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An imperative command-line-interface for AI workload orchestration

MCP ServersRegistry oficial25 estrellas3 forksPythonApache-2.0Actualizado today
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Last scanned: 9/9/2026
Install in Claude Code / Claude Desktop
Method: UVX (Python) · terradev
Claude Code CLI
claude mcp add terradev -- uvx terradev
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "terradev": {
      "command": "uvx",
      "args": ["terradev"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/theoddden/Terradev and follow its README.
Casos de uso

Resumen de MCP Servers

# Terradev-cli v6.2.19

**Cross-cloud GPU orchestration CLI.**

![Terradev Demo](https://raw.githubusercontent.com/theoddden/Terradev/main/demo/terradev-demo.gif)

**License: Apache 2.0** - Free and open source for commercial and personal use.

https://terradev.cloud/
<!-- mcp-name: io.github.theoddden/terradev -->

Terradev is a cross-cloud compute control plane for AI workloads, not just a provisioning wrapper. 

Combines quoting, provisioning, topology optimization, training orchestration, inference tuning, and cost analytics in one CLI, with an accelerated idempotent runtime underneath.

Continued focus on lower cost, faster provisioning, and topology-aware execution with local credential storage.

Model agnostic. Dataset agnostic. GPU agnostic. Provider agnostic. The only thing Terradev is not agnostic about is correctness: it enforces topology, idempotency, and sequencing.

**NOTES ON v6.2.4**

- Version bumped to **v6.2.4**.
- Provider list consolidated to **17 active GPU cloud and inference providers**:
  - `aws` — Amazon Web Services
  - `azure` — Microsoft Azure
  - `baseten` — Baseten
  - `crusoe` — Crusoe Cloud
  - `digitalocean` — DigitalOcean
  - `e2enetworks` — E2E Networks
  - `gcore` — Gcore
  - `gcp` — Google Cloud Platform
  - `huggingface` — Hugging Face
  - `hyperstack` — Hyperstack
  - `inferx` — InferX
  - `latitude` — Latitude.sh
  - `runpod` — RunPod
  - `siliconflow` — SiliconFlow
  - `tensordock` — TensorDock
  - `vastai` — Vast.ai
  - `yottalabs` — YottaLabs
- Added Gcore (`gcore`) as a new VM provider with full lifecycle support, API token auth, and drift monitoring.
- Removed OVHcloud from the active provider list.
- Removed Jio Cloud and Oracle Cloud from the active provider list; removed Alibaba Cloud references from the CLI UI.
- Onboarding, `configure`, `setup`, `quote`, `credential_prompt`, and MCP tool schemas now all reflect the 17-provider set.

**NOTES ON Multi-Stage Training Pipeline (v6.1.1)**

- New `terradev train` subcommands for the full post-pretraining lifecycle:
  - `terradev train sft --model <id> --data <path> --nodes <ips>`
  - `terradev train dpo --base-checkpoint <sft-ckpt> --data <pairs> --algorithm <dpo|simpo|kto|orpo>`
  - `terradev train grpo --base-checkpoint <dpo-ckpt> --data <prompts> --framework <unsloth|openrlhf|trl>`
  - `terradev train pipeline --config examples/training_pipeline.yaml`
- `terradev_cli/core/training_stages.py` and `terradev_cli/core/training_pipeline.py` provide declarative SFT / DPO / GRPO stage configs, provider-aware quote selection, auto-provisioning, checkpoint handoff, and DAG sequencing via the Python `DAGExecutor`.
- `TrainingOrchestrator` now supports multi-node remote SSH launch and end-to-end completion tracking. Training scripts and embedded configs are staged to every node and the master process is polled until the job finishes.
- Unsloth GRPO uses the native `unsloth.GRPOTrainer` with a default rule-based reward instead of a TRL fallback.
- CLI-style frameworks (`axolotl`, `llama-factory`, `ms-swift`, `trl`, `openrlhf`) are wrapped in self-contained Python scripts that write their embedded config files at runtime, so they are safe to copy to remote nodes.
- See `examples/training_pipeline.yaml` for a SFT → DPO → GRPO pipeline sample.

**NOTES ON 6.0.8**

- **New agent subcommands** — `terradev agent sandbox`, `terradev agent mesh`, and `terradev agent mcp` are now real, dependency-resolving commands instead of placeholders:
  - `terradev agent sandbox` runs untrusted payloads with hardware-isolated runtimes (`bwrap`, `runsc`, `firecracker`, and Linux Landlock LSM), all discoverable/downloadable via `DependencyManager`.
  - `terradev agent mesh` creates a decentralized peer-to-peer agent mesh using real `libp2p` (`go-libp2p-daemon` + `p2pclient`), A2A HTTP, and `WireGuard` encrypted transports.
  - `terradev agent mcp` is a dynamic Model Context Protocol bridge with stdio and HTTP transports and a multi-server bridge.

**NOTES ON 6.0.0**

- **Unsloth** (`terradev train unsloth`): optimized local LLM training, serving, and coding agents with 70% lower VRAM usage and faster training via Triton kernels. Subcommands: `run`, `start`, `stop`.
  - `terradev train unsloth run --model unsloth/Llama-3.1-8B`
  - `terradev train unsloth run --model unsloth/Qwen3.6-7B-GGUF:Q4_K_M --port 8080`
  - `terradev train unsloth start claude --model unsloth/Llama-3.1-8B`
  - `terradev train unsloth stop`

- **Weaviate** (`terradev database weaviate`): vector database operations with local, embedded, cloud, and custom environments. Subcommands: `up`, `list-collections`, `create-collection`, `delete-collection`, `insert`, `query`, `hybrid-search`.
  - `terradev database weaviate up --environment local`
  - `terradev database weaviate create-collection --name Article --vector-size 384`
  - `terradev database weaviate insert --collection Article --objects '[{"properties": {"title": "Hello"}, "vector": [0.1, ...]}]'`
  - `terradev database weaviate query --collection Article --vector '[0.1, ...]' --top-k 5`
  - `terradev database weaviate hybrid-search --collection Article --query "generative AI" --top-k 5`

- **Letta** (`terradev agent letta`): stateful agents with long-horizon memory across sessions. Subcommands: `create`, `list`, `chat`, `status`, `delete`, `remember`.
  - `terradev agent letta create --name my-agent --model openai/gpt-4.1`
  - `terradev agent letta list`
  - `terradev agent letta chat --agent-id <id> --message "hello"`
  - `terradev agent letta remember --agent-id <id> --text "Our staging cluster is on us-east-1" --label fact`
  - `terradev agent letta status --agent-id <id>`
  - `terradev agent letta delete --agent-id <id>`

- **Removed integrations**: `terradev ml databricks` and `terradev ml langsmith` command groups, all related MCP tools, and Jaeger tracing endpoints in the Helm values have been removed.
- Version bumped to **6.0.0** to reflect these breaking changes.

**NOTES ON 5.7.10**

- **Local Ollama integration** (`terradev ml ollama`): list, pull, generate, chat, inspect, and check running models on a local Ollama server (`list`, `pull`, `generate`, `chat`, `info`, `ps`).
- **DeepEval integration** (`terradev ml deepeval`): install, run test suites, list metrics, and evaluate single LLM outputs with metrics like `AnswerRelevancyMetric`, `FaithfulnessMetric`, and `HallucinationMetric` (`install`, `init`, `run`, `metrics`, `evaluate`).
- **MCP tool surface** now at 237 tools, including `ollama_*` and `deepeval_*` tool definitions and handlers.

**NOTES ON 5.7.7**

- **Vault command** (`terradev vault`): store, sync, and use cloud API secrets from environment variables or the encrypted local vault. Designed for GitHub Actions / CI/CD pipelines where secrets are provided as `TERRADEV_<PROVIDER>_<KEY>` env vars.
  - `terradev vault sync` imports supported `TERRADEV_<PROVIDER>_<KEY>` secrets into the encrypted `~/.terradev/credentials.json` (use `--all` to also import custom keys).
  - `terradev vault run -- <command>` injects vault secrets into a sub-process and zeroizes them afterwards.
  - `terradev` now automatically falls back to `TERRADEV_*` environment variables when the local vault file is missing, so `terradev up` works directly from GitHub Secrets without a separate `configure` step.

- **Database command** (`terradev database`): universal database and vector store operations with SQLite, PostgreSQL, Qdrant, and Redis adapters. Subcommands: `terradev database up`, `database down`, `database crud`, `database search`, `database sql`, and `database qdrant` with its own `search`, `scroll`, `upsert`, `create-collection`, and `delete-collection` operations.

**NOTES ON 5.6.0**

Added **API Gateway for inference serving** with OpenAI/Anthropic/custom API entry and exit points:

- **Gateway Service** (`core/gateway_service.py`): FastAPI-based gateway that provides OpenAI-compatible, Anthropic-compatible, and custom workflow API endpoints for inference serving. Integrates with Terradev's inference router and KV cache management for intelligent routing.

- **Gateway CLI command** (`terradev gateway`):
  ```bash
  terradev gateway
  terradev gateway --host 0.0.0.0 --port 8080
  terradev gateway --no-anthropic --max-concurrent 50
  terradev gateway --model meta-llama/Llama-3.1-8B-Instruct
  ```

- **OpenAI-compatible endpoints**:
  - POST /v1/chat/completions
  - POST /v1/completions

- **Anthropic-compatible endpoints**:
  - POST /v1/messages
  - POST /v1/messages/batches

- **Custom workflow endpoints**:
  - POST /v1/custom/entry/{workflow_id}
  - POST /v1/custom/exit/{workflow_id}

- **Management endpoints**:
  - GET /health
  - GET /v1/gateway/status

- **Features**:
  - Streaming response support
  - Configurable CORS, concurrent requests, timeouts
  - Integration with inference router for intelligent routing
  - Request/response transformation and validation

**NOTES ON 5.3.9**

Added **LoRAX (LoRA eXchange) integration** and **HuggingFace PEFT import** for production-grade multi-LoRA inference serving:

- **LoRAX Service** (`ml_services/lorax_service.py`): Async HTTP client for Predibase LoRAX multi-LoRA inference server that serves thousands of fine-tuned models on a single GPU with dynamic adapter loading, heterogeneous continuous batching, and adapter exchange scheduling.

- **LoRAX CLI commands** (`terradev lora lorax`):
  ```bash
  terradev lora lorax deploy -m mistralai/Mistral-7B-Instruct-v0.1 --docker
  terradev lora lorax test --host localhost --port 8080
  terradev lora lorax list-adapters
  terradev lora lorax load-adapter -a vineetsharma/qlora-adapter-Mistral-7B-Instruct-v0.1-gsm8k
  terradev lora lorax unload-adapter -a my-adapter
  terradev lora lorax generate -p "What is 2+2?" -a my-adapter
  terradev lora lorax sync-registry
  ```

- **PEFT Import Service** (`ml_services/peft_import_service.py`): Download, validate, and prepare LoRA adapters from HuggingFace using the PEFT library with auto-detec
agentic-aiagentic-workflowcloud-gpudisaggregated-inferencedistributed-inferencegpu-clustergpu-provisioninghuggingfacekubernetesllm-inferencemcp-servermixture-of-expertsmlopsmulti-cloudollamaraysglangvllm

Lo que la gente pregunta sobre Terradev

¿Qué es theoddden/Terradev?

+

theoddden/Terradev es mcp servers para el ecosistema de Claude AI. An imperative command-line-interface for AI workload orchestration Tiene 25 estrellas en GitHub y su última actualización registrada es del 2026-09-08.

¿Cómo se instala Terradev?

+

Puedes instalar Terradev clonando el repositorio (https://github.com/theoddden/Terradev) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar theoddden/Terradev?

+

Nuestro agente de seguridad ha analizado theoddden/Terradev y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene theoddden/Terradev?

+

theoddden/Terradev es mantenido por theoddden. La última actividad registrada en GitHub es del 2026-09-08, con 13 issues abiertos.

¿Hay alternativas a Terradev?

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