Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-28 05:07 UTC

ModelEngine-Group / nexent

Nexent is a zero-code platform for auto-generating production-grade AI agents using Harness Engineering principles — unified tools, skills, memory, and orchestration with built-in constraints, feedback loops, and control planes.

Python · TypeScriptMIT★ 5,839 stars⑂ 724 forkssince Apr 2025View on GitHub ↗
KindNetwork serviceMCP serverWeb interfaceLibraryhow this is determined
#agent#agentic-ai#agentic-framework#agentic-rag#agentic-workflow#ai#llm#rag#harness#harness-engineering#mcp#multi-agent

ModelEngine-Group/nexent holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on AI Readiness (68/100). It was last updated today. 3 contributors account for most of its recent work.

98
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

98
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

ModelEngine-GroupOrganization
856 followers21 public repossince Feb 2025

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPInexent1.8.1568166 days agoagentaiframework
PyPIbackendpoints to another repo — not scored0.2.4.112,08884573 days agodjangobackend

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

100Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 3,280 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year3,280
human_commit_share1
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 50 releases published
36/36Release recency — latest release 22 days ago
27/27Release cadence — a release every ~14.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count50
latest_release_tagv2.4.0
releases_from_tagsno
days_since_latest_release22
mean_days_between_releases14.4
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

91Excellent · 17% of overall

Popularity & adoption

97Exceptional
How it's scored
60/60Stars — 5,839 stars
23.8/25Forks — 724 forks
13.4/15Watchers — 255 watchers
Inputs used
forks724
stars5,839
watchers255
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

81Excellent · 23% of overall

Maintainer resilience (bus factor)

77Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
17.2/22.5Commit distribution — top contributor authored 23% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 4 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled100
top_contributor_share0.234

Issue & PR responsiveness

87Excellent
How it's scored
36.8/42Issue resolution — 88% of issues closed
25.3/30PR acceptance — 1,766/2,093 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 27/30 approved changesets -- score normalized to 9
Inputs used
merged_prs1,766
open_issues166
closed_issues1,182
prs_merged_7d22
prs_decided_7d24
prs_merged_30d41
prs_decided_30d44
issue_closed_ratio0.877
closed_unmerged_prs327
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

64Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
21.1/25Owner reach — 856 followers of ModelEngine-Group
12.8/25Track record — 21 public repos, account ~1 yr old
Inputs used
followers856
owner_typeOrganization
is_verifiedno
owner_loginModelEngine-Group
public_repos21
account_age_days553

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 166 days ago
20/20Version history — 8 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesnexent
ecosystemspypi
any_deprecatedno
min_days_since_publish166

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

90Excellent
How it's scored
24/24CI workflows — 17 workflow(s)
24/24Tests present
16/16Linter config — .eslintrc.json, sdk/pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
6.4/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://modelengine-group.github.io/nexent/
10/10Repository description
10/10Topics — 12 topics
0/10Wiki
Inputs used
topicsagent, agentic-ai, agentic-framework, agentic-rag, agentic-workflow, ai, llm, rag, harness, harness-engineering, mcp, multi-agent
has_wikino
homepagehttp://modelengine-group.github.io/nexent/
docs_sitehttp://modelengine-group.github.io/nexent/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

68Good · 16% of overall

Security posture

72Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-Review — Found 27/30 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 4 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.2
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

54Moderate
How it's scored
4.9/35Direct dependencies free of known advisories — 3 affected: fastmcp 2.12.0 (critical 10.0), smolagents 1.23.0 (critical 10.0), mcp 1.22.0 (high 7.1)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
23.8/40No advisories left outstanding — 3 advisory-carrying package(s) unaddressed past 90 days; oldest published 302 days ago
Inputs used
sourceosv
advisories26
affected_packages3
assessed_packages124
unassessed_packages0
affected_by_severitycritical 2, high 1
direct_affected_packages3
Matched the pypi:nexent@1.8.1 runtime dependency closure — what installing the published package pulls in — 124 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

68Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — .cursor/rules/backend/app_layer_rules.mdc, .cursor/rules/backend/database_layer_rules.mdc, .cursor/rules/backend/service_layer_rules.mdc, .cursor/rules/english_comments.mdc, .cursor/rules/environment_variable.mdc, .cursor/rules/frontend/component_layer_rules.mdc, .cursor/rules/frontend/frontend_overview_rules.mdc, .cursor/rules/frontend/hook_layer_rules.mdc, .cursor/rules/frontend/page_layer_rules.mdc, .cursor/rules/frontend/service_layer_rules.mdc, .cursor/rules/frontend/type_layer_rules.mdc, .cursor/rules/frontend/ui_standards_rules.mdc, .cursor/rules/pytest_unit_test_rules.mdc, AGENTS.md, doc/docs/en/sdk/core/agents.md, doc/docs/zh/sdk/core/agents.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 98 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.98
agent_instruction_files.cursor/rules/backend/app_layer_rules.mdc, .cursor/rules/backend/database_layer_rules.mdc, .cursor/rules/backend/service_layer_rules.mdc, .cursor/rules/english_comments.mdc, .cursor/rules/environment_variable.mdc, .cursor/rules/frontend/component_layer_rules.mdc, .cursor/rules/frontend/frontend_overview_rules.mdc, .cursor/rules/frontend/hook_layer_rules.mdc, .cursor/rules/frontend/page_layer_rules.mdc, .cursor/rules/frontend/service_layer_rules.mdc, .cursor/rules/frontend/type_layer_rules.mdc, .cursor/rules/frontend/ui_standards_rules.mdc, .cursor/rules/pytest_unit_test_rules.mdc, AGENTS.md, doc/docs/en/sdk/core/agents.md, doc/docs/zh/sdk/core/agents.md
agent_instruction_max_bytes6,449

Verify loop (build / test / typecheck)

69Good
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — .eslintrc.json, sdk/pyproject.toml ([tool.ruff])
11/11Static type checking — frontend/tsconfig.json
10/10Reproducible environment — devcontainer, Dockerfile
10/10Demonstrated agent practice — 9 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configsfrontend/tsconfig.json
agent_commit_share0.09
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

79Good
How it's scored
27/45Type-checkable code — Python with type-check config (frontend/tsconfig.json)
52.2/55Manageable file sizes — 82/1,588 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes704,286
source_files_sampled1,588
oversized_source_files82

Machine-readable interfaces

20At Risk
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

5,839GitHub stars
100contributors
3,280commits, last 12 months
0days since last push
50releases
3bus factor
166open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'backend' points at a different repository (https://github.com/tr4n2uil/backend.django); excluded from ecosystem scoring

More detail

Star and fork history 0 ★ / 724 ⇿
0Stars
724Forks
50Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0125250375500625750723342025-052025-122026-08
Major 2Minor 13Patch 17

Each point covers 2 days.

OpenSSF Scorecard 7.2 / 10
7.2aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-28 05:07 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 27/30 approved changesets -- score normalized to 9
10Contributorsproject has 4 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 136
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All dependencies 160

Full resolved dependency set from the GitHub dependency graph: 125 direct and 35 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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npmreact-pdf10.1.0direct
npmreact-shiki^0.10.1direct
npmreact-syntax-highlighter^16.1.0direct
npmrecharts^2.15.0direct
npmrehype-katex^7.0.1direct
npmrehype-raw^7.0.0direct
npmremark-gfm^4.0.1direct
npmremark-math^6.0.0direct
npmremark-parse^11.0.0direct
npmremark-rehype^11.1.0direct
npmtailwind-merge^3direct
npmtw-animate-css^1.4.0direct
npmtw-shimmer^0.4.11direct
npmunified^11.0.0direct
npmunist-util-visit^5.0.0direct
npmuuid^11.1.0direct
npmzustand^5.0.9direct
PyPIaiofiles—direct
PyPIaiohttp—direct
PyPIauthlib—direct
PyPIboto3—direct
PyPIbotocore—direct
PyPIcharset-normalizer—direct
PyPIcroniter—direct
PyPIcryptography—direct
PyPIdefusedxml—direct
PyPIdocker—direct
PyPIebooklib0.20direct
PyPIelasticsearch8.17.2direct
PyPIemail-validator—direct
PyPIexa-py1.14.0direct
PyPIfastapi—direct
PyPIfastmcp—direct
PyPIgreenlet—direct
PyPIhttpx—direct
PyPIijson3.5.0direct
PyPIjieba—direct
PyPIjsonref—direct
PyPIkubernetes—direct
PyPIlangchain—direct
PyPIlangchain-text-splitters1.1.2direct
PyPImatplotlib—direct
PyPImcp—direct
PyPImcpadapt—direct
PyPInumpy—direct
PyPIopenai—direct
PyPIopenjiuwen—direct
PyPIopenpyxl—direct
PyPIorjson3.10direct
PyPIparamiko—direct
PyPIpillow—direct
PyPIpsycopg2-binary—direct
PyPIpydantic—direct
PyPIpydantic-settings—direct
PyPIpyjwt—direct
PyPIpymssql—direct
PyPIpymysql—direct
PyPIpypandoc1.17direct
PyPIpypdf6.9.1direct
PyPIpython-docx—direct
PyPIpython-dotenv—direct
PyPIpython-multipart—direct
PyPIpython-pptx1.0.2direct
PyPIpyyaml—direct
PyPIrapidfuzz—direct
PyPIredis—direct
PyPIreportlab—direct
PyPIrequests—direct
PyPIrich—direct
PyPIruamel-yaml0.19.1direct
PyPIscikit-learn—direct
PyPIsetuptools—direct
PyPIsmolagents1.23.0direct
PyPIsqlalchemy—direct
PyPIsupabase—direct
PyPItiktoken—direct
PyPIuvicorn—direct
PyPIwebsocket-client—direct
PyPIwebsockets—direct
PyPIxlrd—direct
npm@tailwindcss/postcss^4indirect
npm@types/js-yaml^4.0.9indirect
npm@types/mdast4.0.4indirect
npm@types/node22.15.16indirect
npm@types/papaparse^5.3.16indirect
npm@types/react18.3.20indirect
npm@types/react-dom18.3.6indirect
npm@types/unist3.0.3indirect
npmeslint^9.34.0indirect
npmeslint-config-next15.5.7indirect
npmeslint-config-prettier^9.1.0indirect
npmeslint-plugin-prettier^5.1.3indirect
npmpostcss^8indirect
npmpostcss-nested^7indirect
npmprettier^3.2.5indirect
npmtailwindcss^4indirect
npmtypescript5.8.3indirect
npmvitepress^1.6.3indirect
PyPIcelery—indirect
PyPIflower—indirect
PyPIhuggingface-hub—indirect
PyPIlangfuse2.60.10indirect
PyPInest-asyncio—indirect
PyPIopentelemetry-api—indirect
PyPIopentelemetry-exporter-otlp—indirect
PyPIopentelemetry-instrumentation—indirect
PyPIopentelemetry-instrumentation-fastapi—indirect
PyPIopentelemetry-instrumentation-requests—indirect
PyPIopentelemetry-sdk—indirect
PyPIopentelemetry-semantic-conventions—indirect
PyPIpytest—indirect
PyPIray—indirect
PyPIruff—indirect
PyPIunstructured0.18.14indirect
PyPIunstructured-inference1.2.0indirect
Dependency advisories 3

Installing pypi:nexent@1.8.1 pulls in 124 packages, direct and transitive: 3 carry known advisories, of which 3 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
fastmcp2.12.0directcritical143.2.0
smolagents1.23.0directcritical6—
mcp1.22.0directhigh61.28.1

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

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Related records

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100deepset-ai/haystackOpen-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.Exceptional97langgenius/difyBuild Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.Exceptional98bytedance/deer-flowAn open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.Exceptional99ag2ai/ag2AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3xExceptional97strands-agents/harness-sdkBuild an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.Exceptional99google/adk-pythonAn open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.Exceptional

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.