Agentspan
github.com/agentspan-ai/agentspan- Category
- AI Agents
- Rank
- No. 639Tools index
Previous survey · No. 632 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- agentspan-ai
- GitHub
- 496 stars
- Latest release
- v0.4.4
- Date
About
A distributed, durable runtime for AI agents that survive process crashes, scale across machines, and can pause for human approval for extended periods. Works with existing frameworks like OpenAI Agents SDK, LangChain, and LangGraph while adding crash recovery and distributed execution.
What it does
Agentspan moves agent coordination into a persistent Conductor server while separate workers execute tools. It records each step, exposes execution history and a visual interface, and can turn an LLM-produced task graph into a fixed workflow. Agents may run directly or arrive from supported third-party frameworks.
Why it's ranked here
A strong choice when agent execution must behave like production infrastructure rather than an in-memory loop. Server-held state, independently scaled workers, scheduled runs, approval handling, metrics, and cross-language SDKs form a coherent system. The main reservation is operational weight: even local use introduces a Java server, while production expects PostgreSQL and Conductor.
What's good
The planner produces one JSON task graph, then the server fixes orchestration, branching, parallelism, and retries without further model decisions. Worker processes remain stateless and scale independently. Executions expose identifiers for later status checks, streaming, approval, cancellation, and history. Credentials stay server-side, use authenticated encryption at rest, and are injected only during tool execution.
Tradeoffs
Local startup downloads a roughly 50 MB server JAR and requires Java. SQLite suits development, but the production guidance calls for PostgreSQL. Scheduled executions currently lack skip-if-running or queue policies, per-schedule retry overrides, and optimistic concurrency. The scheduling documentation also says event, webhook, file, and stream triggers are specified but not built, which conflicts with broader event-trigger claims.
How to use it well
Use it for long-running research, support, approval, scheduled, or multi-step automation where recovery and audit history justify a persistent control plane. Keep business tools in stateless workers, use PostgreSQL for production, and set explicit turn limits, timeouts, guardrails, authentication, and monitoring. It does not replace an agent framework or solve schedule-overlap policy for you.
Technical notes+
docs/quickstart.md says AgentRuntime.run() compiles an Agent into a server workflow and starts a worker for decorated tool calls; HTTP, API, and MCP tools instead execute server-side. docs/worker-types.md lists 17 worker categories, server-registered task definitions, two retries with linear backoff, hourly response timeouts, credential injection through /api/workers/secrets, five-second worker health checks, and SDK cleanup on AgentRuntime.shutdown(). docs/deployment.md documents SQLite WAL locally, PostgreSQL with Docker Compose for production, and Kubernetes manifests plus Helm charts. docs/scheduling.md defines declarative schedule reconciliation and explicitly lists overlap controls and event-style triggers as unfinished. pyproject.toml declares Python 3.9 or newer with no project dependencies, while package.json is private and contains only development tooling.
Observed
- License
- MIT
- SDK languages
- Python, TypeScript/JavaScript, Java, and C#/.NET
- Install surface
- Python package, npm packages, .NET package, Maven or Gradle artifact, and a Go CLI binary
- Interfaces
- CLI, language SDKs, REST API, visual web UI, Prometheus metrics, and optional OpenTelemetry
- Platform support
- CLI installers are documented for macOS, Linux, and Windows
- Deployment
- SQLite for local development; PostgreSQL with Docker Compose or Kubernetes for production
Read from README.md, package.json, pyproject.toml, docs/cli.md, docs/sdk.md, docs/index.md, docs/ai-models.md, docs/providers.md, docs/deployment.md, docs/quickstart.md, docs/scheduling.md, docs/integrations.md, docs/self-hosting.md, docs/worker-types.md.
What it can do
Recover AI agents after process crashes
Crashed AI agent state and context → Restored AI agent execution from last checkpoint
Distribute AI agent execution across multiple machines
AI agent workload and available machine resources → Distributed agent processes running on multiple machines
Pause AI agent execution for human approval
AI agent requiring human intervention → Paused agent state awaiting human approval for extended periods
Resume AI agent execution after human approval
Human approval decision and paused agent state → Continued AI agent execution from pause point
Integrate existing AI frameworks with durability features
OpenAI Agents SDK, LangChain, or LangGraph applications → Enhanced AI applications with crash recovery and distributed execution
Checkpoint AI agent state for persistence
Running AI agent execution state → Persistent checkpoints for crash recovery
Tags
Tech Stack
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.