erlangchain

Minimal building blocks for talking to LLMs from Erlang, without third-party dependencies.

Module	Role
`llm`	one-call chat client for OpenAI, Anthropic, and OpenRouter-hosted open-source models, with tool-use and multimodal support
`llm_datasource`	create and manage provider-backed vector-store datasources
`json_util`	dependency-free JSON encode/decode

Install

%% rebar.config
{deps, [
    {erlangchain, "~> 0.2.0"}
]}.

Set OPENAI_API_KEY, ANTHROPIC_API_KEY, and/or OPENROUTER_API_KEY in the environment (a .env file in the working directory is loaded automatically if present).

llm

%% Simple completion (defaults to openai/small):
{ok, #{content := Text}} = llm:chat([#{role => user, content => <<"hello">>}]),

%% Pick provider + size:
{ok, Resp} = llm:chat(openai, big, Messages),

%% Open-source models through OpenRouter:
{ok, Resp} = llm:chat(opensource, small, Messages),

%% Frontier tier, or an exact model slug:
{ok, Resp} = llm:chat(openai, frontier, Messages),
{ok, Resp} = llm:chat("openai", "exact-model-slug", Messages),

%% Tool use — pass tool specs, get back tool_calls to run and feed back:
{ok, #{tool_calls := Calls}} = llm:chat(openai, big, Messages, Tools),

%% OpenAI file search — pass a vector store id before Opts:
{ok, Resp} = llm:chat(openai, big, Messages, Tools, <<"vs_product_docs">>, #{}).

%% Datasource lifecycle — file paths are uploaded and attached as one batch:
{ok, DatasourceId} = llm_datasource:create(openai, <<"Product docs">>),
{ok, #{file_ids := FileIds}} =
    llm_datasource:files_add(
        openai, DatasourceId, ["docs/guide.pdf", "docs/api.md"]
    ),
ok = llm_datasource:files_remove(openai, DatasourceId, FileIds),
ok = llm_datasource:delete(openai, DatasourceId).

Messages are maps like #{role => system|user|assistant, content => binary()}, plus #{role => tool_result, tool_use_id => Id, content => Bin} to return tool output. The frontier models are gpt-5.6-sol for OpenAI, fable-5 for Anthropic, and moonshotai/kimi-k3 for the opensource OpenRouter provider. The other opensource defaults are openai/gpt-oss-20b for small and z-ai/glm-5.2 for big. A tier can be replaced with an exact model slug as a string or binary; provider names also accept atoms, strings, or binaries. Alternatively, pass #{model => "provider/model"} in Opts. Datasource is none or an OpenAI vector store id. Other providers do not support managed vector-store datasources. See the header of src/llm.erl for the full message/response shapes. Deleting datasource files detaches them from that vector store; it does not permanently delete the uploaded OpenAI files.

json_util

<<"{\"a\":1}">> = json_util:encode(#{<<"a">> => 1}),
#{<<"a">> := 1} = json_util:decode(<<"{\"a\":1}">>).

License

MIT — see LICENSE.