I've been running BabyAGI for a few weeks now, trying to automate a standard data pipeline analysis task: ingesting a list of new data sources, classifying their schema, and generating a proposed merge strategy. The agent runs, it completes all the tasks the system generates, and the execution log shows green checks. But when I go to look at the final "report," it's a garbled, repetitive, and often contradictory mess. It feels like watching a highly competent worker diligently file papers into a shredder.
The core problem is the output aggregation. BabyAGI's default orchestration handles task completion and queuing, but the final synthesis step—where the results of all those tasks are supposed to be compiled into a coherent answer—is either non-existent or fundamentally broken in most implementations I've seen. The agent will often just concatenate the raw results from the last few tasks, including all its internal chain-of-thought, leading to a dump of disjointed text.
Here's a simplified example of the kind of output structure I'm getting, based on my logging:
```python
Final Output from BabyAGI Agent:
---
TASK 1 RESULT: Identified source A as PostgreSQL. Schema has tables: users, orders.
TASK 2 RESULT: Identified source B as MongoDB. Collections: events, logs.
TASK 3 RESULT: Proposed join key on user_id. Need to handle data type mismatch.
TASK 2 RESULT: (Again) Identified source B as MongoDB. Collections: events, logs.
TASK 4 RESULT: Join key is user_id. Date formats differ.
REPORT: TASK 1 RESULT: Identified source A as PostgreSQL. Schema has tables: users, orders. TASK 3 RESULT: Proposed join key on user_id. Need to handle data type mismatch. Join key is user_id. Date formats differ.
```
Notice the repetition of Task 2 and the nonsensical "REPORT" that is just a string mash-up. The agent lacks a dedicated, final "synthesis" task with clear instructions to digest all previous results into a new, clean, structured format.
What I've tried so far:
* Increasing the `max_iterations` just leads to more repetitive tasks and a longer, more garbled final string.
* Tweaking the system prompt to emphasize "produce a final, synthesized report" has minimal effect; the underlying logic for output compilation seems to be separate.
* I modified the task creation prompt to try and *always* generate a "Synthesize final report" task as the last item. This works slightly better, but the synthesis task still receives the raw, concatenated mess as its context, so the LLM struggles.
Is this a fundamental architectural limitation of the vanilla BabyAGI loop? Are any of you running modified versions that actually produce clean, usable final deliverables? I'm looking for concrete implementation fixes—changes to the `execute_task` function, the context accumulation logic, or the task creation logic—not just prompts.
Specifically:
* How are you managing context window overflow from accumulating all task results?
* Have you implemented a separate summarization or structuring step *outside* the main loop?
* Is there a proven fork or alternative agent architecture that handles this output synthesis phase better, while keeping the autonomous task generation?
The community examples all focus on getting the agent to *run*, not on getting it to produce a *usable output*. I need the latter for any real engineering work.
—davidr
—davidr