Agent CRM

An open-source CRM built for agents, not dashboards.

Agent CRM keeps track of companies, contacts, prospects, opportunities, research, follow-ups, forecasts, and sales-pipeline state. It also points out unsupported forecasts and work that is falling through the cracks. The difference is intentional: there is no graphical interface. Agents work with it directly through a JSON CLI or typed MCP tools.

Data stays local in SQLite, and both interfaces use the same application service.

Learn more at crmemento.com.

Why agent-first?

Most CRMs are designed around humans clicking through screens. Agent CRM is designed around agents reading structured context, taking explicit actions, and leaving an auditable trail.

• No interface: Use the CLI or MCP tools.
• Local by default: SQLite is the source of truth.
• Pipeline-aware: Track prospects from identification through won, lost, or do-not-contact.
• Revenue-aware: Forecast weighted, best-case, commit, and closed-won revenue.
• Constructively skeptical: A critical CRO review challenges weak assumptions with record-level evidence.
• Built for coordination: Project isolation, immutable activity history, and optimistic version checks help multiple agents work safely.
• System of record only: It logs outreach, but never sends messages or contacts anyone.

Quick start

Agent CRM requires Python 3.11 or later.

git clone https://github.com/clayton/agent-crm.git
cd agent-crm

export CRM_ACTOR=codex
./bin/crm init
./bin/crm project create "My Pipeline" --slug pipeline
./bin/crm project list

All CLI output is JSON. Writes require an actor through --actor or CRM_ACTOR.

By default, data is stored at ~/.codex/memories/agent-crm/crm.sqlite3. Set CRM_DB=/path/to/crm.sqlite3 or use the global --db option to choose another location.

What it tracks

• Projects that isolate separate pipelines
• Companies and contacts
• Prospects and pipeline stages
• Forecastable opportunities, amounts, close dates, probabilities, and next steps
• Sourced research notes
• Follow-up tasks and due dates
• Email, call, message, and meeting logs
• Immutable activity history
• Revenue targets and re-runnable project readiness checks

The default pipeline is:

identified → researching → qualified → ready_to_contact
→ contacted → replied → meeting_booked → won

Every non-terminal stage can also exit to lost, not_a_fit, or do_not_contact.

MCP server

Install the optional official MCP Python SDK:

uv sync --extra mcp
uv run crm-mcp

Example MCP client configuration:

{
  "mcpServers": {
    "agent-crm": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/agent-crm", "run", "crm-mcp"]
    }
  }
}

The server exposes typed tools for projects, companies, contacts, prospects, pipeline transitions, notes, tasks, inbox queries, search, and timelines.

Two agent-oriented read commands make common reviews direct:

# A ranked queue of 3-5 tasks and follow-ups
./bin/crm next-actions pipeline

# Prospects grouped in configured sales-stage order
./bin/crm pipeline pipeline
./bin/crm pipeline pipeline --include-terminal

The equivalent MCP tools are crm_next_actions and crm_pipeline. They return structured data so the calling agent can format it naturally for the conversation.

Bootstrap and onboarding

Bootstrap configures forecast defaults and returns a readiness checklist. It is deliberately safe to re-run: unchanged settings are not rewritten and no records are duplicated.

./bin/crm bootstrap pipeline \
  --target-amount 100000 \
  --target-period 2026-Q3 \
  --currency USD \
  --default-owner codex \
  --actor codex

The result identifies missing configuration, unowned prospects, incomplete opportunities, and the next setup steps. The MCP equivalent is crm_bootstrap.

Forecasting and honest revenue reviews

A prospect becomes forecastable only when explicitly qualified with an amount, expected close date, and concrete next step:

./bin/crm opportunity qualify pro_ID \
  --amount 25000 \
  --expected-close-at 2026-09-15T00:00:00Z \
  --next-step "Schedule the decision call" \
  --forecast-category commit \
  --probability 80 \
  --actor codex

./bin/crm forecast pipeline --period 2026-Q3
./bin/crm conversions pipeline
./bin/crm review pipeline --period 2026-Q3

forecast returns open pipeline, weighted forecast, best case, commit, closed-won revenue, target attainment, coverage, and missing forecast data. conversions derives directional stage conversion rates from the immutable activity history.

review is the critical CRO adversary. It flags unsupported commit claims, weak coverage, overdue or expired next steps, incomplete forecast evidence, and other assumptions that deserve scrutiny. Findings include severity, underlying records, and a recommended corrective action.

The MCP equivalents are crm_qualify_opportunity, crm_forecast, crm_conversions, and crm_cro_review.

Staying ahead of dropped work

The v0.3 action engine ranks work across tasks and pipeline risks. Every recommendation carries a score, plain-language reasons, a suggested action, and an effort estimate.

./bin/crm next-actions pipeline
./bin/crm next-actions pipeline --mode close --time-budget 45
./bin/crm next-actions pipeline --mode pipeline_build
./bin/crm risks pipeline

risks finds unowned, unscheduled, stale, overdue, uncontactable, expired, and forecast-incomplete records. It is read-only by default. To explicitly create idempotent repair tasks:

./bin/crm risks pipeline --create-tasks --actor codex

The MCP equivalent is crm_pipeline_risks.

SDR workbench

Agent CRM prepares top-of-funnel work without sending messages:

./bin/crm sdr-queue pipeline
./bin/crm research-brief pro_ID
./bin/crm outreach-brief pro_ID

The SDR queue prioritizes enrichment and outreach preparation using fit, contactability, and research completeness. Research briefs separate sourced facts from unsourced context and missing information. Outreach briefs combine verified context, prior interactions, prerequisites, and a suggested angle while preserving the system-of-record-only boundary.

The MCP equivalents are crm_sdr_queue, crm_research_brief, and crm_outreach_brief.

Agent skill

The reusable agent skill lives in skill/agent-crm. Symlink or copy that directory into your agent’s skills directory, then configure the skill to point to your checkout.

Development

The core test suite has no third-party dependencies:

PYTHONPATH=src python3 -m unittest discover -s tests -v

License

MIT