Syrin - Static Contract Analysis for MCP Servers
Catch MCP failures before the agent does
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>log in to comment🚀 Syrin CLI is live on DevHunt Syrin CLI is a testing, linting, and static contract checker for MCP servers. It helps you validate MCP tools before they are used by an LLM — catching broken schemas, unsafe contracts, and non-deterministic behaviour early. What Syrin CLI does 1. Lints MCP tool schemas 2. Statistically validates tool contracts 3. Tests MCP servers without an LLM in the loop 4. Catches unsafe or ambiguous tool definitions early Why this matters? MCP servers are becoming the backbone of LLM tooling, but: 1. Schemas drift 2. Contracts break silently 3. LLMs fail in non-obvious ways 4. Syrin CLI treats MCPs like APIs that deserve real tooling, not prompt-time guesswork. Links: 1. GitHub: https://github.com/syrin-labs/cli 2. NPM: https://www.npmjs.com/package/@syrin/cli If you’re building or maintaining MCP servers, support the project and share feedback — this is early infra, shaped directly by builders.
Syrin is a Python library that adds budget enforcement, memory management, sandboxed code execution and guardrails to LLM agents.
- for
- Python developers building production AI agents.
- pricing
- open source
- license
- ISC
Key features
- First-class budget enforcement — Set dollar limits per agent; stop, warn or switch models when the budget is exceeded.
- Budget-aware persistent memory — Four memory types with decay, import-rank and token-cost awareness, persisting across sessions.
- Isolated sandbox execution — Run LLM-generated Python, Bash or JavaScript in subprocesses with timeouts and no shared state.
- 72+ lifecycle hooks — Typed events fire on every LLM request, tool call, memory read, sandbox exec, etc., for observability.
- Built-in guardrails — PII redaction, prompt-injection detection, content filtering, fact verification and output length limits.
- Multi-agent orchestration — Swarm topologies and recursive sub-agent spawning share budget, memory and observability.
- Agent identity & signing — Each agent has a cryptographic Ed25519 identity; messages are signed to prevent impersonation.
- Token-Oriented Object Notation (TOON) — Compact schema format reduces token usage on tool calls by ~40%.
Use cases
- Limit runaway LLM costs in a multi-agent research system.
- Build a customer-support bot with strict budget and memory policies.
- Run untrusted LLM-generated code safely in production.
- Create reproducible, checkpointed AI workflows that survive server restarts.
- Orchestrate a swarm of agents for complex data processing while sharing a budget.
Syrin - Static Contract Analysis for MCP Servers vs alternatives
| Best for | Budget-controlled, safe multi-agent AI systems | General AI-agent development | LLM observability | Sandbox runtime for agents | Fast code release automation |
|---|---|---|---|---|---|
| Pricing | Open source | Free | Subscription | Free | Subscription |
| DevHunt upvotes | 24 | 75 | 89 | 73 | 85 |
| Launched | Feb 2026 | Jan 2023 | Jan 2023 | Apr 2026 | Apr 2024 |
- Syrin - Static Contract Analysis for MCP Servers vs Fine: Fine focuses on building software with AI agents but lacks built-in budget enforcement and sandboxed execution.
- Syrin - Static Contract Analysis for MCP Servers vs Langfuse: Langfuse provides observability for LLM apps, while Syrin adds safety, budgeting and sandboxing at the library level.
- Syrin - Static Contract Analysis for MCP Servers vs Pocketenv: Pocketenv offers a universal sandbox runtime but does not include the full agent framework, budgeting or memory features of Syrin.
- Syrin - Static Contract Analysis for MCP Servers vs Devzero: Devzero accelerates code release pipelines; Syrin is a runtime library for safe, cost-controlled AI agents.
Syrin - Static Contract Analysis for MCP Servers FAQ
How does Syrin prevent runaway token costs?+
You define a max_cost in the Budget; when the agent reaches it Syrin can stop execution, warn, or switch to a cheaper model.
Can I run LLM-generated code safely?+
Yes, the Sandbox runs Python, Bash or JavaScript in isolated subprocesses with hard timeouts, memory caps and no parent-process access.
What observability hooks are available?+
Every lifecycle event emits a typed Hook, which you can subscribe to for logging, alerting (e.g., Datadog, PagerDuty) or custom debugging.
Do I need to write configuration files?+
No, all configuration is declarative within Python classes—budget, memory, model, tools and hooks are defined as class attributes.
Is Syrin compatible with existing LLM providers?+
Syrin wraps OpenAI, Anthropic, Google, Ollama and custom models via the Model API.
Summarized by DevHunt from docs.syrin.dev · Sep 27, 2026. Details may change; check the official site.