toolnexus (Elixir)

Your LLM, with MCP tools and agent skills built in — in 3 lines, on the BEAM.

toolnexus unifies every tool source an agent needs — MCP servers (stdio + streamable-HTTP), agent skills (SKILL.md folders), your own functions, HTTP endpoints, ten built-in shell/file tools, and remote A2A agents — behind one uniform Tool, emits the schema in OpenAI / Anthropic / Gemini formats, and ships a unified client with a built-in tool-calling loop (hooks, parallel tool calls, retries, conversation memory, suspension/resume, metrics).

It is the Elixir port of toolnexus, byte-identical in behavior with the JS, Python, Go, Java, and C# ports — same config files, same outputs, same wire formats. The MCP client is implemented in-house on OTP (supervised connections, no third-party MCP SDK), which is also why this port ships the full elicitation bridge (form and URL mode).

Install

def deps do
  [{:toolnexus, "~> 0.10"}]
end

Zero to agent

{:ok, toolkit} = Toolnexus.create_toolkit(mcp_config: "mcp.json", skills_dir: ["skills"])
client = Toolnexus.Client.create(base_url: System.get_env("OPENAI_BASE_URL"),
                                 style: "openai", model: "gpt-4.1",
                                 api_key: System.get_env("OPENAI_API_KEY"))
result = Toolnexus.Client.run(client, "What tools do you have? Use one.", toolkit)
IO.puts(result.text)

mcp.json is the standard Claude-desktop-style config (mcpServers / servers / mcp top-level keys all accepted).
skills/ is a folder of **/SKILL.md files with YAML frontmatter — loaded on demand through the single skill tool (progressive disclosure).
Remote MCP headers values expand ${ENV_VAR} at call time and are never logged.

Why the BEAM port

Long-running agents want supervision. Every MCP connection is a supervised process; a crashed stdio server is isolated (status "failed") without taking your toolkit down; parallel tool calls ride Task.async_stream. Same contract as the other five ports, native OTP underneath.

Sub-agents & teams

An Agent is a Tool: a system prompt × a scoped toolkit view × the client loop. One agent delegates to another in-process via one task tool — isolated context, one result back, tokens rolled up, hierarchical budgets, durable suspension (SPEC.md §7D). Each handle is a GenServer with an inbox-as-state; interrupt kills only the in-flight Run, never the agent.

alias Toolnexus.Agents

explore = Agents.agent("explore", does: "read-only research", uses: %{tools: [lookup]})

coder =
  Agents.agent("coder",
    does: "implements changes",
    soul_file: "AGENTS.md",
    team: [explore],                     # team = the task tool's only targets; no team ⇒ no task tool
    budget: %{max_tokens: 10_000}
  )

r = Agents.run(coder, [llm: %{base_url: "https://openrouter.ai/api/v1", style: "openai", model: "openai/gpt-4o-mini"}], "fix the failing test")
IO.puts("#{r.status} #{r.text} #{r.total_tokens}")

Full guide: Sub-agents & teams.

Docs

Full documentation (all six languages, one site): https://muthuishere.github.io/toolnexus/