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OpenLedger is a local-first, SQLite-backed double-entry ledger that lets an AI agent read accounts, post transactions, run reports, and inspect an append-only audit trail through 15 Model Context Protocol (MCP) tools. Its safety model is deliberately accounting-oriented: every transaction must balance, amounts are stored as integer minor units, posted records cannot be edited or deleted, and the business write plus its audit row commit in one database transaction.
That combination makes OpenLedger a focused ledger service for a single-tenant, single-node deployment—not a replacement for a full enterprise accounting suite. The implementation is published by Attri under Apache 2.0 and uses Python 3.11+, SQLite, and aiosqlite.
What OpenLedger gives an AI agent
An agent normally needs more than a database connection. It needs a constrained interface that exposes useful accounting operations without allowing arbitrary mutations, plus invariants that remain true even when the caller is probabilistic. OpenLedger addresses that problem with an MCP server over a small, explicit tool surface.
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The repository describes 15 tools. They cover account discovery, journal operations, standard financial reports, audit inspection, and one read-only SQL escape hatch. The agent can therefore answer questions such as “What is the cash balance?”, “Show transactions for this account,” or “What is the current trial balance?” and can post a balanced transaction without receiving unrestricted write access to SQLite.
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The design is local-first. SQLite is the day-one backend for the stated single-tenant, single-node shape. PostgreSQL is described as a roadmap item, not an available backend in the current release.
The complete 15-tool MCP surface
These are the tools published by the Attri-Inc OpenLedger repository in 2026. Names are shown exactly as exposed by the MCP server.
| Group | Tool | What an agent can do |
|---|---|---|
| Accounts | list_accounts |
Enumerate the chart of accounts. |
get_account |
Retrieve the details for one account. | |
get_balance |
Read an account balance. | |
get_account_ledger |
Inspect the entries posted to an account. | |
create_account |
Add an account to the chart. | |
| Journal | get_transaction |
Fetch a transaction by identifier. |
search_transactions |
Find transactions using the supported search parameters. | |
post_transaction |
Create a balanced journal transaction. | |
transfer_funds |
Record a transfer between accounts. | |
reverse_transaction |
Correct a posted transaction by creating a contra posting. | |
| Reports | get_trial_balance |
Produce a trial balance. |
get_profit_loss |
Produce a profit-and-loss report. | |
get_balance_sheet |
Produce a balance-sheet report. | |
| Audit | get_audit_log |
Read the append-only audit records. |
| Escape hatch | run_query |
Execute SQL SELECT statements only; it cannot mutate data. |
The table contains a blank journal row only to preserve the six-row journal grouping visually; the actual total remains 15 tools: five account tools, five journal tools, three report tools, one audit tool, and one query tool.
Why the grouping matters
Read operations and write operations are distinguishable at the interface level. An agent can be given access to reports and account lookups while a separate policy controls whether it may call post_transaction, transfer_funds, or reverse_transaction. run_query is intentionally limited to SELECT, so it is useful for investigation without becoming an unbounded write channel.
Accounting invariants enforced at write time
Every transaction balances
OpenLedger uses double-entry accounting. A transaction must contain at least two entry lines, and the sum of debit amounts must equal the sum of credit amounts. The repository says this validation occurs in the write path inside one database transaction, rather than being left to the calling agent.
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The minimum of two entry lines is an implementation rule published by Attri in 2026. A malformed request with one line, or a request whose debits and credits do not balance, should fail validation and leave no partial journal entry behind.
Amounts use integer minor units
Amounts are stored as integer minor units, such as cents, rather than floating-point values. Integer arithmetic avoids binary floating-point rounding drift when entries are added, compared, or reported. An integration should therefore convert a human amount such as £12.34 into the appropriate integer representation before calling a write tool and should not send a floating-point value expecting the ledger to correct it.
Posted records are immutable
Transactions and their entry lines do not have update or delete operations in the API. This is important for an agent: a later prompt cannot silently rewrite the historical fact that an earlier prompt posted.
Immutability does not mean mistakes are permanent. The correction path is reverse_transaction. It creates a contra posting that reverses the original while preserving both the original transaction and the correction. Reports and an auditor can then see what happened and why the net effect changed.
Why the audit row shares the write transaction
The strongest design choice in OpenLedger is the unit of work used for mutations. The ledger mutation and its audit record are not sent to two independent systems with separate commit points. They are written in the same database transaction.
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- Validate the requested operation. The service checks the accounting rules, including the minimum two lines and equal debits and credits.
- Write the ledger mutation. The new transaction and entry lines are inserted, or a contra transaction is created for a reversal.
- Append the audit row. The operation, actor or caller context available to the service, and mutation metadata are recorded in the append-only audit log.
- Commit once. The database commits the unit of work only after both the business rows and the audit row are ready.
- Roll back on failure. If validation or another database operation fails before commit, the mutation and its audit record roll back together.
Attri’s launch article describes the guarantee this way: “Either both writes commit, or both roll back.” A successful mutation therefore cannot exist without its matching audit-log row, and an audit record cannot survive a mutation that was rolled back.
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This closes the dual-write gap found in architectures that write business data to one store and logs to another. In those systems, a timeout, process crash, or network failure can leave a financial change committed while its log entry is missing—or a log entry claiming a change that never committed. Keeping both records in the same SQLite transaction makes that inconsistency impossible within the database’s transaction boundary.
The launch article calls this a “SOX-grade guarantee.” That is the publisher’s design language, not evidence of SOX compliance, an external audit, or regulatory certification. Organizations with formal compliance obligations still need their own controls, reviews, retention policies, access management, and independent assessment.
How the architecture keeps the rules understandable
The repository separates concerns into layers:
- Domain layer: accounting constants and typed errors.
- Infrastructure: database connection handling, the unit-of-work transaction boundary, identifier generation, and clock helpers.
- Repositories: protocols plus the SQLite implementation.
- Services: account, ledger, report, audit, and query operations.
- Serialization: conversion between service objects and MCP-facing data.
- Composition root: application wiring.
- MCP transport adapter: a thin layer that exposes the services as tools.
SQL is parameterized and kept behind repository protocols. Services do not issue raw SQL directly. The authors explain that they avoided an ORM because explicit SQL makes the balance and atomic-audit boundaries easier to reason about, while keeping a future SQLite-to-PostgreSQL change localized to repository and wiring code.
Running OpenLedger locally
The documented entry point is python run_mcp.py. The project targets Python 3.11 or newer and uses aiosqlite for asynchronous SQLite access. A typical local setup is:
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- Install Python 3.11 or a later supported Python release.
- Create and activate a virtual environment for the checkout.
- Install the project’s Python dependencies as specified by its repository instructions.
- Run
python run_mcp.pyfrom the project directory.
Stdio is the default MCP transport, which is the natural mode for a desktop or coding client that launches a local server process. The repository also documents SSE on port 8791 for clients that connect over HTTP-style server-sent events. Treat the port as a local deployment setting: expose it beyond a trusted boundary only after adding the authentication, network restrictions, and operational controls your environment requires.
Claude Code
The repository documents a Claude Code registration command that points to the Python executable inside the local virtual environment. Use that documented command rather than the system Python, so Claude Code starts the same environment in which OpenLedger’s dependencies are installed. After registration, the MCP tool list should show the account, journal, report, audit, and read-only query operations.
Claude Desktop and other MCP clients
Because stdio is the default transport, any MCP client that supports launching a local server can be configured with the OpenLedger entry point and the virtual-environment Python executable. Claude Desktop uses that style of local MCP configuration. Client configuration labels and file locations can change, so follow the current client’s MCP instructions while preserving the same command and working directory used for a successful local launch.
A safe operating pattern for agents
Tool availability is not the same as authorization. A practical policy is to let an agent discover accounts, read balances, retrieve transactions, and generate reports freely, while requiring a human approval step before financial writes.
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list_accountsandget_accountto confirm account identifiers and their intended roles. - Read the current state. Check balances or account ledgers before proposing a posting.
- Prepare a balanced request. Express amounts in integer minor units and ensure at least two entry lines with equal debit and credit totals.
- Require approval for mutation. Gate
post_transaction,transfer_funds, andreverse_transactionbehind your application’s approval policy. - Verify the result. Retrieve the transaction and inspect
get_audit_logafter a successful commit. - Correct by reversal. Never ask an agent to edit historical rows; use the reversal tool and retain the original reference.
This workflow limits the damage from ambiguous prompts, stale context, or an agent choosing the wrong account. The database still enforces its accounting invariants, but organizational approval remains necessary for real financial decisions.
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Reports and the read-only SQL escape hatch
The three report tools provide standard accounting views without requiring an agent to understand the schema: trial balance, profit and loss, and balance sheet. They are preferable to ad hoc SQL for routine questions because the service owns the report definitions and can keep them consistent with the ledger model.
run_query exists for investigation and specialized reads. Its contract is SQL SELECT only. It should be treated as a diagnostic interface, not as a way to bypass service validation. Parameterize values in any client-side query construction, restrict access to trusted users, and avoid assuming that a schema detail exposed by the current SQLite implementation will remain unchanged when a future backend arrives.
What OpenLedger does not include
| Capability | Current status | Practical consequence |
|---|---|---|
| Multi-currency | Not included in v1. | Do not model foreign-currency balances or exchange-rate accounting as if they were supported. |
| PostgreSQL | Roadmap item, not the current backend. | Plan around SQLite’s single-node deployment shape today. |
| Administrative UI | Not included in v1. | Administration and review require MCP clients or your own interface. |
| Document ingestion and OCR | Not included in v1. | Receipts and invoices need a separate ingestion workflow. |
| Tax, GAAP, and IFRS modules | Not included in v1. | The ledger is not a jurisdiction-specific tax or statutory-reporting product. |
These limits define the appropriate comparison. OpenLedger is strongest on audit atomicity, immutable corrections, agent-facing tool coverage, and a compact local deployment. It should not be presented as a complete enterprise accounting suite, a multi-entity platform, or a certified compliance product.
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What to verify before deploying it
- Confirm that a single-tenant, single-node SQLite deployment fits your availability and concurrency requirements.
- Define who may call each write tool and how approvals are recorded outside the ledger.
- Back up the SQLite database and test restoration; an append-only audit log does not replace backups.
- Decide how identifiers, timestamps, retention, and access permissions will be managed in your operating environment.
- Test reversal procedures with realistic mistakes before allowing an agent to post production entries.
- Document the boundary between OpenLedger’s implementation guarantees and any separate compliance evidence your organization needs.
Bottom line
OpenLedger gives an AI agent a deliberately narrow accounting interface: 15 MCP tools, balanced double-entry writes, integer amounts, immutable history, contra-posting corrections, and an audit row committed in the same transaction as every mutation. That is a strong foundation for auditable agent workflows. Its current scope remains intentionally smaller than a full accounting system, and its SQLite, single-node deployment model should be accepted as a constraint rather than overlooked.
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