flatagents 1.1.0

A lightweight framework for building LLM-powered agents.

FlatAgents

Define LLM agents in YAML. Run them anywhere.

What is FlatAgents?

FlatAgents is a lightweight framework for single LLM calls. Each agent is a YAML config that specifies:

• Model configuration
• System and user prompts
• Structured output schema

For multi-agent orchestration, state machines, and workflows, see flatmachines.

Why?

• Composition over inheritance — compose stateless agents with simple config
• Compact structure — easy for LLMs to read and generate
• Inspectable — every agent is readable config
• Language-agnostic — reduce code in any particular runtime
• Common TypeScript interface — single schema for agents

Inspired by Kubernetes manifests and character card specifications.

Versioning

All specs (flatagent.d.ts, profiles.d.ts) and SDKs use lockstep versioning. A single version number applies across the entire repository.

Quick Start

pip install flatagents[all]

from flatagents import FlatAgent

agent = FlatAgent(config_file="reviewer.yml")
result = await agent.call(query="Review this code...")
print(result.output)

Example Agent

reviewer.yml

spec: flatagent
spec_version: "1.0.0"

data:
  name: code-reviewer

  model: "smart-expensive"  # Reference profile from profiles.yml

  system: |
    You are a senior code reviewer. Analyze code for bugs,
    style issues, and potential improvements.

  user: |
    Review this code:
    {{ input.code }}

  output:
    issues:
      type: list
      items:
        type: str
      description: "List of issues found"
    rating:
      type: str
      enum: ["good", "needs_work", "critical"]
      description: "Overall code quality"

What the fields mean:

• spec/spec_version — Format identifier and version
• data.name — Agent identifier
• data.model — Profile name, inline config, or profile with overrides
• data.system — System prompt (sets behavior)
• data.user — User prompt template (uses Jinja2, {{ input.* }} for runtime values)
• data.output — Structured output schema (the runtime extracts these fields)

Model Profiles

Centralize model configurations in profiles.yml and reference them by name:

profiles.yml

spec: flatprofiles
spec_version: "1.0.0"

data:
  model_profiles:
    fast-cheap:
      provider: cerebras
      name: zai-glm-4.6
      temperature: 0.6
      max_tokens: 2048

    smart-expensive:
      provider: anthropic
      name: claude-3-opus-20240229
      temperature: 0.3
      max_tokens: 4096

  default: fast-cheap      # Fallback when agent has no model
  # override: smart-expensive  # Uncomment to force all agents

Agent usage:

# String shorthand — profile lookup
model: "fast-cheap"

# Profile with overrides
model:
  profile: "fast-cheap"
  temperature: 0.9

# Inline config (no profile)
model:
  provider: openai
  name: gpt-4
  temperature: 0.3

Resolution order (low → high): default profile → named profile → inline overrides → override profile

Output Types

output:
  answer:      { type: str }
  count:       { type: int }
  score:       { type: float }
  valid:       { type: bool }
  raw:         { type: json }
  items:       { type: list, items: { type: str } }
  metadata:    { type: object, properties: { key: { type: str } } }

Use enum: [...] to constrain string values.

Multi-Agent Workflows

For orchestration, install flatmachines:

pip install flatmachines[flatagents]

from flatmachines import FlatMachine

machine = FlatMachine(config_file="workflow.yml")
result = await machine.execute(input={"query": "..."})

FlatMachine provides: state transitions, conditional branching, loops, retry with backoff, error recovery, and distributed worker patterns—all in YAML.

Features

• Python SDK (TypeScript SDK in progress)
• Structured output extraction
• Multiple LLM backends (LiteLLM, AISuite)
• Schema validation
• Metrics and logging
• Model profile management

Specs

TypeScript definitions are the source of truth:

• flatagent.d.ts
• profiles.d.ts

Python SDK

pip install flatagents[litellm]

LLM Backends

from flatagents import LiteLLMBackend, AISuiteBackend

# LiteLLM (default)
agent = FlatAgent(config_file="agent.yml")

# AISuite
backend = AISuiteBackend(model="openai:gpt-4o")
agent = FlatAgent(config_file="agent.yml", backend=backend)

Schema Validation

from flatagents import validate_flatagent_config

warnings = validate_flatagent_config(config)

Logging & Metrics

from flatagents import setup_logging, get_logger

setup_logging(level="INFO")  # Respects FLATAGENTS_LOG_LEVEL env var
logger = get_logger(__name__)

Env vars: FLATAGENTS_LOG_LEVEL (DEBUG/INFO/WARNING/ERROR), FLATAGENTS_LOG_FORMAT (standard/json/simple)

For OpenTelemetry metrics:

pip install flatagents[metrics]
export FLATAGENTS_METRICS_ENABLED=true

Metrics are enabled by default and print to stdout every 5s. Redirect to file or use OTLP for production:

# Metrics print to stdout by default
python your_script.py

# Save to file
python your_script.py >> metrics.log 2>&1

# Disable if needed
FLATAGENTS_METRICS_ENABLED=false python your_script.py

# Send to OTLP collector for production
OTEL_METRICS_EXPORTER=otlp \
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 \
python your_script.py

Env vars for metrics:

Variable	Default	Purpose
FLATAGENTS_METRICS_ENABLED	true	Enable OpenTelemetry metrics
OTEL_METRICS_EXPORTER	console	console (stdout) or otlp (production)
OTEL_EXPORTER_OTLP_ENDPOINT	—	OTLP collector endpoint
OTEL_METRIC_EXPORT_INTERVAL	5000 / 60000	Export interval in ms (5s for console, 60s for otlp)
OTEL_SERVICE_NAME	flatagents	Service name in metrics

Migration from Pre-1.0

If you were importing machine classes from flatagents:

# Old (deprecated)
from flatagents import FlatMachine, MachineHooks

# New
from flatmachines import FlatMachine, MachineHooks

The flatmachines package contains all orchestration functionality:

• FlatMachine and state machines
• Hooks (MachineHooks, LoggingHooks, etc.)
• Execution types (retry, parallel, MDAP)
• Persistence and checkpointing
• Distributed worker patterns
• GCP backends (Firestore)

Install with FlatAgent support:

pip install flatmachines[flatagents]