Declarative, self-improving language-model programs for Elixir.

Imp is a full port of DSPy to the BEAM. You describe what each language-model step takes and returns, choose how it thinks, and let an optimizer improve it against examples of what good looks like. You get signatures, modules, optimizers, agent loops and retrieval, running with the reliability and concurrency of OTP.

DSPy makes each call to a model a declared, typed function that you can measure and improve. On the BEAM, an agent is a process: it keeps its own state, receives messages, and runs under a supervisor alongside the rest of your application. With both, you can build anything from one typed call to many long-running agents, and improve each part by measuring it.

lm = Imp.req_llm("openai:gpt-5.4-mini", api_key: System.fetch_env!("OPENAI_API_KEY"))

triage =
  "issue -> kind: enum[bug,feature,question], summary"
  |> Imp.signature("Triage a GitHub issue.")
  |> Imp.predict(lm: lm)

{:ok, prediction} =
  Imp.call(triage, %{issue: "App crashes on startup since 0.4 with ** (KeyError) key :lm not found"})

{Imp.get(prediction, :kind), Imp.get(prediction, :summary)}
#=> {"bug", "App crashes on startup since version 0.4 with a KeyError for `:lm` not found."}

You never write a prompt or a parser. Imp builds the prompt from the signature, checks the reply against it, and gives you typed fields: kind is always one of the three values, or the call returns an error. To make the same task reason first, use Imp.chain_of_thought/2; to give it tools, use Imp.react/3. The signature stays the same.

Give Imp labeled examples and a metric, and it scores the program and optimizes it. You need three lists of issues you have already labeled: trainset, which the optimizer learns from; valset, which it uses to choose between the programs it tries; and testset, which you score on before and after. strong_lm is a more capable model that GEPA uses to read failures and write new instructions.

# Each set is a list of labeled issues like this one:
example =
  Imp.example(%{issue: "Please add a dark mode to the dashboard", kind: "feature"})
  |> Imp.with_inputs([:issue])

metric = Imp.exact_match(:kind)

Imp.evaluate(triage, testset, metric).score

optimizer = Imp.Optimizer.GEPA.new(metric, reflection_lm: strong_lm, max_metric_calls: 300)
improved = Imp.optimize!(triage, optimizer, trainset, valset)

Imp.evaluate(improved, testset, metric).score

GEPA runs the program, reads where it failed, and rewrites its instructions. Other optimizers choose worked examples (LabeledFewShot, BootstrapFewShot), search over combinations of instructions and examples (MIPROv2), learn rules and examples from the program's own better and worse attempts (SIMBA), or train the model's weights (fine-tuning, GRPO). The result is a new program whose instructions and examples you can read, save as JSON, and review as a diff.

A tool is an Elixir function. Imp.react/3 builds an agent that calls tools until it can answer. This one reads web pages with Req. Imp depends on Req; if your own code calls it, as this tool does, add {:req, "~> 0.6"} to your dependencies:

fetch =
  Imp.tool(:fetch, "Read a web page as text.", fn %{"url" => url} -> Req.get!(url).body end,
    schema: %{"type" => "object", "properties" => %{"url" => %{"type" => "string"}}, "required" => ["url"]}
  )

researcher = Imp.react("question -> answer", [fetch], lm: lm)

question =
  "What version does https://raw.githubusercontent.com/elixir-lang/elixir/v1.18.0/VERSION say? " <>
    "Reply with just the version."

{:ok, prediction} = Imp.call(researcher, %{question: question})
Imp.get(prediction, :answer)
#=> "1.18.0"

Imp.call/2 runs a program in your process. Imp.start_run/3 runs it as its own supervised process instead, so you can watch it, stop it, and decide which tool calls it may make:

{:ok, run} =
  Imp.start_run(researcher, %{question: question},
    authorize: fn call ->
      url = call.arguments["url"] || ""

      if String.starts_with?(url, "https://raw.githubusercontent.com/"),
        do: :allow,
        else: {:deny, :untrusted_host}
    end
  )

{:ok, prediction} = Task.await(run.task, :infinity)

for event <- Imp.Run.events(run), do: event.kind
#=> [:run_started, :tools_sent, :model_request, :model_response, :tool_call,
#    :tool_result, :model_request, :model_response, :run_finished]

Imp also includes:

• MCP: import the tools of any MCP server you approve, and they work like your own.
• ACP: serve any Imp program as an agent to Zed and other ACP clients.
• OTP: a run is a process you can watch, stop and limit, and a run ends when the process that started it does. Model requests are cut to a deadline you set. A tool call that may already have taken effect is reported as unknown, never silently retried.
• More shapes: RLM for inputs far larger than a context window, CodeAct and program of thought, which compute with small sandboxed expressions, and your own modules composed from these.

The optimizers work on agents too. GEPA reflects on whole agent runs and rewrites the instructions that steer them. Optimize Anything rewrites any text or JSON you can score, such as an agent's tool descriptions.

Imp needs Elixir 1.19 or later and a C++ compiler for one dependency (erlexec). It reaches models through ReqLLM, so any provider ReqLLM supports works.

Imp 0.5 is experimental and is its first release on Hex. Its API may still change, and its optimizers need large-scale benchmarking. Bug reports and pull requests are welcome.

• Getting started builds one program step by step, from the first call to a supervised server, with real scores.
• Coming from DSPy maps DSPy's names to Imp's.
• Tutorials are Livebook notebooks you can run offline or with a key.
• The cheatsheet has the common calls on one page.

Imp is MIT licensed.