LoopGain Loop Controller
Stop measurable-error agent loops when progress ends, and roll back before the output degrades.
- Source repo
- loopgain-ai/loopgain
- Stars
- ★ 126
- Last updated
- 18d ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- Insufficient evidence
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API (Partial support)
- Cost
- Free, no paid service needed
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A coding team can use failing test counts as the error signal in a code generation loop and retain the best code seen if the loop diverges.
- Not a fit if
- Workflows without a measurable error signal
- Teams expecting LoopGain to verify answer correctness
- Source review
- Insufficient evidence 8 safety controls not found
What does this agent do, and when should you use it?
LoopGain is a pure Python library for monitoring iterative AI workflows with a measurable error signal. Integrate it into your own loop with LoopGain, observe(), and should_continue(); it classifies the error trajectory and tells the loop to continue, stop, or roll back, while recording the best output and iteration details. Optional adapters cover LangGraph, CrewAI, AutoGen, LangChain, the OpenAI Agents SDK, and the Claude Agent SDK; custom workflows can use the raw API. The core has no runtime dependencies and supports Python 3.10+, while framework packages are installed as needed. Optional telemetry is caller-initiated and can use the hosted receiver or a self-hosted receiver and dashboard.
The caller supplies a non-negative error value on each iteration, or a sequence whose length LoopGain treats as the error magnitude; lower values mean better output. LoopGain.observe(errors, output=...) records the error and optional output. The monitor classifies the full error trajectory, and should_continue() tells the surrounding loop whether to proceed. Reaching target_error stops immediately; stalling, oscillation, or divergence can also terminate the loop, returning the lowest-error recorded output where applicable. result exposes the outcome, iteration count, best output, and error data. The caller remains responsible for checking correctness.
- A coding team can use failing test counts as the error signal in a code generation loop and retain the best code seen if the loop diverges.
- A structured extraction developer can track unresolved schema violations and use them to decide whether another revision is useful.
- A RAG team with self-correction can measure required facts still missing and stop when retrieval and revision stop improving.
- A LangGraph, CrewAI, or AutoGen developer can monitor graph updates, task callbacks, or team messages through the corresponding adapter.
- An engineer using the OpenAI Agents SDK or Claude Agent SDK can wrap streamed execution and extract error signals with a custom
error_fn.
How do you install or deploy this agent?
The core library requires Python 3.10 or later and installs with pip; it has no runtime dependencies. Install an optional extra for the framework adapter you plan to use. LoopGain itself does not require model credentials; model calls belong to your own workflow.
How do you use this agent?
Create a LoopGain monitor, calculate an error on each iteration, call observe(), and use should_continue() to control the loop. Pass output to keep a best-so-far buffer. Read result when the loop ends. The README's basic usage is:
from loopgain import LoopGain
lg = LoopGain(target_error=0.1)
while lg.should_continue():
errors = verifier.verify(output)
lg.observe(errors, output=output)
output = reviser.revise(output, errors)
result = lg.result
print(result.outcome)
print(result.best_output)
print(result.iterations_used)
print(result.savings_vs_fixed_cap)What are this agent's strengths and limitations?
- Uses error trajectory and statistical trend to decide when to stop instead of relying only on a fixed iteration cap.
- Keeps a best-so-far output buffer and can roll back to the lowest-error version on oscillation or divergence.
- The core is dependency-free pure Python and supports Python 3.10+.
- Includes optional adapters for six agent frameworks and a raw API for custom loops.
- The caller must supply a useful non-negative error signal; stopping quality depends on the verifier behind it.
- It detects convergence, not correctness; a loop may stall above its target, so callers should inspect
best_error. - The project labels itself alpha and says the API may change before 1.0; the README recommends pinning the version.
- Framework adapters require their optional packages, and each integration still needs a caller-provided
error_fn.
How does this agent compare with similar options?
The README contrasts LoopGain with the common max_iterations=N fixed-cap policy, which can waste compute or stop too early. LoopGain instead uses the error trajectory to stop and can roll back to the best recorded output.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| LoopGain Loop Controller This agent | Insufficient evidence | Library / SDKFree | ★ 126 | 18d ago | Python | — |
| 500+ AI Agent Projects & Use Cases | 21 · Major gaps | CLIFree + model costs | ★ 39k | 2mo ago | Python | — |
| Octopoda | 43 · Major gaps | Library / SDKFreemium | ★ 486 | 3mo ago | Python | Claude Code · Claude.ai · OpenAI API |
| PandaProbe | 41 · Major gaps | Self-hosted serviceFreemium | ★ 791 | 14d ago | Python | — |
How does FollowAgents rate this agent?
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- This assessment could not read or verify the listed files; the description, benchmark figures, and test badge were not independently confirmed.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v0.6.5 LoopGain 0.6.5
Release notes excerpt
Changes - Defer unnecessary classifier regression and residual deviation work ([PR #20](https://github.com/loopgain-ai/loopgain/pull/20)). Public feature extraction keeps the exact statistics.pstdev calculation; thresholds and classification policy are unchanged. - Include the previously merged running-process telemetry consent refresh and safer examples: read-only SQL verification and mandatory local container isolation for generated Python. - Expand verification across supported Python versions, real framework adapters, security checks, and built packages. Measured performance …View on GitHub ↗
FAQ
Does LoopGain guarantee that an output is correct?
result.best_error or your own pass condition.What error signal do I need to provide?
Is it tied to one model or framework?
Does telemetry automatically send my prompts or outputs?
send_telemetry() is explicitly called; the README says it sends numeric error magnitudes and loop summaries, not prompts, completions, error contents, or the output buffer.