swarmstate

Drop-in state backend for LangGraph, CrewAI & custom agent loops - Rust core, framework-agnostic, built for production.

Constant-time checkpoint reads — get_tuple stays at ~7 µs whether a thread holds 5 or 2 000 checkpoints, where LangGraph's InMemorySaver climbs from ~5 µs to ~40 µs — and O(1) state snapshots: ~0.5 µs at any size, against 50 ms to deepcopy a 50 000-entry state. Durable writes land on par with SqliteSaver at the same fsync policy (~1.9× faster than its shipped default, which buys stronger durability). Method, hardware and raw numbers → benchmarks/.

swarmstate is a state and checkpointing backend with a Rust core and a Python API for multi-agent systems. It does not compete with visible agent frameworks; it acts as low-level infrastructure - much like engines such as DuckDB, ClickHouse, Arrow, or Polars sit underneath data applications without replacing them.

It solves three production pains:

  1. State lock-in across frameworks - a framework-agnostic store so migrating frameworks doesn't lose state.
  2. Checkpoint reads that get slower as threads grow - a Rust-backed implementation of LangGraph's checkpointer interface that resolves "the latest checkpoint" by lookup instead of scanning a thread's keys.
  3. Deterministic routing paid for in tokens - a native handoff graph that resolves rule-based transitions in microseconds.

Installation

pip install swarmstate            # prebuilt abi3 wheels, no compiler required
uv add swarmstate                 # or with uv

Optional extras: swarmstate[langgraph], swarmstate[crewai], swarmstate[redis], swarmstate[disk], swarmstate[postgres], swarmstate[otel], swarmstate[all].

Usage

import swarmstate as ss

store = ss.Store()                              # in-memory, msgpack codec
store.set("workflow", "onboarding", {"step": 3, "data": {...}})
snap = store.snapshot()                          # cheap, immutable snapshot
store.set("workflow", "onboarding", {"step": 4})
store.restore(snap)                              # rollback
store.get("workflow", "onboarding")              # -> {"step": 3, "data": {...}}

snap2 = store.snapshot()
snap2.diff(snap)                                 # {"added": [...], "removed": [...], "changed": [...]}

# Retention is opt-in: a snapshot the store keeps pins the state it saw
hist = ss.Store(max_history=10)                  # 0 (default) keeps none, None keeps all
hist.history()                                   # -> [Snapshot, ...], oldest first

# Batch ops: one GIL release / round-trip for the whole set
store.set_many([("workflow", "a", {...}), ("workflow", "b", {...})])
store.get_many([("workflow", "a"), ("workflow", "b")])   # -> [..., ...], order preserved

# Deterministic, LLM-free routing (resolved natively in Rust)
g = ss.HandoffGraph()
g.add_edge("triage", "billing", when="category == 'billing'")
g.add_edge("triage", "human")                    # unconditional default
g.route("triage", {"category": "billing"})       # -> "billing"

Drop-in LangGraph checkpointer (pip install "swarmstate[langgraph]"):

from swarmstate.integrations.langgraph import SwarmStateSaver

graph = builder.compile(checkpointer=SwarmStateSaver())   # replaces SqliteSaver, 1 line

Bounded memory for long-running threads — checkpointers keep every step by default, which for a service that never restarts means growth without end:

saver = SwarmStateSaver(max_checkpoints_per_thread=8)     # keep the newest N per thread

Older checkpoints are dropped with their pending writes and channel blobs. On a 300-invocation thread that is 0.5 MB instead of 28 MB, and the thread still resumes; time travel is limited to the retained window, so size it to taste.

Optional metrics on checkpoint operations (opt-in, zero overhead when unused):

from swarmstate.observability import InMemoryMetrics       # or OpenTelemetryMetrics

metrics = InMemoryMetrics()
saver = SwarmStateSaver(metrics=metrics)
# ... run the graph ...
metrics.summary()   # {"put": {"count": 12, "p50_ms": 0.006, ...}, "get_tuple": {...}}

OpenTelemetry tracing (each checkpoint op becomes a swarmstate.checkpoint.<op> span):

from swarmstate.observability import get_tracer     # needs swarmstate[otel]

saver = SwarmStateSaver(tracer=get_tracer())         # composes with metrics=...

Status

Early development.

  • M0 (scaffolding) ✅ - Rust core builds; import swarmstate works.
  • M1 (Rust store) ✅ - concurrent KV store, msgpack codec, O(1) immutable snapshots, incremental diffs, GIL released on hot paths.
  • M2 (HandoffGraph) ✅ - deterministic conditional routing with a safe Rust condition evaluator (no eval), cycle detection.
  • M3 (LangGraph adapter) ✅ - SwarmStateSaver, a drop-in BaseCheckpointSaver backed by the Store; snapshot/roll back the whole checkpoint DB at once.
  • M4 (Benchmarks) ✅ - durable-vs-durable and in-memory-vs-in-memory comparisons on LangGraph's interface, read latency as a thread grows, Store.snapshot() vs deepcopy, and concurrency scaling. Reproducible: benchmarks/run.py; method and results in benchmarks/README.md.
  • M5 (CrewAI adapter + backends) ✅ - persistent, drop-in checkpointer backends RedisStore, DiskStore (SQLite) and PostgresStore, all msgpack wire-format, plus SwarmStateStorage (portable memory backed by a shared Store).
  • M6 (docs · wheels · PyPI) ✅ - full docs site, benchmarks, cross-platform abi3 wheels, and PyPI publishing via Trusted Publishing (OIDC).
  • Observability ✅ - opt-in metrics hooks and OpenTelemetry tracing on checkpoint ops (put / put_writes / get_tuple): an in-memory sink, an OpenTelemetry metrics sink, and per-op spans (swarmstate[otel]). Zero overhead when unused. Strict mypy in CI.
  • Free-threaded (no-GIL) ready ✅ - the Rust core declares free-threaded support, so on a free-threaded CPython build (cp313t) the store doesn't collapse under threads the way the GIL build does: on a set+get workload at 8 threads it sustains ~1.8M ops/s vs ~130k on GIL Python (over 10x), where the GIL build gets much slower as threads are added. (These workloads are allocation-bound, so neither scales linearly with cores; the win is avoiding the GIL's collapse.) Version-specific cp313t and cp314t wheels ship for Linux (x86_64/aarch64), macOS (arm64) and Windows (x64) alongside the abi3 ones.
  • Batch API ✅ - Store.set_many / get_many (and on every backend) amortize the per-call overhead over a batch: one GIL release for the in-memory core, one round-trip for networked backends. On free-threaded at 8 threads, set_many is ~3x the throughput of individual sets. SwarmStateSaver uses it internally: put_writes (and the incremental channel blobs) flush all writes of a step in a single set_many, so fan-out steps that emit many pending writes pay one lock/round-trip instead of one per write.

Examples

Runnable, offline, deterministic demos in examples/:

  • support_triage.py - a LangGraph workflow tying together HandoffGraph routing, SwarmStateSaver checkpointing and snapshot/restore time-travel.
  • state_portability.py - state as standard msgpack bytes, read back and cross-checked against the msgpack package.

Documentation

Guide, tutorials and API reference: swarmstate.github.io — the store, snapshots & diffs, the LangGraph checkpointer, persistent backends, the handoff graph and the benchmark method. The site is built from swarmstate/swarmstate.github.io.

Development

python -m venv .venv && source .venv/bin/activate
pip install maturin pytest
maturin develop --release     # compile the Rust core and install it locally
cargo test                    # Rust core tests
pytest -q                     # Python API tests

Citing

If you use swarmstate in academic work, please cite it. GitHub's "Cite this repository" button (from CITATION.cff) produces ready-made APA and BibTeX entries. To cite the archived software release, use its Zenodo DOI (10.5281/zenodo.XXXXXXXX):

@software{salmeron_swarmstate,
  author    = {Salmeron, Jose L.},
  title     = {{swarmstate}: A state and checkpointing backend for multi-agent
               systems with a Rust core},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.XXXXXXXX},
  url       = {https://github.com/swarmstate/swarmstate}
}

The DOI is minted when the first release is archived on Zenodo. Replacing 10.5281/zenodo.XXXXXXXX here, in CITATION.cff and on the docs site is all it takes — the placeholder is deliberate, so that nothing cites an identifier that does not resolve.

License

MIT

Project links

Data verified by PyPI on Aug 23, 2026
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Avatar for salmeron from gravatar.com salmeron

Credits

Author: Jose L. Salmeron

GitHub Statistics

Data verified by PyPI on Aug 23, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Provides Extra

all bench crewai dev disk langgraph otel postgres redis

Tags

agents langgraph crewai checkpoint state rust multi-agent

Classifiers

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