Solace Agent Mesh
Orchestrate specialized AI agents through Solace’s event-driven messaging fabric.
- Source repo
- SolaceProducts/solace-agent-mesh
- Stars
- ★ 4.9k
- Last updated
- 1d ago
- License
- NOASSERTION
- Primary language
- Shell
- FA score
- 35/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An internal-platform team answering questions about a sample coffee-company database can follow the SQL Database Tutorial to add database-backed query capability.
- Not a fit if
- Teams without access to Solace Platform event brokers
- Users needing a maintained project with security updates
- Source review
- 35/100 · Major gaps 6 safety controls not found
What does this agent do, and when should you use it?
Solace Agent Mesh (SAM) is an open-source framework for event-driven, multi-agent AI applications. It combines Solace AI Connector (SAC) with Google Agent Development Kit (ADK) to provide a configurable “Universal A2A Agent Host,” using Solace Platform as its message transport. Specialized agents can expose distinct models, instructions, and tools, discover peers, and delegate multi-step work over the A2A protocol. SAM includes REST API, web UI, and Slack integration paths, alongside artifact handling, dynamic embeds, and SQL, JQ, and visualization tools. It is a fit for teams building coordinated systems on message infrastructure, rather than teams seeking a standalone chatbot or a broker-free lightweight library.
SAM uses SAC for broker connections, configuration loading, and component lifecycle management, while Google ADK supplies agent runtime behavior, LLM interaction, tool execution, and state management. Agent capabilities—including LLM model, instructions, and tools—can be defined primarily in SAC YAML configuration, and Solace Platform transports standard A2A communication. Running agents can dynamically discover peers in the same ecosystem, delegate tasks, and exchange data and artifacts. Built-in file tools create, list, and load artifacts with automatic metadata handling; built-in SQL, JQ, and visualization tools support analysis, while Dynamic Embeds resolve context-dependent placeholders in responses.
- An internal-platform team answering questions about a sample coffee-company database can follow the SQL Database Tutorial to add database-backed query capability.
- An application developer who needs live weather data can use the Weather Agent Plugin tutorial to add a weather capability to an agent mesh.
- A team that already operates an MCP Server can follow the MCP Integration Tutorial to connect it for use by configured agents.
- A workplace-tools team wanting employees to converse with the system from Slack can use the documented Slack integration path.
- A workflow application that must pass generated image, audio, or report files between specialized agents can use the documented artifact-management and multimodal-agent capabilities.
How do you install or deploy this agent?
Provide Python 3.10.16–3.13.x, pip, an LLM API key, and macOS, Linux, or Windows with WSL. Create and install a project:
mkdir my-sam && cd my-sam
python3 -m venv .venv && source .venv/bin/activate
sam -v
pip3 install solace-agent-meshFor an earlier installation, the documentation advises running pip3 uninstall solace-agent-mesh and starting again; in-place upgrades are not officially supported. Then run sam init --gui; its initialization UI uses port 5002.
How do you use this agent?
After sam init --gui, start the project with:
sam runOpen http://localhost:8000 for the web chat interface and submit a question. Add an agent with sam add agent --gui, or install an existing plugin with sam plugin add <your-component-name> --plugin <plugin-name>. Run tests with hatch test; for direct pytest usage, run pip install -e .[test] first, then pytest.
What are this agent's strengths and limitations?
- Uses Solace Event Mesh for agent communication, with an explicitly asynchronous and decoupled multi-agent architecture.
- Combines SAC’s connection, configuration, and lifecycle responsibilities with Google ADK’s agent runtime, tool execution, and state management.
- Supports peer discovery and task delegation through the A2A protocol rather than only fixed in-process workflow chains.
- Includes artifact management, dynamic embeds, SQL, JQ, and visualization tools, with REST, web UI, and Slack integration paths.
- Its core transport depends on Solace Platform, so adoption requires access to or integration with that event-messaging infrastructure.
- Local operation requires a supported Python version, pip, an LLM API key, and macOS, Linux, or Windows with WSL.
- The documentation says upgrades from earlier versions are not officially supported; existing installations are generally expected to be removed and rebuilt.
- The README does not specify broker deployment, authentication configuration, supported model-provider list, or production failure-recovery configuration.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Solace Agent Mesh This agent | 35 · Major gaps | CLIFree + model costs | ★ 4.9k | 1d ago | Shell | — |
| AnyAgent | 41 · Major gaps | Library / SDKFree + model costs | ★ 1.3k | 1mo ago | Python | — |
| Nexent | 52 · Major gaps | Self-hosted serviceFree + model costs | ★ 6.1k | 1d ago | Python | OpenAI API |
| Google ADK for TypeScript | 51 · Major gaps | CLIFree + model costs | ★ 1.5k | 7d ago | TypeScript | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows security fix annotations for dependencies (e.g., CVE-2026-59950), but no evidence of least privilege, user confirmation, data flow transparency, sensitive data handling, or external effects. Rollback not mentioned. Source attribution partially via author field and GitHub link, but publisher unverified.
Self-consistency: README, pyproject.toml, and CI workflows are largely consistent, but no detailed failure messages. Dependency availability: dependency list complete, but no lock file or verification. Failure messages: no documentation of error handling or user prompts.
Audience and scenarios: README describes multiple integrations (weather, SQL, MCP, Slack), but target audience not explicit. Capability boundaries: no clear limitations or boundaries. Trigger precision: no trigger conditions or precise control. Environment fit: system requirements provided (Python, OS), but no detailed deployment config.
Information architecture: README well-structured with TOC and links. Install notes: quick start provided, but no detailed configuration guide. Naming stability: no naming conventions or API stability. Examples and FAQ: multiple tutorials, but no FAQ. Known limitations: not mentioned. License: Apache-2.0 with LICENSE file. Versioning/changelog: no CHANGELOG, but dynamic version in pyproject. Maintenance responsibility: contributing guide, but maintainers not explicit.
Output usability: Web UI and CLI provided, but no output format or integration examples. Marginal value: framework offers multi-agent orchestration and event-driven architecture, unique value. Cost-benefit: no performance or resource data.
Claim traceability: feature claims in README lack implementation or test evidence. Cross-source corroboration: no external validation or independent testing. Fact-inference separation: no clear distinction between facts and inferences.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity unverified; treat source with caution.
- Dependencies have security fix annotations but no lock file; actual installs may vary.
- No security configuration guidance, such as least privilege or data encryption.
- No rollback or downgrade mechanism documented.
- No known limitations or troubleshooting information.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
Solace Agent Mesh v2.542.1 Solace Agent Mesh is an event-driven framework for building and orchestrating multi-agent AI systems. It has been rebuilt in Go for better performance and scale, with a new decoupled architecture that lowers cost and improves monitoring in production deployments. > [!NOTE] > The Solace Agent Mesh source code is coming to this repository very soon. Get Started 1. Download the Agent Mesh Desktop Application for your platform from the Assets section below. The application runs all of Agent Mesh on your computer, and includes the sam CLI. …View on GitHub ↗ -
Release notes excerpt
Solace Agent Mesh v2.493.0 Solace Agent Mesh is an event-driven framework for building and orchestrating multi-agent AI systems. It has been rebuilt in Go for better performance and scale, with a new decoupled architecture that lowers cost and improves monitoring in production deployments. > [!NOTE] > The Solace Agent Mesh source code is coming to this repository very soon. Get Started 1. Download the Agent Mesh Desktop Application for your platform from the Assets section below. The application runs all of Agent Mesh on your computer, and includes the sam CLI. …View on GitHub ↗ -
Release notes excerpt
Solace Agent Mesh v2.455.0 Solace Agent Mesh is an event-driven framework for building and orchestrating multi-agent AI systems. It has been rebuilt in Go for better performance and scale, with a new decoupled architecture that lowers cost and improves monitoring in production deployments. > [!NOTE] > The Solace Agent Mesh source code is coming to this repository very soon. Get Started 1. Download the Agent Mesh Desktop Application for your platform from the Assets section below. The application runs all of Agent Mesh on your computer, and includes the sam CLI. …View on GitHub ↗