One typed conversation for distributed AI.
Vantar builds Colloq, an open-source language for AI systems, services, and accelerators that coordinate through one typed, verifiable conversation. We also build Coldstack, the retrieval layer for robot data.
Built on the stack teams already run
Featured · Colloq
Open sourceA programming language for machines that coordinate machines.
Colloq replaces separate client programs, server programs, and disconnected protocol definitions with one typed conversation graph. The compiler projects that agreement into compatible endpoint plans while preserving one inspectable identity across the data center.
One global program
The conversation is the computation.
Typed choices, streams, cancellation, and failures project into one checked local machine per role.
Authenticated data plane
Meaning survives the network.
Persistent Iroh identities and exact role/plan policy let independent servers execute the same identified Colloq Wire.
Machine-authored change
Evolution stays governed.
Automerge sync runs as an authenticated Colloq conversation, then passes through conflict, validation, and promotion gates.
58
Tests passing
4
Transports
2
Wire encodings
v0.1
Research prototype
Colloq
Research · v0.1The language servers speak to think together.
Colloq begins with one typed conversation instead of separate client and server programs. Its compiler projects that graph into compatible endpoint plans, then preserves the same semantics across memory, TCP, authenticated QUIC, or Iroh. Automerge protects collaborative draft promotion, while Miren packages the current multi-node testbed.
conversation Generate(prompt: Prompt) -> stream<Token> {
roles gateway, router, expert[*]
gateway -> router: prompt within 2ms
choice router {
cached { router -> gateway: CachedResult; end }
infer(expert) {
router -> expert: prompt
expert -> gateway: stream<Token>
}
}
}Coldstack
Design partnersRobot data belongs on object storage.
Fleets generate 0.5–2 TB per robot per day. Coldstack keeps your raw MCAP in your own S3 bucket, builds a compact index, and answers one composable query — semantic, time-series, and metadata together. The retrieval layer for robot data.
import coldstack
ns = coldstack.Client(api_key=...).namespace("fleet-a")
results = ns.query(
text="gripper slipping on transparent object", # visual semantic
signal="torque_z > 5 for 2s and velocity < 0.1", # time-series pattern
filter={"robot_id": ["r-204", "r-207"]}, # metadata
limit=50,
)≥5×
Cheaper (target)
vs hot-storage stacks
1–3%
Index footprint
of raw log size
<100ms
Warm query
hot-namespace target
0 bytes
Raw data moved
stays in your bucket
Working engine, validated on real public robot logs — index footprint measured at 0.02% on a camera/lidar recording. In design-partner recruitment; not yet benchmarked at fleet scale.
Why Vantar
The tooling hasn't kept up. We build the missing pieces.
Robot data has no retrieval layer
Fleets generate 0.5–2 TB per robot per day. ~99.9% is never read again, but the failures and edge cases that matter need fast search across the entire corpus. Hot storage is ruinous, Glacier is unsearchable. Coldstack makes petabyte-scale robot logs searchable on object storage.
Distributed AI has no shared language
Models, tools, memory, accelerators, and services are still stitched together with separate APIs and deployment files. Colloq starts from one typed conversation and compiles compatible endpoint plans for every participant.
Distributed AI needs
one shared language.
Colloq is open source and early. Tell us about the workload you want to coordinate, or the robot logs you need to search.
Early access · Colloq design partners & Coldstack
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