SLIM v1.0: Secure Low-Latency Interactive Messaging for Agentic AI

The AI landscape is rapidly evolving from isolated models to interconnected networks of intelligent agents. As AI systems become more sophisticated, they need to communicate, collaborate, and coordinate in real-time. Protocols like A2A (Agent-to-Agent) define what agents say to each other: standardized messages, task delegation, and coordination semantics. But A2A needs a robust transport layer that defines how agents securely exchange these messages across network boundaries.

Existing messaging frameworks weren’t designed for the unique demands of agentic AI. They either sacrifice security for performance, lack proper group communication primitives, or can’t scale to support distributed multi-agent systems. Others rely on centralized brokers that can’t be safely exposed across network boundaries, limiting agents to run only where the broker is deployed.

Enter SLIM (Secure Low-Latency Interactive Messaging), a next-generation communication framework that provides the secure, scalable transport layer for agentic AI protocols like A2A. SLIM handles the “piping”: secure connections, message routing, group communication, and end-to-end encryption, so protocol implementations can focus on agent semantics, not infrastructure.

The Problem: Agent Protocols Need Secure Transport

Modern AI applications are increasingly agentic. They don’t just respond to prompts, they take action, use tools, coordinate with other agents, and make autonomous decisions. The A2A protocol has emerged as a standard for defining how agents communicate: specifying message formats, task delegation patterns, and coordination semantics. A2A comes with SDKs in multiple languages (Python, JavaScript, Java, C#, Go) that implement these agent-level semantics.

However, A2A and similar agent protocols need a robust transport layer beneath them. Whether you’re building:

• Multi-agent AI systems where specialized agents collaborate to solve complex problems.
• AI-powered workflows that orchestrate between LLMs, tools, and data sources.
• Real-time agent collaboration where agents need to share context and coordinate actions.
• Distributed AI services that span data centers, edge devices, and browsers.

You need a messaging infrastructure that can handle:

1. Real-time, low-latency communication — AI agents can’t wait seconds for responses.
2. End-to-end security — Agent communications must remain confidential even through untrusted infrastructure.
3. Flexible communication patterns — From point-to-point tool calls to group coordination.
4. Scale — Supporting thousands of concurrent agents across distributed environments.
5. Protocol flexibility — Supporting A2A, MCP, and custom agent protocols
6. Simplicity — Developers shouldn’t need to be cryptography or distributed systems experts.
7. Flexible deployment — Agents should be reachable without requiring direct server exposure.

Traditional messaging systems fall short. MQTT and AMQP weren’t designed for interactive AI workflows. gRPC provides excellent RPC semantics but lacks group capabilities and end-to-end encryption. Message queues add latency and complexity. Rolling your own solution means reinventing security, scaling, and reliability.

A critical networking challenge also emerges: when A2A runs on direct HTTP/gRPC connections, servers must be continuously exposed and directly reachable. However, A2A can instead use SLIM as its transport layer. SLIM’s registration-based model allows agents to register with the SLIM network and become reachable by peers without exposing server ports. While SLIM routing nodes themselves need to be exposed, agent endpoints can run behind NATs and firewalls while remaining fully accessible, significantly simplifying deployment.

SLIM was built to solve exactly these challenges. It provides the secure, scalable “piping” that agent protocols like A2A need to operate across network boundaries.

What is SLIM?

SLIM is a secure, scalable, and developer-friendly messaging framework that provides the transport layer for agent communication protocols like A2A.

While A2A defines what agents say (message formats, task semantics, coordination patterns), SLIM defines how these messages are securely delivered across distributed networks.

At its core, SLIM combines:

• gRPC’s performance and reliability — Built on HTTP/2 for efficient, multiplexed transport.
• Messaging capabilities — Native support for channels and group communication.
• End-to-end encryption — Using Message Layer Security (MLS) protocol.
• Native RPC support — SRPC (SLIM RPC) for request-response patterns alongside messaging.
• Distributed architecture — Separate control and data planes for scalability and management.
• Protocol flexibility — Transport layer for A2A, MCP, and custom agent protocols.

SLIM enables AI agents to communicate securely whether they’re running in a data center, in a browser, on mobile devices, or across organizational boundaries, all while maintaining sub-second latencies and strong security guarantees.

Key Strengths and Capabilities

The strengths and capabilities of SLIM are:

• Scalable Distributed Architecture
• End-to-End Security by Design
• Two Powerful Session Types
• Intelligent Routing and Naming
• Low Latency Architecture
• Developer-Friendly APIs
• Native RPC Support with SRPC

Scalable Distributed Architecture

SLIM is architected with three main components that cleanly separate concerns:

The Data Plane

The data plane’s sole purpose is to route messages across SLIM nodes. It handles:

• Efficient message forwarding based on hierarchical names.
• Connection management between SLIM nodes.
• Network topology and routing table maintenance.
• Transport optimization without needing to inspect message contents.

The data plane operates purely on message metadata, forwarding packets without understanding application-level sessions or encryption.

The Session Layer

The session layer sits on top of the data plane and handles the complex requirements of secure agent communication:

• Reliable delivery: automatic retries, acknowledgments, and ordering guarantees.
• Encryption: end-to-end encryption using the Message Layer Security (MLS) protocol.
• Session management: group creation, invites, membership changes, and state synchronization.

While the session layer is required for applications to achieve secure, reliable communication, it is not used by SLIM nodes themselves—they only need the data plane.

The Control Plane

The control plane manages the distributed network of SLIM nodes:

• Configuration management for routing nodes.
• Monitoring and telemetry collection (planned for future releases).
• Network orchestration and service discovery.
• Administrative operations across the SLIM infrastructure.

Architecture Diagram

The following diagram illustrates how these components are distributed across applications and intermediate routing nodes:

graph TB
    subgraph "Application Node (Agent A)"
        A1[Session Layer 1]
        A2[Session Layer 2]
        A3[Data Plane Client]
        A1 -.->|encrypted messages| A3
        A2 -.->|encrypted messages| A3
    end
    
    subgraph "Intermediate SLIM Node 1"
        I1[Data Plane]
    end
    
    subgraph "Intermediate SLIM Node 2"
        I2[Data Plane]
    end
    
    subgraph "Application Node (Agent B)"
        B1[Session Layer 1]
        B2[Session Layer 2]
        B3[Data Plane Client]
        B1 -.->|encrypted messages| B3
        B2 -.->|encrypted messages| B3
    end
    
    subgraph "Control Plane"
        CP[Configuration & Monitoring]
    end
    
    A3 -.->|encrypted messages| I1
    I1 -.->|route| I2
    I2 -.->|encrypted messages| B3
    
    CP -.->|manages| I1
    CP -.->|manages| I2
    
    style A1 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style A2 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style A3 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff
    style B1 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style B2 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style B3 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff
    style I1 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff
    style I2 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff
    style CP fill:#7f8c8d,stroke:#5a6970,stroke-width:2px,color:#fff

Distributed Artifacts

This architecture dictates how SLIM is distributed:

• Pure Data Plane

  We distribute the slim binary and Docker images as pure data-plane artifacts. Since SLIM routing nodes only forward messages and don’t participate in application sessions, they don’t need the session layer. This keeps the infrastructure lightweight, fast, and simple to deploy.

• Language Bindings

  We distribute libraries (Python, Go, etc.) that include both the data plane client and the session layer on top. Applications use these bindings to get the full stack: secure, reliable, encrypted communication with automatic session management.

This separation means you can run a global network of SLIM routing nodes without any application logic, while your agents use the rich, full-featured language bindings for their communication needs. The control plane manages the routing infrastructure independently, ensuring the network operates efficiently.

End-to-End Security by Design

SLIM leverages the Message Layer Security (MLS) protocol (RFC 9420) to provide end-to-end encryption for all agent communications. Unlike TLS, which only secures point-to-point connections, MLS ensures that messages remain encrypted even when passing through intermediate nodes or experiencing TLS termination along the path.

This means:

• Messages are confidential. Only authorized group members can decrypt content.
• Authentication is built-in. Cryptographic identities verify agent authenticity.
• Zero-trust architecture. You don’t need to trust intermediate infrastructure
• Group security. Add or remove agents from secure groups without rekeying everything.

For AI applications, this is crucial. Your agent conversations, tool outputs, and coordination messages stay private even when traversing cloud infrastructure you don’t control.

Two Powerful Session Types

SLIM provides two session abstractions that map perfectly to agentic AI patterns: point-to-point and group sessions.

Point-to-Point Sessions

Perfect for tool invocations and direct agent-to-agent communication:

# Discover and connect to a specific service instance
session_config = slim_bindings.SessionConfig(
    session_type=slim_bindings.SessionType.POINT_TO_POINT,
    enable_mls=True,
    max_retries=5,
    interval=datetime.timedelta(seconds=5),
    metadata={}
)

# Create session and wait for establishment
session_context = await app.create_session_async(session_config, remote_name)
await session_context.completion.wait_async()
session = session_context.session

# Send a request
await session.publish_async(
    b"execute_tool: search(query='AI agents')", None, None
)

# Get the response
received_msg = await session.get_message_async(
    timeout=datetime.timedelta(seconds=30)
)
result = received_msg.payload

The session automatically:

• Discovers an available service instance.
• Establishes a secure MLS group (if enabled).
• Binds all subsequent communication to that instance.
• Provides reliable delivery with automatic retries.

Group Sessions

Ideal for multi-agent coordination, broadcast updates, and collaborative workflows:

# Create a coordination channel
session_config = slim_bindings.SessionConfig(
    session_type=slim_bindings.SessionType.GROUP,
    enable_mls=True,
    max_retries=5,
    interval=datetime.timedelta(seconds=5),
    metadata={}
)

# Create group session and wait for establishment
session_context = await app.create_session_async(session_config, channel_name)
await session_context.completion.wait_async()
session = session_context.session

# Invite other agents to join
handle = await session.invite_async(agent_name)
await handle.wait_async()

# Broadcast to all participants
await session.publish_async(
    b"task_update: analysis complete", None, None
)

Group sessions support:

• Many-to-many communication: all messages delivered to all participants.
• Dynamic membership: add or remove agents on the fly.
• Moderation: channel creators control membership.
• Secure group state: MLS ensures all members share cryptographic context.

Intelligent Routing and Naming

SLIM uses a hierarchical naming system based on Decentralized Identifiers (DIDs) that enables both human-readable names and secure, globally unique identifiers:

organization/namespace/service/instance

For example: agntcy/production/search-agent/a3f9k2

This structure supports:

• Anycast routing: send to org/ns/service and reach any available instance.
• Unicast routing: target a specific instance with the full four-part name.
• Service discovery: find agents by their organizational and service identifiers.
• Scalable forwarding: hierarchical names aggregate efficiently in routing tables.

No more hardcoded endpoints or complex service registries. SLIM’s naming system just works.

Low Latency Architecture

SLIM is optimized for interactive AI workloads where every millisecond counts:

• Multiplexed streams: multiple logical streams over single connections.
• Efficient message routing: intelligent forwarding tables minimize hops.
• Zero-copy operations: where possible, avoid unnecessary data copying.
• Async-first design: non-blocking I/O throughout the stack.
• HTTP/2 transport: proven, efficient binary protocol.

Real-world deployments show sub-second end-to-end latencies even with MLS encryption enabled.

Developer-Friendly APIs

SLIM provides high-quality bindings for multiple languages that abstract complexity. Here’s an example in Python:

import slim_bindings
import datetime

# Initialize the global service
service = slim_bindings.get_global_service()

# Create a SLIM instance with shared secret auth
local_name = slim_bindings.Name("org", "namespace", "my-agent")
app = service.create_app_with_secret(
    local_name, "my-shared-secret"
)

# Connect to SLIM node
client_config = slim_bindings.new_insecure_client_config(
    "http://127.0.0.1:46357"
)
conn_id = await service.connect_async(client_config)

# Subscribe to receive messages
await app.subscribe_async(local_name, conn_id)

# Listen for incoming session
session = await app.listen_for_session_async(None)

# Receive and reply to messages
received_msg = await session.get_message_async(
    timeout=datetime.timedelta(seconds=30)
)
payload = received_msg.payload
await session.publish_async(b"response", None, None)

No need to manage MLS groups manually, handle connection pooling, or worry about encryption keys — SLIM’s session layer handles it all.

Native RPC Support with SRPC

Many agent-facing protocols (like A2A and MCP) are RPC-oriented. SLIM includes SRPC (SLIM RPC), which provides gRPC-like streaming RPC semantics directly over the SLIM transport.

SRPC supports all four gRPC patterns:

• Unary → Unary — Simple request-response
• Unary → Stream — Server streaming responses
• Stream → Unary — Client streaming requests
• Stream → Stream — Bidirectional streaming

Define services with Protocol Buffers:

service AgentService {
  rpc ExecuteTool(ToolRequest) returns (ToolResponse);
  rpc StreamResults(Query) returns (stream Result);
  rpc CollectInputs(stream Input) returns (Summary);
  rpc Coordinate(stream Message) returns (stream Message);
}

The SLIM RPC compiler generates language-specific stubs that work seamlessly with SLIM’s secure messaging layer. You get familiar RPC semantics with SLIM’s security and group communication benefits. We are also studying how to extend these RPC interaction patterns to support group communication scenarios, enabling multi-agent RPC workflows.

Single Implementation, Multiple Languages

SLIM is implemented in Rust, providing a high-performance, memory-safe core that powers all language bindings. This approach ensures:

• Consistent behavior: all language bindings use the same battle-tested implementation.
• Performance: Rust’s zero-cost abstractions deliver native speed across all platforms.
• Security: Memory safety guarantees prevent entire classes of vulnerabilities.
• Maintainability: Bug fixes and improvements benefit all language ecosystems simultaneously.

Currently Available:

• Python. Full-featured bindings with async/await support
• Go. Native Go bindings for seamless integration (see our technical blog posts on integrating Rust and Go with UniFFI and distributing C artifacts for Go modules)

Coming Soon:

• C#. For .NET and Unity applications.
• JavaScript/TypeScript. Browser and Node.js support.
• Kotlin. Android and JVM applications.

This multi-language strategy means you can build heterogeneous agent systems where different components use their most appropriate language, all communicating seamlessly through SLIM’s unified protocol.

One of the key reasons SLIM provides multiple language bindings is to support agent communication protocols like A2A across different programming ecosystems. A2A comes with SDKs in multiple languages (Python, JavaScript, Java, C#, Go), and SLIM’s language bindings enable these A2A SDKs to communicate securely. An A2A Python agent can securely communicate with an A2A Java agent, with SLIM handling the underlying transport transparently.

Real-World Use Cases

SLIM + A2A: Agent-to-Agent Communication

SLIM and A2A are designed to work together:

• A2A SDKs (Python, JavaScript, Java, C#, Go) implement agent-level semantics — task delegation, message formats, coordination patterns.
• SLIM bindings provide the secure transport, handling connection management, routing, encryption, and delivery guarantees.

Currently, A2A sits on top of SLIM RPC (SRPC), enabling secure point-to-point agent interactions. We provide slim-a2a-python, a Python A2A SDK implementation built on SLIMRPC that enables developers to build A2A-compliant agents with SLIM as the transport layer. We are actively working to add A2A SDK support for the remaining SLIM language bindings (Go, C#, JavaScript/TypeScript, Kotlin).

Looking ahead, A2A is evolving to support bidirectional streaming for real-time, interactive agent conversations and group communication for multi-agent coordination and collaboration.

SLIM naturally provides both capabilities at the transport level through SRPC and MLS-based end-to-end encryption for groups, which many pub-sub systems lack. This positions SLIM as an ideal transport foundation for A2A’s continued evolution toward richer, more secure agent communication patterns.

The following diagram illustrates how A2A and SLIM capabilities combine:

graph LR
    subgraph Protocol["Protocol Layer (Application Semantics)"]
        direction TB
        A1["A2A Go SDK<br/>Task Delegation<br/>Coordination"]
        B1["A2A C# SDK<br/>Task Delegation<br/>Coordination"]
        C1["A2A Python SDK<br/>Task Delegation<br/>Coordination"]
    end
    
    subgraph Transport["Transport Layer (SLIM)"]
        direction TB
        subgraph Endpoints["Agent Endpoints"]
            A2["SLIM Go Bindings<br/>SRPC + Session + Data Plane"]
            B2["SLIM C# Bindings<br/>SRPC + Session + Data Plane"]
            C2["SLIM Python Bindings<br/>SRPC + Session + Data Plane"]
        end
        
        subgraph Network["SLIM Network Infrastructure"]
            SN1[SLIM Node 1<br/>Data Plane]
            SN2[SLIM Node 2<br/>Data Plane]
        end
    end
    
    A1 -->|uses| A2
    B1 -->|uses| B2
    C1 -->|uses| C2
    
    A2 -.->|"SRPC (P2P)<br/>encrypted via MLS"| SN1
    B2 -.->|"SRPC (P2P)<br/>encrypted via MLS"| SN1
    C2 -.->|"SRPC (Groups)<br/>encrypted via MLS"| SN2
    
    SN1 -.->|routes| SN2
    SN2 -.->|routes| SN1
    
    style Protocol fill:#e8f4f8,stroke:#2980b9,stroke-width:3px
    style Transport fill:#fef5e7,stroke:#f39c12,stroke-width:3px
    style Endpoints fill:#fff,stroke:#95a5a6,stroke-width:2px
    style Network fill:#fff,stroke:#95a5a6,stroke-width:2px
    
    style A1 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style B1 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style C1 fill:#4a90e2,stroke:#2e5c8a,stroke-width:2px,color:#fff
    style A2 fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#fff
    style B2 fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#fff
    style C2 fill:#27ae60,stroke:#1e8449,stroke-width:2px,color:#fff
    style SN1 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff
    style SN2 fill:#f39c12,stroke:#d68910,stroke-width:2px,color:#fff

Architecture Overview:

• Protocol Layer (blue, left): A2A SDKs implement agent-level semantics — defining what agents say through standardized task delegation, message formats, and coordination patterns.
• Transport Layer (green/orange, right): SLIM provides the secure infrastructure for how messages are delivered:
  • SLIM Bindings (green): Include SRPC for RPC semantics, Session Layer for reliability and encryption, and Data Plane client for connectivity.
  • SLIM Network (orange): Pure data plane nodes route encrypted messages without understanding agent semantics.

Communication Patterns:

• Current: A2A uses SRPC for point-to-point (P2P) agent interactions with MLS encryption.
• Future: SLIM naturally provides SRPC group capabilities and MLS-based group encryption, ready to support A2A’s evolution toward groups and bidirectional streaming.

SLIM also provides the transport layer for other agent communication protocols:

• MCP (Model Context Protocol) for context sharing between agents and tools.
• Custom protocols for domain-specific agent interactions.

For more information about A2A, see the official A2A documentation.

Getting Started

Installation

Install the SLIM node:

# Docker
docker pull ghcr.io/agntcy/slim:v1.0.0

# Cargo
cargo install agntcy-slim

# Helm (Kubernetes)
helm pull oci://ghcr.io/agntcy/slim/helm/slim --version v1.0.0

Install Python bindings:

pip install slim-bindings

Quick Example

Create your first SLIM agent in minutes:

import asyncio
import slim_bindings
import datetime

async def main():
    # Initialize the global service
    service = slim_bindings.get_global_service()
    
    # Create app with shared secret authentication
    local_name = slim_bindings.Name("org", "namespace", "agent")
    app = service.create_app_with_secret(local_name, "my-shared-secret")
    
    # Connect to SLIM node
    client_config = slim_bindings.new_insecure_client_config(
        "http://127.0.0.1:46357"
    )
    conn_id = await service.connect_async(client_config)
    
    # Subscribe to local name
    await app.subscribe_async(local_name, conn_id)
    
    # Listen for incoming session
    session = await app.listen_for_session_async(None)
    
    # Handle messages
    while True:
        received_msg = await session.get_message_async(
            timeout=datetime.timedelta(seconds=30)
        )
        payload = received_msg.payload
        print(f"Received: {payload.decode()}")
        await session.publish_async(b"Acknowledged: " + payload, None, None)

asyncio.run(main())

The Road Ahead

SLIM is being actively developed as an open-source project under the Apache 2.0 license. The protocol specification has been submitted as an Internet-Draft to the IETF, reflecting its ambition to become a standard for agentic AI communication.

Current capabilities include:

• Rust core with multi-language bindings (Python, Go, and more coming)
• MLS-based end-to-end encryption
• Point-to-point and group sessions
• SRPC for RPC patterns
• Control plane for management
• Kubernetes deployment support

The community is working on:

• Additional language bindings (C#, JavaScript/TypeScript, Kotlin)
• Enhanced monitoring and observability
• Integration with popular AI frameworks
• Extended A2A support for groups and bidirectional streaming
• Reference implementations of MCP over SLIM
• Performance optimizations

Why SLIM Matters

As AI systems evolve from monolithic models to distributed networks of specialized agents, the communication infrastructure becomes critical. SLIM provides a foundation that:

• Ensures security: end-to-end encryption without complexity.
• Scales with demand: from prototype to production.
• Simplifies development: intuitive APIs for complex capabilities.
• Future-proofs architecture: standards-based, extensible design.

Whether you’re building a multi-agent AI system, an agentic workflow platform, or the next generation of AI applications, SLIM provides the messaging infrastructure you need.

Get Involved

SLIM is open source and welcomes contributions:

• GitHub
• Documentation
• Specification

You can find comprehensive Python and Go examples in the GitHub repository.

Join the community building the communication layer for agentic AI. Try SLIM in your next project with our language binding examples, contribute to the codebase, or share your use cases.

The future of AI is collaborative, distributed, and secure. SLIM makes it possible.

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SLIM is developed by AGNTCY Contributors and released under the Apache 2.0 License.