agent-coherence

agent-coherence stops one agent from silently clobbering another's work on a shared plan.md, store key, or memory.json — a vendor-neutral MESI + optimistic-concurrency coordinator for agent state on a single host, with the safety invariants machine-checked in TLA+.

Two agents share an artifact — a plan.md, a store key, a memory.json. One reads it and works; meanwhile a peer commits a newer version; the first writes back anyway. Last write wins, the peer's work is silently gone, nothing errors, and every downstream decision builds on the wrong version. agent-coherence turns that silent clobber into a loud, typed refusal: MESI-style ownership and invalidation over shared artifacts, optimistic commit-CAS for concurrent writers, and a read-generation fence for crash-reclaimed ones — a stale write is denied or returned as a retryable conflict, never silently applied. Same library, same protocol, across LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, plain files shared across processes (CoherentVolume), any MCP client (the stale-write-guard-fs server, via the mcp extra), and any custom orchestrator. Same behavior regardless of which model provider (Anthropic, OpenAI, Google, Mistral, open-source) the agents talk to.

mcp-name: io.github.Cohexa-ai/stale-write-guard-fs

# Requires Python 3.11+
pip install "agent-coherence[langgraph]"        # LangGraph drop-in
pip install "agent-coherence[crewai]"           # CrewAI adapter
pip install "agent-coherence[openai-agents]"    # OpenAI Agents SDK adapter (experimental)
pip install "agent-coherence[diagnose]"         # ccs-diagnose CLI
pip install "agent-coherence[mcp]"              # stale-write-guard-fs MCP server
pip install "agent-coherence[all]"              # everything

# Before
from langgraph.store.memory import InMemoryStore
store = InMemoryStore()

# After — one import change, no node code changes
from ccs.adapters import CCSStore
store = CCSStore(strategy="lazy")

store.get(), store.put(), store.search() keep working unchanged. CCSStore adds read-side coherence: a peer's commit invalidates your cached view, so your next read is a fresh miss. It does not deny a stale write-back — put is not version-CAS; for write-side lost-update prevention, route writes through CoherentVolume or write_cas (below).

The one-import swap assumes your store namespaces carry the agent identity in namespace[0] — a (user_id, "memories") shape would merge users onto one shared artifact. See the namespace convention.

# Plain files shared across processes / sessions — no framework required
from ccs.adapters.coherent_volume import CoherentVolume

vol = CoherentVolume(workspace_root, managed=("plans/**",))
plan = vol.read("plans/plan.md")           # tracked read — your view is registered
vol.write("plans/plan.md", revised_plan)   # stale view? denied fail-closed → vol.reacquire() and re-derive

agent-coherence-replay — invariant-replay for any CoherenceAdapterCore-mediated agent system. LangGraph capture verified in v1 via CCSStore.record_to(path); CrewAI / AutoGen wired through the same seam but unverified — file an issue if it breaks.

What it guarantees

Each row is a safety invariant model-checked with TLA+/TLC. make tla-check runs all six specs in CI on every push, and every spec carries a documented mutant that must fail — the invariants are load-bearing, not decorative.

Scope, honestly: the guarantees hold for writers that go through the coordinator, under a single coordinator (one host). Concurrent same-key writers on one host are covered; cross-host fencing is on the roadmap, demand-gated — if you need it, open an issue. Edits that bypass the coordinator entirely (a human in an editor, a formatter, a regenerating script) are caught at the workspace boundary by content-hash checks — the foreign-edit guards below, enforced by tests rather than TLA+. Specs, the invariant ↔ implementation map, and the mutant recipes live in formal/tla/.

Correctness is the wedge; the token savings come with it. Writes publish ~12-token invalidation signals instead of rebroadcasting full artifacts, so read-heavy fleets stop re-paying for state they already hold:

Measured on real LangGraph graphs; see docs/reproduce.md and the user guide.

Those are the spatial savings (more agents sharing one artifact). The temporal dimension — a single agent whose source drifts between its turns — has its own pre-registered benchmark, TC-1 (#116): a reproducible savings-regime map of how many re-fetches coherence-gating avoids as the change-rate rises. The metric is re-fetches-avoided — a proxy, a regime map, not a token/dollar invoice. Reproduce with python tools/run_cost_sweep.py; the locked verdict + numbers (PASS at n=50, crossover r≈0.31) live in benchmarks/cost_preregistration.md. Shipped in v0.9.3.

RAG & shared agent memory

RAG corpora and agent memory are shared mutable state, so the stale-read→write lost update lands there too — and a consistent store doesn't save you: the staleness is in the agent's cached view of a record, not the store. Two agents read a record at v1; one writes v2; the other, still on its v1, writes an edit computed from v1 and clobbers v2. agent-coherence keeps the readers current — CCSStore is a drop-in for langgraph.store (composing with Mem0, Letta, LlamaIndex, a vector store, or a plain file underneath whatever you already use; it stores no vectors and does no ranking), so a peer's commit invalidates the stale cached view (read-side coherence). Preventing the stale write-back itself is the write side — route those writes through CoherentVolume or write_cas.

• Runnable, deterministic demo (offline, no keys): python -m examples.coherent_volume.main reproduces the documented lost update, then prevents it.
• Honest scope: writes that go through the coordinator are caught. Auto-watching an unmanaged external source that changes with no coordinator write (a hand-edited file, an out-of-band re-index) is the source-watcher case — on the roadmap, demand-gated, not shipped today.
• Positioning + FAQ: agent-coherence.dev/rag.

• 📖 User guide — installation, namespace convention, strategies, observability, telemetry, examples, full API reference
• 🔎 RAG & shared memory — coherence for retrieval corpora and agent memory stores, with the runnable lost-update demo
• 🗂️ Coherent workspace — CoherentVolume, the data-plane appliance for plain files shared across processes
• 🧱 BYO substrate — CoherentRow / CoherentObject, the same coherence over a Postgres row or an S3 object you already run
• 🛡️ Foreign-edit guards — catch out-of-band edits (a human, a formatter, a script) at the read/write boundary
• 🔌 MCP server — stale-write-guard-fs, the same guarantee for any MCP client, no Python integration required
• 🚦 Effect-ordering gate — gate(), fire an agent's effect only on the input version it decided from
• 📸 Multi-artifact snapshot sessions — read several artifacts as one consistent cut; no torn reads
• 📦 Atomic multi-file publish — atomic_publish, land a set of files all-or-nothing; never a torn pair
• 🧮 Formal verification — the TLA+ specs, invariant ↔ implementation map, mutant recipes
• 🩺 ccs-diagnose CLI — find divergent reads in your existing LangGraph graph without changing any code
• 🧩 Claude Code plugin — cross-session coherence for the prose rules (CLAUDE.md, plan.md) parallel Claude Code sessions share
• 🔍 Why coherence matters — the gap across LangGraph, CrewAI, AutoGen, and Claude Agent SDK
• 🧭 The MESI-derived approach — how the protocol maps each documented gap to a shipped surface, with boundaries
• 🔐 Security & supply chain — kill switches, hash-pinned install, attestation verification, threat model
• 📜 Changelog — version history
• 📄 Paper on arXiv (2603.15183) — formal protocol, TLA+ verification, simulation results

How it works

Each shared artifact is cached locally per agent and reads serve from the local cache when that copy is fresh. Writes commit to a coordinator, which sends lightweight invalidation signals (~12 tokens) to peers so the next read fetches the new version instead of rebroadcasting the full artifact. Consistency is single-writer-multiple-reader per artifact with bounded staleness — peers re-fetch on next read.

Two write disciplines share the same guarantee. Pessimistic: acquire EXCLUSIVE, commit; a writer whose view went stale is denied and must reacquire(). Optimistic: write_cas — read, compute, commit-CAS; the loser of a race gets a typed conflict and bounded retry. Crash recovery composes with both: reclaiming a stalled grant bumps the artifact's ownership epoch, so a reclaimed writer that completes later is rejected at commit even when the version is unchanged (the read-generation fence). On the read side, a snapshot session pins a consistent cut across several artifacts, so a multi-artifact read never sees a torn mix of versions.

Five synchronization strategies ship out of the box: lazy (default), eager, lease (TTL-based), access_count, and broadcast. Pick the one that matches your workload's read/write ratio and how aggressively cached reads should refresh.

Architecture

• Protocol (ccs.core, ccs.strategies) — coherence state machine and synchronization strategies; no framework dependencies.
• Coordinator (ccs.coordinator) — authority service tracking directory state, publishing invalidations, arbitrating commit-CAS, and reclaiming stale grants (crash recovery + read-generation fence).
• Adapters (ccs.adapters) — framework integrations for LangGraph, CrewAI, and AutoGen (~100 lines each), plus an experimental OpenAI Agents SDK adapter (Session-cache coherence + RunHooks).
• Coherent workspace (ccs.adapters.coherent_volume) — the data-plane appliance: an out-of-process coordinator client that brings the same guarantee to plain files on disk, no framework required. See Coherent workspace.
• MCP server (ccs.mcp) — the stale-write-guard-fs stdio server that exposes the coherent-workspace guarantee to any Model Context Protocol client over five swg_* tools. See MCP server.
• Simulation (ccs.simulation) — deterministic tick-driven engine for scenario benchmarks with failure injection.
• Event bus (ccs.bus) — the transport for invalidation signals; in-memory / in-process today (InMemoryEventBus). Networked transports (Redis, Kafka, NATS, gRPC) for a multi-host deployment are on the roadmap, demand-gated.

Protocol safety properties — single-writer, monotonic versioning, the crash-recovery sweep invariants, the OCC no-lost-update, the reclamation fence's no-stale-apply, version retention's no-collected-read, and the snapshot session's no-read-skew-within-cut — are model-checked with TLA+/TLC. The tla-check CI job runs all six specs on every push and PR.

Coherent workspace: the data plane for shared files

The framework adapters wrap a store. CoherentVolume is the other half — the data-plane appliance, the building block that makes a shared workspace coherent for plain files on disk, with no framework in the loop. Architecturally it's an out-of-process coordinator client, not an in-process wrapper: it writes the policy, spawns (or attaches to) a local coordinator over SQLite-WAL, and routes reads and writes through it. Your content stays on the real filesystem; the coordinator holds only MESI state, a content hash, and a version per managed file. Point a sibling volume in another process at the same workspace and it attaches to the same coordinator, so a single-host fleet shares one coherent view.

from ccs.adapters.coherent_volume import CoherentVolume

vol = CoherentVolume(workspace_root, managed=("plans/**", "memory/**"))
data = vol.read("plans/plan.md")              # bytes — registers a SHARED view
vol.write("plans/plan.md", revise(data))      # stale view? denied fail-closed
data = vol.reacquire("plans/plan.md")         # recover: re-mint identity + mandatory fresh read

The explicit read / write / reacquire / write_cas API is the supported primitive (write_cas(path, make_content) is the optimistic counterpart for same-key contention — the loser gets a typed conflict, never a silent drop). For code you'd rather not rewrite, an opt-in, demo-grade open() shim routes managed-path opens through the volume so existing open() / pathlib calls get coherence unchanged:

from ccs.adapters.coherent_volume import coherent_workspace

with coherent_workspace(workspace_root, managed=("plans/**",)):
    text = open("plans/plan.md").read()       # registers a SHARED view
    open("plans/plan.md", "w").write(edit)    # stale view? raises out of close()

Scope, honestly. Plain write() prevents the sequential stale-read→write lost update for a single-host fleet sharing one workspace (A reads v1, B reads v1, A commits v2, B's stale write is denied → B re-reads); concurrent same-key racers go through write_cas — one winner, the loser gets a typed conflict and re-derives, never a silent drop. Edits that bypass the volume entirely are caught at the boundary by the foreign-edit guards. It does not catch an agent that re-reads fresh bytes and then writes a buffer computed from older ones. The open() shim is convenience, not the contract: it covers open()/pathlib text+binary read/write, but not raw os.open, subprocess redirection, mmap, or append/update modes — those delegate to the original open() unchanged. Run it yourself: python -m examples.coherent_volume.main (offline, deterministic, no keys), or read the positioning + FAQ.

Foreign-edit guards: writes that bypass the coordinator

Coordination covers writers that opt in — but real workspaces also get edited from outside: a human fixes a file in an editor, a formatter rewrites it, a CI script regenerates it. Without a guard, the next agent write silently buries that edit, and the next agent read silently builds on bytes the coordinator never saw. CoherentVolume guards both boundaries with a content-hash check:

• Write boundary — on by default. Before writing, the volume checks whether the managed file's on-disk bytes changed out-of-band since it last read or wrote them. If they did, the write raises StaleView instead of clobbering the foreign edit — recover with reacquire() (fresh read → re-derive → re-write). Opt out with CoherentVolume(on_stale_write="allow") to restore last-writer-wins.

• Read boundary — opt-in. With CoherentVolume(on_stale_read="raise"), re-reading a managed file whose bytes changed out-of-band raises StaleView instead of returning bytes your other state wasn't computed from; in strict mode the coordinator enforces the same check server-side. A volume never denies its own just-written bytes — the benign commit→disk-write lag window is recognized and suppressed.

vol = CoherentVolume(workspace_root, managed=("plans/**",), on_stale_read="raise")
# a formatter rewrites plans/plan.md out-of-band …
vol.write("plans/plan.md", revised)   # StaleView — the foreign edit survives
fresh = vol.reacquire("plans/plan.md")  # recover: fresh read, re-derive, re-write

Scope, honestly. These are content-hash checks at the volume's read/write boundary — best-effort point-in-time detection, not filesystem interception. A write that never goes through the volume is caught at the next volume read/write of that file, not blocked as it happens; watching unmanaged external sources is on the roadmap, demand-gated. These guards are enforced by tests, not TLA+ — the model-checked invariants cover the protocol state machine, not disk bytes.

MCP server: stale-write-guard-fs

The same guarantee for agents that speak Model Context Protocol — Claude Code, Cursor, or a custom runtime — with no Python integration at all. stale-write-guard-fs is a stdio MCP server that wraps CoherentVolume and exposes coordinated file access as five tools:

pip install "agent-coherence[mcp]"

{
  "mcpServers": {
    "stale-write-guard-fs": {
      "command": "stale-write-guard-fs",
      "env": { "SWG_ROOT": "/path/to/shared/workspace" }
    }
  }
}

The server binds one workspace per session (SWG_ROOT, defaulting to its working directory; the whole workspace is guarded unless SWG_MANAGED — a comma-separated glob list — narrows it), rejects path traversal and any access to the coordinator's own state directory, and fails closed on IO errors. Denials come back as typed, machine-readable payloads — an agent can parse recover: reacquire and self-heal instead of retrying blindly. Run the red→green demo: python -m examples.mcp_stale_write_guard.main (offline, deterministic, no keys).

Scope, honestly. Same contract as the volume it wraps: single-host, managed paths, cooperative — it guards agents that route file access through the tools; it cannot see edits made around them (those are caught at the next tool call on that file by the foreign-edit guards).

Effect-ordering gate

Agents don't only overwrite files — they fire effects (a deploy, a PR, a shell command) computed from inputs they read earlier. If the input moved in between, the effect fires on stale state. gate() narrows that window: it captures the input's version at decision time, re-reads at the effect boundary, and fires only if the input is unchanged at that re-read — otherwise it holds the effect before it runs.

from ccs.adapters import CoherentVolume, gate

vol = CoherentVolume(workspace_root, managed=("deploy/**",))

# fires run_deploy(plan) only if deploy/config.txt is unchanged since decide() read it;
# else raises StaleView before the deploy runs — reacquire() and re-decide.
gate(vol, "deploy/config.txt", decide=plan_deploy, effect=run_deploy)

It's plain Python, so the same call drops into a LangGraph node, a CrewAI task, or a raw script unchanged.

Scope, honestly. The gate orders effects, it does not roll them back: it fires pre-effect and never undoes one, so for an escaping effect there's a residual re-read→fire window it narrows but can't close. It's single-host and cooperative — the agent opts in. For a pure write effect, use vol.write_cas_at(path, expected_version, content) directly, which is the atomic, no-window path. Gating several mutually-consistent inputs at once is a snapshot-session operation on the coordinator, not this single-input wrapper. Run it: python -m examples.effect_gate.main (offline, deterministic, no keys), or add --baseline to see the stale fire it catches.

Multi-artifact snapshot sessions

gate() protects one input. But an agent that reads several artifacts one by one — a plan, a config, a memory file — can see a torn combination: plan.md from before a peer's commit and config.json from after it. Every individual read was current; the set never coexisted (read-skew). A snapshot session closes that window: it pins a consistent cut of the artifacts you name, captured at a single point, and serves every session read from that cut while peers keep writing.

Against a running coordinator (the same one CoherentVolume spawns), over HTTP:

POST /session/begin      {session_id, read_set: ["plans/plan.md", "config/app.json"]}
                         → {session_token, cut: {path: version}, …}
POST /session/read       {session_id, session_token, path}
                         → the artifact at its PINNED version — never a newer one
POST /session/commit     {session_id, session_token, path, content}
                         → wins only if no peer moved the artifact since the cut
POST /session/heartbeat  {session_id, session_token} — keep the session's lease alive

Or in-process: CoordinatorService.begin_session(read_set=…, owner=…) → session_read(…) / session_commit(…). The cut is an inspectable {artifact: version} map, not an opaque handle — you can read exactly which versions your session is pinned to.

Fail-closed by construction: reading an artifact that was not in the pinned read-set is refused with a typed rejection — never silently served from live state. Sessions have a bounded lifetime backed by a heartbeat lease: a session whose heartbeat lapses, or that is lost to a coordinator restart, is invalidated — later reads get a typed "session invalidated" rejection telling the agent to re-establish, never a quiet fall-through to whatever is current. Model-checked: NoReadSkewWithinCut and PinAlwaysRetained (formal/tla/Snapshot.tla).

Scope, honestly. This prevents read-skew — torn reads across artifacts. It does not add write-skew prevention: commits validate per-artifact against the pinned base through the same optimistic CAS as write_cas, so two sessions that read one cut and write different artifacts can still interleave. Single coordinator, single host. When the coordinator retains version bodies it serves the pinned bytes directly; otherwise it returns the pinned version and content hash as a typed signal and the caller fetches the bytes from its own data plane.

Atomic multi-file publish

write_cas_at lands one file if it hasn't moved. But an agent often edits a set of files that must stay consistent — a plan and its manifest, a config split across files — and must land them together or not at all, never a torn pair where one file references another that already changed. atomic_publish is that all-or-nothing batch:

from ccs.adapters import CoherentVolume

vol = CoherentVolume(workspace_root, managed=("proj/**",))

# lands BOTH files iff each is still at the version the agent read; otherwise the
# WHOLE publish is held (StaleView / CasVersionConflict) with NO file written.
versions = vol.atomic_publish([
    ("proj/plan.md",     plan_version,     new_plan_bytes),
    ("proj/manifest.md", manifest_version, new_manifest_bytes),
])   # -> {"proj/plan.md": 2, "proj/manifest.md": 3}

Either every member's version advances or none does, and a moved member holds the whole batch — a torn commit is never a reachable state, formally specified as the NoPartialPublish invariant in formal/tla/AtomicPublish.tla. A single-file publish takes the direct CAS path; a multi-file publish opens a snapshot session so the versions it checks are captured at one point (no member read across a peer commit). Run it: python -m examples.atomic_publish.main (offline, deterministic, no keys), or add --baseline to see the file-by-file torn pair it prevents.

Scope, honestly. The all-or-nothing guarantee is at the coordinator commit — that is what NoPartialPublish covers. Disk materialization happens after the commit and is best-effort: every file is staged to a temp then renamed into place, so a disk fault fails before any rename (disk stays uniformly old) and a rename failing partway raises a typed PublishMaterializationError naming exactly which files landed — never a bare error implying nothing published. This shrinks, but a crash between renames can't fully eliminate, the multi-file disk window (there is no POSIX multi-file atomic rename); on that error the coordinator is ahead of disk and you re-read + re-materialize. It is single-host and cooperative. The multi-file path also adds a small capture→commit window (the session open); a peer winning it holds the publish rather than tearing it. This is all-or-nothing publish of a file set — not rollback of effects that already escaped, and not write-skew prevention across sessions.

BYO substrate: coherence over the store you already run

CoherentVolume puts coherence over files on disk. But shared agent state often lives in a store you already run — a Postgres row, an S3 object. BYO-substrate bindings bring the same coherence over that store, with the coordinator holding only metadata (a version, per-agent MESI, a fixed-width content_hash, an opaque substrate token) — never the bytes:

from ccs.adapters.coherent_row import CoherentRow      # pip install "agent-coherence[coherent-row]"
from ccs.adapters.coherent_object import CoherentObject  # pip install "agent-coherence[coherent-object]"

# agent A reads a row it will edit over several steps
row = CoherentRow(dsn=..., table="workspaces", artifact_id="ws-42")
data, token = row.read("ws-42")
# ... meanwhile agent B commits a new version through the binding ...
# A's next binding-mediated read/act is DENIED before A writes:
row.commit("ws-42", expected_token=token, new_bytes=revised)  # -> StaleView; reacquire() and re-decide

The value over the substrate's own conditional write (UPDATE … WHERE version=?, S3 If-Match): a bare CAS rejects A's write at write time; the binding tells A its cached view went stale before it acts, in the same typed vocabulary a file (CoherentVolume) or a store key (CCSStore) uses — one coherence surface over a row, an object, or a file. A declarative Coherence Manifest wires each artifact to a substrate and an honest guarantee tier, with credentials as references (secret-file: / aws-default, never literals) and SSRF-constrained connection targets.

Scope, honestly. v1 ships two native-CAS bindings (Postgres + S3) and a forward-only tier for action backends (a Slack post, a Gmail send — decision-input freshness only, no CAS). The value is invalidation-before-act + cross-substrate uniformity; the read-generation fence over a substrate is a documented roadmap item, not claimed — v1 OCC writers ride admit-on-absent + the version-CAS. Single-host and cooperative; when the substrate is itself distributed (S3, managed Postgres) the no-lost-update guarantee is the substrate's and identical with or without this layer. Run it: python -m examples.coherent_row.main / examples.coherent_object.main (offline, deterministic, no keys — an in-memory substrate stand-in; production points the same binding at real Postgres / S3). Full API, per-binding least-privilege, and the honest tier table: BYO substrate bindings.

Status

v0.13.0 released — BYO-substrate bindings (CoherentRow for Postgres, CoherentObject for S3) and subagent identity in the Claude Code adapter. BYO-substrate puts the same deny-stale-writes coherence over state in a store you already run: the coordinator holds only metadata (a version, per-agent MESI, a content hash, an opaque substrate token) — never your bytes — and adds the cross-agent layer a bare conditional write can't provide: a peer's commit marks your cached read stale, so the next binding-mediated read or write is denied before you act on state that already moved, with the same typed conflict and reacquire() recovery on every substrate. Subagent identity makes each Claude Code subagent a first-class coherence peer — correct last_writer attribution, sibling-collision detection, and a scoped grant release on SubagentStop (main-thread behavior unchanged when no subagent id is present). Single-host scope unchanged. See CHANGELOG.md.

See CHANGELOG.md for the full version history and releases for tagged artifacts. Alpha — APIs may change before v1.0.

Paper

Token Coherence: Adapting MESI Cache Protocols to Minimize Synchronization Overhead in Multi-Agent LLM Systems arXiv:2603.15183

@article{parakhin2026token,
  title   = {Token Coherence: Adapting MESI Cache Protocols to Minimize
             Synchronization Overhead in Multi-Agent LLM Systems},
  author  = {Parakhin, Vladyslav},
  journal = {arXiv preprint arXiv:2603.15183},
  year    = {2026}
}

Community

Questions, war stories, and ideas welcome in Discussions. If you've hit a stale-read bug in a multi-agent workflow, open an issue — I'd like to hear about it.

License

Apache-2.0. See LICENSE.