AI Workstation Open Source Intelligence Server

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Enables researching, verifying, comparing, and composing open-source AI projects with transparent evidence and uncertainty boundaries through read-only tools.

автор: zxhwolfe-dev

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Описание

Enables researching, verifying, comparing, and composing open-source AI projects with transparent evidence and uncertainty boundaries through read-only tools.

README

A distribution layer for researching, verifying, comparing and composing open-source AI projects with explicit evidence and uncertainty boundaries.

Status

M1 Alpha / pre-release. The repository now contains:

three complete Skill workflows;
a validated Skills-only Codex plugin package and repo-scoped marketplace;
six read-only project-intelligence tools;
a transport-neutral Python core;
a deterministic offline mock provider;
a fail-closed HTTP provider for AI Workstation's public Radar API;
strict field-level evidence boundaries for repository metadata, analysis projection fields and direct license evidence;
stdio and guarded Streamable HTTP MCP transports;
privacy-minimized structured tool telemetry;
live public-contract probes, sanitized capture, validation and offline replay;
a deterministic Skills-only alpha ZIP builder with SHA-256 checksums;
a consolidated four-level release-readiness report;
a non-root container and localhost-only hosted-alpha compose example;
remote MCP compatibility smoke tests;
automated unit, plugin, MCP, workflow, packaging and deployment-policy tests.

This is not yet a broad public hosted MCP service or public-directory release. The hosted transport is ready for local/private-alpha deployment behind a trusted private network or authenticated TLS gateway. Native public OAuth, production quotas/rate limiting/abuse controls, legal publication gates and platform registration remain deliberately unresolved.

Product boundaries

Every tool result separates:

verified source facts;
analysis and recommendations;
unknown or unverified information;
risks and limitations.

A value being present in data.project does not automatically make it a verified fact. The live hardened provider distinguishes:

verified_public_metadata
verified_direct_evidence
public_projection_only
unknown

Stable repository/public-release metadata can cross the fact boundary after same-snapshot validation. Summary, deployment classification, categories and use cases remain public_projection_only unless a future field-specific evidence contract verifies them. License is stricter: a license label is verified only when public transparency includes a direct License source and excerpt.

The first release is read-only. It does not execute repository code, mutate GitHub projects, save collections, authenticate end users or process payments.

Skills

open-source-project-research
open-source-project-comparison
open-source-stack-planner

Tools

search_ai_projects
get_project_facts
get_license_evidence
compare_ai_projects
find_alternatives
compose_ai_stack

Repository layout

.
├── .codex-plugin/               Skills-only plugin manifest
├── .agents/plugins/             repo-scoped marketplace manifest
├── .github/workflows/           CI, live validation and alpha packaging
├── skills/                      reusable Skill workflows
├── src/aiworkstation_osi/       core, providers, MCP and validation tools
├── schemas/                     input manifest and unified result schema
├── evals/                       bilingual tool and workflow cases
├── tests/                       unit, provider, workflow and MCP tests
├── examples/                    invocation and Codex configuration examples
├── docs/                        architecture, deployment and release runbooks
├── Dockerfile
├── compose.hosted.example.yml
├── CHANGELOG.md
├── SECURITY.md
├── PRIVACY.md
└── SUPPORT.md

Local setup

python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[mcp]"
python -m unittest discover -s tests -v
osi-validate-plugin --root .
osi-readiness --root .

Before real operational evidence exists, the expected readiness state is:

code_ready=true
external_alpha_ready=false
hosted_private_alpha_ready=false
public_launch_ready=false

code_ready=true is a target of the repository checks; do not claim it has been observed until the current local/CI suite actually runs green.

Install the local Skills plugin

codex plugin marketplace add zxhwolfe-dev/aiworkstation-open-source-intelligence --ref main
codex plugin marketplace list

For a local clone:

codex plugin marketplace add /ABSOLUTE/PATH/TO/aiworkstation-open-source-intelligence

The plugin package installs the three Skills only. It deliberately does not yet claim a bundled or registered live MCP connection. Configure the stdio MCP server separately until the hosted connection identity and authorization model are stable.

See docs/plugin-packaging.md.

Offline mock usage

The default provider performs no network access:

osi-m0 provider-info
osi-m0 list-tools
osi-m0 invoke search_ai_projects \
  --arguments '{"query":"self-hosted RAG with Docker and web UI"}'

MOCK_DATA warnings are intentional and prevent fixture output from being mistaken for current verified project intelligence.

Public Radar HTTP provider

Enable the live read-only adapter explicitly:

export OSI_PROVIDER=http
export AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn
export OSI_HTTP_TIMEOUT_SECONDS=30
export OSI_HYDRATE_LIMIT=5

osi-m0 provider-info
osi-m0 invoke get_project_facts \
  --arguments '{"project_id":"infiniflow/ragflow","locale":"en"}'

The adapter:

requires public snapshot identity;
rejects mixed snapshots and unsafe selector contracts;
rejects upstream redirects instead of silently following them;
keeps near matches outside formal recommendations;
exposes field_evidence_status for project-detail fields;
requires direct public License evidence before a license enters verified_facts;
converts missing/indirect/sentinel licenses into explicit unknowns;
flags non-standard license labels for manual review;
never imports private akaiagents modules.

stdio MCP server

Run a local MCP server for Codex or another stdio-capable host:

OSI_PROVIDER=mock osi-mcp

Use live Radar data:

OSI_PROVIDER=http \
AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn \
osi-mcp

The server exposes exactly six annotated read-only tools and preserves the fact/recommendation/unknown/risk boundary.

Runtime telemetry defaults to warnings/errors on stderr so stdio protocol output is not polluted. Set OSI_LOG_LEVEL=INFO to record successful tool name, outcome, duration and safe aggregate counts. Tool arguments, queries, project IDs and raw request IDs are not logged; request IDs are reduced to a short SHA-256 fingerprint.

See docs/codex-setup.md.

Guarded Streamable HTTP MCP

Validate the default local configuration without opening a socket:

osi-mcp-http --check-config

Run a loopback-only development endpoint:

OSI_PROVIDER=mock osi-mcp-http

The endpoint is:

http://127.0.0.1:8000/mcp

Verify it with a real MCP client:

osi-remote-smoke --url http://127.0.0.1:8000/mcp --invoke-search --locale en

A non-loopback bind requires all of the following:

OSI_PROVIDER=http
OSI_MCP_HTTP_PUBLIC_BIND_ACK=reverse-proxy-or-private-network
AIWORKSTATION_RADAR_BASE_URL=https://aiworkstation.cn
OSI_MCP_HTTP_ALLOWED_HOSTS=mcp.example.com,mcp.example.com:*

The server also caps MCP request bodies at 256 KiB by default. Browser clients must additionally use explicit HTTPS OSI_MCP_HTTP_ALLOWED_ORIGINS plus matching narrow CORS policy.

The Host/origin allowlists are passed into MCP transport security for DNS-rebinding/Host-header protection. The public-bind acknowledgement is not authentication. Do not expose the endpoint directly to the Internet without a trusted authenticated TLS gateway or future native OAuth.

Container/private-alpha example:

docker build -t aiworkstation-osi-mcp:0.1.0 .
docker compose -f compose.hosted.example.yml up --build

The example maps only 127.0.0.1:8000 on the host, runs non-root, uses a read-only filesystem and drops Linux capabilities. See docs/hosted-mcp.md.

Validate the public Radar contract

Run both language probes:

osi-probe --base-url https://aiworkstation.cn --locale en
osi-probe --base-url https://aiworkstation.cn --locale zh

Capture, validate and replay one language:

osi-capture-contracts \
  --base-url https://aiworkstation.cn \
  --locale en \
  --project-id infiniflow/ragflow \
  --output-dir tmp/public-validation/contracts-en

osi-validate-contracts \
  --directory tmp/public-validation/contracts-en

osi-replay-contracts \
  --directory tmp/public-validation/contracts-en \
  --output tmp/public-validation/replay-en.json

Replay derives locale and project identity from the sanitized capture manifest. The probe requires a reported license either to have direct verified evidence or to remain an explicit unknown. The manual live-contract-validation workflow runs the bilingual chain and uploads artifacts only after validation, replay and forbidden-key scanning pass.

See docs/production-validation.md.

Build the Skills-only alpha package

osi-build-alpha --root . --output-dir dist/alpha
(
  cd dist/alpha
  sha256sum --check SHA256SUMS
)

The archive contains the plugin manifests, three Skills, changelog, security, privacy, support and tester documentation plus an embedded per-file SHA-256 manifest. It excludes Python runtime code and the live MCP server so the package does not imply live-data access by itself.

The manual .github/workflows/alpha-package.yml workflow runs release gates before uploading the archive.

Readiness levels

osi-readiness distinguishes:

code_ready
external_alpha_ready
hosted_private_alpha_ready
public_launch_ready

For a Skills-only invited alpha, supply real bilingual contract captures plus CI, Codex and review evidence. For a hosted private alpha, additionally supply a credential-free HTTPS /mcp endpoint, successful remote smoke-test attestation and protected gateway/private-network attestation.

See docs/release-readiness.md.

Architecture

User / plugin / MCP host
          |
Three Skills
          |
  +-------+----------------+
  |                        |
stdio MCP             Streamable HTTP MCP
  |                 (guarded private alpha)
  +-----------+------------+
              |
       ToolRegistry
              |
Mock provider OR hardened AI Workstation HTTP provider
              |
 Current healthy validated Radar release

zxhwolfe-dev/akaiagents remains a read-only reference and private data production system. This repository integrates through explicit public HTTP contracts rather than importing its private Python modules.

Main documentation

What still requires real-world execution or publisher decisions

Code-side M1 Alpha is now substantially complete. Remaining gates are:

observe successful local tests and GitHub Actions on Python 3.10/3.12;
run and review bilingual production contract validation against the real Radar responses;
install the Skills package and test stdio MCP from Codex;
deploy the guarded HTTP service behind a protected gateway/private network;
run English and Chinese osi-remote-smoke against the deployed endpoint;
generate real external/hosted-alpha readiness reports;
fix only production contract/runtime differences demonstrated by those runs;
choose software license and publish final legal/support URLs;
implement the chosen per-user OAuth/identity, revocation, quota, rate-limit and abuse-control model before broad public hosting;
register and review the final hosted MCP/plugin connection with the target distribution platform.

The decisions that cannot safely be invented in code are documented in docs/public-launch-decisions.md.

License

No open-source license has been granted yet. The repository is public for pre-release inspection and development; reuse rights will be defined before the first broad public package release.

from github.com/zxhwolfe-dev/aiworkstation-open-source-intelligence

Установка AI Workstation Open Source Intelligence Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/zxhwolfe-dev/aiworkstation-open-source-intelligence

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