크로스 플랫폼 임베디드 C 언어 AI 에이전트 라이브러리 Adam

Adam Embeddable AI agent library in C. Adam gives you a complete agent loop: tool calling, memory, sessions, voice, streaming, structured output, in one #include . Works with cloud APIs (Anthropic, OpenAI, Google Gemini, Groq, Together, xAI) and local models (llama.cpp) through the same interface. Compiles on macOS, Linux, Windows, iOS, Android, and WASM. Quick Start #include "adam.h" int main ( void ) { adam_init (); adam_settings_t * s = adam_create_settings (); adam_settings_set_provider ( s , ADAM_API_ANTHROPIC , getenv ( "ANTHROPIC_API_KEY" ), "claude-sonnet-4-20250514" ); adam_history_t * h = adam_history_create (); adam_run_result_t r = adam_run ( s , h , "What is the capital of France?" ); printf ( "%s\n" , r . final_response ); // "The capital of France is Paris." adam_run_result_free ( & r ); adam_history_destroy ( h ); adam_settings_destroy ( s ); adam_cleanup (); } make deps # build llama.cpp + whisper.cpp make all # build libadam.a make test # run 161 tests (ASan + UBSan) Features Feature Description Agent loop Tool calling with automatic iteration until final response Three providers Anthropic, OpenAI, Google Gemini + any compatible API + local GGUF via llama.cpp Local vision Multimodal image understanding via llama.cpp + mmproj (Gemma 3, LLaVA, etc.) Image generation Native image output via Gemini image models (gemini-3.1-flash-image-preview) Database extensions SQLite and PostgreSQL extensions — embed Adam as SQL functions that query the same database 13 built-in tools File I/O, shell, calculator, SQL, web fetch/search, HTTP POST, memory, research, multi-agent Long-term memory Hybrid BM25 + vector search via SQLite (sqlite-memory + sqlite-vector) Session persistence Save/load conversations with UUIDv7 keys Telegram bot Full-featured Telegram integration with text, voice, images, tools, and memory Voice STT (Whisper cloud/local) + TTS (cloud/system) + full audio pipeline Streaming Real-time token delivery via callback Structured output adam_run_json() with validation and retry Evolution loop Self-improving agent: iterate, score, refine strategy Research mode Autonomous multi-iteration information gathering with report synthesis Multi-agent Agent A invokes Agent B as a tool, with independent settings/tools Guardrails Pre-send and post-receive validation callbacks Response cache LRU hash table keyed on model + message history History management Clone, summarize (LLM-based compression), token estimation Thread pool Concurrent agent execution with job queue Filesystem sandbox Tools restricted to explicitly allowed directories Cross-platform macOS, Linux, Windows, iOS, Android, WASM (Emscripten) Arena allocator Zero-leak per-iteration memory with automatic cleanup Build make deps # Build llama.cpp, whisper.cpp (+ mbedtls/curl on Linux) make all # Build libadam.a make test # Build & run unit tests (ASan + UBSan) make chat # Interactive text chat (cloud API) make chat GGUF=models/model.gguf # Interactive text chat (local) make vision GGUF=models/model.gguf MMPROJ=models/mmproj.gguf # Local vision test make talk # Voice agent (cloud) make talk LOCAL=1 # Voice agent (fully local) make memory # Memory system tests make clean # Remove all build artifacts API Reference Full API documentation with every function, type, and callback: API.md Examples adam_settings_t * s = adam_create_settings (); adam_settings_set_provider ( s , ADAM_API_ANTHROPIC , api_key , "claude-sonnet-4-20250514" ); adam_history_t * h = adam_history_create (); adam_run_result_t r = adam_run ( s , h , "Explain quantum entanglement simply." ); printf ( "%s\n" , r . final_response ); adam_run_result_free ( & r ); // Continue the conversation — history carries forward r = adam_run ( s , h , "Can you give an analogy?" ); printf ( "%s\n" , r . final_response ); adam_run_result_free ( & r ); adam_history_destroy ( h ); adam_settings_destroy ( s ); More examples are available in the examples/ directory: Example Description simple-conversation Multi-turn chat with a cloud API tool-calling Register a custom tool and let the agent call it local-model Run a local GGUF model via llama.cpp local-vision Image understanding with a local vision model + mmproj google-gemini Use Google Gemini models image-generation Generate images with Gemini sqlite-query Natural language queries on any SQLite database structured-json Get validated JSON output with retry memory Long-term memory with hybrid BM25 + vector search sessions Save and restore conversations streaming Real-time token streaming via callback voice Speech-to-text + agent + text-to-speech pipeline multi-agent Agent A delegates to Agent B as a tool evolution Self-improving agent with scoring loop research Autonomous multi-iteration research with report filesystem-sandbox File and shell tools restricted to allowed directories guardrails Pre-send and post-receive validation response-cache LRU cache for repeated queries thread-pool Concurrent agent execution telegram Telegram bot with text, images, tools, and memory wasm-chat Browser-based chat UI via WebAssembly full-agent All features combined Database Extensions Adam can be embedded directly inside SQLite and PostgreSQL as a SQL extension. The agent can query the same database it's loaded in — ask questions in natural language, get answers from your data. -- SQLite .load adam -- PostgreSQL CREATE EXTENSION adam; -- Configure (both) SELECT adam_config( ' provider ' , ' anthropic ' ); SELECT adam_config( ' api_key ' , ' sk-ant-... ' ); -- Ask about your data — the agent reads the schema and runs SQL SELECT adam_ask( ' How many users signed up last month? ' ); -- → "47 users signed up last month." -- Generate SQL without executing SELECT adam_sql( ' top 5 products by revenue ' ); -- → "SELECT p.name, SUM(oi.quantity * oi.price) AS revenue FROM ..." Function Description adam_config(key, val) Configure provider, API key, model (persisted) adam(msg) Stateless one-shot chat adam_ask(msg) SQL-aware agent — reads schema, queries data, multi-turn adam_sql(question) Generate SQL from natural language adam_create_session() Create session (auto-created on first adam_ask ) adam_get_session() Get current session UUID adam_clear_session() Clear session and history See extensions/sqlite/ and extensions/postgres/ for build instructions. Architecture adam_run() loop: build system prompt (identity + instructions + bootstrap files + memory + datetime) -> check guardrails (on_before_send) -> check cache -> dispatch LLM (mock | local/llama.cpp | remote/HTTP) -> check guardrails (on_after_receive) -> if tool_calls: execute tools -> append results -> loop -> if text: return final response -> auto-save session -> extract memory Platform abstraction : macOS uses NSURLSession, Linux uses libcurl+mbedtls, WASM uses embedder-provided http_fn callback. Memory management : Arena allocators for per-iteration zero-copy work. malloc/free for long-lived structures. Arena-allocated strings are only valid within the current iteration. Feature Gates Define before #include "adam.h" to disable features: Gate Effect ADAM_NO_CURL No libcurl (must provide http_fn callback) ADAM_NO_LOCAL No llama.cpp (no local inference) ADAM_NO_PTHREADS No thread pool, no voice thread ADAM_NO_SQLITE No SQLite (no memory, sessions, or SQL tool) ADAM_NO_VOICE No voice subsystem ADAM_NO_FILESYSTEM No file_read/file_write/list_directory tools ADAM_NO_SHELL No shell_exec tool Dependencies All vendored as git submodules in modules/ : Module Purpose llama.cpp Local LLM inference + GGML compute whisper.cpp Local speech-to-text (shares ggml via symlink) miniaudio Cross-platform audio I/O sqlite Amalgamation build sqlite-memory Hybrid BM25 + vector knowledge store sqlite-vector Vector similarity search mbedtls TLS (Linux only) curl HTTP (Linux only) macOS uses system frameworks (Foundation, Security, Metal, AVFoundation, Accelerate) instead of curl/mbedtls. License MIT