List, filter, and recommend LLM models available in the AgenticFlow AI workspace for use in agents and workforce nodes. Should trigger whenever the user mentions LLM models, choosing a provider/model for an agent, model capabilities (reasoning, speed, cost), model selection for specific tasks, or understanding which models are available and their trade-offs. Use the live `af get /models` as the authoritative source. The rest of this skill provides recommendations and context but never overrides the live response.
List, filter, and recommend LLM models available in the AgenticFlow AI workspace for use in agents and workforce nodes. Should trigger whenever the user mentions LLM models, choosing a provider/model for an agent, model capabilities (reasoning, speed, cost), model selection for specific tasks, or understanding which models are available and their trade-offs. Use the live `af get /models` as the authoritative source. The rest of this skill provides recommendations and context but never overrides the live response.
compatibility
Claude Code, Claude Desktop, Codex, Cursor, Gemini CLI
Choose the right model for your agent based on capability needs, speed requirements, and reasoning depth. Use built-in credits for all models listed here.
When NOT to use this skill
Use agenticflow-built-in-credits skill instead for pricing, credits, or billing questions. Use agenticflow-mcp skill if they need external API keys (BYOK). This skill covers model selection and capabilities, not account management or credit usage.
Orient first
af bootstrap --json
Extract models[] — this is the source of truth for available models in your workspace. Never hardcode model lists; they change between CLI releases and backend deployments. The models below are recommendations, but the live models[] array is the final authority.
Discover & health
af changelog --json # What's new in the CLI — model additions/removals
af context --json # AI agent orientation, env vars, invocation guidance
af bootstrap --strict --json # Health check — exits non-zero if degraded
af bootstrap returns an invocation block and data_fresh boolean. If data_fresh: false, the backend is degraded — don't rely on stale model data from a degraded response. af bootstrap --strict exits non-zero when the backend is unhealthy, so CI/automation can abort before choosing models against a degraded workspace.
Verification rule: Before recommending any model, check models[] from af bootstrap --json. If a model is absent from that list, warn the user and fall back to a confirmed model.
Author's Top 3 Recommendations
These are the author's personal picks based on reliability, reasoning quality, and speed:
Rank
Model
Role
Why
1st
deepseek-v4-flash
Primary default
Best all-rounder — strong reasoning, reliable tool use, good speed. Replaces the older GLM 4.7 Flash default.
2nd
gemini-2.5-flash-lite
Fallback / media
Fastest option with media support. Use when speed matters more than reasoning depth.
3rd
qwen-3.5-flash
Deep verification
Deepest thinker — use when reasoning depth and verification matter most.
Note:deepseek-v4-flash and gemini-2.5-flash-lite may not appear in every CLI release's hardcoded KNOWN_MODELS list if they ship between releases. Always verify against af bootstrap --json > models[]. If absent, the backend may still serve them — proceed with a dry-run to confirm.
Upstream Canonical Models
The CLI's built-in validator recognizes these models as of v1.10.5. They are always safe to use:
Deprecated — still served but being replaced by 2.5 Flash Lite
agenticflow/gpt-4o-mini
Fast
Light
Default for blueprints (v1.8.1+) — follows system prompts reliably, good for tool calling
agenticflow/qwen-3.5-flash
Medium-Deep
Very strong
Deep verification, complex reasoning
Default model change (v1.8.1+)
Before v1.8.1: Default was agenticflow/gemini-2.0-flash
After v1.8.1: Default is agenticflow/gpt-4o-mini
Reason for change: Gemini 2.0 Flash refuses web_search on "latest X" prompts citing knowledge cutoff, even with explicit system prompt rules. GPT-4o-mini follows system prompts and calls tools reliably.
Model selection guide
Need a default?
# Author's primary recommendation — deepseek-v4-flash
af agent create --body '{"name":"My Agent","model":"deepseek-v4-flash","project_id":"<id>"}' --json
# Or use the upstream blueprint default — gpt-4o-mini
af agent create --body '{"name":"My Agent","model":"agenticflow/gpt-4o-mini","project_id":"<id>"}' --json
# For maximum speed with media support
af agent create --body '{"name":"My Agent","model":"gemini-2.5-flash-lite","project_id":"<id>"}' --json
Need reasoning?
Deep verification: qwen-3.5-flash — deepest thinker
Reliable tool use + reasoning: deepseek-v3.2 or deepseek-v4-flash
Need speed only?
Fastest correct: gpt-4o-mini or gemma-4-31b-it
Workforce model selection
All agents in a workforce inherit the model:
# Default (v1.8.1+)
af workforce init --blueprint dev-shop --model agenticflow/gpt-4o-mini --name "My Team" --json
# Or use author's primary pick
af workforce init --blueprint dev-shop --model deepseek-v4-flash --name "My Team" --json
Reasoning configuration
Expose reasoning tokens (where supported):
af schema agent --field model_user_config --json
Check the full agent schema (for all fields):
af schema agent --json
For models with hidden reasoning (e.g. some Gemini variants), configure via thinking_config to expose reasoning tokens.
Verify model availability
# Always dry-run first
af agent create --body @agent.json --dry-run --json
The CLI validates the model string at create time. Typos fail fast with an actionable hint listing known models. If you pass a vendor/model-name-shaped string not in the known list, the CLI warns but allows it to proceed — so brand-new models work before the CLI is updated.
Avoid these models
Based on upstream changelog and known issues:
Model
Issue
agenticflow/gemini-2.0-flash
Deprecated, replaced by 2.5 Flash Lite. Still served but default changed to gpt-4o-mini.
agenticflow/gemini-2.0-flash-lite
Deprecated, replaced by 2.5 Flash Lite
In general, if a model is absent from af bootstrap --json > models[], it may have been deprecated or renamed. Check the hint field on 400/422 errors for alternatives.
Fallback model guide
If your preferred model is unavailable:
Run af bootstrap --json and check models[]
Pick the closest match from the confirmed list above
Use --dry-run on create to validate before deploying
For reasoning-heavy tasks: fall back to qwen-3.5-flash or deepseek-v3.2
For speed-first tasks: fall back to gpt-4o-mini or gemma-4-31b-it
Cleanup
Test agents consume credits. Delete when done:
af agent delete --agent-id <id> --json
On errors
400 / Invalid model → Check models[] from bootstrap; model may have been renamed or is not yet in the CLI's hardcoded list. Try --dry-run first.
402 / Payment Required → Model requires credits; see agenticflow-built-in-credits skill
422 / Model not available → Model temporarily unavailable; the hint suggests alternatives
finish_reason=length → Increase max_tokens in model_user_config
When hint is non-empty, follow it before retrying.
