langgraph-runtime-inmem
Inmem implementation for the LangGraph API server.
Decision gist · record as of 2026-08-14
Yes, if you are building with LangGraph and need a lightweight, low-friction runtime for development, testing, or single-process deployments. The active maintenance and low install friction are positive signals. However, clarify the Elastic-2.0 license terms for your use case first, and confirm that in-memory execution meets your durability and scaling requirements before using in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Low install friction with a pure Python wheel.
- Active maintenance as of 1 day ago.
License · maintenance · safety
Elastic-2.0 (unclear) — License treatment is unclear; Elastic-2.0 may carry restrictions or obligations not immediately apparent from standard SPDX classification. Review the license terms before adopting in production.
last release 2026-08-13 (1 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,955,561 downloads/mo, #2,807 on PyPI
Alternatives
Verify before relying
pip install langgraph-runtime-inmem
from langgraph_runtime_inmem import InMemoryRuntime
runtime = InMemoryRuntime()- Whether Elastic-2.0 license permits commercial or proprietary use without additional licensing
- Whether the in-memory runtime is suitable for production workloads or intended for development/testing only
- Performance characteristics and memory scaling limits for large or long-running graphs
What it is and what it does
langgraph-runtime-inmem is the in-memory backend for LangGraph's runtime API, allowing you to execute graph-based workflows directly in process without requiring a separate persistence layer. It sits within the LangGraph ecosystem alongside dependencies like langgraph, langgraph-checkpoint, starlette, and structlog, providing a lightweight option for running stateful workflows.
The package is designed as a drop-in runtime implementation for the LangGraph API server contract. It handles graph execution, state management, and scheduling (via croniter for timed tasks) entirely in memory, making it suitable for scenarios where you want to prototype, test, or run workflows without external infrastructure. The in-memory design trades persistence and horizontal scaling for simplicity and low operational overhead.
Use it for
- Prototyping and testing LangGraph workflows locally before deploying to a persistent runtime
- Running short-lived or development-stage AI agent workflows that don't require state recovery
- Building single-process applications that orchestrate LangGraph-based tasks with built-in scheduling
- Integrating LangGraph execution into existing Starlette-based web services for real-time agent interactions
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building with LangGraph and need a lightweight, low-friction runtime for development, testing, or single-process deployments.
The active maintenance and low install friction are positive signals. However, clarify the Elastic-2.0 license terms for your use case first, and confirm that in-memory execution meets your durability and scaling requirements before using in production.
Install
langgraph-runtime-inmem on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance as of 1 day ago. Requires Python 3.11 or later.
Requires Python 3.11 or later.
License in practice
License treatment is unclear; Elastic-2.0 may carry restrictions or obligations not immediately apparent from standard SPDX classification. Review the license terms before adopting in production.
Quickstart
pip install langgraph-runtime-inmem
from langgraph_runtime_inmem import InMemoryRuntime
runtime = InMemoryRuntime()Verify before relying
- Whether Elastic-2.0 license permits commercial or proprietary use without additional licensing
- Whether the in-memory runtime is suitable for production workloads or intended for development/testing only
- Performance characteristics and memory scaling limits for large or long-running graphs
Package facts
| License | Elastic-2.0 unclear |
| Python support | Supports the current Python release >=3.11.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 7 packagesblockbustercroniterlanggraph-checkpointlanggraphsse-starlettestarlettestructlog |
| Maintenance | Actively maintained 1 days since the last release |
| First released | |
| Downloads | 2,955,561 / month, #2,807 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: langgraph_runtime_inmem-0.32.4-py3-none-any.whl
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See also langgraph-api · langgraph · langgraph-checkpoint · langgraph-cli · langgraph-checkpoint-aws · langgraph-sdk · aegra-api · ag-ui-langgraph · langgraph-supervisor · graphrag