lambdagent/agentpaas

AgentPaaS

Agent Platform as a Service — wraps lambdagent into a production-grade platform.

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Stats: 50 Python files | 2,000+ lines | 28 REST endpoints | 7 LLM providers built-in

Architecture

┌─────────────────────────────────────────────────────┐
│                     AgentPaaS                       │
│                                                     │
│  ┌─────────┐ ┌──────────┐ ┌───────┐ ┌───────────┐  │
│  │ Gateway  │ │ Registry │ │Engine │ │   CLI     │  │
│  │ Auth/Rate│ │ Version  │ │Queue  │ │ Provider  │  │
│  │ Limit   │ │ Traffic  │ │Sandbox│ │ Mgmt      │  │
│  └────┬────┘ └────┬─────┘ └───┬───┘ └───────────┘  │
│       │           │           │                     │
│  ┌────▼───────────▼───────────▼──────────────────┐  │
│  │         Tenant / Billing / Secrets / Metrics   │  │
│  └───────────────────┬───────────────────────────┘  │
│                      │                              │
├──────────────────────┼──────────────────────────────┤
│                      ▼                              │
│  ┌───────────────────────────────────────────────┐  │
│  │              lambdagent (kernel)               │  │
│  │  from_config · Runtime · Executor · Context    │  │
│  └───────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────┘

Quick Start

# Install
pip install fastapi uvicorn pyyaml

# Start server (dev mode, SQLite)
python -m agentpaas serve --dev

# Create a tenant + get API key
python -m agentpaas create-tenant --name "My Org"
# → API Key: ap_xxxxxxxxxxxxxxxx

Create & Run an Agent

# Create agent
curl -X POST http://localhost:8000/api/v1/agents \
  -H "Authorization: Bearer ap_xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "assistant",
    "config": {
      "type": "simple",
      "systemPrompt": "You are a helpful assistant.",
      "model": {"provider": "dashscope", "name": "qwen3-max"}
    }
  }'

# Run agent
curl -X POST http://localhost:8000/api/v1/agents/ag_xxx/run \
  -H "Authorization: Bearer ap_xxx" \
  -d '{"input": "Hello!"}'

LLM Provider Management

# List all providers (7 built-in)
python -m agentpaas provider list

# Add API key for a provider
python -m agentpaas provider add dashscope --api-key sk-xxx

# Test provider connection
python -m agentpaas provider test dashscope --prompt "Say hello"

# Add custom OpenAI-compatible provider
python -m agentpaas provider add my-llm \
  --name "My LLM" \
  --base-url https://my-llm.com/v1 \
  --api-key xxx \
  --models "model-a,model-b"

# List available models
python -m agentpaas provider models dashscope

Built-in Providers: Anthropic (Claude), OpenAI (GPT), DashScope (Qwen), DeepSeek, Zhipu AI (GLM), Moonshot (Kimi), Ollama (local)

API Endpoints (28 routes)

Agent Management

Method	Path	Description
POST	/api/v1/agents	Create agent
GET	/api/v1/agents	List agents
GET	/api/v1/agents/{id}	Get agent
PUT	/api/v1/agents/{id}	Update (auto-version)
DELETE	/api/v1/agents/{id}	Soft delete
POST	/api/v1/agents/{id}/rollback	Rollback version
GET	/api/v1/agents/{id}/versions	Version history

Execution

Method	Path	Description
POST	/api/v1/agents/{id}/run	Synchronous execution
GET	/api/v1/agents/{id}/runs	Execution history
GET	/api/v1/jobs/{id}	Async job status
POST	/api/v1/jobs/{id}/cancel	Cancel job

Auth & Admin

Method	Path	Description
POST	/api/v1/auth/keys	Create API key
GET	/api/v1/auth/keys	List keys
DELETE	/api/v1/auth/keys/{id}	Revoke key
POST	/api/v1/admin/tenants	Create tenant
PUT	/api/v1/admin/tenants/{id}/quota	Set quota
GET	/api/v1/admin/tenants/{id}/usage	Usage stats

Observability & Billing

Method	Path	Description
GET	/api/v1/traces/{run_id}	Execution trace
GET	/api/v1/billing/usage	Usage report
GET	/api/v1/metrics/overview	Platform metrics
GET	/.well-known/agent.json	A2A discovery
GET	/health	Health check

Full OpenAPI docs at: http://localhost:8000/docs

Module Structure

agentpaas/
├── api/                    # FastAPI routes + middleware
│   ├── app.py              #   Application entry (28 routes)
│   ├── deps.py             #   Dependency injection
│   ├── middleware/
│   │   └── auth.py         #   API Key auth (SHA-256)
│   └── v1/
│       ├── agents.py       #   CRUD + /run execution
│       ├── jobs.py         #   Async job management
│       ├── auth.py         #   API Key management
│       ├── admin.py        #   Tenant management
│       ├── billing.py      #   Usage queries
│       ├── traces.py       #   Execution traces
│       ├── metrics.py      #   Platform metrics
│       └── discovery.py    #   A2A agent discovery
├── engine/                 # Execution engine
│   ├── dispatcher.py       #   Sync/async/stream dispatch
│   ├── queue.py            #   Job queue (memory + Redis)
│   ├── sandbox.py          #   L0 in-process / L1 subprocess
│   ├── worker.py           #   Background job consumer
│   ├── cache.py            #   Compile cache (LRU)
│   ├── retry.py            #   Retry + circuit breaker
│   └── stream.py           #   SSE + WebSocket adapters
├── registry/               # Agent registry
│   ├── store.py            #   CRUD + search
│   ├── version.py          #   Version management + rollback
│   ├── traffic.py          #   Canary weighted routing
│   └── discovery.py        #   A2A Agent Card
├── tenant/                 # Multi-tenancy
│   ├── quota.py            #   Token quota + concurrency
│   ├── rbac.py             #   Admin / Developer / Viewer
│   └── isolation.py        #   Data isolation
├── observability/          # Observability
│   ├── logging.py          #   Structured JSON logging
│   ├── metrics.py          #   Prometheus-style metrics
│   └── tracing.py          #   lambdagent trace bridge
├── secrets/                # Secret management
│   ├── vault.py            #   AES-256-GCM encryption
│   └── inject.py           #   Env var injection
├── billing/                # Billing
│   ├── metering.py         #   Token usage recording
│   ├── pricing.py          #   Per-model pricing (6 models)
│   └── report.py           #   Monthly reports
├── db/                     # Data layer
│   ├── models.py           #   7 tables (SQLite → PostgreSQL)
│   └── session.py          #   Connection management
├── cli/
│   └── main.py             #   CLI: serve, create-tenant, provider mgmt
├── config.py               #   Global config (env vars)
├── Dockerfile
├── docker-compose.yml
└── pyproject.toml

Design Principles

#	Principle	Description
P1	Kernel zero-invasion	AgentPaaS only calls lambdagent public API
P2	API First	All features via REST; Dashboard + CLI are API consumers
P3	Progressive complexity	SQLite + memory queue for dev; PostgreSQL + Redis for prod
P4	Formal traceability	Preserves lambdagent β-reduction trace + OTel distributed trace
P5	Open protocols	Native MCP, A2A support
P6	Secure by default	Encrypted secrets, RBAC, audit trail

Deployment

Development (single process)

python -m agentpaas serve --dev    # SQLite, in-memory queue, L0 sandbox

Docker Compose

docker-compose up                  # PostgreSQL + Redis + API + Worker

Kubernetes

• HPA on API pods (CPU > 60%)
• HPA on Worker pods (queue depth > 10)
• Helm chart in deploy/

Testing

python -m pytest tests/ -v        # 70 tests, < 0.3s

License

MIT — see LICENSE