infrarely 0.1.2

InfraRely — Reliable Agent Infrastructure. Production-grade agent framework with zero boilerplate.

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InfraRely

Reliable Agent Infrastructure — Production-grade AI agent framework with zero boilerplate.

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Why InfraRely?

Most AI agents today are unreliable when they move from demos to production.

Common Problems

• Non-deterministic execution — behavior changes between runs, making debugging and incident response difficult
• Hallucination (tool + response) — models invent tool names, parameters, outputs, or unsupported claims
• Poor observability — limited traces make it hard to explain failures and regressions
• Fragile multi-agent coordination — delegation and message passing break under real workload pressure
• Weak trust and accountability — decisions are hard to audit, attribute, and defend in production
• Identity breakdown — unclear agent identity/permissions lead to unsafe cross-agent actions
• Memory problems — stale, conflicting, or ungrounded memory corrupts downstream decisions

InfraRely addresses these failures with infrastructure-first primitives:

• Deterministic execution contracts — router-first control flow with frozen plans and explicit fallbacks
• Capability graphs — dependency-aware workflows that compile and execute predictably
• Verification layers — structural, logical, knowledge, and policy checks on every result
• Multi-agent runtime — scheduler, message bus, shared memory, isolation, and deadlock-aware coordination
• Identity and memory controls — runtime identity/permissions plus scoped memory discipline for safer coordination

The result is an AI agent framework designed for reliability, auditability, and safe production deployment.

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Quick Start

import infrarely

infrarely.configure(llm_provider="openai", api_key="sk-...")

agent = infrarely.agent("helper")
result = agent.run("What is 2+2?")
print(result.output)  # 4 (no LLM call — deterministic math)

Install

pip install infrarely

# With LLM provider extras:
pip install infrarely[openai]
pip install infrarely[anthropic]
pip install infrarely[all-providers]

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Features

Core Framework

• 3-line start — import infrarely → agent() → run()
• Errors-as-data — Result objects with .error, never bare exceptions
• LLM-as-last-resort — Knowledge → Math → Tools → Capabilities → LLM
• Observable by default — traces, metrics, health checks on every agent

7-Layer Architecture

Layer	Name	Description
1	Execution Contracts	Deterministic routing, frozen execution plans, three-gate LLM isolation
2	Capability Graphs	Multi-step workflows with dependency resolution
3	Infrastructure	Execution depth guard, permissions, tool validation, sandboxing
4	Verification	Structural/logical/knowledge/policy checks on every result
5	Adaptive Intelligence	Self-optimizing routing, failure analysis, token optimization
6	Multi-Agent Runtime	OS-like kernel — scheduler, IPC, shared memory, RBAC, deadlock detection
7	Autonomous Evolution	Performance analysis, A/B testing, architecture proposals with policy guards

Tools & Knowledge

@infrarely.tool
def weather(city: str) -> str:
    return f"Sunny in {city}"

agent = infrarely.agent("bot", tools=[weather])
result = agent.run("Weather in NYC?")

agent = infrarely.agent("tutor")
agent.knowledge.add_documents("./notes/")
result = agent.run("Explain photosynthesis")
# LLM bypassed if knowledge confidence >= 85%

Multi-Agent

researcher = infrarely.agent("researcher")
writer = infrarely.agent("writer")
facts = researcher.run("Find facts about Mars")
article = writer.run("Write article", context=facts)

Workflows (DAG)

wf = infrarely.workflow("pipeline", steps=[
    infrarely.step("fetch", fetch_data),
    infrarely.step("process", process, depends_on=["fetch"]),
    infrarely.step("report", generate_report, depends_on=["process"]),
])
results = wf.execute()

Streaming

for chunk in agent.stream("Write a poem"):
    print(chunk.text, end="", flush=True)

Security

• Prompt injection defense (7 injection types)
• Input sanitization (always-on)
• API key rotation
• Tool execution sandboxing
• Compliance audit logging

Human-in-the-Loop

agent.require_approval_for("send_email", auto_approve_after=300)
result = agent.run("Send welcome email")
# Pauses for human approval

CLI

infrarely run "What is 2+2?"
infrarely health
infrarely metrics
infrarely deploy
infrarely verify

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InfraRely Architecture

                          Applications
                               │
                               │
                        AI Agents Layer
                 (Custom Agents Built by Developers)
                               │
                               │
                    InfraRely Agent Control Plane
            ┌─────────────────────────────────────────┐
            │                                         │
            │  Agent Pipeline                         │
            │  • Planning Engine                      │
            │  • Capability Graph                     │
            │  • Tool Router                          │
            │  • Verification Layer                   │
            │                                         │
            │  Platform Services                      │
            │  • Memory System                        │
            │  • Knowledge Engine                     │
            │  • Workflow DAG Engine                  │
            │  • Capability Registry                  │
            │                                         │
            │  Reliability Systems                    │
            │  • Retry & Circuit Breakers             │
            │  • Token Optimization                   │
            │  • Failure Recovery                     │
            │  • Self-Healing Execution               │
            │                                         │
            │  Observability                          │
            │  • Execution Traces                     │
            │  • Metrics & Telemetry                  │
            │  • Token Budget Monitoring              │
            │                                         │
            │  Security                               │
            │  • Input Sanitization                   │
            │  • Tool Sandbox                         │
            │  • Permission Policies                  │
            │  • Compliance Logging                   │
            └─────────────────────────────────────────┘
                               │
                               │
                       InfraRely Runtime
              (Scheduling, Isolation, State, Scaling)
                               │
                               │
                     External Systems / APIs
       Databases • SaaS APIs • Filesystems • LLM Providers

Architecture

InfraRely is structured as a layered Agent Operating System.

1. Applications

   • Developer-built AI applications.

2. Agents

   • Logical workers that execute tasks and coordinate tools.

3. InfraRely Control Plane

   • Planning, routing, verification, and reliability systems.

4. Runtime

   • Execution environment responsible for scheduling, isolation, and scalability.

5. External Systems

   • APIs, databases, and LLM providers used by agents.

Project Structure

infrarely/
├── core/           # Agent, Result, Config, Events, Decorators, Streaming
├── runtime/        # Workflow DAG, async runner, sandbox, scaling, multi-agent kernel
├── router/         # Rule-based intent classification, tool routing
├── agent/          # Execution pipeline, state machine, planning, verification
├── memory/         # Agent memory, knowledge engine, working/structured/long-term
├── security/       # Prompt injection defense, compliance, input sanitization
├── observability/  # Metrics, traces, logging, dashboard
├── optimization/   # Self-optimizing routing, failure analysis, token optimization
├── learning/       # A/B testing, architecture proposals, policy guards
├── platform/       # HITL, evaluation, versioning, marketplace, multitenancy, ACP
├── tools/          # Tool base classes, registry
├── capabilities/   # Multi-step capability definitions
├── integrations/   # GitHub, Gmail, Slack, Postgres, Notion, Webhooks, REST
├── internal/       # Execution engine bridges (private)
└── cli/            # CLI interface

LLM Providers

Provider	Model	Setup
OpenAI	gpt-4o, gpt-4o-mini	infrarely.configure(llm_provider="openai", api_key="sk-...")
Anthropic	claude-sonnet-4-20250514	infrarely.configure(llm_provider="anthropic", api_key="...")
Groq	llama-3.1-8b-instant	infrarely.configure(llm_provider="groq", api_key="...")
Google Gemini	gemini-1.5-flash	infrarely.configure(llm_provider="gemini", api_key="...")
Ollama	llama3.2 (local)	infrarely.configure(llm_provider="ollama")

Configuration

infrarely.configure(
    llm_provider="openai",
    api_key="sk-...",
    llm_model="gpt-4o",
    knowledge_threshold=0.85,
    token_budget=10_000,
    log_level="INFO",
    max_agents=50,
)

Or via environment variables:

export INFRARELY_LLM_PROVIDER=openai
export INFRARELY_API_KEY=sk-...

Documentation

• Quickstart
• Core Concepts
• Architecture
• API Reference
• Vision
• Runtime
• Security Model
• Multi-Agent Runtime
• Verification
• Observability

License

MIT License — see LICENSE for details.