What can you do with this server?

The Tuning Engines server lets you fine-tune open-source LLMs/SLMs on your GitHub repos and manage the full ML lifecycle via CLI or MCP tools. Training Jobs * Create fine-tuning jobs using agents like Cody (code autocomplete) or SIERA (bug-fix specialist) on GitHub repos * Estimate costs before submission; monitor live status including GPU usage and estimated charges * List, view, cancel, and retry failed jobs from checkpoints * Validate S3 credentials before submitting jobs with S3 export Models * List, view, and delete trained/imported models * Import from or export to S3; check import/export status * Browse supported base models (Qwen, Llama, DeepSeek, Mistral, CodeLlama, StarCoder, Gemma, Phi — 1B to 72B parameters) * Use LoRA, QLoRA, or full fine-tuning; iteratively fine-tune previously trained models Datasets * Create, list, view, and delete datasets sourced from S3 for training or evaluation Evaluations * Create, list, cancel, and monitor evaluations against datasets using evaluators like code execution, similarity, or LLM judge * Estimate evaluation costs and view detailed scores/metrics Inference * List available inference models, view API usage stats, and retrieve JWT tokens for direct API access Agents * List and view details of available specialized training agents Account & Billing * Check balance, view transaction history, add credits, and manage account details Configuration * Set API key, override API URL, and view current config

Which integrations are available for this server?

Allows the server to use GitHub repositories as the data source for fine-tuning LLMs and SLMs, enabling the creation of specialized agents like 'Cody' for code autocomplete and 'SIERA' for bug-fix specialization.

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Tuning Engines

by cerebrixos-org

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Tuning Engines CLI & MCP Server

tuning-engines-cli MCP server

npm version MCP Registry License: MIT

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

Related MCP server: ML Lab MCP

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):

CLI generates a device code and opens your browser
Sign up or log in (email/password, Google, or GitHub)
Click "Authorize" to grant CLI access
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.

License

MIT

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license - permissive license

quality

maintenance

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