An Elixir library and standalone agentic engine for building reliable AI agents and workflow projects.

Condukt has two modes. Use it as a Hex library inside an Elixir application when you want agents embedded in your own OTP system. Install it as the condukt engine when you want a single executable that runs agentic workflow projects from the command line, cron, or webhooks.

The engine is built with Burrito and bundles Erlang plus Condukt's bytecode, so workflow projects can run without a local Elixir toolchain. Both modes share the same OTP-native agent runtime, tool system, sandboxing model, and multi-provider LLM support.

Motivation 💡

Condukt grew out of practical work building agentic workflows. We needed a framework that:

• Integrates naturally with OTP supervision trees
• Supports streaming for responsive user experiences
• Works with multiple LLM providers without vendor lock-in
• Provides extensible tooling for domain-specific capabilities

Rather than wrapping JavaScript agent frameworks, we built Condukt from scratch using idiomatic Elixir patterns. We are sharing it because Elixir is an excellent fit for building reliable AI agents.

Features ✨

• OTP-native: Agents are GenServers that integrate naturally with supervision trees
• Streaming: Real-time event streaming for responsive UIs
• Project Instructions: Auto-discovers AGENTS.md, CLAUDE.md, and local skills from the project directory
• Scoped Commands: Expose trusted executables like git, gh, or mix without shell parsing
• Tool System: Extensible tools for file operations, shell commands, and more
• Operations: Compile-time typed entrypoints with JSON Schema input/output validation
• Anonymous Workflows: One-off Condukt.run/2 calls with inline tools and optional structured output
• Sub-agents: Delegate isolated tasks to specialized child sessions with optional structured input/output contracts
• Workflow Engine: Standalone condukt executable for Starlark workflow projects, installable with mise
• Multi-Provider: 18+ LLM providers via ReqLLM (Anthropic, OpenAI, Google, etc.)
• Redaction: Pluggable secret redaction on outbound messages with a regex-based default
• Session Secrets: Resolve credentials from providers such as 1Password and expose them only to tool execution environments
• Telemetry: Built-in observability with :telemetry events

Installation 📦

Library mode

Add condukt to your dependencies in mix.exs:

def deps do
  [
    {:condukt, "~> 0.13"}
  ]
end

Use library mode when Condukt should live inside your own OTP supervision tree.

Engine mode

Install the standalone executable from GitHub Releases with mise:

mise use -g github:tuist/condukt
condukt version

Use engine mode when you want to run a workflow project directly:

condukt workflows check --root .
condukt workflows run triage --root . --input '{"issue":"broken"}'
condukt workflows serve --root . --port 4000

The release assets include Linux x64, macOS x64, macOS arm64, and Windows x64 builds.

See the Workflows guide for creating, running, and sharing workflows. See the Workflow Starlark API reference for every Starlark builtin available to workflow files.

Quick Start 🚀

1. Define an Agent

defmodule MyApp.CodingAgent do
  use Condukt

  @impl true
  def tools do
    Condukt.Tools.coding_tools()
  end
end

2. Start and Use the Agent

# Start the agent with an explicit system prompt override
{:ok, agent} = MyApp.CodingAgent.start_link(
  api_key: System.get_env("ANTHROPIC_API_KEY"),
  system_prompt: """
  You are an expert software engineer.
  Write clean, well-documented code.
  Always run tests after making changes.
  """
)

# Run a prompt
{:ok, response} = Condukt.run(agent, "Create a GenServer that manages a counter")

# Stream responses for real-time output
Condukt.stream(agent, "Add documentation to the counter module")
|> Stream.each(fn
  {:text, chunk} -> IO.write(chunk)
  {:tool_call, name, _id, _args} -> IO.puts("\n📦 Using tool: #{name}")
  {:tool_result, _id, result} -> IO.puts("   Result: #{inspect(result)}")
  :done -> IO.puts("\n✅ Done!")
  _ -> :ok
end)
|> Stream.run()

3. Add to Supervision Tree

defmodule MyApp.Application do
  use Application

  def start(_type, _args) do
    children = [
      {MyApp.CodingAgent,
        api_key: System.get_env("ANTHROPIC_API_KEY"),
        system_prompt: "You are a helpful coding assistant."}
    ]

    Supervisor.start_link(children, strategy: :one_for_one)
  end
end

Operations 🎯

Sometimes you don't want to chat with an agent. You want to call it like a typed function: known input, validated output, no conversation history. The operation macro declares one of those entrypoints at compile time.

defmodule MyApp.ReviewAgent do
  use Condukt

  @impl true
  def tools do
    [
      Condukt.Tools.Read,
      {Condukt.Tools.Command, command: "gh", env: [GH_TOKEN: System.fetch_env!("GH_TOKEN")]}
    ]
  end

  operation :review_pr,
    input: %{
      type: "object",
      properties: %{
        repo: %{type: "string"},
        pr_number: %{type: "integer"}
      },
      required: ["repo", "pr_number"]
    },
    output: %{
      type: "object",
      properties: %{
        verdict: %{type: "string", enum: ["approve", "request_changes", "comment"]},
        summary: %{type: "string"},
        blockers: %{type: "array", items: %{type: "string"}}
      },
      required: ["verdict", "summary", "blockers"]
    },
    instructions: """
    1. Fetch the PR with `gh pr view <number> --repo <repo> --json files,title,body`.
    2. Read each changed file.
    3. Decide a verdict and list concrete blockers.
    """
end

# Each operation generates a typed function on the module.
{:ok, %{verdict: "approve", blockers: []}} =
  MyApp.ReviewAgent.review_pr(%{repo: "tuist/condukt", pr_number: 1})

Each call spins up a transient Condukt.Session with the agent's tools plus a synthetic submit_result tool whose schema is the declared output schema. The agent loop runs until the model calls submit_result; the captured arguments are validated against the output schema and returned. No process needs to be supervised, and no conversation history is kept across calls.

Reach for an operation when you want to:

• Drive agents from CI, webhooks, cron jobs, or .exs scripts
• Compose one agent's typed entrypoint into another agent's tool list
• Get input/output validation identical to what the LLM provider sees

Reach for start_link + Condukt.run/2 when continuity across turns matters: when the next message depends on the last one.

Schemas are JSON Schema maps, validated with JSV. Input validation runs before any LLM call; output validation runs after the model submits its result. Operations also emit [:condukt, :operation, :start | :stop | :exception] telemetry events alongside the inner agent-loop events.

See Condukt.Operation for the full reference.

Anonymous Workflows

For scripts, notebooks, jobs, and one-off automations that do not need a named agent module, call Condukt.run/2 with the prompt first. Condukt creates a transient session, runs the prompt, and shuts it down when the response is ready.

{:ok, text} =
  Condukt.run("Summarize the project README in three bullets.",
    model: "anthropic:claude-sonnet-4-20250514",
    tools: [Condukt.Tools.Read]
  )

Anonymous workflows can also take typed input and return structured output:

{:ok, %{summary: summary}} =
  Condukt.run("Read the supplied file and return a short summary.",
    input: %{path: "README.md"},
    input_schema: %{
      type: "object",
      properties: %{path: %{type: "string"}},
      required: ["path"]
    },
    output: %{
      type: "object",
      properties: %{summary: %{type: "string"}},
      required: ["summary"]
    },
    tools: [Condukt.Tools.Read]
  )

Use anonymous workflows when the whole task is contained in one call. Use a supervised agent when you need conversation history, long-lived state, or OTP supervision.

Sub-agents

Agents can delegate work to specialized child agents by declaring subagents/0. Each child runs as its own Condukt.Session under the parent session's sub-agent supervisor, with separate conversation history and its own tools, model, and system prompt.

defmodule MyApp.LeadAgent do
  use Condukt

  @impl true
  def subagents do
    [
      reviewer:
        {MyApp.ReviewerAgent,
         input: %{
           type: "object",
           properties: %{
             path: %{type: "string"},
             severity: %{type: "string", enum: ["low", "medium", "high"]}
           },
           required: ["path"]
         },
         output: %{
           type: "object",
           properties: %{
             findings: %{type: "array", items: %{type: "object"}},
             summary: %{type: "string"}
           },
           required: ["findings", "summary"]
         }},
      summarizer: [
        model: "anthropic:claude-haiku-4-5",
        system_prompt: "Summarize delegated context into concise notes."
      ]
    ]
  end
end

When sub-agents are registered, Condukt injects a subagent tool into the parent. :input and :output schemas are optional. If an input schema is declared, Condukt validates the tool call's input value before the child starts. If an output schema is declared, Condukt adds a submit_result tool to the child and returns the validated structured value to the parent. Use role: [opts] to register an anonymous child agent inline instead of creating a dedicated module.

See the Sub-agents guide for role declarations, inheritance, supervision, and structured contracts.

LiveBook 📓

Condukt works well in LiveBook notebooks with Mix.install/1:

Mix.install([
  {:condukt, "~> 0.13"}
])

Application.put_env(:condukt, :api_key, System.fetch_env!("ANTHROPIC_API_KEY"))

defmodule NotebookAgent do
  use Condukt

  @impl true
  def tools do
    Condukt.Tools.read_only_tools()
  end
end

{:ok, agent} =
  NotebookAgent.start_link(
    system_prompt: "You are a helpful LiveBook assistant."
  )

{:ok, response} =
  Condukt.run(agent, "Summarize the current notebook context.")

response

For richer notebook output, you can stream events and render them with LiveBook/Kino cells as they arrive.

Configuration ⚙️

API Keys

Set your API key via environment variable, application config, or option:

# Environment variable (recommended) - ReqLLM auto-discovers these
export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."

# Application config
config :condukt,
  api_key: "sk-ant-...",
  system_prompt: "You are a helpful coding assistant."

# Per-agent option
MyApp.CodingAgent.start_link(api_key: "sk-ant-...")

Values passed to start_link/1 take precedence over config :condukt, which takes precedence over agent module defaults.

Agent Options

MyApp.CodingAgent.start_link(
  api_key: "sk-ant-...",                        # Overrides config :condukt, :api_key
  model: "anthropic:claude-sonnet-4-20250514",  # Overrides config/module default
  base_url: "http://localhost:11434/v1",        # Override provider's default URL
  system_prompt: "You are helpful.",            # Overrides config/module default
  thinking_level: :medium,                      # Overrides config/module default
  load_project_instructions: true,              # Auto-load AGENTS.md, CLAUDE.md, and local skills
  cwd: "/path/to/project",                      # Overrides config/default cwd
  session_store: Condukt.SessionStore.Memory,   # Optional session persistence
  redactor: Condukt.Redactors.Regex,            # Optional outbound secret redaction
  name: MyApp.CodingAgent                       # GenServer name
)

Project Instructions

By default, Condukt inspects the project root configured by cwd at startup and appends local project guidance to the effective system prompt:

• AGENTS.md
• CLAUDE.md
• .agents/skills/*/SKILL.md

Discovered skills are listed in the prompt with their file paths so the agent can read the full SKILL.md instructions when needed.

{:ok, agent} =
  MyApp.CodingAgent.start_link(
    cwd: "/path/to/project",
    system_prompt: "You are a helpful coding assistant."
  )

Disable this behavior if you need a fully static prompt:

{:ok, agent} =
  MyApp.CodingAgent.start_link(
    load_project_instructions: false
  )

Session Storage

Persisted sessions are opt-in. Provide a session store to save and restore conversation history plus session settings.

Built-in session stores:

• Condukt.SessionStore.Memory stores snapshots in ETS for reuse within the current VM
• Condukt.SessionStore.Disk persists snapshots to disk across restarts

# Restore within the current VM
{:ok, agent} =
  MyApp.CodingAgent.start_link(
    session_store: {Condukt.SessionStore.Memory, key: {:coding_agent, "/tmp/project"}}
  )

# Persist to disk across restarts
{:ok, agent} =
  MyApp.CodingAgent.start_link(
    cwd: "/tmp/project",
    session_store: Condukt.SessionStore.Disk
  )

# Custom path or custom implementation
{:ok, agent} =
  MyApp.CodingAgent.start_link(
    session_store: {Condukt.SessionStore.Disk, path: "/tmp/condukt.session"}
  )

Compaction

Long-running agents accumulate messages that grow past the model's context window. Pass a compactor to keep history bounded. Condukt applies it after each completed turn, and Condukt.compact/1 triggers it manually.

# Keep the last 40 messages
MyApp.CodingAgent.start_link(
  compactor: {Condukt.Compactor.Sliding, keep: 40}
)

# Elide oversized old tool result payloads, leave the most recent five intact
MyApp.CodingAgent.start_link(
  compactor: {Condukt.Compactor.ToolResultPrune, keep_recent: 5, max_size: 4_000}
)

Built-in strategies:

• Condukt.Compactor.Sliding: keeps the last N messages, drops orphaned tool results.
• Condukt.Compactor.ToolResultPrune: replaces oversized historical tool result payloads with a placeholder, preserving the surrounding reasoning.

Implement Condukt.Compactor to provide your own strategy. Each compaction emits a [:condukt, :compact, :stop] telemetry event with before/after message counts.

Sensitive Data Redaction

Redaction rewrites user input and tool results before they leave the BEAM process and reach the LLM provider. Assistant output and the system prompt are left untouched. The original messages remain in session history; each turn re-runs the redactor on the messages about to be sent.

The built-in Condukt.Redactors.Regex covers common high-precision patterns (emails, JWTs, PEM private keys, Anthropic/OpenAI/GitHub/Google/AWS/Slack tokens) and replaces matches with [REDACTED:KIND] placeholders the LLM can still reason about.

# Use the built-in defaults
{:ok, agent} = MyApp.CodingAgent.start_link(redactor: Condukt.Redactors.Regex)

# Add project-specific patterns to the defaults
{:ok, agent} =
  MyApp.CodingAgent.start_link(
    redactor:
      {Condukt.Redactors.Regex,
       extra_patterns: [{~r/cust_[a-z0-9]+/, "CUSTOMER"}]}
  )

Implement Condukt.Redactor to plug in a custom redactor (e.g. NER-based PII detection):

defmodule MyApp.Redactor do
  @behaviour Condukt.Redactor

  @impl true
  def redact(text, _opts), do: MyApp.PiiScanner.scrub(text)
end

MyApp.CodingAgent.start_link(redactor: MyApp.Redactor)

Supported Providers

Thanks to ReqLLM, Condukt supports 18+ providers:

Built-in Tools 🧰

Default Tool Sets

# Full coding tools: Read, Bash, Edit, Write
def tools, do: Condukt.Tools.coding_tools()

# Read-only: Read, Bash
def tools, do: Condukt.Tools.read_only_tools()

Individual Tools

Scoped Command Grants

Prefer a parameterized Condukt.Tools.Command over Condukt.Tools.Bash when you want to grant access to a specific executable or attach trusted environment variables without exposing them in the prompt.

defmodule MyApp.ReviewAgent do
  use Condukt

  @impl true
  def tools do
    [
      Condukt.Tools.Read,
      {Condukt.Tools.Command, command: "git"},
      {Condukt.Tools.Command, command: "gh", env: [GH_TOKEN: System.fetch_env!("GH_TOKEN")]}
    ]
  end
end

Each scoped command tool accepts:

• args - array of strings passed directly to the configured executable
• cwd - optional working directory
• timeout - optional timeout in seconds

Custom Tools 🛠️

Define custom tools by implementing the Condukt.Tool behaviour:

defmodule MyApp.Tools.Weather do
  use Condukt.Tool

  @impl true
  def name, do: "get_weather"

  @impl true
  def description, do: "Gets the current weather for a location"

  @impl true
  def parameters do
    %{
      type: "object",
      properties: %{
        location: %{type: "string", description: "City name"}
      },
      required: ["location"]
    }
  end

  @impl true
  def call(%{"location" => location}, _context) do
    case WeatherAPI.get(location) do
      {:ok, data} -> {:ok, "Temperature: #{data.temp}°F"}
      {:error, reason} -> {:error, reason}
    end
  end
end

Events and Callbacks 📡

Handle events during agent execution:

defmodule MyApp.LoggingAgent do
  use Condukt

  @impl true
  def handle_event({:tool_call, name, _id, _args}, state) do
    Logger.info("Agent calling tool: #{name}")
    {:noreply, state}
  end

  @impl true
  def handle_event({:text, chunk}, state) do
    # Stream to WebSocket, etc.
    {:noreply, state}
  end

  @impl true
  def handle_event(_event, state), do: {:noreply, state}
end

Telemetry

Condukt emits telemetry events for observability:

:telemetry.attach_many(
  "my-handler",
  [
    [:condukt, :agent, :start],
    [:condukt, :agent, :stop],
    [:condukt, :tool_call, :start],
    [:condukt, :tool_call, :stop],
    [:condukt, :subagent, :start],
    [:condukt, :subagent, :stop],
    [:condukt, :operation, :start],
    [:condukt, :operation, :stop],
    [:condukt, :secrets, :resolve],
    [:condukt, :secrets, :access]
  ],
  fn event, measurements, metadata, _config ->
    Logger.info("#{inspect(event)}: #{inspect(measurements)}")
  end,
  nil
)

Secret telemetry includes environment variable names and counts for auditing, but never includes resolved secret values.

Sub-agent telemetry identifies the parent agent, delegated role, child agent, whether structured input and output contracts are configured, and whether the delegation ended with :ok or :error. It never includes task text, structured input values, or structured output values.

Streaming API

The streaming API returns an enumerable of events:

Condukt.stream(agent, "Hello")
|> Enum.each(fn event ->
  case event do
    {:text, chunk} -> IO.write(chunk)
    {:thinking, chunk} -> IO.write(IO.ANSI.faint() <> chunk <> IO.ANSI.reset())
    {:tool_call, name, id, args} -> IO.inspect({name, args})
    {:tool_result, id, result} -> IO.inspect(result)
    {:error, reason} -> IO.puts("Error: #{inspect(reason)}")
    :agent_start -> IO.puts("Agent started")
    :agent_end -> IO.puts("Agent finished")
    :turn_start -> nil
    :turn_end -> nil
    :done -> IO.puts("\nDone")
  end
end)

License

MIT License - see LICENSE for details.