agenticflow-llm-models

name	agenticflow-llm-models
description	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
metadata	{"author":"Anton Gulin (https://github.com/antongulin)","version":"4.0.0","license":"MIT"}
triggers	["what models are available"]

Author: Anton Gulin · Tool: opencode-skill-creator · GitHub: @antongulin · Registry: skills.sh

AgenticFlow LLM Models

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:

agenticflow/deepseek-v3.2
agenticflow/gemma-4-31b-it
agenticflow/gemma-4-26b-a4b-it
agenticflow/gemini-2.0-flash
agenticflow/gpt-4o-mini
agenticflow/qwen-3.5-flash

Model characteristics

Model	Speed	Reasoning	Best for
agenticflow/deepseek-v3.2	Medium	Strong	Reliable reasoning, good tool use
agenticflow/gemma-4-31b-it	Fast	Light	General purpose, high-volume
agenticflow/gemma-4-26b-a4b-it	Fast	Light	General purpose, slightly smaller context
agenticflow/gemini-2.0-flash	Fast	Light	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.

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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.

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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:

1. Run af bootstrap --json and check models[]
2. Pick the closest match from the confirmed list above
3. Use --dry-run on create to validate before deploying
4. For reasoning-heavy tasks: fall back to qwen-3.5-flash or deepseek-v3.2
5. 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

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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.