Tuning Engines CLI & MCP Server

Govern every AI workflow through one API.

Tuning Engines is a governed AI runtime for model, agent, skill, and MCP workflows. Route inference through one OpenAI-compatible API, apply RBAC and traffic policies, request approvals for high-risk actions, inspect traces and usage, and connect durable orchestration frameworks such as LangGraph and Temporal. The same CLI and MCP server also manage domain-specific fine-tuning of open-source models.

Training Agents

Tuning Engines uses specialized agents that control how your data is analyzed and converted into training data. Each agent produces a different kind of domain-specific fine-tuned model optimized for its use case. Current agents focus on code, with more coming for customer support, data extraction, security review, ops, and other domains.

Cody (code_repo) — Code Autocomplete Agent

Cody fine-tunes on your GitHub repo using QLoRA (4-bit quantized LoRA) via the Axolotl framework (HuggingFace Transformers + PEFT). It learns your codebase's patterns, naming conventions, and project structure to produce a fast, lightweight adapter optimized for real-time completions.

Best for: code autocomplete, inline suggestions, tab-complete, code style matching, pattern completion.

te jobs create --agent code_repo \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-cody-model

SIERA (sera_code_repo) — Bug-Fix Specialist

SIERA (Synthetic Intelligent Error Resolution Agent) uses the Open Coding Agents approach from AllenAI to generate targeted bug-fix training data from your repository. It synthesizes realistic error scenarios and their resolutions, then fine-tunes a model that learns your team's debugging style, error handling conventions, and fix patterns.

Best for: debugging, error resolution, patch generation, root cause analysis, fix suggestions.

te jobs create --agent sera_code_repo \
  --quality-tier high \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-siera-model

Quality tiers (SIERA only):

• low — Faster, fewer synthetic pairs (default)
• high — Deeper analysis, more training data, better results

Coming Soon

Agent	Persona	What it does
Resolve	Mira	Fine-tunes on support tickets, macros, and KB articles for automated ticket resolution
Extractor	Flux	Trains for strict schema extraction from docs, PDFs, and business text
Guard	Aegis	Security-focused code reviewer that catches risky patterns and proposes safer fixes
OpsPilot	Atlas	Incident response agent trained on runbooks, postmortems, and on-call notes

Supported Base Models

Size	Models
3B	Qwen/Qwen2.5-Coder-3B-Instruct
7B	codellama/CodeLlama-7b-hf, deepseek-ai/deepseek-coder-7b-instruct-v1.5, Qwen/Qwen2.5-Coder-7B-Instruct
13-15B	codellama/CodeLlama-13b-Instruct-hf, bigcode/starcoder2-15b, Qwen/Qwen2.5-Coder-14B-Instruct
32-34B	deepseek-ai/deepseek-coder-33b-instruct, codellama/CodeLlama-34b-Instruct-hf, Qwen/Qwen2.5-Coder-32B-Instruct
70-72B	codellama/CodeLlama-70b-Instruct-hf, meta-llama/Llama-3.1-70B-Instruct, Qwen/Qwen2.5-72B-Instruct

Quick Start

npm install -g tuningengines-cli

# Or run without installing
npx -y --package tuningengines-cli@latest te auth status

# Sign up or log in (opens browser — works for new accounts too)
te auth login

# Add credits (opens browser to billing page)
te billing add-credits

# Estimate cost before training
te jobs estimate --base-model Qwen/Qwen2.5-Coder-7B-Instruct

# Train Cody on your repo
te jobs create --agent code_repo \
  --base-model Qwen/Qwen2.5-Coder-7B-Instruct \
  --repo-url https://github.com/your-org/your-repo \
  --output-name my-model

# Monitor training
te jobs status <job-id> --watch

# View your trained models
te models list

# Create a governed orchestration starter
te orchestration init langgraph
te orchestration init temporal
te orchestration init inngest
te orchestration init triggerdev
te orchestration init hatchet
te orchestration init restate
te orchestration init dbos
te orchestration init dapr
te orchestration init prefect
te orchestration init dagster
te orchestration init airflow

MCP Server Setup

The CLI includes a built-in MCP server with 60+ tools. Any AI assistant that supports MCP can fine-tune models, manage training jobs, run evaluations, check inference usage, inspect traces, review approvals, and manage non-secret tenant registry metadata through natural language.

For security, the MCP server intentionally does not expose internal proxy routes. It also refuses MCP-side inference-key creation and raw secret-bearing mutation fields. Use the CLI or web UI for workflows that intentionally create one-time keys, submit raw provider secrets, validate S3 credentials, or import/export S3 assets with raw credentials.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "tuning-engines": {
      "command": "npx",
      "args": ["-y", "--package", "tuningengines-cli@latest", "te", "mcp", "serve"],
      "env": {
        "TE_API_KEY": "te_your_key_here"
      }
    }
  }
}

Claude Code

claude mcp add tuning-engines -- npx -y --package tuningengines-cli@latest te mcp serve

Work Sessions and outcomes

Label the desired outcome for a project without interrupting your coding workflow:

te goal start "Fix flaky checkout retries"
te goal show
te goal complete --result succeeded

Install optional native telemetry hooks for Claude Code or Codex:

te guard claude-code install --mode observe --project .
te guard claude-code doctor
te guard claude-code doctor --probe
te guard codex install

Claude Code writes project-local hooks into .claude/settings.local.json. On Windows, verify with dir .\.claude, type .\.claude\settings.local.json, then restart Claude Code from the same project root and review claude /hooks. doctor --probe is available in tuningengines-cli 0.4.20 and later; it runs synthetic hook events through the installed commands and checks that the trace is visible to Tuning Engines. Hook invocations also write a local redacted status log at .claude/tuning-engines-hook-status.jsonl. Codex project hooks require review and trust from /hooks. Tuning Engines sends pseudonymous session and transcript references by default, not transcript contents or local absolute paths.

Claude Code Plugin

The repository also ships a Claude Code plugin wrapper around the same MCP server. It keeps installation discoverable while preserving the same TE_API_KEY environment-variable boundary:

claude plugin marketplace add cerebrixos-org/tuning-engines-cli
claude plugin install tuning-engines@tuning-engines

VS Code / Cursor / Windsurf

Add to your MCP settings (.vscode/mcp.json or equivalent):

{
  "servers": {
    "tuning-engines": {
      "command": "npx",
      "args": ["-y", "--package", "tuningengines-cli@latest", "te", "mcp", "serve"],
      "env": {
        "TE_API_KEY": "te_your_key_here"
      }
    }
  }
}

What the AI assistant can do

When connected, your AI assistant can:

• "Fine-tune Qwen 7B on my-org/my-repo using the SIERA agent with high quality"
• "How much would it cost to train a 32B model for 3 epochs on this repo?"
• "Check the status of my latest training job"
• "List my trained models"
• "Export my model to s3://my-bucket/models/"
• "Show my account balance"
• "Train a bug-fix specialist on this repo" (auto-selects SIERA)
• "Create an autocomplete model for this codebase" (auto-selects Cody)

The create_job tool description includes full agent details and model lists, so AI assistants automatically select the right agent and model based on what you ask for.

Unified API Endpoint

Tuning Engines can be used anywhere a tool accepts an OpenAI-compatible API base URL. Point the client at:

https://api.tuningengines.com/v1

Use an inference key that starts with sk-te-... for live model calls, and use the model IDs shown by:

te inference models

This lets OpenCode, Temporal activities, LangGraph apps, OpenAI SDK clients, and other custom-provider clients route through the same Tuning Engines control plane for model RBAC, routing, fallbacks, guardrails, AGT policy, traces, usage metering, and cost attribution.

See docs/unified-api-endpoint.md for copy-paste examples for OpenCode, Temporal, Python, JavaScript, and other OpenAI-compatible clients.

Agent Runtime SDK and Orchestration Starters

Use the CLI/MCP package when you want npx tools for assistants. Use the Python SDK when you want your own app to run durable agent workflows while Tuning Engines remains the governed control plane for models, agents, skills, MCP tools, RBAC, AGT policy, audit, usage, and token economics.

Install directly from this repo:

pip install "tuning-agents[langgraph] @ git+https://github.com/cerebrixos-org/tuning-engines-cli.git#subdirectory=packages/tuning-agents"
pip install "tuning-agents[temporal] @ git+https://github.com/cerebrixos-org/tuning-engines-cli.git#subdirectory=packages/tuning-agents"

LangGraph example:

from langgraph.checkpoint.memory import InMemorySaver

from tuning_agents import TuningClient
from tuning_agents.langgraph import create_tuning_langgraph_agent, invoke_with_trace

client = TuningClient(api_key="te_your_key_here")

agent = create_tuning_langgraph_agent(
    client,
    model="llama-3.3-70b-fp8",
    agent_names=["billing-escalation"],
    checkpointer=InMemorySaver(),
    interrupt_before=["tools"],
)

result = invoke_with_trace(
    client,
    agent,
    [{"role": "user", "content": "Triage this ticket and escalate if needed."}],
    thread_id="ticket-123",
)

client.flush_trace(name="ticket-triage", runtime="langgraph", status="succeeded")

Temporal example:

from tuning_agents.temporal import (
    agent_message_activity,
    chat_completion_activity,
    define_temporal_workflow,
    mcp_tool_activity,
)

TuningAgentWorkflow = define_temporal_workflow()
# Register TuningAgentWorkflow plus the three activities in your Temporal worker.

The SDK captures runtime events from LangGraph/Temporal and posts them to POST /api/v1/traces. Each event carries a run_id, request_id, and a normalized event type such as model.call, mcp.tool_call, agent.message, workflow.step, human.edit, action.finalized, outcome.recorded, or state.reference. The app pairs that with inference usage, request capture, policy decisions, approval requests, external state references, audit, and billing logs.

JavaScript/TypeScript users can also import lightweight tracing helpers from the npm package:

import { createOpenAIAgentsTraceAdapter } from "tuningengines-cli/adapters/openai-agents";
import { createClaudeAgentSdkTraceAdapter } from "tuningengines-cli/adapters/claude-agent-sdk";

Both helpers send redacted run, model, tool, handoff, error, goal, and outcome events to the existing trace API. goal_key, goal_status, and goal_score are normalized into the same success-signal analytics as outcome_key.

For decision traces, store redacted signals in metadata.decision, for example proposal_summary, changed_fields, change_summary, final_action, outcome_label, and reason_summary. Do not place raw prompts, provider keys, tenant secrets, or full customer data in trace metadata.

Generate a starter kit:

te orchestration init langgraph --dir ./lg-te-demo
te orchestration init temporal --dir ./temporal-te-demo
te orchestration init inngest --dir ./inngest-te-demo
te orchestration init triggerdev --dir ./trigger-te-demo
te orchestration init hatchet --dir ./hatchet-te-demo
te orchestration init restate --dir ./restate-te-demo
te orchestration init dbos --dir ./dbos-te-demo
te orchestration init dapr --dir ./dapr-te-demo
te orchestration init prefect --dir ./prefect-te-demo
te orchestration init dagster --dir ./dagster-te-demo
te orchestration init airflow --dir ./airflow-te-demo

LangGraph and Temporal starters use the Python runtime SDK. Inngest, Trigger.dev, and Hatchet starters generate TypeScript projects with a small self-contained Tuning Engines helper. Restate, DBOS, and Dapr starters use the same TypeScript helper. Prefect, Dagster, and Airflow starters generate Python workflow examples with a small helper module. All generated examples include governed model calls, trace flushing, registry manifests, policy context metadata, decision metadata, runtime state references, and approval retry patterns.

CLI Commands

Authentication

Command	Description
te auth login	Sign up or log in via browser
te auth logout	Clear saved credentials
te auth status	Show current auth status (email, balance)

Training Jobs

Command	Description
te jobs list	List all training jobs
te jobs show <id>	Show job details
te jobs create	Submit a training job (--agent, --quality-tier, --base-model, --repo-url, --output-name)
te jobs status <id>	Live status (--watch for continuous polling)
te jobs cancel <id>	Cancel a running job
te jobs retry <id>	Retry from last checkpoint
te jobs estimate	Cost estimate before submitting
te jobs validate-s3	Pre-validate S3 credentials

Models

Command	Description
te models list	List your trained models
te models show <id>	Show model details
te models base	List supported base models
te models import	Import a model from S3
te models export <id>	Export a model to S3
te models delete <id>	Delete a model
te models status <id>	Check import/export status

Datasets

Command	Description
te datasets list	List all datasets
te datasets show <id>	Show dataset details
te datasets create	Create a dataset from S3 (--name, --s3-url, --for-evaluation)
te datasets delete <id>	Delete a dataset
te datasets status <id>	Check import/processing status

Evaluations

Command	Description
te evals list	List all evaluations
te evals show <id>	Show evaluation details and scores
te evals create	Run an evaluation (--model, --dataset, --evaluators)
te evals cancel <id>	Cancel a running evaluation
te evals status <id>	Live evaluation progress
te evals evaluators	List available evaluators
te evals estimate	Cost estimate for an evaluation

Inference

Command	Description
te inference models	List available inference models
te inference usage	Show inference API usage stats
te inference jwt	Get a JWT for direct API access
te inference token	Exchange an inference key (sk-te-...) for a short-lived inference JWT

Runtime Traces and Approvals

Command	Description
te traces list	List LangGraph, Temporal, and custom runtime traces
te traces show <run-id>	Show one trace, including events, policy decisions, and approvals when linked
te traces ingest --data '<json>'	Ingest or update a trace using a user API token or inference key
te outcomes list	List observed outcomes, goals, evals, and workflow success signals
te outcomes record --run-id ... --key ... --label ...	Record a success signal for a run
te outcomes map --outcome-key ... --criteria '<json>'	Map unmapped events to an outcome key
te insights list	List Insight Loop recommendations
te insights accept <id>	Accept an insight as valid; does not change production
te insights apply <id>	Apply or queue the approved action for an accepted insight
te doctor simulate --data '<json>'	Simulate inference access, role, endpoint, policy, and resource checks
te policy-decisions list	List AGT YAML policy decisions
te policy-decisions show <id>	Show one policy decision with redacted context
te policy-templates list	List curated AGT YAML policy templates
te policy-templates render <id> --params '<json>'	Render disabled/shadow policy YAML from safe structured parameters
te policy-drafts generate --prompt '<text>'	Generate an AI-assisted disabled/shadow draft for review and testing
te approvals list --status pending	List policy approval requests
te approvals show <id>	Show approval detail and retry metadata
te approvals approve <id>	Approve a pending request
te approvals deny <id>	Deny a pending request

Orchestration Starters

Command	Description
te orchestration init langgraph	Create a LangGraph starter wired to Tuning Engines governance and traces
te orchestration init temporal	Create a Temporal worker starter wired to Tuning Engines governance and traces
te orchestration init inngest	Create an Inngest function starter wired to Tuning Engines governance and traces
te orchestration init triggerdev	Create a Trigger.dev task starter wired to Tuning Engines governance and traces
te orchestration init hatchet	Create a Hatchet workflow starter wired to Tuning Engines governance and traces
te orchestration init restate	Create a Restate service starter wired to Tuning Engines governance and traces
te orchestration init dbos	Create a DBOS workflow starter wired to Tuning Engines governance and traces
te orchestration init dapr	Create a Dapr Workflow starter wired to Tuning Engines governance and traces
te orchestration init prefect	Create a Prefect flow starter wired to Tuning Engines governance and traces
te orchestration init dagster	Create a Dagster asset starter wired to Tuning Engines governance and traces
te orchestration init airflow	Create an Airflow DAG starter wired to Tuning Engines governance and traces

Agents

Command	Description
te agents list	List available agents
te agents show <id>	Show agent details and capabilities

Tenant Admin Automation

These commands require an API token for a tenant owner or tenant admin. They are designed for CI smoke tests and end-to-end product checks. Secret fields can be sent on create/update where the server supports them, but responses never print stored provider keys, AWS secrets, or invitation tokens.

Command	Description
te tenant resources	List supported tenant resource names
te tenant list <resource>	List resources such as inference_keys, inference_roles, model_deployments, routing_profiles, guardrail_policies, governance_policies, mcp_servers, tenant_agents, tenant_skills, and credential_sources
te tenant show <resource> <id>	Show one tenant resource
te tenant create <resource> --data '<json>'	Create a tenant resource from JSON
te tenant update <resource> <id> --data '<json>'	Update a tenant resource from JSON
te tenant delete <resource> <id>	Delete a tenant resource; inference keys are revoked
te tenant validate guardrail_policies --data '<json>' --sample-text 'hello'	Validate/test an unsaved simple guardrail without creating records
te tenant validate governance_policies --data '<json>' --context '<json>'	Validate/test an unsaved Governance Rule without creating records
te tenant test-policy <id> --context '<json>'	Dry-run a Governance Rule
te tenant test governance_policies <id> --context '<json>'	Compatibility alias for governance policy dry-runs
te tenant team list	List tenant members, pending invitations, and allowed domains
te tenant team invite <email> --role member	Invite a user by email; the invite token is emailed and never printed
te tenant team set-role <member-id> --inference-role-id <id>	Assign an inference role to a member
te tenant team disable <member-id>	Disable a member
te tenant team enable <member-id>	Re-enable a member
te tenant team remove <member-id>	Remove a member
te tenant team cancel-invite <invitation-id>	Cancel a pending invitation
te tenant team domains --set "example.com,example.org"	Replace allowed email domains
te tenant capture show	Show inference capture settings
te tenant capture update --data '<json>'	Update inference capture settings

Billing & Account

Command	Description
te billing show	Balance and transaction history
te billing add-credits	Open browser to add credits
te account	Account info

Configuration

Command	Description
te config set-token <key>	Set API key manually
te config set-url <url>	Override API URL
te config show	Show current config

All commands support --json for machine-readable output.

MCP Tools Reference

Training Jobs

Tool	Description
create_job	Fine-tune an LLM on a GitHub repo. Supports agent selection (Cody, SIERA), quality tier, base model, epochs, S3 export.
estimate_job	Cost estimate before training. Returns cost range, balance, sufficiency check.
list_jobs	List training jobs with status filter
show_job	Full job details including agent, model, GPU usage, cost, retry info
job_status	Live status with GPU minutes, charges, delivery progress
cancel_job	Cancel a running/queued job
retry_job	Retry a failed job from its last checkpoint

Models

Tool	Description
list_models	List trained and imported models
show_model	Model details (status, size, base model, training job)
delete_model	Delete a model from cloud storage
model_status	Import/export progress
list_supported_models	Available base models with GPU hours per epoch

Marketplace

Tool	Description
list_catalog_models	Browse pre-built models and datasets
get_catalog_model	Details of a marketplace item
catalog_export_status	Check marketplace export progress

Datasets

Tool	Description
list_datasets	List datasets for training and evaluation
show_dataset	Dataset details and status
create_dataset	Create a dataset from S3
delete_dataset	Delete a dataset
dataset_status	Check dataset import/processing status

Evaluations

Tool	Description
list_evaluations	List model evaluations
show_evaluation	Evaluation details, scores, and metrics
create_evaluation	Run an evaluation against a dataset
cancel_evaluation	Cancel a running evaluation
evaluation_status	Live evaluation progress
list_evaluators	Available evaluators (code_execution, similarity, llm_judge, etc.)
estimate_evaluation	Cost estimate for an evaluation

Inference

Tool	Description
list_inference_models	Models available for inference
inference_usage	Inference API usage statistics
get_inference_jwt	Get JWT token for direct API access
get_inference_token	Exchange an inference key for a short-lived inference JWT

Runtime, Policy, and Approvals

Tool	Description
list_traces	List runtime traces
show_trace	Show a trace with linked events, policy decisions, and approvals
create_trace	Ingest a trace payload without secrets
list_outcomes	List observed outcomes/goals normalized as success signals
list_insights	List Insight Loop recommendations
show_insight	Show one Insight Loop recommendation
doctor_simulate	Simulate inference access, role, endpoint, policy, and resource checks
record_outcome	Record an outcome/goal signal; requires --enable-registry-writes
map_outcome	Create an outcome mapping rule; requires --enable-registry-writes
accept_insight	Accept an insight for review; requires --enable-registry-writes
apply_insight	Apply or queue an accepted insight; requires --enable-registry-writes
list_policy_decisions	List AGT YAML policy decisions
show_policy_decision	Show one decision with redacted context
list_policy_templates	List curated AGT YAML policy templates
render_policy_template	Render disabled/shadow policy YAML from safe structured parameters
generate_policy_draft	Generate an AI-assisted disabled/shadow draft; secret-looking prompts are refused
list_approvals	List policy approval requests
show_approval	Show one approval request
approve_approval	Approve a pending request
deny_approval	Deny a pending request

Tenant Admin MCP Tools

These tools require a tenant owner/admin API token. The MCP server refuses internal proxy routes, inference-key creation, and raw secret-bearing mutation fields.

Tool	Description
list_tenant_resources	List allowlisted tenant resource names
tenant_resource_list	List models, roles, policies, MCP servers, agents, skills, credential sources, and related metadata
tenant_resource_show	Show one resource without returning stored secrets
tenant_resource_create	Create non-secret tenant registry/config metadata
tenant_resource_update	Update non-secret tenant registry/config metadata
tenant_resource_delete	Delete or revoke a tenant resource
tenant_resource_validate	Validate/test unsaved guardrail or AGT policy payloads without creating records
test_governance_policy	Dry-run an AGT YAML governance policy
tenant_team_list	List members, invitations, and allowed domains
tenant_team_invite	Invite a user without returning invitation tokens
tenant_team_set_inference_role	Assign or clear an inference role
tenant_team_disable / tenant_team_enable	Disable or re-enable a member
tenant_team_remove	Remove a tenant member
tenant_invitation_cancel	Cancel a pending invitation
tenant_domains_update	Replace allowed email domains
inference_capture_show / inference_capture_update	Manage request-capture settings using credential-source references

Agents

Tool	Description
list_agents	List available agents
show_agent	Agent details and capabilities

Account

Tool	Description
get_balance	Account balance and recent transactions
get_account	Account details

Environment Variables

Variable	Description
TE_API_KEY	API key (overrides config file)
TE_API_URL	API URL (default: https://app.tuningengines.com)

Tenant management commands keep the configured te_* API token local and exchange it for a short-lived management JWT before calling the API. Inference keys (sk-te-*) are for inference-only flows such as te inference token and proxy calls; they are not accepted for tenant registry management commands.

Inference Smoke Testing

Use te-inference-smoke to exercise inference behavior as a tenant admin and, optionally, real tenant users. The default run is read-only. Set TE_SMOKE_MUTATE=1 to create temporary inference roles, keys, policies, guardrails, MCP servers, agents, and skills, then test permission permutations and clean them up.

If you only have an sk-te-* inference key, set TE_INFERENCE_KEY for proxy-only checks. Full role/user/policy permutations require a tenant-admin app API key that starts with te_.

TE_API_URL=https://app.tuningengines.com \
TE_ADMIN_API_KEY=te_admin_key_here \
TE_USER_API_KEY=te_user_key_here \
npx -y --package tuningengines-cli@latest te-inference-smoke

For actual proxy model calls, enable live calls explicitly:

TE_API_URL=https://app.tuningengines.com \
TE_INFERENCE_BASE=https://api.tuningengines.com/v1 \
TE_ADMIN_API_KEY=te_admin_key_here \
TE_SMOKE_MUTATE=1 \
TE_SMOKE_LIVE_CALLS=1 \
TE_SMOKE_CREATE_MODEL_DEPLOYMENT=1 \
TE_SMOKE_ALLOWED_MODEL=llama-3.1-8b-fast \
TE_SMOKE_DENIED_MODEL=llama-3.3-70b-fp8 \
TE_SMOKE_AGENT_URL=https://httpbin.org/post \
npx -y --package tuningengines-cli@latest te-inference-smoke

TE_SMOKE_CREATE_MODEL_DEPLOYMENT=1 is useful for disposable tenants that do not already have an enabled model. By default the runner treats a provider authentication failure on an allowed model as proof that Tuning Engines RBAC allowed the request through to the provider. Set TE_SMOKE_ALLOW_PROVIDER_AUTH_FAILURE=0 when the tenant has real provider credentials and the allowed call must return 200.

To test multiple tenant users, provide their API tokens:

TE_SMOKE_USERS_JSON='[
  {"email":"member1@example.com","api_key":"te_user_key_1"},
  {"email":"member2@example.com","api_key":"te_user_key_2"}
]' \
TE_ADMIN_API_KEY=te_admin_key_here \
TE_SMOKE_MUTATE=1 \
npx -y --package tuningengines-cli@latest te-inference-smoke

Preview coverage:

npx -y --package tuningengines-cli@latest te-inference-smoke --list

Each run writes a masked JSON report under te-smoke-results/, or to TE_SMOKE_REPORT when that env var is set.

Authentication

te auth login uses a secure device authorization flow (same pattern as gh auth login):

1. CLI generates a device code and opens your browser
2. Sign up or log in (email/password, Google, or GitHub)
3. Click "Authorize" to grant CLI access
4. Token flows back automatically — no copy-paste

Works for both new sign-ups and existing accounts. Token saved to ~/.tuningengines/config.json with 0600 permissions.

Links

• Website
• MCP Registry
• npm
• GitHub

License

MIT