Agentspan
UnclaimedDurable execution for AI agents, ensuring resilience and human collaboration in production.
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TL;DR - Agentspan
- Provides durable execution for AI agents, making them resilient to crashes and process interruptions.
- Enables human-in-the-loop workflows and complex agent pipelines with built-in state persistence and observability.
- Open-source and self-hostable, leveraging Conductor for robust workflow orchestration and production readiness.
Pricing: Free forever
Best for: Individuals & startups
Pros & Cons
Pros
- Ensures agents are highly resilient and can recover from crashes without losing state.
- Facilitates seamless human intervention and approval processes within agent workflows.
- Simplifies the creation and management of complex, multi-agent systems.
- Provides deep visibility into agent execution for debugging and monitoring.
- Offers robust testing capabilities that are fast and reliable.
Cons
- Requires setting up and managing a Conductor server.
- May have a learning curve for users unfamiliar with workflow orchestration concepts.
- Currently focused on Python, potentially limiting use for other language ecosystems.
Key Features
Crash and Resume functionality for agentsHuman-in-the-loop approvals for tool callsMulti-agent pipelines with durable executionObservability for agent runs, tool calls, and LLM requestsDeterministic testing framework for agent logicSelf-hostable server and SDKConductor-based workflow orchestrationPython API for agent definition and execution
Pricing
Free
Agentspan is completely free to use with no hidden costs.
What is Agentspan?
Agentspan is an open-source server and SDK designed to provide durable execution for AI agents. It compiles agent definitions into robust workflows, ensuring that agent execution state persists outside of the immediate process. This means agents can automatically retry tool calls, gracefully handle process crashes (like OOM errors or deployments), and resume from the exact point of failure on any machine, preventing lost work.
The platform is built for production environments, addressing critical needs like crash recovery, human-in-the-loop interactions, and complex agent pipelines. It leverages Conductor, an open-source orchestration engine used by major tech companies, to provide primitives like durable state, per-step retries, full execution history, and replayability. Agentspan offers a clean Python API on top of this foundation, making it accessible for developers building and deploying AI agents. It also includes comprehensive observability tools to monitor every step, tool call, and LLM request, along with robust testing capabilities that allow for deterministic testing of agent logic without needing an LLM or server.
Agentspan is ideal for developers and teams looking to deploy reliable, production-grade AI agents that can withstand failures, integrate human oversight, and orchestrate complex multi-agent workflows. Its self-hostable nature and open-source foundation provide flexibility and control over the agent infrastructure.
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Agentspan FAQ
How does Agentspan achieve durable execution and crash recovery for AI agents?
Agentspan compiles agent definitions into Conductor workflows. Conductor is an open-source orchestration engine that persists the execution state of workflows outside of the agent's process. If an agent's process crashes, the state is preserved on the Agentspan server, allowing the agent to resume from the exact point of failure when reconnected from any machine.
Can Agentspan integrate human approvals into an agent's workflow, and how does that work?
Yes, Agentspan supports human-in-the-loop functionality. By decorating a tool with
@tool(approval_required=True), the agent will pause and hold its state on the server when that tool is called. The workflow can then be resumed (approved or rejected) from various interfaces like Slack, a web portal, or directly through code, without any timeout.What is the underlying technology Agentspan uses for its workflow orchestration?
Agentspan leverages Conductor, an open-source orchestration engine that has been used in production at companies like Netflix, LinkedIn, and Tesla. Conductor provides the primitives for durable state, per-step retries, full execution history, and replay capabilities, which Agentspan builds upon with its Python API.
How does Agentspan facilitate testing for AI agents, especially without needing an LLM or server?
Agentspan includes a
mock_run utility in its testing framework. This allows developers to script exact tool sequences and simulate agent execution deterministically. This means agent logic, error handling, tool routing, and output parsing can be tested in milliseconds without requiring an actual LLM call or a running Agentspan server.What kind of observability features does Agentspan provide for agent runs?
Agentspan offers comprehensive observability by logging every tool call, every LLM request, and the timing for each step within an agent's execution. This data is stored, queryable, and replayable, allowing users to see the full execution history and understand the flow and performance of their agents.
Source: agentspan.ai