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Agents use MCP
Expose Jungle Grid as tools your coding agents, AI agents, or automation systems can call.
MCP + API execution layer
The execution layer foragentic AI workloads.
Submit inference, image generation, batch, training, fine-tuning, and containerized AI jobs through MCP or API. Jungle Grid handles routing, execution, logs, recovery, artifacts, and lifecycle tracking.
Your agent submits work. Jungle Grid manages the infrastructure.
Recognized by F6S
Paste into your coding agentintegration-prompt.txt
Works with Codex, Cursor, Claude Code, Windsurf, and other coding agents.
You are integrating Jungle Grid into my app or AI agent. Goal: Use Jungle Grid as the execution layer for AI workloads. For agents: Connect through the Jungle Grid MCP server so the agent can submit jobs, check status, read logs, and retrieve outputs as tools. For applications: Use the Jungle Grid API to submit jobs directly and track their lifecycle. Build: - Job submission - Job status tracking - Logs and artifact retrieval - Graceful failure handling - A simple production-readable integration Start with a basic inference job. The application should be able to: Submit a job Receive a job ID Poll or check job status Display logs/status to the user Show the final output when completed Show a useful error when failed Do not build provider-specific GPU logic or hardcode cloud GPU infrastructure. Do not make the app or agent manage GPU servers directly. Treat Jungle Grid as the execution layer. Use the Jungle Grid MCP and API docs for the exact tools, endpoints, and request format.
Drop-in workflow
Already have an AI workflow?
Run it on Jungle Grid.
Jungle Grid fits into the way your team already builds. Submit AI jobs, route compute automatically, stream logs, collect artifacts, and stop worrying about provisioning GPU servers.
- Submit inference, training, fine-tuning, image, and batch workloads
- Route automatically across available compute capacity
- Get logs, retries, artifacts, and lifecycle tracking by default
- Avoid manual GPU provisioning and provider dashboards
submit-inference.ts
const response = await fetch("https://api.junglegrid.dev/v1/jobs", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.JUNGLE_GRID_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
name: "summarize-document",
image: "ghcr.io/acme/infer:latest",
workload_type: "inference",
model_size_gb: 8,
command: ["python", "infer.py"],
}),
});
const { job_id } = await response.json();One execution layer
Built for agents and apps.
Agents can call Jungle Grid through MCP tools. Applications can submit jobs directly through the API. Both paths use the same execution layer for routing, logs, status, recovery, and outputs.02
Apps use the API
Submit jobs directly from your product, backend, or workflow.
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Jungle Grid executes
Route to capacity, run the workload, stream logs, track lifecycle, and return artifacts.
Execution demo
Sign up. Run 5 qualifying inference jobs free.
Create an account, submit an inference job, track its lifecycle, stream logs, and retrieve the output. Apps use the API. Agents can use MCP tools. Jungle Grid handles the infrastructure underneath.Same execution layer. API for apps. MCP for agents.
Send an inference job
Describe the work, not the GPU.
POST /v1/jobs
{
"type": "inference",
"model": "llama-3.1-8b",
"input": {
"prompt": "Summarize this document"
}
}Jungle Grid routes and runs it
Capacity and recovery stay behind the API.
{
"job_id": "job_123",
"status": "running",
"logs_url": "/v1/jobs/job_123/logs"
}Queued RunningCompleted
Get output and artifacts
Return the completed work to your product.
{
"status": "completed",
"output": "...",
"artifacts": [ ... ]
} No GPU setup No provider dashboards Logs + status included Artifacts through API
The problem
Agents are starting to do real work. Execution is still messy.
AI products need to run more than a single model call. They trigger inference, image generation, long-running batch jobs, training, and containers.
Developers should not have to hardcode providers, manage GPUs, or build queueing and retry logic for every workload.
What you can run
InferenceImage generationBatch workTraining and fine-tuningContainers
What Jungle Grid is
One execution layer for agents and AI apps.
Submit work through an API. Jungle Grid runs model inference, fine-tuning, image generation, batch processing, and containerized AI tasks without making your app choose cloud providers, manage GPUs, or wire up failure recovery.
You submit the work. Jungle Grid handles execution.
It is an API-first runtime between your agent or application and the compute needed to run its work. The job contract stays stable while the infrastructure stays hidden.
1. Submit
Send an AI workload
Your app or agent sends one API request for inference, training, image generation, batch scripts, or containerized jobs.
2. Route
Jungle Grid routes and executes it
It chooses suitable capacity, starts the run, and handles routing, queue pressure, provider health, and recovery without exposing infrastructure to your app.
3. Get results
Get status, logs, and results
The job moves from queued to running to completed or failed. Your app can read logs and collect generated files, artifacts, or output through the API.
What you stop managing
Instead of opening GPU dashboards, comparing instance types, and building retry logic, your app submits a job and follows its lifecycle through the API.
Provider and capacity routing
Execution, queues, and recovery
Job lifecycle, logs, and outputs
For teams and production workloads
Running serious AI workloads?
Talk to Jungle Grid about inference, training, fine-tuning, batch jobs, agents, or custom GPU execution.