Can AI replace AdaL?

01What it costs

Checked Aug 14, 2026 · source: adalagent.ai. There is no permanently free plan; new accounts get 7 days of free access and then have to pick a paid tier.

Hidden costs: Model inference is included in the subscription rather than billed per token, so the usage multipliers between Pro, Max and Max+ are the real cost lever and are not expressed in published numeric limits.

02Could AI build it for you?

The core job: Wire a terminal agent to several provider APIs, give it file, shell and test tools, and let a planner sub-agent hand tasks to specialized workers in one session.

What a working version needs:

• Node 20 or Python 3.12
• API keys for at least two providers in .env
• Git repo to work against
• Playwright if you want the browser-verification worker

Vendor-adjacent entry: submitted by the maker, written to the same bar as the rest of the row. Verdict is the maintainer's to set.

03What you'd give up

Because the gap between an agent that runs and an agent that finishes is mostly unglamorous harness work, and nobody wants to maintain it on a weekend. The flat subscription across every frontier model is the other half: DIY means holding API accounts with six vendors and watching the meter on every long run.

04Free and cheaper alternatives

05The build prompt

Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version.

Build me a multi-model coding agent CLI to replace AdaL, in an empty folder.
Stack: Python 3.12, one file per module, no framework.

- Providers: Anthropic, OpenAI and Google, behind a single `chat(model, messages, tools)`
  function. Keys from .env via python-dotenv, never hardcoded. A `--model` flag
  switches families mid-session without restarting.
- Tools the agent can call: read_file, write_file, list_dir, run_shell, run_tests.
  run_shell prints the command and waits for y/n unless I pass --yolo.
- An orchestrator loop: a planner call splits my request into numbered steps, then
  each step runs as a fresh sub-agent with its own context window and only the tools
  it needs. Sub-agent results append to a shared markdown scratchpad on disk.
- Persist every session as JSONL under .agent/sessions/ so I can replay or resume.
  A `--resume <id>` flag reloads the scratchpad and continues.
- After any step that edits files, automatically run the test command from
  pyproject.toml and feed failures back to the same sub-agent for one retry.
- Print a running token and dollar total per provider at the end of every turn.
- Out of scope, deliberately: browser automation, a GUI, team accounts, and any
  hosted service. Those are the parts the subscription is actually selling.
- README: setup, the .env template, how to add a fourth provider, and one honest
  paragraph on where this loses to a tuned commercial harness on long tasks.

06Open-source starting points

• AdalFlow: The vendor's own open-source agent library; the building blocks are public even though the product is not.
• LiteLLM: One API shape over every provider, which is the boring half of model routing.