GitHub - sqliteai/adam: An embeddable cross-platform AI agent library written in C. Cloud and local LLMs, tool calling, long-term memory, voice, sessions, research mode, self-evolving loops. The SQLite of agent frameworks: small, portable, just works.

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."