Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agentledger commit that Acme's tier is Growth using the scraper's SHA-256 receipt"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Git-style version control, cryptographic receipts, and cascading rollbacks for multi-agent shared memory.
Terminal demo — Provenance Firewall catch + BFS domino quarantine (zero API keys)
pip install -e ".[dev]" && agentledger demoMulti-agent teams share memory. One hallucinated scrape can become a quote, a proposal, and a CRM dispatch — cascading memory poisoning without a clear rollback path.
AgentLedger puts a Provenance Firewall in front of every write and a Causal Lineage Graph behind every read so you can quarantine the poison root and every derived decision.
Mechanism | Behavior |
Cryptographic Receipts | Facts must cite a registered artifact SHA-256 or they never land |
Confidence Half-Life Decay |
|
Cascading Rollback (BFS) | Walk |
Related MCP server: gpp
flowchart LR
A[Agent Tool Output] --> B[Artifact Hasher<br/>SHA-256]
B --> C[Provenance Firewall]
C -->|VALIDATED| D[Merkle Commit]
D --> E[Main State DAG]
E --> F[Downstream Causal Reads]
F -.->|causal_read_hashes| D
C -->|UNVERIFIED_RECEIPT / LOW_CONFIDENCE| X[Blocked]Feature | AgentLedger | Mem0 / Zep | Raw Redis / JSON |
Cryptographic Receipts | ✅ | ❌ | ❌ |
Confidence Half-Life Decay | ✅ | ⚠️ partial | ❌ |
Cascading Rollback (BFS) | ✅ | ❌ | ❌ |
Model Context Protocol (MCP) | ✅ | ❌ | ❌ |
Embedded Zero-Cloud (SQLite) | ✅ | ❌ | ⚠️ DIY |
git clone https://github.com/ratharo-gitty/agentledger.git
cd agentledger
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
agentledger demoUse the high-level API in tools and agent hops:
from agentledger import ledger
@ledger.watch(key="acme::budget")
def scrape_budget():
return "Verified budget: $500,000"
with ledger.agent("bot-pricer"):
budget = ledger.get("acme::budget") # tracks causal read
ledger.set("acme::quote", 75000) # links lineage automatically{
"mcpServers": {
"agentledger": {
"command": "agentledger",
"args": ["mcp"],
"env": {
"AGENTLEDGER_DB": ".agentledger/ledger.db"
}
}
}
}After
pip install -e ., pointcommandat the absolute path to theagentledgerbinary if Cursor does not inherit your venv PATH.
{
"mcpServers": {
"agentledger": {
"command": "agentledger",
"args": ["mcp"],
"env": {
"AGENTLEDGER_DB": "/absolute/path/to/your/project/.agentledger/ledger.db"
}
}
}
}from agentledger import AgentLedgerEngine, MemoryFact, EvidenceAnchor, SourceType
engine = AgentLedgerEngine(".agentledger/ledger.db")
sha = engine.register_artifact("scrape: acme tier=Growth")
fact = MemoryFact(
namespace="acme_corp",
entity_key="tier",
value="Growth",
initial_confidence=0.92,
evidence=EvidenceAnchor(
source_type=SourceType.TOOL_STDOUT,
source_uri="tool://scraper",
raw_artifact_sha256=sha,
excerpt="tier=Growth",
),
)
commit = engine.commit(agent_id="researcher", facts=[fact])
facts, causal = engine.read_facts(namespace="acme_corp")MCP tools: ledger_register_evidence · ledger_read · ledger_commit · ledger_blame · ledger_revert_cascade
Command | Purpose |
| Self-contained 4-bot GTM simulation (firewall + domino rollback) |
| Sample multi-agent commits for local exploration |
| Rich table of commits, causal parents, decayed confidence |
| Full provenance receipt panel |
| Prefix-aware BFS cascade quarantine |
| Start the FastMCP stdio server |
pip install -e ".[dev]"
pytest -v
ruff check .
agentledger demoSee CONTRIBUTING.md and GOOD_FIRST_ISSUES.md for contributor on-ramps (CrewAI, LangGraph, Postgres, DAG visualizer, and more).
This server cannot be deployed
Hosted agent memory with provenance, contradiction surfacing, and snapshot rollback.
Self-curating memory your AI tools share: provenance, verified citations, and trust tiers.
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Shared persistent memory for AI agents to read, verify, continue, and connect knowledge.