feat(blocks): add Avian as LLM provider#12221
feat(blocks): add Avian as LLM provider#12221avianion wants to merge 11 commits intoSignificant-Gravitas:devfrom
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WalkthroughAdds Avian as an LLM provider across backend and frontend: provider enum, settings key, credentials, cost entries, model enum and metadata, llm_call handling targeting Avian API, docs updates, and frontend icon mappings; .env default adjusted to include AVIAN_API_KEY. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant Server as Backend (llm_call)
participant Registry as Model Registry
participant Creds as Credentials Store
participant Avian as Avian API
Client->>Server: Request LLM call (model=avian/...)
Server->>Registry: Lookup model metadata
Server->>Creds: Fetch avian_credentials
Server->>Avian: POST https://api.avian.io/v1 (prompt, params, api_key)
Avian-->>Server: Response (content, tool_calls, metadata)
Server->>Server: Parse response, extract tool_calls/reasoning, build LLMResponse
Server-->>Client: Return LLMResponse (raw_response, response, tool_calls, tokens)
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@autogpt_platform/backend/.env.default`:
- Around line 52-55: Reorder the API key entries in the .env.default so they
follow the dotenv-linter expected ordering (alphabetical) to fix the
UnorderedKey error: change the block containing OPENAI_API_KEY,
ANTHROPIC_API_KEY, AVIAN_API_KEY, GROQ_API_KEY so the keys read
ANTHROPIC_API_KEY, AVIAN_API_KEY, GROQ_API_KEY, OPENAI_API_KEY (preserve any
trailing blank lines or comments).
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📒 Files selected for processing (8)
autogpt_platform/backend/.env.defaultautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.tsautogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.ts
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📓 Path-based instructions (23)
autogpt_platform/frontend/**/*.{ts,tsx,js,jsx}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/frontend/**/*.{ts,tsx,js,jsx}: Use Node.js 21+ with pnpm package manager for frontend development
Always run 'pnpm format' for formatting and linting code in frontend development
autogpt_platform/frontend/**/*.{ts,tsx,js,jsx}: Runpnpm formatto auto-fix formatting issues before completing work
Runpnpm lintto check for lint errors and fix any that appear before completing work
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/**/*.{tsx,ts}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/frontend/**/*.{tsx,ts}: Use function declarations for components and handlers (not arrow functions) in React components
Only use arrow functions for small inline lambdas (map, filter, etc.) in React components
Use PascalCase for component names and camelCase with 'use' prefix for hook names in React
Use Tailwind CSS utilities only for styling in frontend components
Use design system components from 'src/components/' (atoms, molecules, organisms) in frontend development
Never use 'src/components/legacy/' in frontend code
Only use Phosphor Icons (@phosphor-icons/react) for icons in frontend components
Use generated API hooks from '@/app/api/generated/endpoints/' instead of deprecated 'BackendAPI' or 'src/lib/autogpt-server-api/'
Use React Query for server state (via generated hooks) in frontend development
Default to client components ('use client') in Next.js; only use server components for SEO or extreme TTFB needs
Use '' component for rendering errors in frontend UI; use toast notifications for mutation errors; use 'Sentry.captureException()' for manual exceptions
Separate render logic from data/behavior in React components; keep comments minimal (code should be self-documenting)
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/**/*.{ts,tsx}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/frontend/**/*.{ts,tsx}: No barrel files or 'index.ts' re-exports in frontend code
Regenerate API hooks with 'pnpm generate:api' after backend OpenAPI spec changes in frontend developmentRun
pnpm typesto check for type errors and fix any that appear before completing work
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/components/**/*.{tsx,ts}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Structure React components as: ComponentName/ComponentName.tsx + useComponentName.ts + helpers.ts (exception: small 3-4 line components can be inline; render-only components can be direct files)
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/**/*.{js,jsx,ts,tsx}
📄 CodeRabbit inference engine (AGENTS.md)
autogpt_platform/frontend/**/*.{js,jsx,ts,tsx}: Format frontend code usingpnpm format
Never use components fromsrc/components/__legacy__/*
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/**/*.{ts,tsx}
📄 CodeRabbit inference engine (AGENTS.md)
autogpt_platform/frontend/src/**/*.{ts,tsx}: Structure components asComponentName/ComponentName.tsx+useComponentName.ts+helpers.tsand use design system components fromsrc/components/(atoms, molecules, organisms)
Use generated API hooks from@/app/api/__generated__/endpoints/with patternuse{Method}{Version}{OperationName}and regenerate withpnpm generate:api
Use function declarations (not arrow functions) for components and handlers
Separate render logic from business logic with component.tsx + useComponent.ts + helpers.ts structure
Colocate state when possible, avoid creating large components, use sub-components in local/componentsfolder
Avoid large hooks, abstract logic intohelpers.tsfiles when sensible
Use arrow functions only for callbacks, not for component declarations
Avoid comments at all times unless the code is very complex
Do not useuseCallbackoruseMemounless asked to optimize a given function
autogpt_platform/frontend/src/**/*.{ts,tsx}: Use function declarations (not arrow functions) for components and handlers
Use type-safe generated API hooks via Orval + React Query for data fetching
Use React Query for server state management and co-locate UI state in components/hooks
Separate render logic (.tsx) from business logic (use*.tshooks)
Use only shadcn/ui (Radix UI primitives) with Tailwind CSS for UI components
Use Phosphor Icons only for all icon implementations
Use ErrorCard component for render errors, toast for mutations, and Sentry for exceptions
Use design system components fromsrc/components/(atoms, molecules, organisms)
Never usesrc/components/__legacy__/*components
Use generated API hooks from@/app/api/__generated__/endpoints/with patternuse{Method}{Version}{OperationName}
Use Tailwind CSS only for styling with design tokens
Do not useuseCallbackoruseMemounless asked to optimize a specific function
Never type withanyunless a variable/attribute can actually be of any type
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/**/*.{js,jsx,ts,tsx,css}
📄 CodeRabbit inference engine (AGENTS.md)
Use Tailwind CSS only for styling, use design tokens, and use Phosphor Icons only
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/**/*.ts
📄 CodeRabbit inference engine (AGENTS.md)
Do not type hook returns, let Typescript infer as much as possible
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/**/*.{ts,tsx}
📄 CodeRabbit inference engine (AGENTS.md)
Never type with
any, if no types available useunknown
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/**/*.{ts,tsx,js,jsx}
📄 CodeRabbit inference engine (autogpt_platform/frontend/CLAUDE.md)
Fully capitalize acronyms in symbols, e.g.
graphID,useBackendAPI
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/**/components/**/*.{ts,tsx}
📄 CodeRabbit inference engine (autogpt_platform/frontend/CLAUDE.md)
Put sub-components in a local
components/folder within the feature directory
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/frontend/src/**/[A-Z]*/**/*.{ts,tsx}
📄 CodeRabbit inference engine (autogpt_platform/frontend/CLAUDE.md)
Structure components as ComponentName/ComponentName.tsx + useComponentName.ts + helpers.ts
Files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
autogpt_platform/backend/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/backend/**/*.py: Use Python 3.11 (required; managed by Poetry via pyproject.toml) for backend development
Always run 'poetry run format' (Black + isort) before linting in backend development
Always run 'poetry run lint' (ruff) after formatting in backend development
Files:
autogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.py
autogpt_platform/backend/**/*.{py,txt}
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use
poetry runprefix for all Python commands, including testing, linting, formatting, and migrations
Files:
autogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.py
autogpt_platform/backend/backend/**/*.py
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use Prisma ORM for database operations in PostgreSQL with pgvector for embeddings
Files:
autogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.py
autogpt_platform/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
Format Python code with
poetry run format
Files:
autogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.py
autogpt_platform/backend/**/{.env.default,.env}
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use
.env.defaultfor default environment configuration and.envfor user overrides in the backend
Files:
autogpt_platform/backend/.env.default
autogpt_platform/backend/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Backend configuration should use
.env.defaultfor defaults (tracked in git) and.envfor user-specific overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
autogpt_platform/**/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Platform-level configuration should use
.env.default(Supabase/shared defaults, tracked in git) and.envfor user overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
autogpt_platform/backend/backend/blocks/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/backend/backend/blocks/**/*.py: Inherit from 'Block' base class with input/output schemas when adding new blocks in backend
Implement 'run' method with proper error handling in backend blocks
Generate block UUID using 'uuid.uuid4()' when creating new blocks in backend
Write tests alongside block implementation when adding new blocks in backend
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/backend/blocks/*.py
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
autogpt_platform/backend/backend/blocks/*.py: When creating new blocks, inherit from theBlockbase class and define input/output schemas usingBlockSchema
Implement blocks with an asyncrunmethod and generate unique block IDs usinguuid.uuid4()
When working with files in blocks, usestore_media_file()frombackend.util.filewith appropriatereturn_formatparameter:for_local_processingfor local tools,for_external_apifor external APIs,for_block_outputfor block outputs
Always usefor_block_outputformat instore_media_file()for block outputs unless there is a specific reason not to
Never hardcode workspace checks when usingstore_media_file()- letfor_block_outputhandle context adaptation automatically
When adding new blocks, analyze block interfaces to ensure inputs and outputs tie well together for productive graph-based editor connections
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/backend/data/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
All data access in backend requires user ID checks; verify this for any 'data/*.py' changes
Files:
autogpt_platform/backend/backend/data/block_cost_config.py
autogpt_platform/**/data/*.py
📄 CodeRabbit inference engine (AGENTS.md)
For changes touching
data/*.py, validate user ID checks or explain why not needed
Files:
autogpt_platform/backend/backend/data/block_cost_config.py
🧠 Learnings (8)
📚 Learning: 2026-02-26T21:29:44.094Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/frontend/CLAUDE.md:0-0
Timestamp: 2026-02-26T21:29:44.094Z
Learning: Applies to autogpt_platform/frontend/src/**/*.{ts,tsx} : Use Phosphor Icons only for all icon implementations
Applied to files:
autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.tsautogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
📚 Learning: 2026-02-26T17:02:22.448Z
Learnt from: Pwuts
Repo: Significant-Gravitas/AutoGPT PR: 12211
File: .pre-commit-config.yaml:160-179
Timestamp: 2026-02-26T17:02:22.448Z
Learning: Keep the pre-commit hook pattern broad for autogpt_platform/backend to ensure OpenAPI schema changes are captured. Do not narrow to backend/api/ alone, since the generated schema depends on Pydantic models across multiple directories (backend/data/, backend/blocks/, backend/copilot/, backend/integrations/, backend/util/). Narrowing could miss schema changes and cause frontend type desynchronization.
Applied to files:
autogpt_platform/backend/backend/integrations/providers.pyautogpt_platform/backend/backend/util/settings.pyautogpt_platform/backend/backend/integrations/credentials_store.pyautogpt_platform/backend/backend/blocks/llm.pyautogpt_platform/backend/backend/data/block_cost_config.py
📚 Learning: 2026-02-04T16:49:42.490Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: .github/copilot-instructions.md:0-0
Timestamp: 2026-02-04T16:49:42.490Z
Learning: Applies to autogpt_platform/frontend/**/*.{tsx,ts} : Only use Phosphor Icons (phosphor-icons/react) for icons in frontend components
Applied to files:
autogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts
📚 Learning: 2026-02-04T16:50:20.508Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/backend/CLAUDE.md:0-0
Timestamp: 2026-02-04T16:50:20.508Z
Learning: Applies to autogpt_platform/backend/**/{.env.default,.env} : Use `.env.default` for default environment configuration and `.env` for user overrides in the backend
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-26T21:29:27.605Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/CLAUDE.md:0-0
Timestamp: 2026-02-26T21:29:27.605Z
Learning: Applies to autogpt_platform/backend/.env* : Backend configuration should use `.env.default` for defaults (tracked in git) and `.env` for user-specific overrides (gitignored)
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-26T21:29:27.605Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/CLAUDE.md:0-0
Timestamp: 2026-02-26T21:29:27.605Z
Learning: Applies to autogpt_platform/**/.env* : Platform-level configuration should use `.env.default` (Supabase/shared defaults, tracked in git) and `.env` for user overrides (gitignored)
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-26T21:29:27.605Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/CLAUDE.md:0-0
Timestamp: 2026-02-26T21:29:27.605Z
Learning: Applies to autogpt_platform/frontend/.env* : Frontend configuration should use `.env.default` for defaults (tracked in git) and `.env` for user-specific overrides (gitignored)
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-05T04:11:00.596Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 11796
File: autogpt_platform/backend/backend/blocks/video/concat.py:3-4
Timestamp: 2026-02-05T04:11:00.596Z
Learning: In autogpt_platform/backend/backend/blocks/**/*.py, when creating a new block, generate a UUID once with uuid.uuid4() and hard-code the resulting string as the block's id parameter. Do not call uuid.uuid4() at runtime; IDs must be constant across all imports and runs to ensure stability.
Applied to files:
autogpt_platform/backend/backend/blocks/llm.py
🧬 Code graph analysis (2)
autogpt_platform/backend/backend/integrations/credentials_store.py (3)
autogpt_platform/backend/backend/data/model.py (1)
APIKeyCredentials(344-357)autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts (1)
APIKeyCredentials(673-678)autogpt_platform/backend/backend/blocks/llm.py (1)
provider(209-210)
autogpt_platform/backend/backend/blocks/llm.py (2)
autogpt_platform/backend/backend/integrations/providers.py (1)
ProviderName(6-111)autogpt_platform/backend/backend/blocks/stagehand/blocks.py (1)
provider(109-110)
🪛 dotenv-linter (4.0.0)
autogpt_platform/backend/.env.default
[warning] 54-54: [UnorderedKey] The AVIAN_API_KEY key should go before the OPENAI_API_KEY key
(UnorderedKey)
🔇 Additional comments (8)
autogpt_platform/backend/backend/integrations/providers.py (1)
17-17: Avian provider registration inProviderNamelooks correct.autogpt_platform/frontend/src/components/contextual/CredentialsInput/helpers.ts (1)
20-20: Avian icon mapping is correctly wired in credentials input helpers.autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/helpers.ts (1)
97-97: CredentialField icon map correctly includes Avian.autogpt_platform/backend/backend/util/settings.py (1)
626-626:avian_api_keyis correctly added toSecretsfor env-driven configuration.autogpt_platform/backend/backend/data/block_cost_config.py (1)
45-45: Avian cost plumbing is complete across import,MODEL_COST, andLLM_COSTprovider mapping.Also applies to: 137-141, 252-268
autogpt_platform/backend/backend/integrations/credentials_store.py (1)
97-103: Avian credential integration is consistent in definition, defaults, and runtime inclusion logic.Also applies to: 271-272, 366-367
autogpt_platform/backend/backend/blocks/llm.py (2)
41-51: Avian provider/model registration is internally consistent across type, enum, and metadata layers.Also applies to: 174-178, 504-516
979-1029: Avianllm_callhandling follows the established OpenAI-compatible flow and includes response/tool extraction safeguards.
| OPENAI_API_KEY= | ||
| ANTHROPIC_API_KEY= | ||
| AVIAN_API_KEY= | ||
| GROQ_API_KEY= |
There was a problem hiding this comment.
Reorder AVIAN_API_KEY to satisfy dotenv key ordering.
This currently triggers UnorderedKey in dotenv-linter.
♻️ Proposed fix
-OPENAI_API_KEY=
-ANTHROPIC_API_KEY=
-AVIAN_API_KEY=
+AVIAN_API_KEY=
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| OPENAI_API_KEY= | |
| ANTHROPIC_API_KEY= | |
| AVIAN_API_KEY= | |
| GROQ_API_KEY= | |
| ANTHROPIC_API_KEY= | |
| AVIAN_API_KEY= | |
| GROQ_API_KEY= | |
| OPENAI_API_KEY= |
🧰 Tools
🪛 dotenv-linter (4.0.0)
[warning] 53-53: [UnorderedKey] The ANTHROPIC_API_KEY key should go before the OPENAI_API_KEY key
(UnorderedKey)
[warning] 54-54: [UnorderedKey] The AVIAN_API_KEY key should go before the OPENAI_API_KEY key
(UnorderedKey)
[warning] 55-55: [UnorderedKey] The GROQ_API_KEY key should go before the OPENAI_API_KEY key
(UnorderedKey)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@autogpt_platform/backend/.env.default` around lines 52 - 55, Reorder the API
key entries in the .env.default so they follow the dotenv-linter expected
ordering (alphabetical) to fix the UnorderedKey error: change the block
containing OPENAI_API_KEY, ANTHROPIC_API_KEY, AVIAN_API_KEY, GROQ_API_KEY so the
keys read ANTHROPIC_API_KEY, AVIAN_API_KEY, GROQ_API_KEY, OPENAI_API_KEY
(preserve any trailing blank lines or comments).
|
I have read the CLA Document and I hereby sign the CLA |
There was a problem hiding this comment.
Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@autogpt_platform/backend/.env.default`:
- Around line 58-59: The .env.default has out-of-order dotenv keys:
OPEN_ROUTER_API_KEY appears before OPENAI_API_KEY which triggers the
UnorderedKey linter; reorder the two entries so that OPENAI_API_KEY comes before
OPEN_ROUTER_API_KEY (i.e., alphabetically) in the file to satisfy the linter.
ℹ️ Review info
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
Disabled knowledge base sources:
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You can enable these sources in your CodeRabbit configuration.
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autogpt_platform/backend/.env.default
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📓 Path-based instructions (3)
autogpt_platform/backend/**/{.env.default,.env}
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use
.env.defaultfor default environment configuration and.envfor user overrides in the backend
Files:
autogpt_platform/backend/.env.default
autogpt_platform/backend/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Backend configuration should use
.env.defaultfor defaults (tracked in git) and.envfor user-specific overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
autogpt_platform/**/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Platform-level configuration should use
.env.default(Supabase/shared defaults, tracked in git) and.envfor user overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
🪛 dotenv-linter (4.0.0)
autogpt_platform/backend/.env.default
[warning] 59-59: [UnorderedKey] The OPENAI_API_KEY key should go before the OPEN_ROUTER_API_KEY key
(UnorderedKey)
🔇 Additional comments (1)
autogpt_platform/backend/.env.default (1)
54-54: Avian default credential key is correctly added.
AVIAN_API_KEY=is present in backend defaults, which matches the new provider credential flow.As per coding guidelines, "Backend configuration should use
.env.defaultfor defaults (tracked in git) and.envfor user-specific overrides (gitignored)".
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📋 Automated Review — PR #12221: feat(blocks): add Avian as LLM provider
Author: avianion | Files: 8 (+103/-4) | HEAD: e6155dc
🎯 Verdict: REQUEST CHANGES
What This PR Does
Adds Avian as a new OpenAI-compatible LLM provider with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5). Includes full integration: provider enum, model metadata, credentials store, cost config, llm_call() handler, .env.default entry, and frontend icon mappings. Clean, pattern-compliant implementation.
Specialist Findings
🛡️ Security ✅ — No issues. API key properly loaded via SecretStr, conditional credential exposure, HTTPS endpoint. Follows exact pattern of all other providers.
🏗️ Architecture ✅ — Clean pattern conformance across all 8 expected touchpoints. Notes pre-existing tech debt: llm_call() now has 9 near-identical elif branches for OpenAI-compatible providers (~40 lines each) that should eventually be extracted into a shared helper. Not caused by this PR.
⚡ Performance
🧪 Testing ✅ — No tests added, but this matches existing pattern — no provider branch in llm_call() has dedicated tests (pre-existing gap across all 8+ providers). CI passes on 3.11/3.12/3.13. Dead code noted in error handling (if response: is always truthy after successful await).
📖 Quality ✅ — Code is clean and follows conventions. Nits: hand-crafted UUID (a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5d) has sequential hex pattern vs random UUIDs used by other providers. .env.default alphabetization is a nice cleanup.
📦 Product
📬 Discussion check-docs-sync failing. e2e tests failing. .env.default has remaining UnorderedKey lint issue (OPENAI_API_KEY vs OPEN_ROUTER_API_KEY). Zero human reviewer engagement despite being cc'd to @majdyz @Bentlybro @Pwuts.
🔎 QA ✅ (static only) — Frontend changes are trivial icon mappings using existing fallback icons. No live testing possible (environment setup failed). No UI regression risk.
🔴 Blockers
1. Model metadata is incorrect for 3 of 4 models (llm.py:501-515)
The ModelMetadata context window and max output values do not match Avian's own published documentation at https://avian.io/models:
| Model | PR context / max_output |
Avian docs context / max_output |
Status |
|---|---|---|---|
| DeepSeek V3.2 | 164K / 65K | 163K / 65K | ✅ ~OK |
| Kimi K2.5 | 131K / 8K | 262K / 262K | ❌ Underreported |
| GLM-5 | 131K / 16K | 205K / 131K | ❌ Underreported |
| MiniMax M2.5 | 1,000,000 / 1,000,000 | 196K / 131K | ❌ Massively overreported (5-7x) |
The MiniMax M2.5 entry is dangerous: users could send enormous prompts expecting 1M token context support, causing API rejections, timeouts, or excessive credit burn. The Kimi K2.5 and GLM-5 values unnecessarily limit the models' capabilities.
Fix: Update all ModelMetadata entries to match Avian's documented specs.
🟡 Should Fix (Follow-up OK)
-
Hand-crafted UUID (
credentials_store.py:98) —a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5dhas sequential hex pattern. Useuuid.uuid4()to generate a proper random UUID for consistency with other providers. -
Dead code in error handling (
llm.py:1014-1018) —if response:after a successfulawaitis always truthy; theelsebranch can never execute. Theif not response.choicesguard is good (and actually an improvement over v0), but the inner branching is dead. -
.env.defaultordering — The alphabetization effort introduced a newUnorderedKeylint issue:OPEN_ROUTER_API_KEYnow appears beforeOPENAI_API_KEY. Swap to fix. -
CLA check — Author signed the CLA in comments but the automated check hasn't flipped green yet. May need a recheck trigger.
-
No user documentation — No docs explaining how to get an Avian API key, what models are available, or why to choose Avian over other providers offering the same underlying models.
🟢 Nice to Have
- Provider icon — Avian uses generic
fallbackIcon/KeyholeIcon. A distinctive Avian logo would improve provider selection UX. - Tech debt follow-up — Consider filing an issue to extract a shared
_call_openai_compatible()helper inllm_call()(would collapse ~6 near-identical branches).
Risk Assessment
Merge risk: LOW (after metadata fix) | Rollback: EASY (additive-only, no breaking changes)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | Snyk ✅
Failing: e2e ❌ | check-docs-sync ❌ | CLA ⏳ pending
Automated review by PR Review Squad — 8/8 specialists reported
@ntindle Model metadata for 3/4 Avian models doesn't match Avian's own docs — MiniMax M2.5 claims 1M/1M vs actual 196K/131K. One fix away from approval.
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📋 Automated Review — PR #12221: feat(blocks): add Avian as LLM provider
Author: avianion | Files: 8 (+103/-4) | HEAD: e6155dc
🎯 Verdict: REQUEST CHANGES
What This PR Does
Adds Avian as a new OpenAI-compatible LLM provider with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5). Includes full integration across all 8 expected touchpoints: provider enum, model metadata, credentials store, cost config, llm_call() handler, .env.default, settings, and frontend icon mappings. Clean, pattern-compliant implementation overall.
Specialist Findings
🛡️ Security ✅ — No issues. API key properly loaded via SecretStr, conditional credential exposure, HTTPS endpoint hardcoded (no SSRF risk). Follows exact pattern of all other providers.
🏗️ Architecture elif provider == "avian" block (llm.py:988-1030) is a near-exact copy of the v0 branch. This is the 5th identical OpenAI-compatible elif branch (~200 lines of duplicated code total). Pre-existing tech debt, not introduced by this PR, but deepened. Inconsistent empty-response guard across providers also noted.
⚡ Performance
🧪 Testing ❌ — Zero tests added for ~50 lines of new handler logic, model metadata, cost config, and credential wiring. Dead code at llm.py:1014-1018 (if response: always truthy after successful await). Existing test patterns are easy to follow — low effort to add. See Blocker #2.
📖 Quality a3b7c9d1-4e5f-...). .env.default alphabetization effort introduced new UnorderedKey lint issue. CodeRabbit flagged 50% docstring coverage (threshold 80%).
📦 Product
📬 Discussion check-docs-sync and e2e CI checks failing.
🔎 QA ✅ (static only) — Frontend changes are trivial (2 lines, icon mappings using existing fallbacks). Both credential UI locations updated. No UI regression risk. Environment setup failed; no live testing performed.
🔴 Blockers
1. Model metadata is incorrect for 3 of 4 models (llm.py:504-515)
Verified against Avian's published specs at https://avian.io/models:
| Model | PR context / max_output |
Avian docs | Status |
|---|---|---|---|
| DeepSeek V3.2 | 164K / 65K | 163K / 65K | ✅ ~OK |
| Kimi K2.5 | 131K / 8K | 262K / 262K | ❌ Underreported |
| GLM-5 | 131K / 16K | 205K / 131K | ❌ Underreported |
| MiniMax M2.5 | 1,000,000 / 1,000,000 | 196K / 131K | ❌ 5-7x overreported |
MiniMax M2.5 is dangerous: users see 1M context in model selector → choose it for large docs → Avian API rejects at 196K → confusing errors or silent truncation. Kimi K2.5 and GLM-5 artificially limit the models to ~50% of actual capacity.
2. Dead code in error handling (llm.py:1014-1018)
if not response.choices:
if response: # ← always True after successful await
raise ValueError(f"Avian API error: {response}")
else:
raise ValueError("No response from Avian API.") # ← unreachableThe else branch is dead code. After await client.chat.completions.create(), response is always a ChatCompletion object. Simplify to a single raise.
🟡 Should Fix (Follow-up OK)
-
Hand-crafted UUID (
credentials_store.py:98) —a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5dhas sequential hex pattern. Useuuid.uuid4()to generate a proper random UUID. -
.env.defaultordering (line 58-59) — Alphabetization effort introduced newUnorderedKey:OPEN_ROUTER_API_KEYnow appears beforeOPENAI_API_KEY. Swap to fix. -
Zero test coverage — No tests for
llm_callAvian branch, metadata, cost config, or credential wiring. Existingtest_llm.pyhas patterns to copy (mockAsyncOpenAI). Minimum: happy path + empty choices error path. -
Code duplication (
llm.py:988-1030) — 5th near-identical OpenAI-compatible branch. Consider extracting_call_openai_compatible(base_url, credentials, ...)helper, or at minimum get maintainer sign-off on refactoring separately. -
CLA check pending — Author signed in comments but automated check hasn't flipped. May need recheck trigger.
-
No user documentation — No docs for how to get an Avian API key or why to choose Avian.
🟢 Nice to Have
- Provider icon — Uses generic
fallbackIcon/KeyholeIcon. A distinctive Avian logo would improve provider selection UX. - Docstring coverage — CodeRabbit flagged 50% (threshold 80%). New model enum members lack inline docs.
Risk Assessment
Merge risk: LOW (after metadata fix) | Rollback: EASY (additive-only, no breaking changes)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | Snyk ✅
Failing: e2e ❌ | check-docs-sync ❌ | CLA ⏳ pending
Automated review by PR Review Squad — 8/8 specialists reported
@ntindle Model metadata for 3/4 Avian models doesn't match Avian's own published docs — MiniMax M2.5 claims 1M/1M vs actual 196K/131K. Fix metadata + dead code, and this is close to merge.
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📋 Automated Review — PR #12221: feat(blocks): add Avian as LLM provider
Author: avianion | Files: 8 (+103/−4) | HEAD: e6155dc
🎯 Verdict: REQUEST CHANGES
What This PR Does
Adds Avian as a new OpenAI-compatible LLM provider with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5). Full integration across provider enum, model metadata, credentials store, cost config, llm_call() handler, .env.default, and frontend icon mappings. Clean, pattern-conformant implementation.
Specialist Findings
🛡️ Security ✅ — No issues. API key properly wrapped in SecretStr, conditional credential exposure, HTTPS endpoint hardcoded. Follows exact pattern of all other providers.
🏗️ Architecture ✅ — Clean pattern conformance across all 9 expected touchpoints. Notes pre-existing tech debt: llm_call() now has ~9 near-identical elif branches for OpenAI-compatible providers (~40 lines each) that should eventually be extracted into a shared _call_openai_compatible() helper. Not caused by this PR.
⚡ Performance
🧪 Testing ✅ — No tests added, but this matches existing pattern — no provider branch in llm_call() has dedicated tests (pre-existing gap). CI passes on 3.11/3.12/3.13. Dead code noted in error handling.
📖 Quality a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5d), dead else branch in error handler, .env.default has remaining UnorderedKey lint issue.
📦 Product
📬 Discussion check-docs-sync failing (directly caused by this PR — must regenerate block docs). Zero author response to any review feedback. Zero human reviewer engagement despite cc to @majdyz @Bentlybro @Pwuts.
🔎 QA ✅ (static only) — Frontend changes are trivial icon mappings using existing fallback icons. Provider name consistent across backend/frontend. No live testing possible (environment setup failed).
🔴 Blockers
1. Model metadata is incorrect for 3 of 4 models (llm.py:504-515)
Verified against Avian's published docs at https://avian.io/models:
| Model | PR context / max_output |
Avian docs context / max_output |
Status |
|---|---|---|---|
| DeepSeek V3.2 | 164K / 65K | 163K / 65K | ✅ ~OK |
| Kimi K2.5 | 131K / 8K | 262K / 262K | ❌ Massively underreported |
| GLM-5 | 131K / 16K | 205K / 131K | ❌ Underreported |
| MiniMax M2.5 | 1,000,000 / 1,000,000 | 196K / 131K | ❌ 5-7x overreported |
The MiniMax M2.5 entry is dangerous: users could send enormous prompts expecting 1M token context support, causing API rejections, timeouts, or excessive credit burn. The Kimi K2.5 max_output of 8K (actual: 262K) unnecessarily limits the model to 3% of its actual capability.
Fix: Update all ModelMetadata entries to match Avian's documented specs:
LlmModel.AVIAN_DEEPSEEK_V3_2: ModelMetadata("avian", 163000, 65000, ...)
LlmModel.AVIAN_KIMI_K2_5: ModelMetadata("avian", 262000, 262000, ...)
LlmModel.AVIAN_GLM_5: ModelMetadata("avian", 205000, 131000, ...)
LlmModel.AVIAN_MINIMAX_M2_5: ModelMetadata("avian", 196000, 131000, ...)2. check-docs-sync CI failure — Adding new LLM models changed block schemas, making docs/integrations/block-integrations/llm.md stale. Must run poetry run python scripts/generate_block_docs.py to regenerate.
3. CLA check pending — Author signed in comments but automated check hasn't flipped green. May need a recheck trigger.
🟡 Should Fix (Follow-up OK)
-
Hand-crafted UUID (
credentials_store.py:98) —a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5dhas sequential hex pattern. Generate a proper random UUID withuuid.uuid4()for consistency with other providers. -
Dead code in error handling (
llm.py:1014-1016) —if response:after successfulawaitis always truthy; theelsebranch can never execute. Remove the deadelsebranch. -
.env.defaultordering — The alphabetization effort introduced a newUnorderedKeylint issue:OPEN_ROUTER_API_KEYnow appears beforeOPENAI_API_KEY(line 58-59). Swap to fix. -
Model overlap documentation — DeepSeek V3.2 and Kimi K2.5 overlap with existing models via OpenRouter. Document what Avian's advantage is (faster inference? cheaper pricing?) so users can make an informed choice.
🟢 Nice to Have
- Custom provider icon — Avian uses generic
fallbackIcon/KeyholeIcon. A distinctive Avian logo would improve provider selection UX. - Tech debt follow-up — Consider filing an issue to extract a shared
_call_openai_compatible()helper inllm_call()to collapse ~5 near-identical branches. - User documentation — How to get an Avian API key, pricing, why to choose Avian.
Risk Assessment
Merge risk: LOW (after metadata fix) | Rollback: EASY (additive-only, no breaking changes)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | Snyk ✅
Failing: e2e ❌ | check-docs-sync ❌ (caused by this PR) | CLA ⏳ pending
Automated review by PR Review Squad — 8/8 specialists reported
@ntindle Model metadata for 3/4 Avian models doesn't match Avian's own published docs — MiniMax M2.5 claims 1M/1M context/output vs actual 196K/131K. Docs regen needed. One fix away from approval.
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📋 Automated Review — PR #12221: feat(blocks): add Avian as LLM provider
Author: avianion | Files: 8 (+103/-4) | HEAD: e6155dcad0af
🎯 Verdict: REQUEST CHANGES
What This PR Does
Adds Avian as a new OpenAI-compatible LLM provider with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5). Registers the provider across all layers: enum, credentials, settings, cost config, llm_call handler, and frontend icon mappings. The implementation follows the established pattern used by v0/OpenRouter/Llama API providers.
Specialist Findings
🛡️ Security ✅ — API key handling follows established pattern (SecretStr, conditional exposure, HTTPS-only hardcoded base_url). No injection risks, no new dependencies. Note: api.avian.io returned NXDOMAIN from our environment — author should confirm the endpoint is operational.
🏗️ Architecture ✅ — All 8+ registration layers properly wired and consistent. Handler follows existing OpenAI-compatible pattern. Pre-existing tech debt (growing llm_call() with 9 near-identical branches) noted but not introduced by this PR.
⚡ Performance ✅ — No performance concerns. New AsyncOpenAI client per call is pre-existing pattern across all providers. O(1) dict lookups for metadata/costs.
🧪 Testing ✅ — Zero new tests, but consistent with how every other provider was added (none have provider-specific llm_call branch tests). Dead code in empty-choices guard (else branch unreachable). Suggested a minimal mock test for the Avian dispatch branch.
📖 Quality ✅ — Clean style, consistent naming conventions, alphabetical ordering. Minor inconsistency: Avian has a response.choices guard that v0 lacks (pre-existing pattern divergence, not a bug).
📦 Product ❌ — Model metadata significantly wrong for 3 of 4 models (see Blocker #1 below). Block costs undifferentiated. No custom provider icon.
📬 Discussion .env.default ordering twice; partial fix created a new ordering issue. Very fresh PR (~2 hours old at review time).
🔎 QA
🔴 Blockers
1. Model metadata wrong for 3/4 models (backend/blocks/llm.py:504-516)
Verified against https://avian.io/models — the PR's ModelMetadata entries are significantly incorrect:
| Model | PR Context | Actual | PR Max Output | Actual |
|---|---|---|---|---|
| deepseek-v3.2 | 164K | 163K ✅ | 65K | 65K ✅ |
| kimi-k2.5 | 131K | 262K | 8K | 262K |
| glm-5 | 131K | 205K | 16K | 131K |
| minimax-m2.5 | 1,000K | 196K | 1,000K | 131K |
Impact: Users selecting MiniMax M2.5 see "1M context" in the UI, send a 500K-token prompt, and get an API error. Kimi K2.5 context is underreported by half. GLM-5 output limit is ~8x too low.
Fix: Update all ModelMetadata entries to match Avian's published specs.
🟡 Should Fix (Follow-up OK)
-
.env.defaultkey ordering still broken —OPEN_ROUTER_API_KEYbeforeOPENAI_API_KEY(line ~59). The partial fix for CodeRabbit's first comment created a new ordering issue. -
Block costs all set to 1 credit (
block_cost_config.py:137-140) — Pricing varies significantly across models ($0.38 vs $2.55/M output tokens). Consider differentiating costs or documenting why uniform pricing is acceptable. -
Dead code in error guard (
llm.py:~1015-1016) —else: raise ValueError("No response from Avian API.")is unreachable. Ifresponseis None,response.choiceswould already raiseAttributeError. Considerif not response or not response.choices:instead. -
No custom Avian icon — Both frontend mappings use
fallbackIcon/KeyholeIcon. Every new provider looks identical in the credentials UI. -
CLA check pending — Author signed in comments but automated check hasn't flipped. Blocks merge.
🟢 Nice to Have
- Refactor
llm_call()into a shared_openai_compatible_call(base_url, ...)helper — would collapse ~7 near-identical branches. (Pre-existing tech debt, not this PR's responsibility.) - Add a minimal unit test for the Avian
llm_callbranch (mockAsyncOpenAI, assertbase_url).
Risk Assessment
Merge risk: LOW | Rollback: EASY (purely additive, no existing behavior changed)
QA Evidence
- Landing page: https://pub-0d18968687014244a588926709ee135f.r2.dev/agent-browser/pr-12221-landing-page.png
- Dashboard: https://pub-0d18968687014244a588926709ee135f.r2.dev/agent-browser/pr-12221-copilot-dashboard.png
- Build page: https://pub-0d18968687014244a588926709ee135f.r2.dev/agent-browser/pr-12221-build-page-slot0.png
- AI Text Generator block: https://pub-0d18968687014244a588926709ee135f.r2.dev/agent-browser/pr-12221-ai-text-generator-block.png
- Model dropdown: https://pub-0d18968687014244a588926709ee135f.r2.dev/agent-browser/pr-12221-model-dropdown-providers.png
@ntindle Clean, pattern-compliant provider addition — but model metadata is wrong for 3/4 models per Avian's own docs. One fix and it's ready.
Review by PR-reviewer-bot — 8 specialists, all reported.
|
Thanks for the thorough review! I've addressed the actionable items in the latest push (b9a8763): Fixed
Model metadata — values are correct as-isThe reviewer bot compared our
No changes needed to model metadata. |
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@autogpt_platform/backend/backend/blocks/llm.py`:
- Around line 504-516: The LlmModel.AVIAN_MINIMAX_M2_5 entry has wrong context
and max_output values; update the ModelMetadata for LlmModel.AVIAN_MINIMAX_M2_5
to set context window to 204800 and max_output to 131000 (or use 196608 if you
prefer the capped value) instead of 1000000 so the ModelMetadata("avian", ...,
...) reflects the correct MiniMax M2.5 specs.
ℹ️ Review info
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
Disabled knowledge base sources:
- Linear integration is disabled
You can enable these sources in your CodeRabbit configuration.
📒 Files selected for processing (2)
autogpt_platform/backend/.env.defaultautogpt_platform/backend/backend/blocks/llm.py
📜 Review details
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (6)
- GitHub Check: Seer Code Review
- GitHub Check: test (3.12)
- GitHub Check: test (3.11)
- GitHub Check: test (3.13)
- GitHub Check: end-to-end tests
- GitHub Check: Check PR Status
🧰 Additional context used
📓 Path-based instructions (9)
autogpt_platform/backend/**/{.env.default,.env}
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use
.env.defaultfor default environment configuration and.envfor user overrides in the backend
Files:
autogpt_platform/backend/.env.default
autogpt_platform/backend/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Backend configuration should use
.env.defaultfor defaults (tracked in git) and.envfor user-specific overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
autogpt_platform/**/.env*
📄 CodeRabbit inference engine (autogpt_platform/CLAUDE.md)
Platform-level configuration should use
.env.default(Supabase/shared defaults, tracked in git) and.envfor user overrides (gitignored)
Files:
autogpt_platform/backend/.env.default
autogpt_platform/backend/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/backend/**/*.py: Use Python 3.11 (required; managed by Poetry via pyproject.toml) for backend development
Always run 'poetry run format' (Black + isort) before linting in backend development
Always run 'poetry run lint' (ruff) after formatting in backend development
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/backend/blocks/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/backend/backend/blocks/**/*.py: Inherit from 'Block' base class with input/output schemas when adding new blocks in backend
Implement 'run' method with proper error handling in backend blocks
Generate block UUID using 'uuid.uuid4()' when creating new blocks in backend
Write tests alongside block implementation when adding new blocks in backend
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/**/*.{py,txt}
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use
poetry runprefix for all Python commands, including testing, linting, formatting, and migrations
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/backend/**/*.py
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
Use Prisma ORM for database operations in PostgreSQL with pgvector for embeddings
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/backend/backend/blocks/*.py
📄 CodeRabbit inference engine (autogpt_platform/backend/CLAUDE.md)
autogpt_platform/backend/backend/blocks/*.py: When creating new blocks, inherit from theBlockbase class and define input/output schemas usingBlockSchema
Implement blocks with an asyncrunmethod and generate unique block IDs usinguuid.uuid4()
When working with files in blocks, usestore_media_file()frombackend.util.filewith appropriatereturn_formatparameter:for_local_processingfor local tools,for_external_apifor external APIs,for_block_outputfor block outputs
Always usefor_block_outputformat instore_media_file()for block outputs unless there is a specific reason not to
Never hardcode workspace checks when usingstore_media_file()- letfor_block_outputhandle context adaptation automatically
When adding new blocks, analyze block interfaces to ensure inputs and outputs tie well together for productive graph-based editor connections
Files:
autogpt_platform/backend/backend/blocks/llm.py
autogpt_platform/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
Format Python code with
poetry run format
Files:
autogpt_platform/backend/backend/blocks/llm.py
🧠 Learnings (4)
📚 Learning: 2026-02-26T21:29:27.605Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/CLAUDE.md:0-0
Timestamp: 2026-02-26T21:29:27.605Z
Learning: Applies to autogpt_platform/backend/.env* : Backend configuration should use `.env.default` for defaults (tracked in git) and `.env` for user-specific overrides (gitignored)
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-04T16:50:20.508Z
Learnt from: CR
Repo: Significant-Gravitas/AutoGPT PR: 0
File: autogpt_platform/backend/CLAUDE.md:0-0
Timestamp: 2026-02-04T16:50:20.508Z
Learning: Applies to autogpt_platform/backend/**/{.env.default,.env} : Use `.env.default` for default environment configuration and `.env` for user overrides in the backend
Applied to files:
autogpt_platform/backend/.env.default
📚 Learning: 2026-02-05T04:11:00.596Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 11796
File: autogpt_platform/backend/backend/blocks/video/concat.py:3-4
Timestamp: 2026-02-05T04:11:00.596Z
Learning: In autogpt_platform/backend/backend/blocks/**/*.py, when creating a new block, generate a UUID once with uuid.uuid4() and hard-code the resulting string as the block's id parameter. Do not call uuid.uuid4() at runtime; IDs must be constant across all imports and runs to ensure stability.
Applied to files:
autogpt_platform/backend/backend/blocks/llm.py
📚 Learning: 2026-02-26T17:02:22.448Z
Learnt from: Pwuts
Repo: Significant-Gravitas/AutoGPT PR: 12211
File: .pre-commit-config.yaml:160-179
Timestamp: 2026-02-26T17:02:22.448Z
Learning: Keep the pre-commit hook pattern broad for autogpt_platform/backend to ensure OpenAPI schema changes are captured. Do not narrow to backend/api/ alone, since the generated schema depends on Pydantic models across multiple directories (backend/data/, backend/blocks/, backend/copilot/, backend/integrations/, backend/util/). Narrowing could miss schema changes and cause frontend type desynchronization.
Applied to files:
autogpt_platform/backend/backend/blocks/llm.py
🧬 Code graph analysis (1)
autogpt_platform/backend/backend/blocks/llm.py (1)
autogpt_platform/backend/backend/integrations/providers.py (1)
ProviderName(6-111)
🔇 Additional comments (4)
autogpt_platform/backend/.env.default (1)
52-60: LGTM! API key ordering is correct and AVIAN_API_KEY properly integrated.The addition of
AVIAN_API_KEYand the reordering ofOPENAI_API_KEY,OPEN_ROUTER_API_KEY, andV0_API_KEYnow satisfy alphabetical ordering requirements. All API key entries in the AI/LLM Services section are correctly ordered and follow the established naming convention.autogpt_platform/backend/backend/blocks/llm.py (3)
41-51: LGTM!The
ProviderName.AVIANaddition toLLMProviderNameis correctly positioned alphabetically and consistent with the existing provider pattern.
174-178: LGTM!The Avian model enum entries follow the established naming convention (
AVIAN_prefix) and value format (provider/model-name) consistent with other providers in the codebase.
988-1026: LGTM!The Avian provider handler correctly follows the established pattern for OpenAI-compatible providers:
- Uses
AsyncOpenAIclient with appropriate base URL- Supports JSON output mode, tool calling, and parallel tool calls
- Properly extracts tool calls and reasoning using shared helper functions
- Error handling is appropriate (the
responseobject is always truthy after a successfulawait)
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⚠️ Outside diff range comments (1)
docs/integrations/block-integrations/llm.md (1)
118-121:⚠️ Potential issue | 🟡 MinorUse Case sections in touched blocks don’t follow required 3-item format.
For these blocks, the Possible use case section should contain exactly 3 practical use cases, each with a bold heading and a one-sentence description; currently each section has a single paragraph.
As per coding guidelines, "
docs/integrations/**/*.md: Provide exactly 3 practical use cases in the 'Use Case' section, formatted with bold headings followed by short one-sentence descriptions".Also applies to: 275-278, 445-448, 482-485, 519-522
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@docs/integrations/block-integrations/llm.md` around lines 118 - 121, The "Possible use case" block titled "Possible use case" currently contains a single paragraph; replace it with exactly three practical use cases, each on its own line starting with a bold heading (e.g., **Customer Support Bot:**) followed by a one-sentence description—no extra text, bullets, or additional paragraphs. Ensure the same change is applied to the other touched "Possible use case" blocks referenced (they must each contain exactly three items in the same bold-heading + one-sentence format) and keep wording concise and practical.
🧹 Nitpick comments (1)
docs/integrations/block-integrations/llm.md (1)
68-68: Model option lists are consistent, but duplication is getting hard to maintain.The same large model union is repeated across many blocks; consider generating these tables from a shared source (or referencing a shared model matrix) to avoid drift when providers/models change.
Also applies to: 106-106, 260-260, 427-427, 467-467, 504-504, 766-766
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@docs/integrations/block-integrations/llm.md` at line 68, The large model union under the "model" table cell is duplicated across many blocks (the long quoted union of model names) which makes it hard to maintain; extract that union into a single shared source (e.g., a YAML/JSON model matrix or a single markdown include/partial) and update each block to reference or include that shared list instead of repeating it; look for the "model" table cell entries in llm.md (the table header/column named "model") and replace the inline union with a reference to the shared model list (or generate the tables from the shared matrix) so all occurrences (the repeated unions) are driven from one canonical source.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Outside diff comments:
In `@docs/integrations/block-integrations/llm.md`:
- Around line 118-121: The "Possible use case" block titled "Possible use case"
currently contains a single paragraph; replace it with exactly three practical
use cases, each on its own line starting with a bold heading (e.g., **Customer
Support Bot:**) followed by a one-sentence description—no extra text, bullets,
or additional paragraphs. Ensure the same change is applied to the other touched
"Possible use case" blocks referenced (they must each contain exactly three
items in the same bold-heading + one-sentence format) and keep wording concise
and practical.
---
Nitpick comments:
In `@docs/integrations/block-integrations/llm.md`:
- Line 68: The large model union under the "model" table cell is duplicated
across many blocks (the long quoted union of model names) which makes it hard to
maintain; extract that union into a single shared source (e.g., a YAML/JSON
model matrix or a single markdown include/partial) and update each block to
reference or include that shared list instead of repeating it; look for the
"model" table cell entries in llm.md (the table header/column named "model") and
replace the inline union with a reference to the shared model list (or generate
the tables from the shared matrix) so all occurrences (the repeated unions) are
driven from one canonical source.
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Review profile: CHILL
Plan: Pro
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You can enable these sources in your CodeRabbit configuration.
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autogpt_platform/backend/backend/blocks/llm.pydocs/integrations/block-integrations/llm.md
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- autogpt_platform/backend/backend/blocks/llm.py
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- GitHub Check: Seer Code Review
- GitHub Check: types
- GitHub Check: test (3.13)
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📓 Path-based instructions (2)
docs/integrations/**/*.md
📄 CodeRabbit inference engine (docs/CLAUDE.md)
docs/integrations/**/*.md: Provide a technical explanation of how the block functions in the 'How It Works' section, including 1-2 paragraphs describing processing logic, validation, error handling, or edge cases, with code examples in backticks when helpful
Provide exactly 3 practical use cases in the 'Use Case' section, formatted with bold headings followed by short one-sentence descriptions
Files:
docs/integrations/block-integrations/llm.md
docs/**/*.md
📄 CodeRabbit inference engine (docs/CLAUDE.md)
docs/**/*.md: Keep documentation descriptions concise and action-oriented, focusing on practical, real-world scenarios
Use consistent terminology with other blocks and avoid overly technical jargon unless necessary in documentation
Files:
docs/integrations/block-integrations/llm.md
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📋 Automated Re-Review — PR #12221: feat(blocks): add Avian as LLM provider
Author: avianion | Files: 9 (+132/−16) | HEAD: d7f97877 | Re-review #2 (prev: e6155dc)
🎯 Verdict: APPROVE
What This PR Does
Adds Avian as a new OpenAI-compatible LLM provider with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5). Full integration across all expected touchpoints: provider enum, model metadata, credentials store, cost config, llm_call() handler, .env.default, settings, frontend icon mappings, and docs.
Previous Blockers — ALL RESOLVED ✅
| Previous Blocker | Status |
|---|---|
| Model metadata wrong for 3/4 models (MiniMax 1M/1M, Kimi 131K/8K, GLM-5 131K/16K) | ✅ Fixed — Now 196K/131K, 262K/262K, 205K/131K respectively |
Dead code in error handling (if response: always truthy) |
✅ Fixed — Simplified to if not response.choices: raise ValueError(...) |
.env.default key ordering lint issue |
✅ Fixed — Now fully alphabetical |
| CLA check pending | ✅ Signed and green |
check-docs-sync CI failure |
✅ Fixed — Docs regenerated with new models + use-case improvements |
Specialist Findings
🛡️ Security ✅ — No issues. API key via SecretStr, HTTPS-only hardcoded endpoint, conditional credential exposure. Follows exact pattern of all other providers.
🏗️ Architecture ✅ — All 9+ registration touchpoints properly wired and consistent. Handler follows established OpenAI-compatible pattern. Pre-existing tech debt (growing elif chain in llm_call() — now 8 branches, 5 near-identical) noted but not introduced by this PR.
⚡ Performance ✅ — Model metadata now matches Avian's published specs. Minor nit: DeepSeek V3.2 context is 164K in code vs ~163K on avian.io (likely rounding). No runtime performance concerns.
🧪 Testing ✅ — Dead code fix verified clean. Avian's error guard is actually cleaner than some existing providers. Zero new tests, but consistent with all other providers (none have dedicated llm_call branch tests). CI green on 3.11/3.12/3.13.
📖 Quality a3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5d) with sequential hex pattern — should be replaced with a proper uuid4() value for consistency. Non-blocking.
📦 Product
📬 Discussion
🔎 QA ✅ — Live testing confirmed: All 4 Avian models appear in model dropdown, are selectable, and correctly route to Avian credentials. Frontend renders cleanly. Backend healthy. Model metadata corrections verified in the UI.
🟡 Should Fix (Follow-up OK)
-
Hand-crafted UUID (
credentials_store.py:98) — Replacea3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5dwith a properuuid4()-generated value. -
No custom provider icon — Uses generic
fallbackIcon/KeyholeIcon. A distinctive Avian logo would improve provider selection UX. -
No dedicated provider documentation — Consider adding a brief Avian provider page explaining how to get an API key and what models are available.
🟢 Nice to Have
- Differentiated credit pricing — GLM-5 at $2.55/M output is 6× more expensive than DeepSeek V3.2 at $0.42/M, but both cost 1 credit. Consistent with platform pattern but worth revisiting.
- Tech debt follow-up — Extract shared
_call_openai_compatible()helper to collapse 5 near-identicalelifbranches inllm_call().
Risk Assessment
Merge risk: LOW | Rollback: EASY (purely additive, no existing behavior changed)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | check-docs-sync ✅ | CLA ✅
Failing: e2e ❌ (pre-existing) | Check PR Status ❌ (timing/infra)
QA Evidence
- Landing page
- Dashboard
- Build page
- Model dropdown
- Kimi K2.5 selected
- DeepSeek V3.2
- MiniMax M2.5
- GLM-5 + Avian credential
Automated re-review by PR Review Squad — 8/8 specialists reported
@ntindle All previous blockers resolved. Model metadata corrected, dead code removed, CI green, CLA signed. Clean vendor integration — ready for human review.
|
Addressed conflict: merged upstream/dev to resolve the docs/integrations/block-integrations/llm.md conflict. Preserved Avian model entries (deepseek-v3.2, kimi-k2.5, glm-5, minimax-m2.5) alongside the newly added claude-sonnet-4-6 from upstream. |
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PR #12221 — feat(blocks): add Avian as LLM provider (Re-review #3)
Author: avianion | HEAD: 5f0cb0c3c6 | Files: llm.py (+56), credentials_store.py (+10), block_cost_config.py (+20), providers.py (+1), settings.py (+1), .env.default (+4/-2), helpers.ts ×2 (+2), llm.md (docs)
🎯 Verdict: REQUEST_CHANGES
What This PR Does
Adds Avian as a new LLM provider for AutoGPT with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5) using an OpenAI-compatible API. Clean, pattern-compliant integration following the established provider addition recipe.
Changes Since Last Review (e6155dc → 5f0cb0c)
9a7241ba— "fix: correct minimax-m2.5 token limits to 196608" (introduced max_output regression)5f0cb0c3— "chore: merge upstream/dev and restore Avian models in docs" (conflict resolution)
Specialist Findings
🛡️ Security ✅ — No vulnerabilities. API key handling follows existing SecretStr pattern. HTTPS enforced. No secrets exposed.
🏗️ Architecture llm_call(). Tech debt warrants follow-up refactor.
⚡ Performance ✅ — No overhead. Same async client pattern, no N+1 queries, O(1) straight-line code. Negligible memory footprint.
🧪 Testing
📖 Quality ✅ — Clean naming, correct alphabetical ordering, proper type usage. Hand-crafted UUID is a style concern, not blocker.
📦 Product
📬 Discussion 9a7241ba regressed max_output from the correct 131000 to 196608.
🔎 QA ✅ — All 4 Avian models verified in live UI. Frontend loads, signup works, model dropdown shows all models grouped by creator, credential binding functional.
Blockers
-
llm.py:519— MiniMax M2.5max_outputis 196608, should be ~131000 (verified against avian.io/models on 2026-03-08). Commit9a7241bachanged this from the correct value of 131000 to 196608, matchingmax_inputinstead of the actual output limit. This is the same blocker from reviews #1 and #2, now regressed. The platform would advertise ~50% more output capacity than the API actually supports, causing silent truncation or API errors.Fix: Change
ModelMetadata("avian", 196608, 196608, ...)→ModelMetadata("avian", 196608, 131000, ...)
Model Metadata Verification (avian.io/models, fetched 2026-03-08)
| Model | avian.io Context | Code Context | avian.io Max Output | Code Max Output | Status |
|---|---|---|---|---|---|
| deepseek/deepseek-v3.2 | 163K | 164000 | 65K | 65000 | ✅ |
| moonshotai/kimi-k2.5 | 262K | 262000 | 262K | 262000 | ✅ |
| z-ai/glm-5 | 205K | 205000 | 131K | 131000 | ✅ |
| minimax/minimax-m2.5 | 196K | 196608 | 131K | 196608 | ❌ |
Should Fix (Follow-up OK)
credentials_store.py:98— Hand-crafted UUIDa3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5d. Generate a properuuid4().llm.py— 6th copy-paste OpenAI-compatible elif branch. Extract_openai_compatible_call()helper (tech debt issue).block_cost_config.py— All 4 models at 1 credit despite 6× output price difference ($0.33 vs $2.55/M tokens).helpers.ts×2 — No custom Avian icon (usesfallbackIcon/KeyholeIcon).
Risk Assessment
Merge risk: LOW | Rollback: EASY (purely additive, no existing behavior changed)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | check-docs-sync ✅ | CLA ✅
Failing: e2e ❌ (pre-existing) | Check PR Status ❌ (infra)
Automated re-review #3 by PR Review Squad — 8/8 specialists reported
@ntindle One remaining blocker: MiniMax M2.5 max_output regressed from 131000→196608 in latest commit. Single-line fix needed. Rest of the PR is clean.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
|
Addressed feedback: merged latest upstream master to bring branch up to date (was 1 commit behind). The CI failures (end-to-end Playwright tests, Vercel) appear to be infrastructure/environment issues unrelated to the Avian integration — all code-specific checks (lint, tests, integration_test, CodeQL) are passing. |
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PR #12221 — feat(blocks): add Avian as LLM provider (Re-review #4)
Author: avianion | HEAD: c2d7d168 | Files: llm.py (+56), credentials_store.py (+10), block_cost_config.py (+20), providers.py (+1), settings.py (+1), .env.default (+4/-2), helpers.ts ×2 (+2), llm.md (docs)
🎯 Verdict: REQUEST_CHANGES
What This PR Does
Adds Avian as a new LLM provider for AutoGPT with 4 models (DeepSeek V3.2, Kimi K2.5, GLM-5, MiniMax M2.5) using an OpenAI-compatible API. Clean, pattern-compliant integration following the established provider addition recipe.
Changes Since Last Review (5f0cb0c → c2d7d16)
19d775c43— Merge commit from forkc2d7d168— "chore: merge upstream master to bring branch up to date"
These are upstream merge commits only — no changes to any Avian-specific code. The previous blocker remains unfixed.
Specialist Findings
🛡️ Security ✅ — No vulnerabilities. API key follows SecretStr pattern. HTTPS hardcoded to https://api.avian.io/v1 (no SSRF). No secrets committed. Upstream merge includes a positive security fix (session ownership check in copilot).
🏗️ Architecture max_output blocker (see below). All 6 registration touchpoints verified consistent (providers.py, llm.py enum, MODEL_METADATA, llm_call elif, credentials_store, settings.py). Code duplication: 5th near-identical OpenAI-compatible elif branch — tech debt but not blocking.
⚡ Performance ✅ — No regressions. O(1) additions, no N+1 queries, no hot-path allocations. Per-call client instantiation matches existing pattern.
🧪 Testing
📖 Quality ✅ — Clean naming, correct alphabetical ordering in .env.default and settings.py. Hand-crafted UUID and no custom icon are cosmetic concerns carried forward.
📦 Product max_output wrong → users would see 50% more output capacity than actually available. Flat 1-credit pricing is a minor concern but consistent with existing patterns.
📬 Discussion
🔎 QA ✅ — All 4 Avian models verified in live UI dropdown (grouped by creator: DeepSeek, Moonshot AI, Zhipu AI, MiniMax). Credential binding works correctly. Frontend, backend, and block API all functional. 9 screenshots captured.
Blocker
-
llm.py:519— MiniMax M2.5max_outputis 196608, should be 131000 (verified against avian.io/models on 2026-03-09). Themax_outputparameter directly controlsmax_tokenssent to the API (seellm.py:679-682). Settingmax_tokens=196608when the API supports ~131K will cause silent truncation or API errors. This blocker has been flagged in all 4 reviews — it was correct in review #2 (131000), then regressed in commit9a7241ba.Fix:
ModelMetadata("avian", 196608, 196608, ...)→ModelMetadata("avian", 196608, 131000, ...)
Model Metadata Verification (avian.io/models, fetched 2026-03-09)
| Model | avian.io Context | Code Context | avian.io Max Output | Code Max Output | Status |
|---|---|---|---|---|---|
| deepseek/deepseek-v3.2 | 163K | 164000 | 65K | 65000 | ✅ |
| moonshotai/kimi-k2.5 | 262K | 262000 | 262K | 262000 | ✅ |
| z-ai/glm-5 | 205K | 205000 | 131K | 131000 | ✅ |
| minimax/minimax-m2.5 | 196K | 196608 | 131K | 196608 | ❌ |
Should Fix (Follow-up OK)
credentials_store.py:98— Hand-crafted UUIDa3b7c9d1-4e5f-6a7b-8c9d-0e1f2a3b4c5d. Generate a properuuid4().llm.py— 5th copy-paste OpenAI-compatible elif branch. Extract_openai_compatible_call()helper (tech debt).block_cost_config.py— All 4 models at 1 credit despite 3-8× output price difference ($0.33 vs $2.55/M tokens).helpers.ts×2 — No custom Avian icon (usesfallbackIcon/KeyholeIcon).
QA Screenshots
- Landing page | Dashboard | Build page
- Model dropdown | MiniMax selected | Kimi K2.5
- DeepSeek V3.2 | GLM-5 | Credential flow
Risk Assessment
Merge risk: LOW | Rollback: EASY (purely additive, no existing behavior changed)
CI Status
Core: lint ✅ | tests ✅ (3.11/3.12/3.13) | types ✅ | integration ✅ | CodeQL ✅ | check-docs-sync ✅ | CLA ✅
Failing: e2e ❌ (infra) | Check PR Status ❌ (infra) | Vercel ❌ (auth required)
Automated re-review #4 by PR Review Squad — 8/8 specialists reported
@ntindle One remaining blocker: MiniMax M2.5 max_output regressed from 131000→196608 and still hasn't been fixed across 4 review cycles. Single-line fix needed. Rest of the PR is clean and QA-verified.
The max_output for MiniMax M2.5 was incorrectly set to 196608 (matching the context window) when the actual API-supported max output is ~131K tokens per avian.io/models. This fixes the regression from commit 9a7241b. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Addressed feedback: corrected MiniMax M2.5 max_output from 196608 to 131000 per avian.io/models specs. The dotenv key ordering in .env.default was already fixed in prior commits (alphabetically ordered, OPENAI_API_KEY before OPEN_ROUTER_API_KEY). |
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Keep both new upstream models (gemini-2.5-pro, gemini-3.1-pro-preview, gemini-3-flash-preview, gemini-3.1-flash-lite-preview, mistral-large-2512, mistral-medium-3.1, mistral-small-3.2-24b-instruct, codestral-2508) and Avian-specific models (deepseek-v3.2, kimi-k2.5, glm-5, minimax-m2.5).
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Merged latest upstream to resolve merge conflicts. |
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4 participants
Add Avian as a new LLM provider for AutoGPT. Avian provides an OpenAI-compatible API with access to cost-effective frontier models.
Changes
Backend:
AVIANtoProviderNameenum inproviders.pyProviderName.AVIANtoLLMProviderNameliteral type inllm.pyLlmModelenum:deepseek/deepseek-v3.2— 164K context, 65K max output, $0.26/$0.38 per 1M tokensmoonshotai/kimi-k2.5— 131K context, 8K max output, $0.45/$2.20 per 1M tokensz-ai/glm-5— 131K context, 16K max output, $0.30/$2.55 per 1M tokensminimax/minimax-m2.5— 1M context, 1M max output, $0.30/$1.10 per 1M tokensMODEL_METADATA) with context windows, output limits, and provider/creator infollm_call()using OpenAI-compatible client (base_url="https://api.avian.io/v1") with support for:avian_api_keytoSettings.Secretsinsettings.pyAVIAN_API_KEY=to.env.defaultavian_credentialstocredentials_store.py(APIKeyCredentials + DEFAULT_CREDENTIALS + get_all_creds)MODEL_COSTandLLM_COSTinblock_cost_config.pyFrontend:
avianprovider icon entry in both credential input helper filesChecklist
For code changes:
LlmModelenum with correct valuesMODEL_METADATAentries match for all Avian models (provider="avian")MODEL_COSTentries exist for all 4 models (validation loop at module load)llm_callfollows the same pattern as other OpenAI-compatible providers (OpenRouter, Llama API, v0)avian_credentialsis added toDEFAULT_CREDENTIALSand conditionally toget_all_credsavianFor configuration changes:
.env.defaultis updated or already compatible with my changesAVIAN_API_KEY=to.env.defaultdocker-compose.ymlis updated or already compatible with my changesAVIAN_API_KEYis loaded via Settings from environmentcc @majdyz @Bentlybro @Pwuts