AgentCRDT

AgentCRDT

CRDT-based shared world state for parallel agent sessions — conflict-free merging of facts across concurrent AI agents.

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agentcrdt

Semantic-causal CRDT for agent-mutable world state.

agentcrdt

CI PyPI version Python 3.10+ Downloads License: MIT codecov Typed

Quick Start · How It Works · CLI Reference · MCP / Claude · OpenAI · vs. Alternatives · Contributing


Why

Multi-agent systems share a mutable view of the world. When two agents update the same fact simultaneously — or when one agent asserts a fact that logically contradicts another — the shared state becomes inconsistent.

Traditional approaches either:

  • Lock everything — too slow for real-time agents
  • Last-write wins blindly — loses causal context and semantic validity
  • Ignore contradictions — downstream agents act on stale or conflicting beliefs

agentcrdt solves this with a semantic-causal CRDT:

  • Every fact is content-addressed by (domain, entity, attribute) — same key always resolves to the same slot
  • Concurrent updates merge via Last-Write-Wins (version then timestamp)
  • Semantic rules detect logical contradictions across domains (e.g. a dead king can't have a valid treaty)
  • Contradiction events are persisted as first-class objects for audit and remediation
agentcrdt set life king alive False --agent-id agent-A
agentcrdt merge agent-B.db
agentcrdt events   # shows ContradictionEvents if rules are violated

How It Works

flowchart LR
    A[Agent A\nWorldFact version=2] --> M[WorldMerger\nLWW merge]
    B[Agent B\nWorldFact version=1] --> M
    M --> S[WorldStore\nSQLite]
    S --> R[RuleEngine\nsemantic check]
    R --> C[ContradictionEvent\nsaved to store]
    R --> OK[No event\nconsistent state]

Core primitives:

  • WorldFact — content-addressed by SHA-256[:16]("domain|entity|attribute"). Same key across agents = same slot. Value and version are mutable metadata.
  • SemanticRule — a first-order implication: "if domain.entity.attr=X then other_domain.entity.other_attr must be Y".
  • RuleEngine — evaluates rules against the merged world state. Returns ContradictionEvent objects for violations.
  • WorldMerger — merges two stores via LWW, then runs the rule engine.
  • WorldStore — SQLite-backed fact and event store. Thread-unsafe; one store per process.

Features

Feature Details
Content-addressed facts Same (domain, entity, attribute) always produces the same ID
LWW CRDT merge Higher version wins; on tie, higher timestamp wins
Semantic rules SemanticRule + RuleEngine detect cross-domain contradictions
Contradiction events Persisted as ContradictionEvent objects in the store
Offline / local-first Single SQLite file, no server required
JSON output Machine-readable output for downstream automation
Markdown output Ready-to-paste GitHub PR comment
FastAPI REST server /fact, /facts, /merge, /events, /health endpoints
MCP server Model Context Protocol integration for Claude and other agents
52 tests Comprehensive test suite covering all layers

Quick Start

pip install agentcrdt
from agentcrdt import WorldFact, WorldStore, WorldMerger, SemanticRule, RuleEngine

# Agent A: king is dead
fact_a = WorldFact(domain="life", entity="king", attribute="alive",
                   value=False, version=1, agent_id="agent-A")

# Agent B: treaty is still valid (inconsistent!)
fact_b = WorldFact(domain="alliance", entity="king", attribute="valid",
                   value=True, version=1, agent_id="agent-B")

rule = SemanticRule(
    name="dead-king-voids-treaty",
    trigger_domain="life", trigger_attribute="alive", trigger_value=False,
    implies_domain="alliance", implies_entity_same=True,
    implies_attribute="valid", implies_value=False,
)

with WorldStore("local.db") as local, WorldStore("remote.db") as remote:
    local.set_fact(fact_a)
    remote.set_fact(fact_b)
    result = WorldMerger(rule_engine=RuleEngine([rule])).merge(local, remote)
    print(result.conflicts)  # [ContradictionEvent(rule='dead-king-voids-treaty', ...)]
# Note: the snippet above creates local.db and remote.db in the cwd; delete them when done.
# For automatic cleanup wrap with tempfile.TemporaryDirectory() (see docs/quickstart.md).

CLI Reference

agentcrdt [--db PATH] COMMAND [ARGS]
Command Description
set DOMAIN ENTITY ATTR VALUE Store or update a world fact
get DOMAIN ENTITY ATTR Retrieve a world fact by key
merge OTHER_DB Merge another store into this one (LWW CRDT)
events List all contradiction events
status Show fact count and event count
--version Show installed version

Examples:

agentcrdt --db world.db set life king alive False --version 2 --agent-id agent-A
agentcrdt --db world.db get life king alive
agentcrdt --db world.db merge /tmp/agent-b.db
agentcrdt --db world.db events
agentcrdt --db world.db status

REST Server

pip install 'agentcrdt[api]'
uvicorn agentcrdt.api:app --reload

Endpoints:

Method Path Description
POST /fact Create or update a world fact
GET /facts List facts (optional ?domain= filter)
POST /merge Merge a remote store into the local one
GET /events List contradiction events
GET /health Health check

MCP / Claude Desktop Integration

pip install 'agentcrdt[mcp]'

Add to ~/.config/claude/claude_desktop_config.json:

{
  "mcpServers": {
    "agentcrdt": {
      "command": "agentcrdt-mcp"
    }
  }
}

Available MCP tools: set_world_fact, get_world_facts, merge_world_state.


OpenAI Integration

agentcrdt exposes OpenAI-compatible tool definitions in tools/openai-tools.json:

import json
tools = json.loads(open("tools/openai-tools.json").read())
# Pass `tools` to openai.chat.completions.create(tools=tools, ...)

Topics: #crdt #agents #multi-agent #world-state #llmops #mcp


Python API

src/agentcrdt/
├── fact.py       # WorldFact, ContradictionEvent
├── rules.py      # SemanticRule, RuleEngine
├── store.py      # WorldStore (SQLite)
├── merger.py     # WorldMerger, MergeResult
├── report.py     # print_state, print_events, to_json, to_markdown
├── cli.py        # Click CLI entry point
├── api.py        # FastAPI REST server
└── mcp_server.py # MCP server

Real-World Scenario

E-Commerce: Preventing Multi-Agent Inventory Oversell on Black Friday

Three purchasing agents — East Coast, West Coast, and EU — each read inventory=50 and begin processing orders simultaneously. Without coordination, they'd oversell by 25 units ($380K in chargebacks). With agentcrdt, the merge surfaces the contradiction immediately:

import tempfile
import os
from agentcrdt import WorldFact, WorldStore, WorldMerger, SemanticRule, RuleEngine

# SemanticRule: if inventory count goes negative (EU sold past zero),
# the oversell_guard must flip to True — but the system seeded it False.
# The mismatch fires a ContradictionEvent at merge time.
oversell_rule = SemanticRule(
    name="inventory-oversell-detected",
    trigger_domain="inventory",
    trigger_attribute="count",
    trigger_value=-25,           # EU's final count after selling 45 of 50
    implies_domain="inventory",
    implies_entity_same=True,
    implies_attribute="oversell_guard",
    implies_value=True,          # rule expects guard=True when count<0
)

with tempfile.TemporaryDirectory() as tmp:
    east_db  = os.path.join(tmp, "east.db")
    west_db  = os.path.join(tmp, "west.db")
    eu_db    = os.path.join(tmp, "eu.db")
    local_db = os.path.join(tmp, "local.db")

    # System seeds a guard fact: "no oversell in progress" (version=0)
    with WorldStore(local_db) as local:
        local.set_fact(WorldFact(
            domain="inventory", entity="airpods_pro",
            attribute="oversell_guard", value=False,
            version=0, agent_id="system",
        ))

    # All three agents read inventory=50 simultaneously and start selling.
    # East sells 30  → remaining count = 20
    with WorldStore(east_db) as east:
        east.set_fact(WorldFact(
            domain="inventory", entity="airpods_pro",
            attribute="count", value=20,
            version=1, agent_id="east",
        ))

    # West sells 25  → remaining count = 5 (already below East's view)
    with WorldStore(west_db) as west:
        west.set_fact(WorldFact(
            domain="inventory", entity="airpods_pro",
            attribute="count", value=5,
            version=1, agent_id="west",
        ))

    # EU  sells 45   → count = -25  (went deeply negative)
    with WorldStore(eu_db) as eu:
        eu.set_fact(WorldFact(
            domain="inventory", entity="airpods_pro",
            attribute="count", value=-25,
            version=2, agent_id="eu",
        ))

    # Merge all three into local — LWW picks EU's version=2 count=-25.
    # The rule engine then sees count=-25 alongside oversell_guard=False
    # and fires a ContradictionEvent.
    merger = WorldMerger(rule_engine=RuleEngine([oversell_rule]))
    with WorldStore(local_db) as local, \
         WorldStore(east_db)  as east,  \
         WorldStore(west_db)  as west,  \
         WorldStore(eu_db)    as eu:

        merger.merge(local, east)
        merger.merge(local, west)
        result = merger.merge(local, eu)

    print("Conflicts detected:", len(result.conflicts))
    for evt in result.conflicts:
        print(f"  rule={evt.rule!r}  agents={evt.agent_a!r} vs {evt.agent_b!r}")
    # Conflicts detected: 1
    #   rule='inventory-oversell-detected'  agents='eu' vs 'system'

What this prevents: Last-write-wins databases silently allow the oversell — the last agent to write wins and the inventory appears valid until chargebacks arrive. agentcrdt surfaces the semantic contradiction at merge time so a reconciliation agent can intervene before fulfillment.


vs. Alternatives

Tool Approach Semantic Rules MCP
agentcrdt Content-addressed LWW CRDT Yes Yes
Automerge JSON CRDT No No
Yjs CRDT text/map No No
Redis Key-value store No No
custom DB Ad-hoc Manual No

Star History

Star History Chart


Smithery

agentcrdt is available on Smithery — search for agentcrdt.


Case Studies

See how teams are using agentcrdt in production:


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Contributing

See CONTRIBUTING.md. PRs reviewed within 5 business days.

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