The semantic continuity layer for AI systems.
Venkai
Your systems don't talk to each other.
Agents, tools, data, workflows — each on its own.
Venkai connects, and structures. One typed context layer between everything you already run.
The Forge puts the system to work. Agents execute; Venkai keeps what they establish.
And the loop makes the system better. Cervelet evaluates, Mutation changes the workflow, it runs again.
Your AI stack
Memory, RAG, orchestration, continuity: four questions
Venkai does not require replacing your existing memory, retrieval, orchestration or model infrastructure.
| Layer | Question | Examples | Relationship to Venkai |
|---|---|---|---|
| Memory | What should the system remember about a user, a conversation or an entity? | Mem0, Zep, Letta, assistant memory | Complementary. Venkai does not replace it. |
| RAG | What information should the system retrieve from documents? | Vector databases, document indexes | Complementary. Venkai indexes no documents. |
| Orchestration | How are agents and steps coordinated? | LangGraph, CrewAI, AutoGen, custom loops | Stays in place. Venkai does not decide who runs. |
| Continuity | What context and work state must survive so the next agent, session, model or step can continue? | Venkai | The layer Venkai covers. |
The layers overlap in places: Venkai also stores facts and preferences, but attached to the state of a piece of work, not to a user profile.
What sets Forge apart
It does not only run. It changes.
A workflow that runs without ever changing is an automation. What follows is the shape of the loop that makes it change — and at this stage, only its shape.
Demo
Workflow v1
61 %Success
14Human interventions
120 runs
Workflow v2
74 %Success
8Human interventions
120 runs
Cervelet — what went wrong?
38% of failures were leads enriched, then discarded
Mutation — what changes
Qualification moves ahead of enrichment
Run → Measure → Learn → Mutate
These values illustrate the mechanism. No executions table exists yet, so none of these numbers is measured. The day it exists, this block shows the same fields filled for real.
Forge against the market
What Forge does, and what it does not.
Eight axes. The Forge column is filled because we are its source; the facing column is not, because we have not verified it.
| Capability | Forge + Venkai | NanoCorp |
|---|---|---|
| Model freedom | ✓ | ? |
| Agent orchestration | ✓ | ? |
| Persistent organizational context | ✓Venkai | ? |
| Self-improvement | ✓Cervelet + Mutation | ? |
| Autonomous business creation | ○ | ? |
| Deployment | Cloud | Cloud |
| Pricing | Public, /pricing/ | ? |
| Open source | No | ? |
NanoCorp builds autonomous businesses. Forge makes existing businesses autonomous.
If you start from an idea and want it to become a business on its own, NanoCorp is built for that and we are not.
No competitor cell is ticked without a source on record. A table where we win everywhere is a table nobody believes.
The Forge 10
Ten founders. Ten businesses. Six months, in public.
Every slot is a real business, run by a real person, with its results shown — including the bad ones. Two are running. Eight are open.
- 01In production
Clipping Lab
Content and production
Clipping LabView live lab → - 02This week
Trading Lab
Market intelligence
Trading Lab - 03Open
—
E-commerce
Take this slot → - 04Open
—
Real estate
Take this slot → - 05Open
—
Agency and services
Take this slot → - 06Open
—
B2B SaaS
Take this slot → - 07Open
—
Operations
Take this slot → - 08Open
—
Open
Take this slot → - 09Open
—
Open
Take this slot → - 10Open
—
Left open
Take this slot →
- 2 / 10
- Founding operators
- 2
- Live systems
- 0
- Executions
- 0
- Creators
content/forge.ts
content/marketplace.ts
db.executions — table absente
db.creators — table absente
The last two counters read zero because the tables meant to fill them do not exist yet. They will keep reading zero until they do.
Proof
What is verified today.
Adversarial audit, 10 August 2026: checked by reading the source and running the tests, not the documentation.
One agent resumes another's work, including when the next agent is declared on a different model.
The A → B scenario, then A → C with an agent declared on a different model, passes 19 checks out of 19. Nothing Venkai keeps is model-specific. The scenario runs through the SDK — it has not yet been replayed against two genuinely distinct model vendors.
python demos/agent_handoff_demo.py # 19/19 checks- VERIFIED
80/80 core tests
Full pytest suite (`pytest -q`), no regression.
- VERIFIED
Cross-session persistence
Context survives the agent and the process restarting.
- VERIFIED
Isolation, versioning, access
Org filtering, snapshot on every write, JWT and API keys.
- LIMIT
What is not proven
PostgreSQL and Docker unvalidated, default embeddings hashing-based, no real LLM cost measurement. Detail on /architecture/ and /en/research/.
Marketplace
You do not need to build every system from scratch.
Compare twenty agent platforms, deploy a system that already works, or publish your own. Every comparison carries its verification date — or says it has none.
Two entries are verified: ours. The other twenty stay out of the index until we have actually examined them. (0 / 20)
A system you build with Forge can become a product you sell: 80% to you, 20% to Forge.
Pilot
Build a continuity layer for your agents.
Describe your multi-agent workflow and where context goes missing. We answer with a pilot scope, or with the reason Venkai isn't the right piece.
No newsletter. A human reply. · or write directly to contact@venkai.fr