Skip to main content

mult-agentes

PyPI Python CI Release License: Apache 2.0 Tests Coverage

Multi-agent framework: Claude Code in VS Code is the executor; the framework is the observability + governance layer. 684 skills across 39 areas, 7-phase pipeline saga, HMAC-chained audit log, real-time dashboard.

Install

pip install mult-agentes                                # core only
pip install "mult-agentes[dashboard,observability]"     # + FastAPI dashboard + OTel
pip install "mult-agentes[dashboard,observability,llm]" # + Anthropic SDK (optional)

Quickstart (60 seconds)

# 1. Boot the dashboard
make dashboard-real
# → http://localhost:8000 (green banner = connected)

# 2. Register work (replace with whatever you're building)
python -m src.bridge.cli start \
    --intent build_feature --specialist frontend-specialist \
    --description "Implementar autenticação JWT no FastAPI"
# → {"capsule_id": "cap_abc123", ...}

# 3. Record artifacts as you produce them
python -m src.bridge.cli artifact --capsule cap_abc123 --path src/auth.py

# 4. Finalize
python -m src.bridge.cli complete --capsule cap_abc123 --status success \
    --summary "JWT auth shipped + 12 tests passing"

The dashboard updates in real time via WebSocket. No Anthropic API key needed — Claude in your VS Code chat is the LLM.

Architecture in one sentence

You (humano) ⇄ Claude Code (VS Code) ⇄ src.bridge.Recorder ⇄ EventBus + WorldState + Audit + Memory ⇄ Dashboard

See docs/explanation/architecture.md for the full Mermaid diagrams.

What this repo holds

What's inside

Three layers of metadata over a flat collection of agent skills:

  1. Skills — 684 individual SKILL.md files installed under <area>/.agents/skills/<name>/, each with YAML frontmatter (name, description, optional metadata, source_org, etc.)
  2. Curated tier — 127 hand-selected skills with marketplace install counts and tier classification (docs/reference/registries/_skills-registry.yaml)
  3. Capability graph + agents/orchestrators — taxonomy of sub-areas, declarative routing, and the multi-agent hierarchy

The 39 area folders (ai-ml, frontend, backend, cyber-segurança, …) are mapped 1:1 to capability domains in the routing compass.

Documentation

Docs follow the Diátaxis framework:

  • 📘 Tutorials — step-by-step learning
  • 🛠️ How-to guides — goal-oriented recipes
  • 📖 Reference — canonical specs, templates, contracts, registries, policies
  • 💡 Explanation — architecture, constitution, ADRs

Local docs portal: make docs-serve → http://localhost:8000

Implementation maturity

This repo implements a subset of the multi-agent framework specified in GUIA-ARTEFATOS-MULTIAGENT-v2.md (v3.2.2, 168 artifacts across 19 layers). Current coverage: 100% (168/168) — full documental coverage. Camadas 1-6 templated (44 templates including PROJECT-STRUCTURE); Camadas 7-19 specified (122 specs in docs/reference/specs/). Runtime implementation for Camadas 7-19 is staged via the ROADMAP.

v3.2.2 update (2026-05-24): new Camada 19 (Escalabilidade + Ciclo de Vida HomoSapiens) with 11 specs, PROJECT-STRUCTURE template in C3, and Regra 35 (Spec antes do código — SDD) added as 21st absolute rule. See _framework/ARTIFACTS-INVENTORY.md for the per-artifact gap analysis.

Fully documented today (all 19 Camadas):

  • SKILL-CATALOG ↔ docs/reference/registries/_skills-registry.yaml (127 curated, 4 tiers) + registry-full.yaml (684 total)
  • SKILL-TAXONOMY ↔ docs/reference/registries/CAPABILITY-GRAPH.yaml (39 areas × 64 sub_areas)
  • 44 canonical templates in docs/reference/templates/
  • 122 specs in docs/reference/specs/

Layout

.
├── docs/                              # Diátaxis-organized documentation
│   ├── tutorials/                     # Learn by doing
│   ├── how-to/                        # Recipes
│   ├── reference/
│   │   ├── specs/                     # 122 architecture + behavior specs (Camadas 7-19)
│   │   ├── templates/                 # 44 instance-ready templates (Camadas 1-6)
│   │   ├── contracts/                 # 5 JSON schemas
│   │   ├── registries/                # _skills-registry, registry-full, agents, orchestrators, …
│   │   ├── policies/                  # cost, error-handling, observability
│   │   └── runtime-api/               # Per-module Python API reference
│   └── explanation/                   # Architecture, constitution, ADRs
├── src/                               # Runtime (13 modules)
│   ├── pipeline/                      # 7-phase orchestrator (Camada 7)
│   ├── privacy/                       # PII, RBAC, audit chain (Camada 8)
│   ├── ai/                            # Model mgmt, RAG, memory, circuit breaker (Camada 9)
│   ├── skills/                        # Loader, router, invoker, versioning (Camada 10)
│   └── (meta_learning, agent_expansion, autonomy, growth, body, …)
├── _framework/                        # Instance docs + runtime data
│   ├── ARTIFACTS-INVENTORY.md         # Per-artifact gap analysis
│   ├── PRD.md, ROADMAP.md, BLUEPRINT.md, BUDGET.md, SCOPE.md, …
│   ├── memory/                        # Episodic + semantic memory (JSONL)
│   ├── locks/                         # Resource locks (JSON)
│   └── observability/                 # agent_metrics.jsonl
├── scripts/                           # Audits, registry sync, marketplace fetch (12 scripts)
├── tests/                             # pytest suites (unit + smoke)
├── <area>/                            # 39 area folders × 684 SKILL.md files
├── .githooks/                         # Versioned git hooks (pre-commit)
├── .github/workflows/                 # CI: audit + verify
├── pyproject.toml                     # PEP 621 metadata
├── Makefile                           # make help
├── README.md (this file)
├── CHANGELOG.md, LICENSE, CONTRIBUTING.md, CODE_OF_CONDUCT.md, SECURITY.md
└── mkdocs.yml                         # Docs portal config

Tier model

Tier Installs threshold Count
Platinum ≥100K OR official Anthropic 34
Gold 10K–100K 30
Silver 1K–10K (provenance validated) 33
Bronze 100–1K (provenance validated) 30
Experimental <100 (not curated yet)

Tier section comments and _meta counts in _skills-registry.yaml are auto-synced by scripts/sync_registry.py.

Quickstart

git clone https://github.com/claudinoinsights/mult-agentes.git
cd mult-agentes
python -m venv .venv && source .venv/bin/activate     # Windows: .venv\Scripts\activate
pip install -e ".[dev,test,docs]"

# Set up the audit gate
python scripts/setup_hooks.py

# Validate everything (read-only)
make audit       # 5 audits (structural, semantic, paranoid, deep, verify_all)
make test        # pytest + coverage
make docs        # build MkDocs site

Pre-commit hook

Exit codes:

Code Meaning
0 All checks passed
1 Structural or semantic issue (commit aborted)
2 _meta drift detected — run python scripts/sync_registry.py and re-stage

Bypass (not recommended): git commit --no-verify.

CI (GitHub Actions)

.github/workflows/audit.yml runs the same gate on every push to main/master and on every PR. Also verifies registry-full.yaml is in sync with the filesystem.

Catches commits made with --no-verify or by collaborators who haven't run setup_hooks.py.

Adding a skill

npx skills add <owner/repo@skill> -y
mv .agents/skills/<name> <area>/.agents/skills/<name>
python scripts/sync_registry.py
python scripts/gen_registry_full.py
make audit

If promoting to a curated tier, also add an entry under platinum/gold/silver/bronze in docs/reference/registries/_skills-registry.yaml.

Multi-agent hierarchy (Layer 1 → Layer 5)

Layer 1: Cortex (global orchestrator, haiku-4-5, always on)
Layer 2: Domain orchestrators (10 total: frontend, backend, ai_ml, devops, security, qa,
         finance_trading, integrations, iot, meta — sonnet-4-6, always on)
Layer 3: Task orchestrator (ephemeral, sonnet-4-6, spawned per story)
Layer 4: Specialists (20 total: frontend-specialist, backend-node-specialist, …, planner-opus)
Layer 5: Workers (5 total: code-writer, file-operator, api-caller, test-runner, git-worker)

Each agent declares capabilities, preferred_skills, budget_default_usd, and primary_orchestrator. See docs/explanation/architecture.md for the full mental model.

Routing flow

  1. Cortex classifies user intent (one of 18 classes in ROUTING-COMPASS.yaml#intent_classification.classes)
  2. Routing rule selects target domain orchestrator (possibly via classifier_subroutes)
  3. Domain orch decomposes into stories, delegates to Layer 3 task orchestrators
  4. Task orch sequences Layer 4 specialists, which invoke skills via Layer 5 workers
  5. auditor-haiku runs after every artifact for constitutional compliance

See docs/reference/registries/ROUTING-COMPASS.yaml for the full intent → orchestrator mapping.

Zero-bugs rule

Audits run structural + semantic checks on every commit. Any of these fails the gate:

  • YAML/JSON parse errors
  • Missing or invalid SKILL.md frontmatter
  • Curated entry without a matching SKILL.md on disk
  • primary_area references a folder that doesn't exist
  • preferred_skills references a skill name not on disk
  • governs_areas references a folder that doesn't exist
  • sub_areas references a sub_area not in CAPABILITY-GRAPH.yaml
  • _meta counts diverge from filesystem reality
  • Tier classification doesn't match install threshold (with official: true as escape hatch)

To see what's wrong without committing: make audit.

License

Apache 2.0 © 2026 Eric Claudino

Metadata

Release files for mult-agentes 1.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mult-agentes 1.0.2
File Size Uploaded
mult_agentes-1.0.2.tar.gz 41.9 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for mult-agentes 1.0.2
File Interpreter ABI Platform
mult_agentes-1.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 42.1 MB

Release files / mult_agentes-1.0.2.tar.gz

Download URL mult_agentes-1.0.2.tar.gz
Size 41.9 MB
Tags Source
SHA-256 checksum
How to use checksums
ae563764bae2c32a14b41ddaa0d641a39394815cf1c1a221b8eae269ba9a1c5e
BLAKE2b-256 checksum
How to use checksums
d10951383b17616a418da21207e3ad675115b2bffb92308c9b4b7cd64429a1ba
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 27, 2026.

Transparency log

Release files / mult_agentes-1.0.2-py3-none-any.whl

Download URL mult_agentes-1.0.2-py3-none-any.whl
Size 209.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
46964210399ae66cee1233c2b70ff5fc97f53b5a88646c6e29b41f84d1bb401c
BLAKE2b-256 checksum
How to use checksums
7901ea10210fbd82b10c349e5d7a7c36b7ec8d491501ec80003186ae4bde0bd7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 27, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

1.0.2 This release

2 release files

1.0.1

2 release files

1.0.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page