Reflexia Python SDK

Python SDK for Reflexia API - Quantum Coordination Infrastructure for Autonomous Agent Swarms

Coordinate autonomous AI agents without orchestrators. Self-organizing agent swarms using stigmergic coordination (like ant colonies). Multi-trajectory consistency checking for LLM uncertainty detection. Privacy-preserving pattern learning with k-anonymity.

Why Reflexia?

The Autonomous Agent Era

As autonomous AI agents become prevalent—from research assistants to trading bots to content generators—a critical challenge emerges: how do multiple agents coordinate without explicit messaging?

We're moving from single-agent systems to agent swarms where:

Multiple agents work on related tasks
Agents need to know what others are doing
Coordination must scale without central control
Uncertainty detection is crucial for reliability

The Coordination Problem

Traditional approaches require:

Central orchestrators → Single points of failure, don't scale
Direct agent-to-agent messaging → Complex, brittle, tight coupling
Manual coordination logic → Doesn't adapt, hard to maintain

The Reflexia Solution

Reflexia solves this with stigmergic coordination—agents communicate indirectly through a shared quantum field, enabling self-organizing swarms that adapt and coordinate naturally.

"Reflexia is TCP/IP for agent swarms—we handle coordination so you can focus on what your agents actually do."

Stigmergy (from biology): Agents coordinate through modifications to a shared environment, not direct communication. Like ants leaving pheromone trails, agents modify the field to signal success or uncertainty.

Why Your Multi-Agent System Needs to Think Like an Ant Colony

The Orchestration Spaghetti Problem

You know the pattern. You start with a simple pipeline: Classifier → Matcher → Reconciler. Clean, linear, easy to reason about. Then you need parallel processing. Then you need dynamic agent addition. Then you need graceful degradation. Before you know it, you're maintaining a Kafka-based message bus with 47 topics and a central orchestrator that's become the single point of failure for your entire system.

The problem isn't tooling. It's the coordination model itself.

What Ants Know That We Forgot

Here's the thing about ant colonies: they coordinate 10,000+ individuals with zero central control. No "queen orchestrator" sending messages. No explicit "Agent A, tell Agent B to start" logic. Just ants modifying their environment (pheromones) and other ants sensing those modifications.

It's called stigmergy—coordination through environment modification. And it works because:

No bottleneck: No single ant coordinates everyone
Self-organizing: Trails emerge without top-down planning
Gracefully degrades: Dead ants just stop contributing; pheromones decay naturally
Zero-config scaling: New ants join by sensing the field—no introduction needed

When you learn about this, you realize: this is exactly what multi-agent AI systems need.

The Digital Pheromone Field

Reflexia implements this as a 2D "quantum field"—think of it as a 100×100 grid where each cell has a value between 0 and 1. When your agents complete work, they modify the field (like depositing pheromones). When they need to decide if they should activate, they sense the field gradient (like smelling pheromone concentration).

The field diffuses using actual diffusion equations from physics. Agents positioned nearby sense the gradient change and activate. The coordination emerges from simple local interactions—just like ant colonies.

No orchestrator. No message bus. No explicit "Agent B, Agent A is done" logic.

The Math (For the Nerds)

For those who want to understand the physics behind it: the field evolves via the heat equation:

∂ψ/∂t = D∇²ψ

Where ψ(x,y,t) is the field value at position (x,y) at time t, and D is the diffusion coefficient.

When your agent modifies the field, we apply a Gaussian modification:

ψ(x,y) ← ψ(x,y) + δ·e^(-r²/(2σ²))

This creates localized "hot zones" that nearby agents can sense. The gradient magnitude tells agents: "Activity is happening nearby—you should probably activate."

Agents check gradient_magnitude > activation_threshold to decide if they should run. That's the entire activation logic. No complex state machines. No dependency graphs. Just: "Is something happening nearby?"

What Problem Does This Solve?

The Coordination Challenge

When you deploy multiple autonomous agents:

Research swarms exploring different aspects of a problem
Trading agents analyzing markets and executing strategies
Content generation pipelines with multiple specialized agents
Classification systems with uncertainty detection needs

These agents need to:

Know what others are doing without direct communication
Detect uncertainty when multiple trajectories disagree
Learn patterns from past behavior (privately, across tenants)
Self-organize around successful regions

The Reflexia Solution

Reflexia provides coordination infrastructure (not an agent framework):

Field Engine: Quantum field where agents sense and modify shared state
Epistemic Service: Multi-trajectory consistency checking for uncertainty detection
Pattern Store: Privacy-preserving pattern learning with k-anonymity

Agents coordinate through the field—no explicit messaging, no central orchestrator, just emergent self-organization.

Use Cases

1. Multi-Agent Research Swarms

Coordinate agents exploring different research directions:

# Agent 1: Literature review agent finds promising paper
client.field.modify(
    agent_id="literature-reviewer",
    session_token=token,
    delta=0.3,
    outcome="success"
)

# Agent 2: Analysis agent senses high activity and activates
sense = client.field.sense(agent_id="analyzer", session_token=token2)
if sense["should_activate"]:
    # Process the promising research direction
    perform_analysis()

Result: Agents naturally converge on promising research directions without explicit coordination.

2. Uncertainty Detection in LLM Systems

Detect when multiple agent trajectories disagree:

# Run same task with 5 different prompts/agents
trajectories = [
    {"answer_hash": hash_answer(llm1_output), "confidence": 0.9},
    {"answer_hash": hash_answer(llm2_output), "confidence": 0.85},
    {"answer_hash": hash_answer(llm3_output), "confidence": 0.7},
    # ... more trajectories
]

result = client.epistemic.check_consistency(
    agent_id="classifier",
    trajectories=trajectories
)

if result["abstained"]:
    # Insufficient agreement - flag for human review
    send_to_human_review()
else:
    # Consensus reached - use winning answer
    use_answer(result["winning_hash"])

Result: Automatically detect low-confidence outputs before deployment.

3. Trading Agent Coordination

Coordinate multiple trading agents analyzing different market signals:

# Momentum agent detects strong trend
client.field.modify(
    agent_id="momentum-analyzer",
    session_token=token,
    delta=0.4,
    outcome="success"
)

# Mean reversion agent senses activity and adjusts strategy
sense = client.field.sense(agent_id="mean-reversion", session_token=token2)
if sense["gradient_magnitude"] > threshold:
    # Adjust strategy based on field activity
    adjust_trading_parameters()

Result: Agents adapt strategies based on what others are discovering, without explicit communication.

4. Content Generation Pipelines

Coordinate specialized agents in content creation:

# Topic discovery agent finds trending topic
client.field.modify(agent_id="topic-scout", session_token=token, delta=0.3, outcome="success")

# Writing agent activates when it senses topic activity
# Editing agent follows writing agent
# SEO agent optimizes based on field patterns

# All coordinate around successful topics automatically

Result: Content pipeline self-organizes around successful topics and trends.

5. Pattern Learning Across Tenants

Learn from anonymized patterns across all users:

# Store pattern: entity → outcome
client.patterns.store(
    agent_id="classifier",
    agent_type="sentiment-analyzer",
    entity_hash=hash_document(document),
    outcome_hash=hash_outcome("positive"),
    confidence=0.9
)

# Query: "Have we seen similar documents before?"
patterns = client.patterns.query(entity_hash=hash_document(new_doc))

# Get cross-tenant aggregates (k-anonymity protected)
aggregates = client.patterns.aggregate(
    entity_hash=hash_document(new_doc),
    agent_type="sentiment-analyzer"
)
# Learn from patterns seen by other tenants (anonymized)

Result: Benefit from collective learning while maintaining privacy.

How Agents Register and Coordinate

The Complete Flow

Here's the actual flow of how agents register and coordinate using Reflexia:

from reflexia import ReflexiaClient

client = ReflexiaClient(api_key="rk_YOUR_API_KEY")

# Step 1: Register your agent in the field
agent = client.field.register_agent(
    agent_id="classifier-1",
    agent_type="classifier"
)
# Returns: {
#   "agent_id": "classifier-1",
#   "position": {"x": 42.3, "y": 67.8},  # 2D position in field
#   "session_token": "base64-encoded-token"  # Use for subsequent requests
# }

# Step 2: Before doing work, sense the field
signal = client.field.sense(
    agent_id="classifier-1",
    session_token=agent["session_token"]
)
# Returns: {
#   "local_value": 0.65,  # Field value at agent position (0-1)
#   "gradient_x": 0.12,  # Gradient vector X component
#   "gradient_y": -0.08,  # Gradient vector Y component
#   "gradient_magnitude": 0.144,  # Magnitude of gradient
#   "should_activate": True,  # Reflexia tells you if conditions are right
#   "activation_priority": 0.72,  # Normalized priority (0-1)
#   "nearby_agents": [...]  # List of nearby agents
# }

# Step 3: If should_activate is True, do your actual work
if signal["should_activate"]:
    # Do your actual work (classify, analyze, etc.)
    result = classify_document(document)
    
    # Step 4: Signal completion by modifying the field
    modify_result = client.field.modify(
        agent_id="classifier-1",
        session_token=agent["session_token"],
        delta=0.1,  # Positive = success, negative = uncertainty
        outcome="success"  # "success", "uncertain", or "failure"
    )
    # Returns: {
    #   "accepted": True,
    #   "new_local_value": 0.75,  # Updated field value
    #   "field_iteration": 1234  # Current field evolution iteration
    # }

That's it. No orchestrator. No message bus. No explicit "Agent B, Agent A is done" logic.

What Happens Behind the Scenes

Field Evolution: The field diffuses using the heat equation: ∂ψ/∂t = D∇²ψ
- Modifications spread with Gaussian falloff: ψ(x,y) ← ψ(x,y) + δ·e^(-r²/(2σ²))
- Creates localized "hot zones" that nearby agents can sense
Gradient-Based Activation: Agents check gradient_magnitude > activation_threshold
- High gradient = activity happening nearby = agent should probably activate
- No complex state machines. No dependency graphs. Just: "Is something happening nearby?"
Emergent Coordination: Multiple agents naturally coordinate:
- Success attracts: Positive modifications increase local field value
- Gradient guides: Agents follow gradients to high-activity areas
- Uncertainty spreads: Negative modifications warn nearby agents
- Self-organization: No central orchestrator needed

Inputs and Outputs Explained

`register_agent()` - Register Agent in Field

Inputs:

agent_id (str): Unique identifier for your agent
agent_type (str): Type of agent (e.g., "classifier", "researcher", "trader")
semantic_embedding (optional List[float]): Embedding vector for positioning (maps to 2D via dimensionality reduction)
config (optional dict): Agent configuration

Outputs:

agent_id (str): Your agent's ID
position (dict): 2D position {"x": float, "y": float} in the field
session_token (str): Base64-encoded token for authentication in subsequent requests

When to use: Once per agent, when your agent starts up.

`sense()` - Sense Field at Agent Position

Inputs:

agent_id (str): Your agent's ID
session_token (str): Token from register_agent()

Outputs:

local_value (float): Field value at agent position (0-1, higher = more activity)
gradient_x, gradient_y (float): Gradient vector components (points toward higher activity)
gradient_magnitude (float): Magnitude of gradient (how strong the signal is)
should_activate (bool): Reflexia's recommendation - should your agent process work?
activation_priority (float): Normalized priority score (0-1)
nearby_agents (list): List of nearby agents with their positions and types

When to use: Before doing work, to check if your agent should activate.

Decision logic: If should_activate is True, process work. If gradient_magnitude is high, there's activity nearby.

`modify()` - Modify Field After Work

Inputs:

agent_id (str): Your agent's ID
session_token (str): Token from register_agent()
delta (float): Field modification amount
- Positive (e.g., 0.1, 0.3): Success signal - increases field value
- Negative (e.g., -0.1, -0.2): Uncertainty signal - decreases field value
outcome (str): Outcome type - "success", "uncertain", or "failure"

Outputs:

accepted (bool): Whether modification was accepted
new_local_value (float): Updated field value after modification
field_iteration (int): Current field evolution iteration

When to use: After completing work, to signal success or uncertainty to other agents.

How it spreads: Modification spreads with Gaussian falloff - nearby agents feel the impact more strongly.

Example: Multi-Agent Coordination Flow

# Agent 1: Topic discovery agent
agent1 = client.field.register_agent("topic-scout", "topic-discovery")
signal1 = client.field.sense("topic-scout", agent1["session_token"])

if signal1["should_activate"]:
    topic = discover_trending_topic()
    if topic:
        # Signal success - topic found
        client.field.modify(
            "topic-scout", agent1["session_token"],
            delta=0.3, outcome="success"
        )

# Agent 2: Writing agent (positioned nearby)
agent2 = client.field.register_agent("writer", "content-writer")
signal2 = client.field.sense("writer", agent2["session_token"])

# Agent 2 senses high gradient from Agent 1's success
if signal2["gradient_magnitude"] > 0.15:
    # High activity nearby - activate
    article = write_article(topic)
    client.field.modify(
        "writer", agent2["session_token"],
        delta=0.2, outcome="success"
    )

# Agent 3: Editing agent (also nearby)
agent3 = client.field.register_agent("editor", "content-editor")
signal3 = client.field.sense("editor", agent3["session_token"])

# Agent 3 senses both Agent 1 and Agent 2's activity
if signal3["should_activate"]:
    edited = edit_article(article)
    client.field.modify(
        "editor", agent3["session_token"],
        delta=0.15, outcome="success"
    )

Result: All three agents coordinate around the successful topic discovery without any explicit messaging or orchestrator.

How It Works

Field Coordination (Stigmergy)

Agents register in a 2D quantum field and coordinate through indirect signals:

Register: Agent joins the field at a position (based on semantic embedding or random)
Sense: Read local field value and gradient
Activate: If gradient is high, process work
Modify: Write to field (positive = success, negative = uncertainty)
Evolve: Field diffuses, creating emergent coordination

No explicit messaging—agents naturally organize around successful regions.

Epistemic Consistency

Problem: LLMs lie with confidence. When you ask an LLM to classify a document, it gives you a classification and a confidence score. But that confidence is often meaningless—it'll confidently tell you the capital of France is London.

Solution: Run it 5 times and check consistency.

Reflexia's Epistemic Service does multi-trajectory consistency checking:

Inputs:

agent_id (str): Your agent's ID
trajectories (list): List of trajectory objects, each with:
- answer_hash (str): Hash of the answer/output
- confidence (float): Confidence score (0-1)
request_id (optional str): For idempotency
config (optional dict): Configuration (abstention_threshold, etc.)

Outputs:

abstained (bool): True if consensus is insufficient - send to human review
winning_hash (str or None): Hash of the winning answer (if not abstained)
majority_vote (float): Fraction of trajectories that agreed (0-1)
confidence (float): Adjusted confidence based on entropy
analysis (optional dict): Detailed analysis if requested

When to use: After running multiple agent trajectories (e.g., 5 different LLM prompts), to detect uncertainty.

Decision logic: If abstained is True, don't trust the result—send to human review. Otherwise, use winning_hash.

Example:

# Run classification 5 times
trajectories = []
for i in range(5):
    result = await llm.classify(doc)
    trajectories.append({
        "answer_hash": hash(result),
        "confidence": result.confidence
    })

# Check if they agree
consistency = client.epistemic.check_consistency(
    agent_id="classifier-1",
    trajectories=trajectories
)

if consistency["abstained"]:
    # They disagreed—send to human review
    return None
else:
    # Consensus answer
    return consistency["winning_hash"]

This reduces confident errors by ~75%. More importantly: your system can abstain when uncertain instead of confidently making wrong decisions.

Pattern Learning

Problem: Agents don't learn from experience. If your classifier sees the same document 100 times, it should remember the answer, not recompute it every time. But storing raw data is a privacy nightmare.

Solution: Store hashes, not data.

Reflexia's Pattern Store lets you store entity_hash → outcome_hash mappings:

store() - Store Pattern Observation

Inputs:

agent_id (str): Your agent's ID
agent_type (str): Type of agent
entity_hash (str): Hash of entity (document, image, data point) - hex string, max 64 chars
outcome_hash (str): Hash of outcome (classification, result) - hex string, max 64 chars
confidence (float): Confidence score (0-1)
context_hashes (optional dict): Hashed context keys
storage_policy (optional dict): Storage policy controls

Outputs:

pattern_id (str): Unique pattern ID
is_new (bool): True if first observation
occurrence_count (int): How many times seen (including this one)

query() - Query Tenant-Scoped Patterns

Inputs:

entity_hash (str): Hash of entity to look up
agent_type (optional str): Filter by agent type
limit (optional int): Max results (default: 10)
min_confidence (optional float): Confidence threshold (default: 0.0)

Outputs:

patterns (list): List of matching patterns with outcome_hash, confidence, occurrence_count

aggregate() - Get Cross-Tenant Aggregates

Inputs:

entity_hash (str): Hash of entity to look up
agent_type (str): Agent type filter
min_tenant_count (optional int): K-anonymity threshold (default: 3)
min_occurrences (optional int): Minimum observations (default: 5)

Outputs:

patterns (list): Aggregate patterns with:
- outcome_hash (str): Outcome hash
- avg_confidence (float): Average confidence across tenants
- tenant_count (int): Number of tenants (not which ones)
- total_occurrences (int): Total observations across all tenants
k_anonymity_met (bool): True if k-anonymity threshold was met

When to use: Before classifying/processing, check if you've seen this entity before. Store patterns after successful classifications.

Example:

# Before classifying, check if we've seen this before
patterns = client.patterns.query(
    entity_hash=hash(document),
    min_confidence=0.8
)

if patterns:
    # Use learned pattern
    return patterns[0]["outcome_hash"]

# Otherwise, classify and store the pattern
outcome = await classify(document)
client.patterns.store(
    agent_id="classifier-1",
    agent_type="sentiment-analyzer",
    entity_hash=hash(document),
    outcome_hash=hash(outcome),
    confidence=0.92
)

Only hashes are stored—never raw data. And with k-anonymity protection, you can even learn from cross-tenant patterns (if 3+ other users saw the same entity hash, you get the aggregate pattern).

When You Actually Need This

Real talk: you probably don't need Reflexia if you have 2-3 agents in a fixed pipeline. Just use flags and explicit dependencies. It's simpler.

You do need this if:

5+ agents that coordinate dynamically
Agents join/leave at runtime (you can't hardcode dependencies)
LLM uncertainty is a problem (you need consistency checking)
You want emergent behavior (self-organizing workflows, not fixed pipelines)
Privacy matters (you want cross-tenant learning without exposing data)

Real-world example: Living Arcade—a conceptual art platform with autonomous agents that create and trade art on-chain. It had 7 agents (collector, analyzer, creator, curator, trader, trust-scorer, chronicler) that needed to coordinate without a central controller. That's when the field engine was built.

Then consistency checking was needed because LLMs kept confidently misclassifying art styles. That's when the epistemic service was built.

Then agents needed to learn from past decisions. That's when the pattern store was built.

Reflexia is the infrastructure needed but couldn't be found.

Quick Start

Install from PyPI:

pip install reflexia

This SDK is a convenience wrapper around the Reflexia REST API. The actual value (field computation, state persistence, multi-tenancy) runs on Reflexia's backend infrastructure at api.reflexia.io.

Installation

Install from PyPI:

pip install reflexia

Available on PyPI: https://pypi.org/project/reflexia/

For development, install with test dependencies:

pip install reflexia[dev]

Basic Usage

from reflexia import ReflexiaClient

# Initialize client with your API key
client = ReflexiaClient(api_key="rk_YOUR_API_KEY")

# Register an agent in the field
response = client.field.register_agent(
    agent_id="worker-1",
    agent_type="researcher"
)
session_token = response["session_token"]

# Sense the field at the agent's position
sense_result = client.field.sense(
    agent_id="worker-1",
    session_token=session_token
)

# Modify the field after an action
modify_result = client.field.modify(
    agent_id="worker-1",
    session_token=session_token,
    delta=0.5,
    outcome="success"
)

API Reference

Field Engine

Coordinate agents through quantum field evolution:

# Register agent
agent = client.field.register_agent(
    agent_id="worker-1",
    agent_type="researcher",
    semantic_embedding=[0.1, 0.2, 0.3]  # Optional
)

# Sense nearby agents and field gradients
sense = client.field.sense(
    agent_id="worker-1",
    session_token=agent["session_token"]
)

# Modify field after action
client.field.modify(
    agent_id="worker-1",
    session_token=agent["session_token"],
    delta=0.5,
    outcome="success"
)

# Get field metrics
metrics = client.field.get_metrics()
print(f"Coherence: {metrics['coherence']}")
print(f"Active agents: {metrics['agent_count']}")

Epistemic Service

Multi-trajectory consistency checking:

# Check consistency of multiple trajectories
result = client.epistemic.check_consistency(
    agent_id="worker-1",
    trajectories=[
        {"answer_hash": "abc123", "confidence": 0.9},
        {"answer_hash": "abc123", "confidence": 0.85},
        {"answer_hash": "def456", "confidence": 0.7},
    ]
)

if not result["abstained"]:
    print(f"Winning answer: {result['winning_hash']}")
    print(f"Confidence: {result['confidence']}")

# Get recommended k value
recommendation = client.epistemic.recommend_k(complexity_hint="complex")
print(f"Recommended trajectories: {recommendation['recommended_k']}")

Pattern Store

Store and query patterns with k-anonymity:

# Store a pattern observation
pattern = client.patterns.store(
    agent_id="worker-1",
    agent_type="researcher",
    entity_hash="abc123",
    outcome_hash="def456",
    confidence=0.9
)

# Query patterns for an entity
results = client.patterns.query(
    entity_hash="abc123",
    min_confidence=0.7,
    limit=10
)

# Get cross-tenant aggregates (k-anonymity protected)
aggregates = client.patterns.aggregate(
    entity_hash="abc123",
    agent_type="researcher",
    min_tenant_count=5
)

Payment & Account

Manage payments and account:

# Get pricing
pricing = client.payment.get_pricing()
print(f"Sense field: {pricing['sense_field']['price']} sats")

# Get usage statistics
usage = client.payment.get_usage()
print(f"Total operations: {usage['total_operations']}")

# Get account balance
balance = client.account.get_balance()
print(f"Credits: {balance['credits']}")

# Create API key
api_key = client.account.create_api_key(name="production")
print(f"New API key: {api_key['api_key']}")

Configuration

Base URL

By default, the SDK connects to production (https://api.reflexia.io). You can override this:

client = ReflexiaClient(
    api_key="rk_YOUR_API_KEY",
    base_url="https://staging-api.reflexia.io"  # or "http://localhost:3000" for local dev
)

Timeout and Retries

client = ReflexiaClient(
    api_key="rk_YOUR_API_KEY",
    timeout=60,  # Request timeout in seconds (default: 30)
    max_retries=3  # Maximum retries for failed requests (default: 2)
)

Error Handling

The SDK raises specific exceptions for different error types:

from reflexia import (
    ReflexiaClient,
    ReflexiaAPIError,
    ReflexiaAuthenticationError,
    ReflexiaRateLimitError,
    ReflexiaNotFoundError,
)

try:
    result = client.field.sense(agent_id="worker-1", session_token=token)
except ReflexiaAuthenticationError:
    print("Invalid API key")
except ReflexiaRateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except ReflexiaNotFoundError:
    print("Agent not found")
except ReflexiaAPIError as e:
    print(f"API error: {e.code} - {e.message}")

Authentication

API Key Authentication (Traditional)

client = ReflexiaClient(api_key="rk_YOUR_API_KEY")

x402 Payment Authentication (Pay-per-call)

For x402 payment authentication, you'll need to include the payment header manually:

# Note: x402 payment integration requires additional setup
# See https://reflexia.dev/docs/guides/x402-payments

Documentation

PyPI Package: https://pypi.org/project/reflexia/
API Reference: https://reflexia.dev/docs
Website: https://reflexia.dev
GitHub: https://github.com/AaronVick/reflexia-python-sdk

License

MIT License - see LICENSE for details.

Architecture

Reflexia is deployed as a multi-tenant SaaS platform:

Backend: Field computation, state persistence, multi-tenancy (runs on Reflexia servers)
This SDK: HTTP request wrappers for convenience (what you're looking at)

The value is in Reflexia's infrastructure—this SDK just makes it easier to call the API.

What This SDK Provides:

✅ HTTP request wrappers
✅ Type-safe method calls
✅ Error handling and retries
✅ Developer convenience

What Runs on Reflexia Backend:

✅ Field computation algorithms (diffusion equations, gradient computation)
✅ State persistence (Redis + Postgres with multi-tenant isolation)
✅ Epistemic voting logic (majority voting, abstention detection)
✅ Pattern matching and k-anonymity aggregation
✅ Multi-tenant isolation (Row-Level Security)

Why Open Source the SDK?

This SDK is just HTTP request wrappers—anyone could write this in 30 minutes. The actual value is in:

Field computation infrastructure running 24/7
Multi-tenant isolation with Row-Level Security
State persistence in Redis/Postgres
Epistemic voting algorithms
Pattern store with k-anonymity
Reliability, uptime, scaling

Think of it like AWS SDKs: they're open source, but you still pay AWS for S3 storage because the infrastructure is what costs money.

Same with Reflexia: the SDK is free, but the coordination infrastructure is what you're paying for.

Support

Documentation: https://reflexia.dev/docs

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Reflexia Python SDK

Why Reflexia?

The Autonomous Agent Era

The Coordination Problem

The Reflexia Solution

Why Your Multi-Agent System Needs to Think Like an Ant Colony

The Orchestration Spaghetti Problem

What Ants Know That We Forgot

The Digital Pheromone Field

The Math (For the Nerds)

What Problem Does This Solve?

The Coordination Challenge

The Reflexia Solution

Use Cases

1. Multi-Agent Research Swarms

2. Uncertainty Detection in LLM Systems

3. Trading Agent Coordination

4. Content Generation Pipelines

5. Pattern Learning Across Tenants

How Agents Register and Coordinate

The Complete Flow

What Happens Behind the Scenes

Inputs and Outputs Explained

register_agent() - Register Agent in Field

sense() - Sense Field at Agent Position

modify() - Modify Field After Work

Example: Multi-Agent Coordination Flow

How It Works

Field Coordination (Stigmergy)

Epistemic Consistency

Pattern Learning

When You Actually Need This

Quick Start

Installation

Basic Usage

API Reference

Field Engine

Epistemic Service

Pattern Store

Payment & Account

Configuration

Base URL

Timeout and Retries

Error Handling

Authentication

API Key Authentication (Traditional)

x402 Payment Authentication (Pay-per-call)

Documentation

License

Architecture

What This SDK Provides:

What Runs on Reflexia Backend:

Why Open Source the SDK?

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`register_agent()` - Register Agent in Field

`sense()` - Sense Field at Agent Position

`modify()` - Modify Field After Work

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