Adds the web_search tool to every agent.toml and hand HAND.toml that did
not already declare it. Without this capability the runtime gates the
tool with 'Capability denied: tool not in allowed list', leaving agents
unable to perform web searches even when a search provider is
configured.
For tools arrays that already contained web_fetch, web_search is
inserted directly after it (its natural companion). For arrays without
web_fetch, web_search is appended to the end.
24 files updated total: 21 agents and 3 hands.
On dual-stack hosts (notably macOS), `localhost` resolves to both ::1
and 127.0.0.1 with IPv6 tried first. Local LLM servers (Ollama, vLLM,
LM Studio) installed via the standard scripts bind IPv4 only, so the
IPv6 connection attempt fails immediately and Happy Eyeballs fallback
to IPv4 isn't reliably triggered for connection-refused errors,
producing spurious "Configured local provider offline" warnings in
the daemon even when the server is up and reachable via curl.
Companion to librefang/librefang#3112 which fixes the hardcoded URL
constants in the main repo. After both land, existing installs pick
up the fix on their next registry sync.
OpenAI-compatible LLM gateway. Pairs with the librefang-llm-drivers
registration (librefang PR #3076) so Novita models surface in the
dashboard model picker without each user having to add them via
/api/models/custom.
Pricing and limits sourced from GET /openai/v1/models on 2026-04-25
(input_token_price_per_m / output_token_price_per_m, divided by 10000
to get USD per million tokens). Curated to a small popular subset:
- deepseek/deepseek-v3.2 (frontier)
- moonshotai/kimi-k2-thinking (frontier, supports_thinking)
- minimax/minimax-m2 (smart)
- meta-llama/llama-3.3-70b-instruct (smart)
- qwen/qwen3-coder-30b-a3b-instruct (balanced)
- zai-org/glm-4.7-flash (balanced)
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)
These 11 templates are the general-purpose ones that don't depend on any
particular model at the primary level (`[model] provider = "default"`).
They also ship a secondary `[[fallback_models]]` block pointing at
`gemini-2.0-flash` with `api_key_env = "GEMINI_API_KEY"`.
That default hurts everyone who doesn't happen to have `$GEMINI_API_KEY`
set — every agent boot logs `WARN Fallback driver 'gemini' failed to
init: Missing API key`, once per turn per agent. The templates that
actually intend to use Gemini as their primary model (analyst, coder,
researcher, code-reviewer, debugger, legal-assistant, data-scientist,
academic-researcher, test-engineer) are left untouched — those
declare Gemini in `[model]`, which is an intentional design choice, not
a hidden fallback.
Users who want a Gemini fallback chain for generic agents can add
`[[fallback_models]]` themselves in `~/.librefang/workspaces/agents/...`
once they've set `$GEMINI_API_KEY`.
Removed from:
assistant, customer-support, devops-lead, doc-writer, email-assistant,
meeting-assistant, planner, recruiter, sales-assistant, social-media,
writer
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"`.
* refactor: migrate icon fields from emoji to lucide:<name> tokens
Every TOML manifest's `icon = "<emoji>"` line is replaced with
`icon = "lucide:<kebab-name>"` — a reference to a lucide-react icon,
which the librefang.ai site and dashboard render as crisp SVG. Reasons
for the switch:
- Emoji render very differently across OS/browser/font stacks; the
registry catalog looked inconsistent from one row to the next.
- Five manifests (clip / creator / linkedin / reddit / twitter) had
their icons stored as literal Python-style escape strings
("\\U0001F3AC") because the TOML parser upstream never decoded
them. Switching away from emoji drops that class of bug entirely.
- As a drive-by, also decode the \\uXXXX accent escapes in the
[i18n.fr] block of hands/creator/HAND.toml so "Créateur" shows
up correctly.
87 files touched. example manifests left untouched (still "TODO").
* fix: backfill i18n name + drop the single-member email category
- Every existing [i18n.<lang>] block now has a `name` field. 60 files
previously translated description but kept the English name
implicitly — which rendered as "some English some Chinese" in the
registry UI. Fill in the missing name from the English brand (or a
known localized equivalent: DingTalk→钉钉, Feishu→飞书, Email→
电子邮件 / メール / E-Mail / Correo / Courriel, and a handful of
hands that have Chinese product names like 视频剪辑 Hand).
- channels/email.toml was the only item under category="email";
reclassify it as "messaging" so the sub-category filter chip list
on the category page isn't littered with singletons.
* feat(i18n): localize 76 agents/integrations/plugins into 7 languages
Adds full [i18n.zh], [i18n.zh-TW], [i18n.ja], [i18n.ko], [i18n.de],
[i18n.es], [i18n.fr] blocks with name + description to every manifest
that previously shipped English-only.
Coverage:
- 32 agents (academic-researcher, analyst, architect, assistant,
code-reviewer, coder, customer-support, data-scientist, debugger,
devops-lead, doc-writer, email-assistant, health-tracker,
hello-world, home-automation, legal-assistant, meeting-assistant,
ops, orchestrator, personal-finance, planner, recipe-assistant,
recruiter, researcher, sales-assistant, security-auditor,
social-media, test-engineer, translator, travel-planner, tutor,
writer)
- 33 integrations (AWS, Azure, Bitbucket, Brave Search, Discord,
Dropbox, Elasticsearch, Exa Search, Fetch, Filesystem, GCP, Git,
GitHub, GitLab, Gmail, Google Calendar, Google Drive, Google Maps,
Jira, Linear, Memory, MongoDB, Notion, PostgreSQL, Puppeteer, Redis,
Sentry, Sequential Thinking, Slack, SQLite, Teams, Time, Todoist) —
brand names kept as-is across all locales, only descriptions
translated.
- 11 plugins (auto-summarizer, context-decay, conversation-logger,
episodic-memory, guardrails, keyword-memory, mempalace-indexer,
sentiment-tracker, todo-tracker, topic-memory, user-profile)
The descriptions are one-line summaries — hand-translated rather than
machine-generated, so technical terms (MCP, PR, CI/CD, etc.) stay
consistent across locales.
* feat(i18n): close remaining per-lang gaps for channels, workflows, devteam
Third pass on i18n coverage. Every non-example manifest now carries a
full set of [i18n.zh], [i18n.zh-TW], [i18n.ja], [i18n.ko], [i18n.de],
[i18n.es], [i18n.fr] blocks.
- 44 channel adapters: added French descriptions (zh/zh-TW/ja/ko/de/es
were already present). Brand names kept as-is in all locales so users
recognize Discord / Slack / LINE / etc. consistently.
- 22 workflows: filled zh-TW / ja / ko / de / es / fr blocks. Each
translation mirrors the existing zh one in structure and tone so the
catalog reads consistently across locales.
- hands/devteam/HAND.toml: added the four langs that were missing
(zh-TW, de, es, fr).
Only the 6 templates under examples/ are left without i18n blocks on
purpose — they still contain "TODO:" placeholders.
The librefang MCP security check blocks shell interpreters (sh, bash)
as commands. Use `command = "npx"` with `$HOME` in args — the runtime
now expands env vars in args natively.
The provider creation form only collected api_key_env (the env var
name) but not the actual key value. New providers were always created
as "unconfigured" because no key was stored.
Add an optional secret api_key field so the dashboard can pass the
key value during creation. The backend strips it from the TOML and
saves it to secrets.env instead.
- Move example skills to examples/skills/
- Move scaffolding templates to examples/{agents,hands,channels,plugins,providers,integrations,skills}/
- Remove legacy skill.toml from example skills; SKILL.md is the canonical format
- Delete top-level templates/ directory
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
Squashed replay of the original 9-commit branch onto current main. The
original branch was 30+ commits behind, forked from before the skills
refactor (PR #42) and workflow template expansion (PR #36), so a
standard rebase hit heavy add/add conflicts on workflows/*.toml that
are unrelated to the wiki hand.
This replay keeps only the final hands/wiki/ tree state, which is the
actual intent of the PR (the author iterated several times on the same
files; squashing matches that).
Implements the "LLM Wiki" pattern (Andrej Karpathy) for building a
personal, Obsidian-compatible knowledge base. Instead of on-the-fly
RAG, the wiki hand incrementally maintains a Markdown vault:
hands/wiki/
├── HAND.toml # hand manifest + [agents.*] sections
├── README.md # user-facing docs
├── SKILL-main.md # Librarian (coordinator) routing + FS ops
├── SKILL-ingestor.md # Source extraction + [[wikilink]] writing
├── SKILL-analyst.md # Synthesis with provenance citations
└── SKILL-linter.md # Broken link / orphan / contradiction audit
Closeslibrefang/librefang-registry#44 (via replay, not merge).
Standardize on Claude Code's SKILL.md format as every skill's source of
truth. skill.toml becomes an optional metadata layer for runtime, input
schema, and versioning — never for the prompt body.
- validate.py: require SKILL.md in every skill dir; when skill.toml also
exists, cross-check name/description consistency to prevent drift
- Add SKILL.md to the two custom-skill examples
- Move the meeting-agenda prompt body out of skill.toml into SKILL.md
- Rewrite skills/README.md to document the md-first, toml-as-metadata convention
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Claude Code-style skills use SKILL.md with YAML frontmatter instead of
skill.toml. Validator now accepts either form, unblocking the 60 bundled
skills restored in #42.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(hands): add devteam hand -- autonomous software development team
Multi-agent hand with 7 roles (PM, Architect, Frontend, Backend, DevOps, QA, Designer)
and 3 team size tiers (simple/standard/full) for different project scales.
PM coordinator auto-scans GitHub issues, triages, assigns tasks to specialists,
and tracks progress on an in-memory project board.
* refactor(hands): slim devteam to 3 agents (PM + Engineer + QA)
7 agents with serial agent_send = massive token waste and info loss at every
handoff. Merge architect/frontend/backend/devops into one Engineer with full
context. Keep QA separate for independent verification. Drop designer.
Tiers: lite (PM + Engineer) and standard (PM + Engineer + QA).
* fix(hands/devteam): fix workspace isolation and git workflow gaps
- PM uses GitHub API for code browsing, no repo clone needed
- Engineer explicitly clones repo, branches, commits, pushes, creates PR
- QA explicitly clones repo, checks out branch under review
- PM tracks last_scan timestamp to filter already-triaged issues
- approval_mode now means PR stays open for review, not skip commit
* fix(hands/devteam): use shared repo checkout instead of per-agent clones
All 3 agents share one checkout at ../shared/repo/. Engineer clones it
on the first task; PM and QA read from the same path. Eliminates
duplicate clones and cross-workspace visibility issues.
* fix(hands/devteam): read issue comments before triaging
Comments contain clarifications, reproduction steps, duplicate markers,
and resolution status. Also skip already-assigned and wontfix issues.
* fix(hands/devteam): fix interactive git add, add merge/close APIs, add fix iteration flow
- Replace git add -p (interactive) with git add <specific files>
- PM prompt now has explicit merge PR and close issue API calls
- Engineer has explicit fix-request handling (same branch, push, no new PR)
* feat(hands/devteam): add full GitHub interaction -- PR review, issue comments, labels
PM:
- Labels issues during triage, comments triage status
- Scans open PRs for external review requests
- Comments on issues linking merged PRs
Engineer:
- Replies to review comments on PR after fixing
- Reviews external PRs with APPROVE/REQUEST_CHANGES + line comments
QA:
- Leaves PR review (APPROVE or REQUEST_CHANGES with line comments)
- All findings visible on GitHub, not just via agent_send
SKILL.md:
- Added PR diff, reviews, review comments, reply, merge API references
* fix(hands/devteam): enforce English comments, line-level reviews, comment-before-close
- All GitHub comments/reviews must be in English (added global rule)
- PR reviews must use comments[] with path+line, not body-only
- Comment on issue with resolution details BEFORE closing/merging
- Improved comment templates with structured info
* fix(hands/devteam): 8 logic fixes from end-to-end workflow review
1. Filter PRs from Issues API (pull_request key)
2. PM sends PR number to QA for review
3. Deduplicate PR scanning via devteam_reviewed_prs
4. QA reports test gaps instead of pushing code to shared branch
5. branch_strategy wired into Engineer (gitflow branches from develop)
6. approval_mode: ON = wait for human, OFF = auto-merge after QA
7. scan_interval mapped to schedule_create every_secs
8. git checkout -B instead of -b to handle existing branches
* fix(hands/devteam): second-pass review — 6 more logic fixes
1. Engineer extracts PR number from create-PR API response
2. PM falls back to GitHub Contents API when shared repo not yet cloned
3. QA gets external PR review flow (was only on Engineer)
4. PM checks CI status + mergeable before merging
5. PM handles merge conflict (409) by sending back to Engineer to rebase
6. i18n approval_mode description synced with actual semantics
* fix(hands/devteam): third-pass — runtime scenarios
1. Deduplicate cron schedule on daemon restart (check schedule_list first)
2. Max 3 review rounds before escalating to user (prevent infinite loop)
3. Clean working directory before switching tasks (git checkout -- . && git clean)
4. Add user direct commands (work on #42, status, review PR #50)
5. Pass tech_stack to Engineer in task delegation
6. Fix duplicate step numbering in Review Cycle
* fix(hands/devteam): fourth-pass — state consistency and edge cases
1. QA force-syncs to remote branch (git checkout -B origin/branch) for force-push safety
2. Board sync step: reconcile with GitHub each scan cycle (catch external closes/merges)
3. Prune devteam_reviewed_prs of closed PRs, cap done list at 30
4. PM checks CI before sending to QA (don't waste QA on red builds)
5. Stop/cancel command: remove from board, comment on issue
6. Explicit rebase commands for Engineer (fetch + rebase + force-with-lease)
* fix(hands/devteam): fifth-pass — crash prevention
1. Guard empty repo_url: stop and tell user to configure it
2. Add python3 to requires (all JSON parsing depends on it)
3. Engineer git config user.name/email on first clone (prevents commit rejection)
4. Explicit build/lint/test commands per tech stack (Rust/TS/Python/Go/Java/Swift)
5. event_publish on task completion so user gets notified
6. Global rule: check API HTTP status before parsing JSON
* feat(hands/devteam): add gh CLI / MCP / curl API three-layer fallback
- Add GitHub MCP integration (mcp_servers = ["github"])
- Add gh CLI as optional requirement (preferred over curl)
- All 3 agents: gh > MCP > curl priority for GitHub operations
- Add issue_tracker setting (github/linear/jira)
- Add agent_list to shared tools
- SKILL.md: add full gh CLI reference section
- i18n: add issue_tracker translation
* feat(hands/devteam): full MCP/integration/notification layer
MCP allowlist: github, linear, jira, sentry, slack, discord
- Sentry: Engineer reads crash reports/stack traces when fixing bugs
- Slack/Discord: PM posts status updates (triaged, completed, QA results)
- Linear/Jira: alternative issue trackers
New settings: notify_channel (none/slack/discord), issue_tracker (github/linear/jira)
New optional requires: npx (MCP runtime), SENTRY_AUTH_TOKEN
PM prompt: notification section, channel-aware status posting
Engineer prompt: Sentry context lookup for bug fixes
i18n: added translations for new settings
* feat(hands/devteam): workflows, onboarding, knowledge, standup, rollback
Workflows (8 integrated):
- PM: bug-triage, product-spec, weekly-report, incident-postmortem
- Engineer: code-review, test-generation, refactor-plan, api-design
- QA: code-review, test-generation
New capabilities:
- Repo onboarding: first activation analyzes repo structure/stack/CI
- Knowledge accumulation: store lessons per issue, detect module hotspots
- Daily standup: cron schedule, board summary via notify_channel
- Rollback: gh pr revert + postmortem workflow + re-open issue
Also:
- Added workflow_run to tools, skills = [] (all allowed)
- Rewrote README with full architecture, lifecycle, workflow table
- PM prompt now has 15 sections covering full lifecycle
* feat(hands/devteam): per-agent capabilities, resources, profiles, fallbacks
Each agent now has full AgentManifest config (not just system_prompt):
PM:
- profile: automation
- capabilities: web, memory, schedule, knowledge, event, workflow, agent_send
- shell: gh, curl, cat, python3
- resources: 200k tokens/hr
Engineer:
- profile: coding
- capabilities: file r/w, shell, web, memory, knowledge, workflow
- shell: cargo, npm, python, go, swift, mvn, git, gh, docker, make
- resources: 300k tokens/hr, 10 concurrent tools
- network: * (needs to push to GitHub)
QA:
- profile: coding (read-heavy, no file_write)
- capabilities: file read, shell (test/lint commands only), web, workflow
- shell: cargo test/clippy/audit, npm test, pytest, go test, gh
- resources: 150k tokens/hr
All agents have fallback_models configured.
* feat(hands/devteam): rewrite with proper resource composition
First hand to use the new composition features:
Agents:
- PM: base=planner, capabilities restricted to gh/git shell only
- Engineer: base=coder, full shell access, network=*
- QA: base=code-reviewer, tool_blocklist=[file_write], test/lint shells only
Composition:
- base: inherit from agents/planner, agents/coder, agents/code-reviewer
- mcp_servers: github (agents interact via MCP, not curl in prompts)
- workflows: bug-triage, code-review, test-generation via workflow_run tool
- plugins: todo-tracker, auto-summarizer, episodic-memory
- per-agent skills: SKILL-pm.md, SKILL-engineer.md, SKILL-qa.md
- per-agent capabilities: QA can't write files, PM can't run builds
Prompts are clean and focused (role + methodology + principles),
not stuffed with curl commands. GitHub interaction goes through
MCP tools or gh CLI.
* fix(devteam): complete planner methodology in PM prompt
Added SCOPE/SEQUENCE/RISK/MILESTONE keywords from the planner
base template's methodology into the PM's triage workflow.
* docs: update hands README, fix repo_url reference in prompts
- hands/README.md: document full composition model (base, MCP, workflows,
plugins, per-agent skills, per-agent capabilities)
- Updated hand count to 15 (added devteam)
- Engineer prompt: clarify repo_url comes from User Configuration, not
a template variable
- PM prompt: same clarification
* fix(devteam): override name/description from base templates
Without explicit name, agents inherit base names (planner/coder/code-reviewer)
instead of hand-specific names (pm/engineer/qa). This affects display and
the prefixed name used in agent registry (devteam:pm vs devteam:planner).
* style: format HAND.toml with taplo
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
* feat: convert schema.toml to machine-parseable format
Replace comment-based documentation format with structured TOML that
can be deserialized into the RegistrySchema Rust type. All 6 content
types (provider, agent, hand, integration, skill, plugin) preserved
with every field, description, enum option, and nested section.
* fix: format options arrays in schema.toml for taplo compliance
Evan Hu