Best BabyAGI workflow for a 3-step research agent

I'm Emily, and I've been running a small marketing agency's support ops for about three years, where I've implemented several AI research assistants to help with content planning; my current production workflow uses a modified BabyAGI script to analyze competitor data.

For your 3-step research agent, the biggest practical differences come down to the foundation you build on. Here's a breakdown of the four main approaches I've tested:

* **Initial Setup & Code Clarity:** The original BabyAGI from Yohei Nakajima is the clearest for learning, but its single-objective loop is a mismatch for your fixed three-step process. You'll spend 4-6 hours immediately refactoring its `execution_agent` and `task_creation_agent` to prevent infinite new tasks. The LangChain implementation wraps it in a more abstract chain, which adds a layer of complexity that can obscure understanding for a beginner.
* **Cost Control & Task Drift:** Managing the autonomous task list is your primary challenge. With a standard setup, each "suggest related questions" step can spawn 3-5 new research tasks, escalating API calls quickly. You must implement a hard stop after the third step. I insert a task list length check before the `task_creation_agent` runs; without this, my costs for a single research topic ballooned from roughly $0.12 to over $1.50 in testing because it decided to "deeply explore" tangential questions.
* **Tooling Integration Effort:** The "gather sources" step requires connecting to search or a browser. Using the `google-search-results` package with SerpAPI is the fastest path (maybe 2 hours to integrate), but it's a paid service. I use it in prod and it adds about $50/month at our volume. Building a free alternative with `requests` and `BeautifulSoup` is possible but you'll easily spend a day or two handling blocks, parsing, and rate limiting.
* **Output Quality & Summarization Focus:** The quality of your summary depends almost entirely on the prompt you give to the agent performing that step. I found the original `execution_agent` too brief. You need to structure its system prompt explicitly for conciseness, citation of source URLs, and extraction of claims versus opinions. This isn't a limitation of a specific script, but a required tuning step that takes 15-20 iterative tests per topic domain to get right.

My recommendation is to start with a heavily simplified fork of the original BabyAGI script, because your workflow is fixed and finite. You need to tell us your expected monthly research volume and whether you have a budget for a search API, because that dictates whether the quick SerpAPI integration is sensible or if you need to build a custom fetcher.

Totally agree on the cost control point. I had the same issue with the task list spiraling.

For a hard stop, I found it simpler to just override the task creation function after step three. Instead of checking list length, I set a global step counter and return an empty list once it hits three. Works reliably and keeps the logic in one place.

What's your experience with the summary step? I've found the quality varies wildly with the initial sources gathered.

Three steps? You've already spotted the core failure mode. Everyone misses it until the first bill arrives.

Start with the original Nakajima script. Strip out the entire `task_creation_agent` logic from the main loop. Replace it with a hardcoded list: `['gather_sources', 'summarize_key_points', 'suggest_related_questions']`. Iterate through it, call your execution agent, append results to context. Loop ends. No chance for drift.

Your real problem won't be the loop control. It'll be the quality of step one dictating the garbage-in-garbage-out chain. What's your validation step for those gathered sources? Without one, the summary is just confident nonsense.

Post the postmortem when it tries to summarize a 404 page as a key insight.