AgentForge Labs provides the infrastructure to deploy, coordinate, and scale autonomous AI agents — from research labs prototyping multi-agent systems to enterprises running production agent fleets.
Declare agent topology in YAML. AgentForge handles spawning, health checks, retries, and graceful teardown across your GPU cluster.
Per-agent metrics, token usage, latency profiles, and cost tracking. Drill into any agent's full execution trace with one click.
Pub/sub message bus between agents with backpressure, deduplication, and priority queues. Agents discover each other automatically.
Intelligent placement of agent workloads across A100, H100, and L40S instances. Bin-packing for cost efficiency, oversubscription for throughput.
Every agent runs in an isolated container with resource limits, network policies, and time-to-live constraints. No noisy neighbors.
Run on any LLM provider — OpenAI, Anthropic, Groq, self-hosted models via vLLM. Unified API across all of them. BYO model, we handle the rest.
Built on GCP Compute Engine (GPU instances) + Vertex AI for model inference + BigQuery for analytics pipeline
Run MMLU, GSM8K, HumanEval across 50+ models simultaneously. Each model gets isolated GPU allocation, results stream to BigQuery. Paper-ready charts in hours, not weeks.
Deploy 100 specialized agents to review a large PR — each agent focuses on one aspect: security, performance, style. Results merge into a single actionable report.
Hundreds of agents monitor mempools, DEX liquidity, and MEV opportunities in parallel. Agent mesh shares signals in real-time for sub-second decision making.
Orchestrate text-to-text, text-to-image, and text-to-audio agents in a DAG pipeline. Automatic retry on failed generations, GPU bin-packing for throughput.
AI agent researcher and builder. 4+ years shipping production ML systems. Previously built quantitative trading infrastructure.
Cloud infrastructure specialist. Experienced with GCP, Kubernetes, and GPU cluster provisioning for ML workloads.
Specializes in LLM inference optimization and agent architectures. Active in open-source AI community.
Private alpha. We're running internal benchmarks with 1,000-agent swarms on GCP A100 instances. Planning beta release Q4 2026.
Our control plane runs on GCP Compute Engine with A100/H100 GPUs for LLM inference, plus Vertex AI for managed model serving. We're actively using Google Cloud credits to scale our alpha cluster.
The agent runtime and SDK will be open source (Apache 2.0). The control plane and observability dashboard will be hosted SaaS with a free tier for researchers.
Join the waitlist. We onboard 5-10 teams per month. Research labs and open-source projects get priority.
Join the private alpha waitlist. First 50 teams get $500 in free compute credits.