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Setup: OpenClaw

Connect the l6e-budget MCP server to OpenClaw for session-scoped budget enforcement.

The agent gates calls using pre-call token estimates. Out of the box, budgets are directionally accurate — calibration makes them billing-accurate. Call l6e_record_usage if you want to feed actual token counts back into the ledger for exact accounting.

Install​

No separate pip install is required if you use uvx (recommended). uvx runs l6e-mcp in an isolated environment on first use.

If you prefer a manual install:

pip install l6e-mcp

Configure​

Edit ~/.openclaw/openclaw.json and add the mcpServers block. If the file already has other keys, merge this into the existing JSON:

{
  "mcpServers": {
    "l6e-budget": {
      "command": "uvx",
      "args": ["l6e-mcp"],
      "env": {
        "L6E_LOG_PATH": "${HOME}/.l6e/runs.jsonl",
        "L6E_API_KEY": "sk-l6e-...",
        "L6E_CLOUD_SYNC": "1"
      }
    }
  }
}

L6E_LOG_PATH is required. OpenClaw's gateway spawns MCP servers as child processes from ~/.openclaw/, not your project directory. Without this env var, runs.jsonl will be written to an unpredictable location.

L6E_API_KEY and L6E_CLOUD_SYNC are optional — omit them to run fully local. When set, session run logs sync to the l6e cloud and gate decisions use your personal calibration factor. See l6e.ai Integration for what cloud sync enables.

If uvx is not on the PATH that OpenClaw sees, use the full path:

which uvx  # then substitute the result in "command"

After editing, restart the gateway to pick up the change:

openclaw gateway restart

Project-level config (team setups)​

OpenClaw also reads openclaw.config.json from the project root, merging it with the global config. For team setups, add the mcpServers block there instead of editing the global file:

{
  "mcpServers": {
    "l6e-budget": {
      "command": "uvx",
      "args": ["l6e-mcp"],
      "env": {
        "L6E_LOG_PATH": "${HOME}/.l6e/runs.jsonl",
        "L6E_API_KEY": "sk-l6e-...",
        "L6E_CLOUD_SYNC": "1"
      }
    }
  }
}

Per-agent scoping (optional)​

By default, all agents can use l6e-budget. To restrict it to a specific agent, add mcpServers to that agent's entry in agents.list:

{
  "agents": {
    "list": [
      {
        "id": "code",
        "mcpServers": ["l6e-budget"]
      }
    ]
  }
}

Verify​

openclaw mcp list
openclaw doctor

l6e-budget should appear with status running and four tools listed:

  • l6e_run_start
  • l6e_authorize_call
  • l6e_record_usage
  • l6e_run_end

openclaw doctor validates the full config and reports any parse errors. If l6e-budget does not appear, check that uvx is on your PATH (which uvx) or that l6e-mcp is installed (pip show l6e-mcp).

Rules for AI​

Add the enforcement rule to AGENTS.md in the agent workspace so it is loaded every session.

  • All agents (default workspace): ~/.openclaw/workspace/AGENTS.md
  • Specific agent: ~/.openclaw/agents/{agent_id}/AGENTS.md

The rule content is in .openclaw/AGENTS.md in the repository. Append its contents to your AGENTS.md.

Example conversation starter​

Use this at the start of a new chat to test the full flow:

Using the l6e-budget MCP tools, call l6e_run_start with budget_usd=1.00,
model="claude-sonnet-4-20250514", client="openclaw". Show me the full JSON
response including session_id. Then add a one-line docstring to any function
in this project. Call l6e_authorize_call before the edit and l6e_run_end
when done.

Note: pass the bare model ID without the provider prefix — use "claude-sonnet-4-20250514", not "anthropic/claude-sonnet-4-20250514". If you are unsure of the active model ID, pass "unknown".

Reading your run log​

After a session ends:

# Most recent session
tail -1 ~/.l6e/runs.jsonl | python -m json.tool

# All sessions — cost summary
cat ~/.l6e/runs.jsonl | python -c "
import sys, json
for line in sys.stdin:
    r = json.loads(line)
    print(f\"{r['run_id']}  \${r['total_cost']:.4f}  {r['calls_made']} calls  {r['reroutes']} reroutes  source={r['source']}\")
"

Verify the log path is correct​

Run a minimal session to confirm runs.jsonl lands in ~/.l6e/ and not somewhere else:

Call l6e_run_start with budget_usd=0.10, model="claude-sonnet-4-20250514",
client="openclaw". Then immediately call l6e_run_end with the session_id.

Then check:

tail -1 ~/.l6e/runs.jsonl | python -m json.tool

If the file doesn't exist or is empty, L6E_LOG_PATH is not being passed to the server process. Re-check the env block in your config and run openclaw gateway restart.

Known limitations​

  • Always call l6e_run_end. If the gateway stops before l6e_run_end is called, the run log for that session is not written.
  • Never import l6e_mcp directly. The session registry lives only in the MCP server process. Importing l6e_mcp.server in a subprocess will always return "Unknown session".
  • Rerouting is advisory only. When l6e_authorize_call returns "action": "reroute", the agent stops work and tells you to switch to a cheaper model. The MCP protocol has no mechanism for forcing a model switch — the response is a signal to you, not an automatic redirect.
  • Gateway restart required. Unlike Cursor, there is no hot-reload for MCP config changes. Run openclaw gateway restart after any edit to mcpServers.
  • Adaptive Model Routing. OpenClaw's adaptiveRouting feature (opt-in via agents.defaults.model.adaptiveRouting) is complementary to l6e's budget enforcement: l6e signals when the budget ceiling is reached, Adaptive Routing handles local-first model selection independently. Both can be active simultaneously.