pyagent-blueprint

Pillar 1 of the PyAgent production stack for multi-agent LLM systems — declare your entire agent system in a single YAML file. Validate, compile, test, diff, and generate from the CLI.

Architecture Pillar: 📋 Blueprint The entry point for all PyAgent systems — declare agents, workflows, providers, and contracts in a single YAML file, then validate, compile, and test without writing Python. Part of the full stack: install pyagent-all for all pillars.

Install

pip install pyagent-blueprint

Depends on: pyagent-patterns, pyagent-router, pyagent-providers, pyagent-context, pydantic, pyyaml, click.

Why Blueprints?

Without blueprints, agent systems are defined in code — hard to version, diff, review, or test without running them. With pyagent-blueprint, your agent system is a YAML document that is:

• Versioned: semantic version + git-diffable
• Validated: Pydantic schema + static analysis (dangling refs, security)
• Compiled: YAML → executable RuntimeGraph in one call
• Testable: contract conformance tests with MockLLM
• Renderable: Mermaid diagrams + Markdown docs auto-generated

Blueprint YAML Schema

api_version: pyagent/v1
metadata:
  name: customer-support
  version: 1.0.0
  description: Support routing system
  owner: platform-team

providers:
  primary:
    model: gpt-4.1-mini
  fallback:
    model: gpt-4.1-nano

context:
  memory:
    working_max_tokens: 128000
  compression:
    policy: semantic_lossless
    target_ratio: 0.6

agents:
  classifier:
    prompt: "Classify into: billing, tech, general"
    provider: primary
  billing:
    prompt: "Handle billing inquiries"
    provider: primary
    guardrails: [pii_redact]
  tech:
    prompt: "Handle technical support"
    provider: primary

workflows:
  support:
    pattern: supervisor
    agents:
      classifier: classifier
      routes:
        billing: billing
        tech: tech
    recovery:
      max_retries: 2
      timeout_seconds: 30

contracts:
  support:
    input:
      type: string
      max_tokens: 2000
    output:
      type: string
    sla:
      latency_p95_ms: 5000
      cost_max_usd: 0.05

observability:
  tracing:
    enabled: true
  cost_budget:
    daily_usd: 100.0
    alert_threshold: 0.8

Loader — Load and Validate

from pyagent_blueprint import load_blueprint, load_blueprint_from_str

# From file
spec = load_blueprint("blueprint.yaml")       # YAML or JSON
print(spec.metadata.name)                      # "customer-support"
print(spec.agents.keys())                      # dict_keys(['classifier', 'billing', 'tech'])

# From string
spec = load_blueprint_from_str(yaml_text)

Compiler — Spec → RuntimeGraph

import asyncio
from pyagent_blueprint import BlueprintCompiler, load_blueprint

spec = load_blueprint("blueprint.yaml")
compiler = BlueprintCompiler()
graph = compiler.compile(spec)

# Run a workflow
result = asyncio.run(graph.run("support", "I can't see my invoice"))
print(result.output)

# Inspect
print(graph.describe())
# {"metadata": {...}, "workflows": {"support": {"pattern_type": "Supervisor"}}}

Validator — Static Analysis

from pyagent_blueprint import BlueprintValidator, load_blueprint

spec = load_blueprint("blueprint.yaml")
validator = BlueprintValidator()
issues = validator.validate(spec)

for issue in issues:
    print(f"[{issue.severity}] {issue.path}: {issue.message}")

Checks performed:

• Dangling agent refs (workflow references undefined agent)
• Dangling provider refs (agent references undefined provider)
• Unknown pattern names (not in pattern registry)
• Contract → workflow ref mismatch
• Unrealistic SLA values
• Security: hardcoded API keys in prompts

Renderer — Mermaid + Markdown

from pyagent_blueprint import BlueprintRenderer, load_blueprint

spec = load_blueprint("blueprint.yaml")
renderer = BlueprintRenderer()

# Mermaid diagram
print(renderer.to_mermaid(spec))
# graph TD
#     classifier[Routes customer requests]
#     billing[Billing specialist]
#     tech[Technical support agent]
#     classifier -->|billing| billing
#     classifier -->|tech| tech

# Full Markdown documentation
md = renderer.to_markdown(spec)

Differ — Semantic Diff

from pyagent_blueprint import BlueprintDiffer, load_blueprint

old = load_blueprint("v1.yaml")
new = load_blueprint("v2.yaml")

differ = BlueprintDiffer()
changes = differ.diff(old, new)

print(differ.summary(changes))
# BREAKING (1):
#   [modified] workflows.support.pattern
# WARNING (2):
#   [modified] agents.billing.prompt
#   [removed] agents.legacy

Severity levels:

• BREAKING: pattern changes, API version changes
• WARNING: prompt changes, provider changes, removals
• INFO: metadata, descriptions, additions

Tester — Contract Conformance

import asyncio
from pyagent_blueprint import BlueprintTester, load_blueprint

spec = load_blueprint("blueprint.yaml")
tester = BlueprintTester()

results = asyncio.run(tester.test(spec))
print(tester.summary(results))
# Contract Tests: 1/1 passed
#   ✓ PASS  support
#          ✓ output_non_empty
#          ✓ output_is_string
#          ✓ input_within_token_limit

Generator — Scaffold from Pattern

from pyagent_blueprint import BlueprintGenerator

generator = BlueprintGenerator()
yaml_str = generator.generate(
    pattern="supervisor",
    agents=["classifier", "billing", "tech"],
    name="customer-support",
)
print(yaml_str)

CLI Reference

# Validate
pyagent-blueprint validate blueprint.yaml

# Compile and inspect
pyagent-blueprint compile blueprint.yaml

# Render Mermaid diagram
pyagent-blueprint render blueprint.yaml
pyagent-blueprint render blueprint.yaml --format markdown -o docs.md

# Run contract tests
pyagent-blueprint test blueprint.yaml

# Semantic diff
pyagent-blueprint diff v1.yaml v2.yaml

# Generate scaffold
pyagent-blueprint generate --pattern supervisor --agents "classifier,billing,tech" --name my-system

Observability Configuration — Tracing & Cost Budgets

The observability section of a blueprint declares tracing and cost budget settings. These are validated by the schema and compiled into configuration objects that consumers wire at runtime.

observability:
  tracing:
    enabled: true          # Enable trace event emission
    exporter: langfuse     # Preferred exporter backend (console, jsonl, otel, langfuse)
  cost_budget:
    daily_usd: 100.0       # Daily cost ceiling in USD
    alert_threshold: 0.8   # Alert when 80% of budget consumed

Schema classes (defined in pyagent_blueprint.schema.observability):

After compilation, these settings are available on the RuntimeGraph but are not automatically wired — consumers must manually set up the TraceEventBus and exporters. The compiler will emit warnings if observability is declared but the consumer has not wired tracing hooks.

Context Configuration — Memory, Compression & Redaction

The context section declares memory, compression, and redaction settings for agents:

context:
  memory:
    backend: sqlite             # json or sqlite
    working_max_tokens: 128000  # WorkingMemory token limit
    session_path: session.db    # SessionMemory persistence path
  compression:
    policy: semantic_lossless   # none, fifo, semantic_lossless, sawtooth
    target_ratio: 0.6           # Target compression ratio (0.0–1.0)
  redaction:
    max_sensitivity: internal   # Filter out PII and confidential items

Schema classes (defined in pyagent_blueprint.schema.context):

Like observability, context configuration is compiled but manually wired by the consumer. The compiler warns if context settings are declared but not connected to agents.

RuntimeGraph — The Compiled Output

BlueprintCompiler.compile() produces a RuntimeGraph containing:

• Resolved providers — ProviderProtocol instances from the ProviderRegistry
• Instantiated agents — Agent objects with LLM callables, system prompts, and metadata
• Wired workflows — Named patterns (Pipeline, Supervisor, etc.) assembled from agents
• Metadata — Blueprint name, version, description for traceability

graph = compiler.compile(spec)

# Run a named workflow
result = await graph.run("support", "I can't see my invoice")

# Inspect the graph structure
print(graph.describe())
# {
#     "metadata": {"name": "customer-support", "version": "1.0.0"},
#     "workflows": {
#         "support": {
#             "pattern_type": "Supervisor",
#             "agents": ["classifier", "billing", "tech"],
#         }
#     },
#     "providers": ["primary", "fallback"],
#     "observability": {"tracing": {"enabled": true}},
#     "context": {"compression": {"policy": "semantic_lossless"}},
# }

Compiler Warnings

The BlueprintCompiler performs static analysis during compilation and emits warnings for common misconfigurations:

Warnings for unwired observability and context are especially important: they alert consumers that configuration was declared in the blueprint but was never connected at runtime. This helps catch integration oversights without breaking backward compatibility.

Integration

Blueprint is the integration hub of the PyAgent ecosystem:

The Blueprint → Runtime Flow

flowchart LR
    YAML[Blueprint YAML] -->|load_blueprint| Spec[BlueprintSpec]
    Spec -->|validate| V[BlueprintValidator]
    Spec -->|compile| C[BlueprintCompiler]
    C -->|resolve providers| PR[ProviderRegistry]
    C -->|instantiate agents| AG[Agent instances]
    C -->|wire patterns| PT[Pattern instances]
    C -->|produce| RG[RuntimeGraph]
    RG -->|run workflow| R[Result]

1. Load — load_blueprint("file.yaml") parses YAML and produces a BlueprintSpec (Pydantic model)
2. Validate — BlueprintValidator.validate(spec) runs static checks (dangling refs, security, SLA)
3. Compile — BlueprintCompiler.compile(spec) resolves providers, instantiates agents, wires patterns
4. Wire hooks — Consumer manually connects TraceEventBus, ContextLedger, CompressMiddleware to agents
5. Run — graph.run("workflow_name", "input") executes the pattern and returns a Result

Full stack

Install all pillars at once: pip install pyagent-all

→ pyagent.org for full documentation.

Full Documentation

See pyagent.org for full API reference and integration guides.

The PyAgent ecosystem

PyAgent is a production stack for multi-agent LLM systems. Each package is independent — install only what you need, or get everything with pip install pyagent-all.

Learn the concepts: The Orchestrator-Worker pattern · Engineering a resilient multi-agent harness · Agent Experience Optimization (AXO)