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ContextLedger

Local, explicitly scoped memory for coding agents.

ContextLedger lets an agent keep useful project knowledge between sessions: architectural decisions, code observations, durable documentation, and failed approaches worth avoiding. It stores conclusions, not chat transcripts.

Many agent-memory products assume organization-wide adoption, cloud infrastructure, or a new company policy. ContextLedger is for the developer who wants durable agent memory today: install it locally, bind it to the projects you choose, and keep the data inside those projects. No cloud deployment, subscription, external account, or company-wide rollout is required.

It is harness- and model-agnostic. ContextLedger exposes standard Model Context Protocol (MCP) tools over stdio, so any MCP client can use it. Codex and Claude Code are included below as concrete setup examples, not privileged integrations. Give the server a project path and it resolves that project's database automatically. For a Git project, storage lives in private Git metadata:

<project>/.git/llm-memory/memory.sqlite

When the project has no .git, the database is stored at <project>/.memory/memory.sqlite. There is no account, cloud service, telemetry, network server, global index, or required team rollout. Linked Git worktrees share a ledger because they share a Git common directory.

Quick start

Requirements: Python 3.11 or newer, SQLite with FTS5, and uv. Normal Python distributions include FTS5. Git is optional.

1. Install

From a clone of ContextLedger:

uv tool install .
command -v context-ledger
context-ledger --help

command -v context-ledger must print the installed executable file, for example /Users/alice/.local/bin/context-ledger. On Windows, use where context-ledger. If the command is missing, run uv tool update-shell, restart the shell, and repeat the block.

2. Add it to an MCP client

ContextLedger uses MCP over standard input/output. Register it once as a global server. Every MCP tool call includes the project path, so the same server works across all projects.

Claude Code

Run this complete block once:

claude mcp add --transport stdio --scope user context-ledger -- \
  "$(command -v context-ledger)" serve
claude mcp get context-ledger

mkdir -p "$HOME/.claude"
if ! grep -Fq "When Context Ledger MCP tools are available" "$HOME/.claude/CLAUDE.md" 2>/dev/null; then
  printf '\n' >> "$HOME/.claude/CLAUDE.md"
  context-ledger snippet >> "$HOME/.claude/CLAUDE.md"
fi

The block adds one global server and appends the ContextLedger snippet to Claude Code's personal instructions once. Start a new Claude Code session and run /mcp to check the connection. See the Claude Code MCP documentation.

Codex

Run this complete block once:

codex mcp add context-ledger -- \
  "$(command -v context-ledger)" serve
codex mcp list

mkdir -p "$HOME/.codex"
if ! grep -Fq "When Context Ledger MCP tools are available" "$HOME/.codex/AGENTS.md" 2>/dev/null; then
  printf '\n' >> "$HOME/.codex/AGENTS.md"
  context-ledger snippet >> "$HOME/.codex/AGENTS.md"
fi

The block adds one global server and appends the ContextLedger snippet to Codex's personal instructions once. Start a new Codex session and run /mcp to check the connection. See the Codex MCP documentation.

Generic MCP client

If the client has no dedicated setup command, add the equivalent stdio server object in its MCP configuration:

{
  "mcpServers": {
    "context-ledger": {
      "command": "/absolute/path/printed/by/command-v/context-ledger",
      "args": ["serve"]
    }
  }
}

Use the exact output of command -v context-ledger for command. The outer property names vary by client; the stdio command and arguments do not.

Append the same harness-neutral snippet to that client's instruction file:

HARNESS_INSTRUCTIONS="/absolute/path/to/your/harness-instructions.md"
mkdir -p "$(dirname "$HARNESS_INSTRUCTIONS")"
if ! grep -Fq "When Context Ledger MCP tools are available" "$HARNESS_INSTRUCTIONS" 2>/dev/null; then
  echo >> "$HARNESS_INSTRUCTIONS"
  context-ledger snippet >> "$HARNESS_INSTRUCTIONS"
fi

If a harness cannot start the server, test the configured executable directly:

/absolute/path/to/context-ledger --help
/absolute/path/to/context-ledger status --project /absolute/path/to/project

Do not test serve directly. It waits silently for MCP messages on standard input.

Related MCP server: ctxmem

CLI reference

Direct CLI commands accept --project PATH after the command and default to the current working directory. The MCP serve command is global and does not take a project; MCP tools provide it per call.

PROJECT_PATH="$(pwd -P)"

# Create the database and print its path
context-ledger init --project "$PROJECT_PATH"

# Show the project, database path, and active counts
context-ledger status --project "$PROJECT_PATH"

# List records
context-ledger list --project "$PROJECT_PATH"
context-ledger list --project "$PROJECT_PATH" --limit 50
context-ledger list --project "$PROJECT_PATH" --all

# Inspect or search records
context-ledger inspect --project "$PROJECT_PATH" 1
context-ledger search --project "$PROJECT_PATH" --tags sqlite "ledger storage"
context-ledger search --project "$PROJECT_PATH" --phrase "why did the old architecture fail" --all
context-ledger search --project "$PROJECT_PATH" --tags architecture --phrase "old approach"

# Retrieve rules for files and their parent directories
context-ledger file-context --project "$PROJECT_PATH" src/context_ledger/server.py tests/test_server.py

# Record durable knowledge
context-ledger record --project "$PROJECT_PATH" decision \
  "Database choice" \
  "Use SQLite with FTS5; no embeddings initially" \
  --authority user_confirmed \
  --source "architecture discussion" \
  --tags sqlite "ledger storage"

# Record compact knowledge for one file or directory
context-ledger record --project "$PROJECT_PATH" observation \
  "MCP handlers stay thin" \
  "Keep storage and matching logic in ledger.py" \
  --authority code_observed \
  --applies-to src/context_ledger/server.py tests/test_server.py

# Preserve lifecycle history
context-ledger supersede --project "$PROJECT_PATH" 1 --replacement 2
context-ledger dispute --project "$PROJECT_PATH" 3

# Inspect packaged instructions
context-ledger snippet
context-ledger snippet --path
context-ledger prompt
context-ledger prompt --path

# Start the MCP stdio server
context-ledger serve

Record kinds are informational metadata: decision, observation, documentation, and failed_attempt. Retrieval does not filter by kind. Evidence authorities are user_confirmed, code_observed, and agent_inferred. Authority describes the source of a claim, not confidence in it.

Command results are JSON except for init, prompt, and snippet.

Project routing

One global MCP server routes each tool call to the supplied project_path:

one ContextLedger server
├── project_path=/projects/a → /projects/a/.git/llm-memory/memory.sqlite
└── project_path=/projects/b → /projects/b/.git/llm-memory/memory.sqlite

The agent must pass the absolute root whose memory the call concerns. For a workspace containing several repositories, it uses each repository root for repo-specific work, or their common workspace root when the knowledge should be shared. A root without Git keeps its ledger in .memory.

Storage, privacy, and behavior

Each MCP tool call receives a project path and resolves storage itself. It uses the Git common metadata directory when <project>/.git exists, so linked worktrees share memory and ordinary Git add and commit operations cannot include the database. Without Git it uses <project>/.memory/memory.sqlite.

Storage is resolved independently for each tool call from its required absolute project_path. That path defines the memory boundary; it may be one repository or a shared workspace. Paths passed to get_file_context.paths and record_memory.applies_to are normalized relative to that root and used for matching, not filesystem lookups.

The database is local but not encrypted. Any user or process that can read its path can read it. Backups or copies containing Git metadata or .memory may also contain the ledger.

The MCP server provides five tools:

  • get_file_context(project_path, paths): retrieve active knowledge attached to files or parent directories.

  • search_memory(project_path, ...): search active or historical records using tags, a phrase, or both.

  • record_memory(project_path, ...): add broad knowledge or rules scoped to an applies_to list.

  • supersede_memory(project_path, ...): retire an obsolete record while preserving history.

  • dispute_memory(project_path, ...): flag unresolved conflicting knowledge.

applies_to is a list of project-relative files or directories governed by a record. File lookup accepts several paths in one call, normalizes separators, and returns records whose scopes match an exact path or one of its parent directories. A rule spanning separate packages lists each governed file or directory; an empty applies_to means genuinely broad project or domain knowledge, not merely multi-package scope.

Agents use both retrieval paths during development: search_memory recalls broad decisions using task language, while get_file_context retrieves rules for the intended or touched files. Recording mirrors that split. Before saving a scoped rule, inspect the instances it claims to govern to verify the scope and preserve real exceptions. Habitual corrections such as “we usually,” “we tend to,” “always,” and “never” are strong signals to record immediately when they express a durable convention.

Search is lexical SQLite FTS5 with BM25 ranking across titles, content, sources, and tags. It accepts one to three exact tag phrases, a free-text phrase whose terms are matched broadly, or both; matches are combined with OR. Tags describe a future task that should retrieve the knowledge—for example, add response field rather than only mappers. There are no embeddings, vector search, automatic code indexing, or project scanning.

Limitations and ideas

Current limitations:

  • Lexical search can miss synonyms and conceptual matches.

  • There is no record editing, deletion command, or automatic deduplication.

  • The server does not verify an agent's claims or whether a user really confirmed one.

  • MCP instructions guide a client but cannot force it to retrieve or record memory.

  • Separate processes rely on normal SQLite locking and may briefly contend.

  • The local database is not an encryption or access-control boundary.

  • ContextLedger is designed for modest local workloads, not a multi-user service.

Possible next work:

  • Measure retrieval misses before considering semantic or vector search.

  • Add provenance-preserving merge and deduplication assistance.

  • Add concurrency and busy-timeout tests.

  • Test packaged MCP integrations end to end in CI.

  • Add a local browser/export workflow for records.

  • Evaluate practical optional encryption at rest.

  • Publish a signed, versioned Python package when release demand warrants it.

Development

Development requires uv:

git clone <repository-url>
cd context-ledger-mcp
uv sync --extra test
uv run pytest -q
uv run context-ledger status

Tests create temporary projects and do not write ledger data into this project.

Run the development checkout as an MCP server:

uv run context-ledger serve

The implementation is intentionally small:

src/context_ledger/cli.py       command-line interface
src/context_ledger/ledger.py    SQLite records, search, and lifecycle
src/context_ledger/paths.py     Project and database paths
src/context_ledger/server.py    MCP server and tools
src/context_ledger/prompts/     server and harness instructions
tests/                          CLI, storage, paths, prompts, and MCP tests

Available Tools

5 tools
dispute_memoryB

Mark a record disputed when evidence conflicts but no replacement is established.

ParametersJSON Schema
NameRequiredDescriptionDefault
record_idYes
project_pathYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden, yet it only implies a mutation. It says nothing about permissions required, whether the dispute is reversible, whether the original record content is preserved, or how the disputed state differs from deletion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the action front-loaded and the qualifying condition appended. No filler, nothing redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no explanation, but for a state-mutating tool with no annotations the description should cover reversibility and required identifiers. It covers the conceptual trigger well while leaving operational details thin.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for two required parameters, and the description never mentions record_id or project_path. It adds no meaning beyond the raw parameter names, leaving the agent to guess formats and scoping.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("mark a record disputed") plus the triggering condition ("evidence conflicts but no replacement is established"). This implicitly separates it from supersede_memory, which presumably applies when a replacement exists, though no sibling is named explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear when-to-use condition tied to the state of the evidence. It stops short of explicitly naming the alternative (supersede_memory) or stating when not to use it, so the routing is inferable but not spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_file_contextC

Get active rules for files, including rules attached to their parent directories.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNo
pathsYes
project_pathYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it only partially does so: it discloses the meaningful inheritance behavior (parent-directory rules are included) and that rules are filtered to 'active'. It says nothing about auth needs, response shape, or how 'active' is determined, leaving real gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight sentence with the core purpose front-loaded and no filler. It is efficient, though its brevity contributes to the coverage gaps elsewhere.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, but a 3-parameter read tool with no annotations and 0% schema coverage needs more than one sentence. The limit/pagination behavior and the interaction between project_path and paths are left entirely undocumented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description compensates only weakly: 'files' loosely implies the paths parameter, but project_path and limit are never addressed. Two of three parameters, including the pagination control, remain semantically opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Get') and resource ('active rules for files'), and adds a scope qualifier ('including rules attached to their parent directories'). It is distinguishable from the memory-oriented siblings, but it never names them to sharpen the contrast.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no prerequisites, and no mention of alternatives among search_memory/record_memory/supersede_memory/dispute_memory. Usage is only implied by the word 'Get'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

record_memoryC

Record durable knowledge, optionally scoped to project-relative files or directories.

ParametersJSON Schema
NameRequiredDescriptionDefault
kindYes
tagsNo
titleYes
sourceNo
contentYes
authorityYes
applies_toNo
project_pathYes

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It signals durability and optional file/directory scoping, but says nothing about how records interact with existing entries, what authority levels imply, whether entries can be edited, or any auth constraints on a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler, and the optional-scope clause is placed sensibly at the end. It is efficient, though arguably too terse for an 8-parameter mutation tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, but for a write tool with 8 parameters, 5 required, 0% schema coverage, and zero annotations, the description is far too thin to let an agent call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 8 parameters, and the description only alludes to one of them (applies_to, via 'project-relative files or directories'). The critical enum fields kind and authority, plus title, content, tags, source, and project_path, receive no explanatory text anywhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb (record) and resource (durable knowledge) and adds a scoping qualifier. It implicitly distinguishes itself from search_memory and the dispute/supersede siblings by being the write path, but it never names those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to record versus when to use supersede_memory or dispute_memory, nor any stated preconditions. The agent must infer usage entirely from the verb.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_memoryC

Search by 1–3 exact tags, a broad free-text phrase, or both.

ParametersJSON Schema
NameRequiredDescriptionDefault
tagsNo
limitNo
phraseNo
project_pathYes
include_inactiveNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and discloses almost nothing: no permission requirements, no result ordering, no pagination behavior despite a limit param, and no note that inactive memories are excluded by default (include_inactive=false). It is a read-style search, but that must be inferred from the verb alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero padding that leads with the query modes. It is efficient, though arguably too terse for a 5-parameter tool with no other documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, but the description still omits behavior an agent needs: the default exclusion of inactive memories, how limit affects results, and how tags and phrase combine when both are supplied. For a search tool with five params and zero schema coverage, this is a meaningful gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 5 parameters, so the description must compensate and only partially does: it clarifies that tags should be 'exact' and limited to 1-3, and that phrase is free-text, which adds real meaning. But limit, project_path, and especially include_inactive (whose default silently drops inactive memories) are left entirely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear verb (search) and the two input modes it accepts (1-3 exact tags, broad free-text phrase, or both), which is specific enough for an agent to know what the tool retrieves. It does not distinguish itself from siblings like get_file_context, which could also surface related content, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says how to query but never when to choose this tool over get_file_context, nor any exclusion or precondition. The only implied guidance is the tag-vs-phrase dichotomy, which is a mechanics hint rather than usage routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

supersede_memoryB

Mark obsolete memory superseded, optionally linking its active replacement.

ParametersJSON Schema
NameRequiredDescriptionDefault
record_idYes
project_pathYes
replacement_idNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden for a mutating tool. It does not say whether superseding is reversible, whether the record is archived or deleted, what permissions are needed, or what happens if the record is already superseded; only the optional replacement link is disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler; the core action is stated first and the optional linkage second.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no explanation, but for a state-changing tool with no annotations the description should have covered irreversibility, permissions, or the effect on the linked replacement. It remains minimally adequate rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does clarify the meaning and optionality of replacement_id ('optionally linking its active replacement'), which is the least self-evident parameter, but project_path and record_id receive no explanation beyond their names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb-plus-resource ('mark obsolete memory superseded') that is readily distinguishable from siblings like record_memory, search_memory, and dispute_memory. It also names the key side effect (linking a replacement), though it does not explicitly contrast itself with dispute_memory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the word 'obsolete' — the agent infers this is for records that are no longer current — but there is no explicit when/when-not guidance or routing between this and dispute_memory, which is a natural alternative for contested records.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 5 tool updatesv0.1.0
    • First observeddispute_memory
    • First observedget_file_context
    • First observedrecord_memory
    • First observedsearch_memory
    • First observedsupersede_memory

TDQS

B3.4/5.0

Scored across 5 tools

Disambiguation4/5

The four memory tools (search, record, supersede, dispute) each map to a clearly distinct lifecycle action, and descriptions reinforce the boundaries. The only mild overlap is between search_memory and get_file_context, both being retrieval tools, but the file-scoped/parent-directory semantics of get_file_context make them distinguishable.

Naming Consistency5/5

All names follow a clean snake_case verb_noun pattern (search_memory, record_memory, supersede_memory, dispute_memory, get_file_context) with no mixing of conventions or vague verbs. The pattern is fully predictable.

Tool Count5/5

Five tools is well-scoped for a memory ledger: one create, two retrieval paths, and two state-transition operations. Each tool earns its place without redundancy or obvious bloat.

Completeness4/5

The surface covers the core ledger lifecycle: recording, searching, retiring (supersede), and flagging (dispute), plus file-scoped retrieval. Minor gaps exist, such as retrieving a single record by id or listing all memories, but these are workarounds via search rather than hard dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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