime executes it natively
import anthropic, psycopg2, smtplib, os, json
from email.mime.text import MIMEText
client = anthropic.Anthropic(api_key=os.environ["KEY"])
conn = psycopg2.connect(os.environ["DB_URL"])
# Query leads from database...
cur = conn.cursor()
cur.execute("SELECT * FROM leads WHERE ...")
leads = cur.fetchall()
# Qualify each lead with LLM...
response = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": ...}]
)
# Parse JSON, validate, set up SMTP,
# format email, handle errors, send...
# (80+ more lines)db = PostgresDatabase
qualify = LlmInference { parseJson: true }
review = HumanQuery "Review Email"
send = EmailSend
db.rows -> qualify.prompt
qualify.draft -> review.body
review.body -> send.bodydatabase + LLM + human review + email, 7 lines
Lead
Subject
Message
Human-in-the-Loop
Approval gates, human reviews, human decisions
Enterprise AI needs human oversight. In Weft, it's one node: HumanQuery. The system pauses, sends a form to the right person, waits hours or days, then resumes exactly where it left off. No webhooks, no polling, no state management.
A browser extension delivers tasks inline. Share a token and anyone joins the review loop without accessing the project. Your client's team can approve actions without ever seeing the underlying system.
Graph-Native Language
Code is a graph
Not a visualization bolted on top, the graph IS the program
AI writes structure, humans see architecture
# Apollo Auto Cold Emailing
pick_hypothesis = ExecPython {
label: "Pick Random Hypothesis"
in: *hypotheses
out: hypothesis
code: <<|
import random
return {"hypothesis": random.choice(hypotheses)}
|>>
}
hypotheses.value -> pick_hypothesis.hypotheses
qualify_llm = LlmInference {
label: "Qualify Company"
parseJson: true
}
qualify_prompt.text -> qualify_llm.prompt
llm_json.config -> qualify_llm.config
gate_qual = Gate "Qualified Only"
unpack_qual.qualified -> gate_qual.pass
extract_company.domain -> gate_qual.value
review = HumanQuery {
label: "Review & Edit Email"
in: *context, *subject, *body
out: subject, body, decision_approved
title: "Review Cold Email"
}
review_context.context_display -> review.context
unpack_draft.subject -> review.subject
unpack_draft.body -> review.body
gate_send = Gate "Approved Only"
review.decision_approved -> gate_send.pass
review.body -> gate_send.value
send_email = EmailSend {
label: "Send Cold Email"
fromEmail: "quentin@weavemind.ai"
}
smtp_config.config -> send_email.config
gate_send.value -> send_email.bodysimplified, see the real thing in the video above
same program, two native views
First-Class Primitives
The vocabulary of 2026
LLM call, database, browser agent, cron job, human approval, API endpoint, all primitives. You don't import them, you don't configure them, they exist in the language the way int exists in C
100 primitives and growing, each one typed and validated at compile time. Adding a new primitive takes one Rust file, and the AI learns it immediately because the structure is uniform
Recursively Foldable
Complexity scales logarithmically
Groups collapse into single nodes. A hundred-node workflow becomes a clean, navigable structure. Your team manages complexity instead of drowning in it.
Previous graph tools (n8n, Make, LangGraph) become spaghetti at scale because they have no recursive scoping. Weft does. And because AI writes the code, the tedium of node-based programming disappears entirely.
The language is growing
This is version 0.1. Every feature below makes the AI more capable, because the language and the AI co-evolve
Agents as Primitives
Long-lived agents in the graph
An agent is a node that persists, manages its own state, and acts through explicit edges. Every tool call visible in the graph, every decision traceable
LLM flexibility with graph-level observability, no black-box agents, the graph IS the agent's reasoning trace
Verified Blocks
Pre-approved building blocks you customize
A RAG pipeline, a moderation layer, an agent with hallucination watchers. Drop a verified block into your program, customize it, and your system inherits its guarantees
We're partnering with insurers to certify these blocks, so systems built with them get coverage faster. Robustness isn't a best practice you hope developers follow, it's a composable building block in the language
Compilation
Weft compiles to a binary
Weft compiles to native Rust. Same performance as hand-written code, zero overhead. The binary provisions its own infrastructure, starts triggers, and runs as a service. One artifact, deploy anywhere.
AI writes a high-level graph. The compiler turns it into systems-level code. Compile once, then run it, serve it as a long-lived process, or manage its infrastructure independently.
Built in Rust
Compiler, runtime, type system, AI builder. All Rust
Compiled
Not interpreted
Type-safe
Compile-time validation
Parallel
Native concurrency
Pricing
Usage-based, transparent. Because WeaveMind is efficient (fewer tokens, fewer API calls), the effective cost is lower than building the same system by hand
Usage
At cost + 40%
Pay as you go, no commitment
- All 100+ primitives
- AI builder (Tangle)
- Human-in-the-loop
- Bring your own API keys
Builder
$100/mo
At cost + 30% · credits roll over
- Everything in Usage
- Lower markup (30%)
- Rolling credits (never expire)
- Priority support
Enterprise
Custom
For agencies and large teams
- Everything in Builder
- White-label deployment
- Custom primitives
- Dedicated support & SLA
- Volume discounts
Weft, the compiler, and the runtime are going open source once stable. The language that runs your systems should be auditable, forkable, and yours
I spent three years breaking AI systems. Red teaming for OpenAI and Anthropic, capability evaluations at METR, building an AI evaluation startup. I presented an autonomous jailbreaking system at the Paris AI Summit.
The same pattern kept showing up. Teams would build impressive AI prototypes, then spend months making them production-ready. Not because the models were bad. Because the tooling was. Every team was reinventing the same plumbing: state management, human approval flows, error handling, API integrations. In languages designed for a different era.
So I stopped fighting the language and built a new one. Weft is a graph-native, type-safe language where LLMs, humans, and infrastructure are first-class primitives. One structure that AI writes naturally, humans review visually, and the runtime executes natively. The teams building production AI shouldn't be spending their time on plumbing.
