LocalAI/AGENTS.md

Build and testing

Building and testing the project depends on the components involved and the platform where development is taking place. Due to the amount of context required it's usually best not to try building or testing the project unless the user requests it. If you must build the project then inspect the Makefile in the project root and the Makefiles of any backends that are effected by changes you are making. In addition the workflows in .github/workflows can be used as a reference when it is unclear how to build or test a component. The primary Makefile contains targets for building inside or outside Docker, if the user has not previously specified a preference then ask which they would like to use.

Building a specified backend

Let's say the user wants to build a particular backend for a given platform. For example let's say they want to build bark for ROCM/hipblas

• The Makefile has targets like docker-build-bark created with generate-docker-build-target at the time of writing. Recently added backends may require a new target.
• At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
  • Use .github/workflows/backend.yml as a reference it lists the needed args in the include job strategy matrix
  • l4t and cublas also requires the CUDA major and minor version
• You can pretty print a command like DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:6.4.4 make docker-build-bark
• Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
• The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.
• Sometimes the user may need extra parameters to be added to docker build (e.g. --platform for cross-platform builds or --progress to view the full logs), in which case you can generate the docker build command directly.

Coding style

• The project has the following .editorconfig

root = true

[*]
indent_style = space
indent_size = 2
end_of_line = lf
charset = utf-8
trim_trailing_whitespace = true
insert_final_newline = true

[*.go]
indent_style = tab

[Makefile]
indent_style = tab

[*.proto]
indent_size = 2

[*.py]
indent_size = 4

[*.js]
indent_size = 2

[*.yaml]
indent_size = 2

[*.md]
trim_trailing_whitespace = false

• Use comments sparingly to explain why code does something, not what it does. Comments are there to add context that would be difficult to deduce from reading the code.
• Prefer modern Go e.g. use any not interface{}

Logging

Use github.com/mudler/xlog for logging which has the same API as slog.

llama.cpp Backend

The llama.cpp backend (backend/cpp/llama-cpp/grpc-server.cpp) is a gRPC adaptation of the upstream HTTP server (llama.cpp/tools/server/server.cpp). It uses the same underlying server infrastructure from llama.cpp/tools/server/server-context.cpp.

Building and Testing

• Test llama.cpp backend compilation: make backends/llama-cpp
• The backend is built as part of the main build process
• Check backend/cpp/llama-cpp/Makefile for build configuration

Architecture

• grpc-server.cpp: gRPC server implementation, adapts HTTP server patterns to gRPC
• Uses shared server infrastructure: server-context.cpp, server-task.cpp, server-queue.cpp, server-common.cpp
• The gRPC server mirrors the HTTP server's functionality but uses gRPC instead of HTTP

Common Issues When Updating llama.cpp

When fixing compilation errors after upstream changes:

1. Check how server.cpp (HTTP server) handles the same change
2. Look for new public APIs or getter methods
3. Store copies of needed data instead of accessing private members
4. Update function calls to match new signatures
5. Test with make backends/llama-cpp

Key Differences from HTTP Server

• gRPC uses BackendServiceImpl class with gRPC service methods
• HTTP server uses server_routes with HTTP handlers
• Both use the same server_context and task queue infrastructure
• gRPC methods: LoadModel, Predict, PredictStream, Embedding, Rerank, TokenizeString, GetMetrics, Health

Tool Call Parsing Maintenance

When working on JSON/XML tool call parsing functionality, always check llama.cpp for reference implementation and updates:

Checking for XML Parsing Changes

1. Review XML Format Definitions: Check llama.cpp/common/chat-parser-xml-toolcall.h for xml_tool_call_format struct changes
2. Review Parsing Logic: Check llama.cpp/common/chat-parser-xml-toolcall.cpp for parsing algorithm updates
3. Review Format Presets: Check llama.cpp/common/chat-parser.cpp for new XML format presets (search for xml_tool_call_format form)
4. Review Model Lists: Check llama.cpp/common/chat.h for COMMON_CHAT_FORMAT_* enum values that use XML parsing:
   • COMMON_CHAT_FORMAT_GLM_4_5
   • COMMON_CHAT_FORMAT_MINIMAX_M2
   • COMMON_CHAT_FORMAT_KIMI_K2
   • COMMON_CHAT_FORMAT_QWEN3_CODER_XML
   • COMMON_CHAT_FORMAT_APRIEL_1_5
   • COMMON_CHAT_FORMAT_XIAOMI_MIMO
   • Any new formats added

Model Configuration Options

Always check llama.cpp for new model configuration options that should be supported in LocalAI:

1. Check Server Context: Review llama.cpp/tools/server/server-context.cpp for new parameters
2. Check Chat Params: Review llama.cpp/common/chat.h for common_chat_params struct changes
3. Check Server Options: Review llama.cpp/tools/server/server.cpp for command-line argument changes
4. Examples of options to check:
   • ctx_shift - Context shifting support
   • parallel_tool_calls - Parallel tool calling
   • reasoning_format - Reasoning format options
   • Any new flags or parameters

Implementation Guidelines

1. Feature Parity: Always aim for feature parity with llama.cpp's implementation
2. Test Coverage: Add tests for new features matching llama.cpp's behavior
3. Documentation: Update relevant documentation when adding new formats or options
4. Backward Compatibility: Ensure changes don't break existing functionality

Files to Monitor

• llama.cpp/common/chat-parser-xml-toolcall.h - Format definitions
• llama.cpp/common/chat-parser-xml-toolcall.cpp - Parsing logic
• llama.cpp/common/chat-parser.cpp - Format presets and model-specific handlers
• llama.cpp/common/chat.h - Format enums and parameter structures
• llama.cpp/tools/server/server-context.cpp - Server configuration options