---
title: "code-eval vs agent-learning-kit"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-future-agi-agent-learning-kit"
tools: ["abacaj-code-eval", "future-agi-agent-learning-kit"]
---

# code-eval vs agent-learning-kit

*GraphCanon updated Aug 5, 2026*

## Verdict

Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; pick agent-learning-kit if agent-learning-kit is a Python framework for evaluating AI-related workflows with modules for faithfulness assessment, embedding similarity analysis, and feedback loop integration via ChromaDB.

[code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [agent-learning-kit](https://futureagi.com) has 118 stars, 43 forks, and 6 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit).

| | [code-eval](/tools/abacaj-code-eval.md) | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) |
| --- | --- | --- |
| Tagline | Run evaluation on LLMs using human-eval benchmark. | Evaluation Framework for all your AI related Workflows |
| Stars | 431 | 118 |
| Forks | 37 | 43 |
| Open issues | 5 | 6 |
| Language | Python | Python |
| Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | Agent-learning-kit is a Python framework for evaluating AI-related workflows with modules for faithfulness assessment, embedding similarity analysis, and feedback loop integration via ChromaDB. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [code-eval](/tools/abacaj-code-eval.md) | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 1058d | 0d |
| Open issues (now) | 5 | 6 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/future-agi-agent-learning-kit/trust.md) |

## Shared compatibility

- **Python**: [code-eval](/tools/abacaj-code-eval.md) - Python runtime; [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) - Python runtime

## Decision facts: code-eval

- **Adopt for:** code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability.

## Decision facts: agent-learning-kit

- **Adopt for:** Agent-learning-kit is a Python framework for evaluating AI-related workflows with modules for faithfulness assessment, embedding similarity analysis, and feedback loop integration via ChromaDB.

## Choose when

### Choose code-eval if…

- License: code-eval is MIT, agent-learning-kit is Apache-2.0.
- Tags unique to code-eval: humaneval, wizardcoder.
- When you need clear comparisons of pass rates for different LLMs using standardized tests

### Choose agent-learning-kit if…

- License: agent-learning-kit is Apache-2.0, code-eval is MIT.
- Tags unique to agent-learning-kit: ai-agents, ci-cd, evaluation, ml.
- When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed.

## When NOT to use code-eval

- If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences
- For real-time or dynamic evaluations as this repo offers pre-computed static results only

## When NOT to use agent-learning-kit

- If your workflow does not align with the specific evaluation criteria and methods supported by agent-learning-kit.
- When you seek a framework that integrates with backend systems other than those provided as optional extras, such as MongoDB or DynamoDB instead of ChromaDB.

## Common questions

### What is the difference between code-eval and agent-learning-kit?

code-eval: Run evaluation on LLMs using human-eval benchmark.. agent-learning-kit: Evaluation Framework for all your AI related Workflows. See the comparison table for live GitHub stats and shared categories.

### When should I choose code-eval over agent-learning-kit?

Choose code-eval over agent-learning-kit when License: code-eval is MIT, agent-learning-kit is Apache-2.0; Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests.

### When should I choose agent-learning-kit over code-eval?

Choose agent-learning-kit over code-eval when License: agent-learning-kit is Apache-2.0, code-eval is MIT; Tags unique to agent-learning-kit: ai-agents, ci-cd, evaluation, ml; When you need comprehensive evaluation of your AI models including faithfulness checks using DeBERTa NLI model installed.

### When should I avoid code-eval?

If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only

### When should I avoid agent-learning-kit?

If your workflow does not align with the specific evaluation criteria and methods supported by agent-learning-kit. When you seek a framework that integrates with backend systems other than those provided as optional extras, such as MongoDB or DynamoDB instead of ChromaDB.

### Is code-eval or agent-learning-kit more popular on GitHub?

code-eval has more GitHub stars (431 vs 118). Stars measure visibility, not whether either tool fits your constraints.

### Are code-eval and agent-learning-kit open source?

Yes - both are open-source projects on GitHub (code-eval: MIT, agent-learning-kit: Apache-2.0).

### Where can I find alternatives to code-eval or agent-learning-kit?

GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/abacaj-code-eval-vs-future-agi-agent-learning-kit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, code-eval or agent-learning-kit?

code-eval: Dormant. agent-learning-kit: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for code-eval and agent-learning-kit?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [agent-learning-kit trust report](/tools/future-agi-agent-learning-kit/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-eval)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_