deep-swe vs teaql-agent-kit
The two are close in size: 1.8k stars for deep-swe, 2.8k for teaql-agent-kit. Over the days we have tracked them deep-swe moved +52.9% and teaql-agent-kit +0.6%, so deep-swe is growing faster right now.
deep-swe leads on context depth and model freedom. teaql-agent-kit does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides long-horizon, behaviorally-graded software-engineering tasks with isolated sandbox execution and separate verifier environments (via Pier).
- Stars
- 1.8k
- Tracked growth
- +52.9%
- Maturity
- ●●●●●
- Last commit
- 54d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Your API key (OpenAI/Anthropic), pay-per-run model costs
Provides a TEAQL-focused, auditable evaluation harness that measures software-engineering discipline and token-efficiency for coding agents rather than offering general-purpose agent automation.
- Stars
- 2.8k
- Tracked growth
- +0.6%
- Maturity
- ●●●●●
- Last commit
- 23d ago
- Language
- Python
- License
- MIT
- Cost to run
- Your API key or model costs may apply
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | deep-swe | teaql-agent-kit |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Whole-repo analysis | ●●●●● Diff + related files |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Behavioral verification | ●●●●● Guides & checkpoints |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config + prompts | ●●●●● Config & prompts |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud via API key | ●●●●● Cloud via API key |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Multiple providers | ●●●●● Single fixed provider |
Setup ease What it takes to get a first useful run out of it. | ●●●●● CLI + API key | ●●●●● Manual setup |
Which one to pick
Pick deep-swe if…
Long-horizon evaluation — choose DeepSWE when you need realistic, multi-step engineering tasks with programmatic verifiers and sandboxed grading to measure end-to-end agent behavior.
- Context depth: Whole-repo analysis (5/5 against 3/5)
- Model freedom: Multiple providers (3/5 against 1/5)
Pick teaql-agent-kit if…
Evaluation-first — pick this when you need reproducible, auditable benchmarks of coding agents working with TEAQL contracts, explicit guardrails, and token-efficiency measurement.
What people want from each one
datacurve-ai/deep-swe
Hacker News: DeepSWE results are unreliable – 3/3 DSv4 "failed" tasks solved with same model drew 3 points and 0 comments.
Questions people ask
Is deep-swe better than teaql-agent-kit?
deep-swe leads on context depth and model freedom. teaql-agent-kit does not take any axis by a clear margin. deep-swe is worth picking when long-horizon evaluation — choose DeepSWE when you need realistic, multi-step engineering tasks with programmatic verifiers and sandboxed grading to measure end-to-end agent behavior.
Which of deep-swe and teaql-agent-kit keeps my code private?
deep-swe: Cloud via API key (3/5). teaql-agent-kit: Cloud via API key (3/5).
What does each one cost to run?
deep-swe: Your API key (OpenAI/Anthropic), pay-per-run model costs. teaql-agent-kit: Your API key or model costs may apply.
Full profiles: datacurve-ai/deep-swe and teaql/teaql-agent-kit. Everything else in Evals & benchmarks.