AgentspanBuild resilient AI agents that never lose their place

Agentspan

Agentspan is an open-source server and SDK that brings durable execution to AI agents. Your agent definitions compile into resilient workflows where execution state lives outside your process, tool calls retry automatically, and human approvals wait and resume cleanly—even if your process crashes.

Product Highlights

• Crash + Resume: Your process dies, but your agent keeps running on the Agentspan server. Reconnect from any machine and resume from the exact step with no lost work.
• Human-in-the-Loop: One decorator marks tools for approval. Agents pause indefinitely, holding state on the server, and resume from Slack, web portals, or code.
• Agent Pipelines: Wire agents into durable pipelines with >>. Each step is logged, crash-safe, and resumable across the full chain.
• Production-Ready Testing: mock_run scripts exact tool sequences for deterministic CI tests—no LLM calls, no server needed, millisecond execution.
• Any LLM, One String: Switch providers from Anthropic to OpenAI to Gemini with a single string change. Mix models within the same pipeline.
• Built-in Guardrails: Validate outputs with regex, LLM checks, or custom functions. Auto-retry, fix, or escalate to humans on failure.

Use Cases

• Long-Running Data Processing: Run agents that process thousands of records over hours or days. Survive crashes, reconnect from different machines, never lose progress.
• Financial Operations with Approval: Build refund processors, invoice validators, or transaction approvers that pause for human sign-off and resume cleanly from any interface.
• Multi-Agent Research Systems: Orchestrate researcher → writer → editor pipelines where each specialist agent runs durably, handoffs are logged, and the entire workflow is replayable.
• Customer Support Automation: Deploy agents with memory across sessions, guardrails for PII protection, and seamless escalation to human agents when needed.

Target Audience

Agentspan is built for engineering teams shipping AI agents to production—developers who need reliability beyond demo frameworks, infrastructure engineers seeking Netflix-grade orchestration, and organizations requiring audit trails, human oversight, and cross-session memory in their agent systems.