FlatAgents

Define LLM agents in YAML. Run them anywhere.

For LLM/machine readers: see MACHINES.md for comprehensive reference.

Why?

- Composition over inheritance — compose stateless agents and checkpointable machines
- Compact structure — easy for LLMs to read and generate
- Simple hook interfaces — escape hatches without complexity; webhook ready
- Inspectable — every agent and machine is readable config
- Language-agnostic — reduce code in any particular runtime
- Common TypeScript interface — single schema for agents, single schema for machines
- Limitations — machine topologies can get complex at scale

Inspired by Kubernetes manifests and character card specifications.

Versioning

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

Core Concepts

Use machines to write flatagents and flatmachines, they are designed for LLMs.

| Term | What it is |
|------|------------|
| FlatAgent | A single LLM call: model + prompts + output schema |
| FlatMachine | A state machine that orchestrates multiple agents, actions, and state machines |

Use FlatAgent alone for simple tasks. Use FlatMachine when you need multi-step workflows, branching, or error handling.

Examples

| Example | What it demonstrates |
|---------|---------------------|
| helloworld | Minimal setup — single agent, single state machine |
| writer_critic | Multi-agent loop — writer drafts, critic reviews, iterates |
| story_writer | Multi-step creative workflow with chapter generation |
| human-in-the-loop | Pause execution for human approval via hooks |
| error_handling | Error recovery and retry patterns at state machine level |
| dynamic_agent | On-the-fly agent generation from runtime context |
| character_card | Loading agent config from character card format |
| mdap | MDAP voting execution — multi-sample consensus |
| gepa_self_optimizer | Self-optimizing prompts via reflection and critique |
| research_paper_analysis | Document analysis with structured extraction |
| multi_paper_synthesizer | Cross-document synthesis with dynamic machine launching |
| support_triage_json | JSON input/output with classification pipeline |
| parallelism | Parallel machines, dynamic foreach, fire-and-forget launches |

Quick Start

``bash
pip install flatagents[all]
`

`python
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
`yaml
spec: flatagent
spec_version: "0.8.2"

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
`yaml
spec: flatprofiles
spec_version: "0.8.2"

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:
`yaml


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

`yaml
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, use FlatMachine (full docs in MACHINES.md):

`python
from flatagents import FlatMachine

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

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

Features

- Checkpoint and restore
- Python SDK (TypeScript SDK in progress)
- MACHINES.md — LLM-optimized reference docs
- Decider agents and machines
- On-the-fly agent and machine definitions
- Webhook hooks for remote state machine handling
- Metrics and logging
- Error recovery and exception handling at the state machine level
- Parallel machine execution (machine: [a, b, c])
- Dynamic parallelism with foreach
- Fire-and-forget launches for background tasks

Planned

- Distributed execution — cross-network machine peering, inter-machine strategies
- SQL persistence backend
- TypeScript SDK
- max_depth config to limit machine launch nesting
- Checkpoint pruning to prevent storage explosion
- $root/ path prefix — resolve agent/machine refs from workspace root, not config dir
- Input size validation — warn when prompt exceeds model context window
- Serialization warnings — flag non-JSON-serializable context values before checkpoint

Specs

TypeScript definitions are the source of truth:
- flatagent.d.ts
- flatmachine.d.ts
- profiles.d.ts

Python SDK

`bash
pip install flatagents[litellm]
`

$3

`python
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)
`

$3

Extend machine behavior with Python hooks:

`python
from flatagents import FlatMachine, MachineHooks

class CustomHooks(MachineHooks):
def on_state_enter(self, state: str, context: dict) -> dict:
context["entered_at"] = time.time()
return context

def on_action(self, action: str, context: dict) -> dict:
if action == "fetch_data":
context["data"] = fetch_from_api()
return context

machine = FlatMachine(config_file="machine.yml", hooks=CustomHooks())
`

Available hooks: on_machine_start, on_machine_end, on_state_enter, on_state_exit, on_transition, on_error, on_action

$3

`yaml
execution:
type: retry # retry | parallel | mdap_voting
backoffs: [2, 8, 16, 35] # Seconds between retries
jitter: 0.1 # ±10% random variation
`

| Type | Use Case |
|------|----------|
| default | Single call |
| retry | Rate limit handling with backoff |
| parallel | Multiple samples (n_samples) |
| mdap_voting | Consensus voting (k_margin, max_candidates) |

$3

`python
from flatagents import validate_flatagent_config, validate_flatmachine_config

warnings = validate_flatagent_config(config)
warnings = validate_flatmachine_config(config)
`

$3

`python
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:

`bash
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:

`bash


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 |