Introduces image-generation models as a first-class [[models]] entry via
a new `modality` field on the model schema ("text" default, "image",
"audio"). When modality != "text", context_window / max_output_tokens
are optional since no conventional context gate exists — OpenAI's
gpt-image-2 docs omit them.
Adds `image_input_cost_per_m` / `image_output_cost_per_m` alongside
existing text token cost fields to cover the 4-price structure OpenAI
uses for image generation (text $5/$10, image $8/$30 per 1M tokens).
Validator updated to:
- accept any modality in {text, image, audio}
- require context_window/max_output_tokens only for modality=text
- range-check the two new cost fields
gpt-image-2 entry added to providers/openai.toml with pricing sourced
from https://developers.openai.com/api/docs/pricing. Snapshot
gpt-image-2-2026-04-21 listed as alias.
* fix(providers): remove ~anthropic, skip ~ prefixes in sync script
OpenRouter uses ~ prefixes for internal auto-routing aliases (e.g. ~anthropic).
These are not real providers — they already route through openrouter.toml.
The generated ~anthropic.toml was confusing (looked like a stale backup)
and redundant with the existing openrouter provider.
- Delete providers/~anthropic.toml
- Skip provider IDs starting with ~ in sync-pricing.py --create-missing
* fix(providers): remove morph, aider, kwaipilot
- morph: specialized code-editing/patching tool, not a general LLM provider
- aider: CLI meta-tool wrapper (base_url empty), redundant with claude-code/codex-cli/gemini-cli/qwen-code
- kwaipilot: Kwai internal coding assistant routed via OpenRouter, niche
* fix(sync): add morph/aider/kwaipilot to SKIP_PROVIDERS to prevent re-creation
* feat(sync): merge OpenRouter-only providers into openrouter.toml
Instead of generating standalone .toml files that just wrap the OpenRouter
endpoint, merge their models directly into openrouter.toml with the
standard 'openrouter/{provider}/{model}' ID convention.
- Add _build_model_fields() and _model_lines() helpers to deduplicate
model rendering between standalone and merged paths
- Add merge_into_openrouter() that appends new models idempotently
- generate_provider_toml() now only runs for providers in PROVIDER_API
- --create-missing routes OpenRouter-only providers to merge_into_openrouter
* fix(providers): remove 14 OpenRouter-only standalone files
These providers have no direct public API and all route through
openrouter.ai/api/v1. Per the new sync-pricing.py policy, their models
will be merged into openrouter.toml on the next CI run instead of
living in separate files that just wrap the OpenRouter endpoint.
Removed: allenai, deepcogito, essentialai, inclusionai, inflection,
liquid, meituan, nex-agi, nousresearch, prime-intellect, relace,
switchpoint, tngtech, writer
* fix(providers): remove 7 niche providers with no driver support
No dedicated LLM driver code exists for these providers — they rely
purely on OpenAI-compatible passthrough with no special handling.
Removing them reduces registry noise; users can still reach them via
openrouter.toml if needed.
Removed: microsoft, ibm-granite, xiaomi, upstage, inception, aion-labs, arcee-ai
* fix(providers): remove ai21, chutes, venice
All three use ApiFormat::OpenAI with no special handling — pure passthrough.
No registry entry needed; users can reach them via openrouter.toml or by
adding a custom provider.
* docs(providers): rewrite README with full provider catalog and inclusion criteria
- List all 46 providers grouped by category with descriptions
- Document why each provider exists (direct API, unique endpoint, dedicated driver, local, CLI)
- Add inclusion criteria section explaining when to create standalone files vs merging into openrouter.toml
- Document sync script routing logic
- Update model counts: 49→46 providers, 339→232 models
* docs: add comprehensive READMEs for all registry sections + deepinfra provider
- agents/README.md: 32 agents across 7 categories with capability field reference
- channels/README.md: 44 channels across 5 categories with protocol reference table
- hands/README.md: 18 hands across 5 categories with HAND.toml format guide
- mcp/README.md: 33 MCP servers across 5 categories with transport/auth format
- plugins/README.md: 12 plugins with hook protocol documentation
- skills/README.md: 60 skills across 9 categories with SKILL.md format guide
- providers/deepinfra.toml: add DeepInfra serverless inference (5 models)
Add `claude-opus-4-7`, Anthropic's current flagship model (per
https://platform.claude.com/docs/en/docs/about-claude/models/overview).
- Context window: 1,000,000 tokens (Opus 4.7 ships with a new tokenizer)
- Max output: 128,000 tokens
- Pricing: $5 / input MTok, $25 / output MTok (unchanged from 4.6)
- Tier: frontier
- Supports tools, vision, streaming, and adaptive thinking
Move the `opus` / `claude-opus` aliases from 4.6 to 4.7 so a user asking
for "opus" gets the current flagship. Opus 4.6 is now in Anthropic's
"Legacy" section of the models overview; keeping it in the registry
entry (for existing callers that pin the exact ID) but without the
generic aliases.
Also **correct Opus 4.6's `context_window`**: it was listed as 200,000
tokens but the official model page has shown 1M tokens since release.
That was a pre-existing bug this PR fixes in passing since it directly
affects anyone who'd have routed queries to 4.6 expecting 1M.
Add a header comment pinning the source URL and explaining the
"latest-first" ordering convention so future additions don't silently
rebind aliases to a previous-generation snapshot.
Add the two GPT-5.4 variants exposed by OpenAI's API (source:
https://developers.openai.com/api/docs/models/gpt-5.4 and
https://developers.openai.com/api/docs/models/gpt-5.4-mini):
- **gpt-5.4** — frontier tier, 1,050,000 context window, 128k max output,
$2.50 / input MTok, $15.00 / output MTok.
- **gpt-5.4-mini** — balanced tier (matching the naming convention used
by `gpt-5-mini`, `gpt-4.1-mini`, etc.), 400,000 context window,
128k max output, $0.75 / input MTok, $4.50 / output MTok.
Ordered right after the GPT-5.2 family and before the Codex variants
section to keep the frontier-GPT chain in release order.
Addresses librefang/librefang-registry#65 — the original request filed
these under the `codex-cli` provider, but Codex CLI's upstream
`models.json` doesn't list `gpt-5.4-mini` (only the full `gpt-5.4`
slug is list-visible there, and it's already tracked in
`providers/codex-cli.toml`). The correct home for OpenAI-API-direct
access is this file; users who want `gpt-5.4-mini` should configure
`provider = "openai"` rather than `provider = "codex-cli"`.
The prior entries (o4-mini, o3, gpt-4.1) no longer appear in
upstream openai/codex's codex-rs/models-manager/models.json and the
Codex CLI actively migrates users away from the old gpt-5 /
gpt-5-codex slugs via tui/src/model_migration.rs.
Replace with the currently-visible ("visibility": "list") slugs:
- gpt-5.4
- gpt-5.3-codex
- gpt-5.2
- gpt-5.2-codex
- gpt-5.1-codex-max
- gpt-5.1-codex-mini
All six share a 272k context window per upstream models.json.
Marking supports_tools / supports_vision / supports_streaming = true
to match the gpt-5-family capability envelope.
Closeslibrefang/librefang#2347
Providers with known public APIs use their official endpoints:
- meta-llama → api.llama.com/v1
- microsoft → models.inference.ai.azure.com (GitHub Models)
- ibm-granite → us-south.ml.cloud.ibm.com/ml/v1 (watsonx)
- tencent → api.hunyuan.cloud.tencent.com/v1
- morph → api.morphllm.com/v1
16 remaining providers without known public APIs route through
OpenRouter (base_url = openrouter.ai/api/v1, OPENROUTER_API_KEY).
sync-pricing.py updated with PROVIDER_API mapping.
Providers without their own public API now use OpenRouter as their
base_url with OPENROUTER_API_KEY, making them testable and usable
when the user has an OpenRouter key configured.
- 20 OpenRouter-only providers: set base_url to openrouter.ai/api/v1
- morph: set correct official API (api.morphllm.com/v1)
- sync-pricing.py: default to OpenRouter routing for new providers
- Merge unique models from duplicate providers into their hand-written
counterparts and remove the duplicates:
- alibaba (tongyi-deepresearch) → qwen
- amazon (nova-2-lite, nova-micro, nova-premier) → bedrock
- bytedance (ui-tars) → volcengine
- nvidia (nemotron-3-nano, nemotron-3-super, etc.) → nvidia-nim
- rekaai (reka-flash-3) → reka
- Set correct official API base_url for providers with public APIs:
arcee-ai, inception, morph, reka, upstage
- Set key_required=false for 20 providers only accessible through
hosting platforms (no public API)
- Update sync-pricing.py with SKIP_DUPLICATES, PROVIDER_API mapping,
and default key_required=false for future auto-generated providers
- Replace tier "free" with "fast" (valid tiers: frontier/smart/balanced/fast/local)
- Remove version suffix from teams-mcp integration id field
- Update sync-pricing.py to not generate invalid tier values
* fix: pin npm package versions in MCP integration templates
Prevent supply chain attacks by pinning exact versions instead of
using unpinned `npx -y @package` which pulls latest on every run.
23 of 25 integrations pinned. sqlite-mcp and aws skipped (packages
not found on npm registry).
* fix: use stable azure/mcp version instead of beta
* feat: add pricing sync script and update model prices from OpenRouter API
- scripts/sync-pricing.py fetches real-time pricing from OpenRouter
- Updated 64 price fields across 13 provider files
- Run periodically or in CI to keep prices current
* feat: add Qwen International and US provider catalogs
* refactor: merge qwen-us into qwen-intl with regions support
* refactor: merge regional providers into single files with regions
Merge qwen-intl.toml into qwen.toml with [provider.regions] for
intl (Singapore) and us (Virginia) endpoints.
Merge minimax-cn.toml into minimax.toml with [provider.regions.china]
including separate api_key_env for China endpoint.
Uses new RegionConfig table format instead of simple string URLs.
* feat: add 4 context engine plugins
- topic-memory: keyword clustering for topic-aware memory recall
- episodic-memory: conversation segmentation and cross-session recall
- user-profile: persistent user profiling from conversation patterns
- context-decay: time-based memory decay with reinforcement dynamics
All plugins use the ingest/after_turn hook protocol with stdin/stdout JSON.
* chore: add plugin scaffolding, update docs and templates
- Add plugin.toml template with {{NAME}} placeholder
- Add new-plugin Makefile target with hooks/ scaffolding
- Update plugins/README.md with all 10 plugins
- Update README.md stats (10 plugins, 220+ models)
- Add Plugin checkbox and checklist to PR template
- Add Plugin to issue template content type dropdown
- Fix CONTRIBUTING.md: last_verified is recommended, not required
* fix: correct model pricing and remove deprecated entries
- openrouter/gemma-2-9b-it: fix pricing from 0.0 to 0.03/0.09 per M tokens
(free variant correctly stays at 0.0)
- github-copilot: remove deprecated copilot/gpt-4 model entry
(GPT-4 retired in favor of GPT-4o for Copilot)
* docs: annotate kimi-coding as membership-gated
Kimi Code CLI uses quota-based membership model (not per-token billing).
Free tier has limited weekly requests; underlying model is K2.5.
Pricing kept at 0.0 consistent with other subscription providers
(chatgpt, github-copilot) but with explanatory comments.
* style: fix trailing newline in github-copilot.toml
* fix: correct Moonshot/Kimi model pricing from official sources
All 5 models had incorrect pricing:
- moonshot-v1-8k: 0.10/0.10 → 0.20/2.00
- moonshot-v1-32k: 0.30/0.30 → 1.00/3.00
- moonshot-v1-128k: 0.80/0.80 → 2.00/5.00
- kimi-k2: 2.00/8.00 → 0.60/2.50
- kimi-k2.5: 2.00/8.00 → 0.45/2.20
Sources: platform.moonshot.ai/docs/pricing/chat, costgoat.com, getmaxim.ai
* feat: add MiniMax M2.7 and M2.7-highspeed models
Released 2026-03-18, MiniMax's latest flagship text model.
10B activated params, 200K context, 128K output, tool use, streaming.
Pricing: $0.30/$1.20 per M tokens (input/output).
Added to both international (minimax.io) and China (minimaxi.com) providers.
* chore: remove router agent
builtin:router has been replaced by LLM intent routing in the kernel.
Assistant is now the sole entry point — see librefang/librefang#1336.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* style: format all TOML files with taplo
Fix CI taplo format check by running `taplo fmt` on all 132 TOML files.
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Each directory (agents, hands, integrations, plugins, providers,
scripts, skills) now has a README documenting its TOML format,
current contents, and contribution steps.
- Add Doubao Seed 2.0 Pro (frontier, 256K context, 128K output)
- Add Doubao Seed 2.0 Code
- Add Doubao Seed 1.8 (multimodal agent model with vision)
- Add Doubao Seed 1.6 Vision
- Update existing 2.0 Lite/Mini with correct specs (256K/128K, vision)
- Update global alias "doubao" to point to 2.0 Pro
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add openrouter/stepfun/step-3.5-flash:free — a free model with 128K
context, tool support, and streaming. Used as default for one-click
cloud deployments.
Community-maintained TOML catalog for LibreFang. New models can be added
via PR without requiring a LibreFang binary release.
Includes validation script, bilingual docs, and GitHub templates.
Evan