Public record
Software health reportschema 0.11.0 · metrics 2.10.0 · 2026-07-15 16:55 UTC

cognizant-ai-lab / neuro-san

Neuro AI System of Agent Networks

PythonApache-2.0★ 136 stars⑂ 43 forkssince Nov 2024View on GitHub ↗
KindLibraryhow this is determined
#agents#ai#ai-agents#mas#multi-agent-systems#mutli-agent#ai-agents-framework#langchain#multi-agent-framework#agentic-ai#agentic-framework#llms#aaosa#sly-data

cognizant-ai-lab/neuro-san holds a health index of 92 out of 100, placing it in the Excellent band. It scores highest on Vitality (100/100) and lowest on AI Readiness (42/100). It was last updated today. A single contributor accounts for most of its recent work.

92
overall / 100
Excellent

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.

92
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 78 is calibrated to 92 on the published index scale (record calibration 2026-08-02).

Ownership

Cognizant AI LabOrganization
188 followers18 public repossince Dec 2018

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIneuro-san0.6.79-1290 days agollmlangchainagentmulti-agent

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 — 3,078 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year3,078
human_commit_share—
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~0.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tag0.6.79
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases0.9

Community & Adoption

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

68Good · 17% of overall

Popularity & adoption

53Moderate
How it's scored
34.6/60Stars — 136 stars
13.5/25Forks — 43 forks
5.3/15Watchers — 10 watchers
Inputs used
forks43
stars136
watchers10
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 (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

72Good · 23% of overall

Maintainer resilience (bus factor)

37Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
10.7/22.5Commit distribution — top contributor authored 53% of commits
13.5/13.5Contributor breadth — 21 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled21
top_contributor_share0.526

Issue & PR responsiveness

79Good
How it's scored
29/42Issue resolution — 69% of issues closed
28.1/30PR acceptance — 709/756 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs709
open_issues102
closed_issues227
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.69
closed_unmerged_prs47
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

85Excellent
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
16.4/25Owner reach — 188 followers of cognizant-ai-lab
21.3/25Track record — 18 public repos, account ~7 yr old
Inputs used
followers188
owner_typeOrganization
is_verified—
owner_logincognizant-ai-lab
public_repos18
account_age_days2,778
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 129 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesneuro-san
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — .flake8, .pylintrc
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://decisionai.ml/neuro-san
10/10Repository description
10/10Topics — 14 topics
10/10Wiki
Inputs used
topicsagents, ai, ai-agents, mas, multi-agent-systems, mutli-agent, ai-agents-framework, langchain, multi-agent-framework, agentic-ai, agentic-framework, llms, aaosa, sly-data
has_wikiyes
homepagehttps://decisionai.ml/neuro-san
docs_sitehttps://decisionai.ml/neuro-san
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

67Good · 16% of overall

Security posture

67Good
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml, requirements-build.txt, requirements.txt, setup.py
has_codeql_workflowyes
has_security_policyyes
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

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.

42Weak · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

60Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — .flake8, .pylintrc
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/519 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes36,180
source_files_sampled519
oversized_source_files0

Machine-readable interfaces

60Moderate
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — neuro_san/api/grpc/agent.proto, neuro_san/api/grpc/chat.proto, neuro_san/api/grpc/concierge.proto, neuro_san/api/grpc/google/api/annotations.proto, neuro_san/api/grpc/google/api/http.proto, neuro_san/api/grpc/mime_data.proto
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_filesneuro_san/api/grpc/agent.proto, neuro_san/api/grpc/chat.proto, neuro_san/api/grpc/concierge.proto, neuro_san/api/grpc/google/api/annotations.proto, neuro_san/api/grpc/google/api/http.proto, neuro_san/api/grpc/mime_data.proto
interfaces_expected_of—

Key facts

136GitHub stars
21contributors
3,078commits, last 12 months
0days since last push
100releases
1bus factor
102open issues
PyPIpackage ecosystems

Data collection warnings

  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

All dependencies 62

Full resolved dependency set from the GitHub dependency graph: 0 direct and 62 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiobotocore—indirect
PyPIaiofiles—indirect
PyPIaiohttp—indirect
PyPIboto3—indirect
PyPIbotocore—indirect
PyPIbs4—indirect
PyPIbuild—indirect
PyPIcmarkgfm—indirect
PyPIcoverage7.6.1indirect
PyPIcroniter—indirect
PyPIflake87.3.0indirect
PyPIgrpcio—indirect
PyPIgrpcio-tools—indirect
PyPIhttpx—indirect
PyPIidna—indirect
PyPIjanus—indirect
PyPIjson-repair—indirect
PyPIjsonschema—indirect
PyPIlangchain—indirect
PyPIlangchain-anthropic—indirect
PyPIlangchain-aws—indirect
PyPIlangchain-classic—indirect
PyPIlangchain-community—indirect
PyPIlangchain-core—indirect
PyPIlangchain-google-genai—indirect
PyPIlangchain-mcp-adapters—indirect
PyPIlangchain-nvidia-ai-endpoints—indirect
PyPIlangchain-ollama—indirect
PyPIlangchain-openai—indirect
PyPIlangchain-openrouter—indirect
PyPIlangfuse—indirect
PyPIleaf-common—indirect
PyPIleaf-server-common—indirect
PyPImcp—indirect
PyPIobjsize—indirect
PyPIopenfga-sdk—indirect
PyPIparameterized—indirect
PyPIprotobuf—indirect
PyPIpsutil—indirect
PyPIpydantic—indirect
PyPIpyhocon—indirect
PyPIpylint4.0.2indirect
PyPIpylint-protobuf0.22.0indirect
PyPIpymarkdownlnt0.9.30indirect
PyPIpyopenssl—indirect
PyPIpytest9.0.3indirect
PyPIpytest-asyncio1.3.0indirect
PyPIpytest-cov5.0.0indirect
PyPIpytest-timeout—indirect
PyPIpytest-timer1.0.0indirect
PyPIpytest-xdist—indirect
PyPIreadme-renderer—indirect
PyPIruamel-yaml—indirect
PyPIsetuptools—indirect
PyPIsetuptools-scm8.1.0indirect
PyPItimedinput—indirect
PyPItimeout-decorator0.5.0indirect
PyPItornado—indirect
PyPIurllib3—indirect
PyPIvalidators—indirect
PyPIwatchdog—indirect
PyPIwheel—indirect
Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Related records

Inspected repositories sharing catalogue tags or ecosystems with cognizant-ai-lab/neuro-san. Read them side by side →

99google/adk-pythonAn open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.Exceptional96google/adk-jsAn open-source, code-first Typescript toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.Exceptional99ag2ai/ag2AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3xExceptional98bytedance/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.Exceptional100deepset-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.Exceptional98ModelEngine-Group/nexentNexent 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.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 v0.11.0 — full methodology · metrics wiki.

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