agentic-ledger 0.3.0a1

Runtime observability for AI agents — see exactly what your agent did, why it did it, and what it cost.

Agentic Ledger

Runtime observability for AI agents — see exactly what your agent did, why it did it, and what it cost.

Alpha — v0.3.0-alpha.1 is an early release of the loop engine, Loop Lens web app, and framework integrations. Interfaces and schema may still change between alpha releases. Found something broken or missing? Open an issue — alpha feedback directly shapes what ships next.

Works with any agent framework, any LLM provider, any model gateway. Zero code changes required. Point your agent at the proxy and everything is captured automatically.

---

How it works

Agentic Ledger runs as a transparent proxy between your agent and the LLM provider. It intercepts every request and response, assigns it an action_id, stores it, and returns the upstream response unmodified. Your agent never knows the proxy is there.

Your Agent  →  Agentic Ledger Proxy  →  OpenAI / Anthropic / LiteLLM / any LLM
                      ↓
               SQLite or Postgres
                      ↓
               Live Dashboard + API

---

Quick Start

Step 1 — Start the proxy

With Docker (recommended, no Python required):

docker run -p 8000:8000 \
  -e AGENTLEDGER_UPSTREAM_URL=https://api.openai.com \
  -v $(pwd)/data:/data \
  ghcr.io/shekharbhardwaj/agentledger:latest

Or with docker compose (SQLite by default — see docker-compose.yml):

AGENTLEDGER_UPSTREAM_URL=https://api.openai.com docker compose up

With uv:

uv add agentic-ledger
AGENTLEDGER_UPSTREAM_URL=https://api.openai.com uv run python -m agentledger.proxy

With pip:

python -m venv venv && source venv/bin/activate
pip install agentic-ledger
AGENTLEDGER_UPSTREAM_URL=https://api.openai.com ./venv/bin/python -m agentledger.proxy

Postgres? Install the extra and set AGENTLEDGER_DSN:

pip install "agentic-ledger[postgres]"
AGENTLEDGER_DSN=postgresql://user:password@localhost/agentledger

Note: the Docker image uses SQLite only. For Postgres with Docker, install via pip instead.

OpenTelemetry? Install the extra and set AGENTLEDGER_OTEL_ENDPOINT:

pip install "agentic-ledger[otel]"
AGENTLEDGER_OTEL_ENDPOINT=http://localhost:4318

Proxy starts on http://localhost:8000. Traces are saved to agentledger.db in the current folder (or /data/agentledger.db in Docker).

---

Step 2 — Point your agent at the proxy

Two changes: set base_url to the proxy and add a session ID header to group calls into a run. Everything else — your API key, model, messages — stays exactly the same.

OpenAI:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",  # ← proxy
    api_key="your-openai-key",
    default_headers={"x-agentledger-session-id": "run-1"},
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Research the top 3 AI trends in 2026"}],
)

Anthropic:

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:8000",  # ← proxy
    api_key="your-anthropic-key",
    default_headers={"x-agentledger-session-id": "run-1"},
)

LiteLLM / OpenRouter / any gateway:

# Point Agentic Ledger at your gateway
AGENTLEDGER_UPSTREAM_URL=http://localhost:4000 uv run python -m agentledger.proxy

# Then point your agent at Agentic Ledger
client = OpenAI(base_url="http://localhost:8000/v1", ...)

---

Step 3 — Open the dashboard

http://localhost:8000/app

The web app updates live via WebSocket as calls come in. No refresh needed.

• Loop Lens — every loop run with status (running / flagged / complete), a cost-per-iteration chart, and a per-iteration table (calls, tokens, cache reads, flags, errors)
• Sessions — every session with expandable call cards: response, thinking, tool calls, tool results, cache tokens, cost, loop flags

The classic single-file dashboard still lives at http://localhost:8000:

• Calls tab — every LLM call with full prompt, system prompt, tool calls, tool results, output, tokens, cost, latency, and errors
• Flow tab — visual DAG of your multi-agent system. Each agent is a node with aggregated cost, latency, and call count. Edges represent handoffs. Click a node to highlight its calls.
• Trace tab — Gantt/waterfall timeline showing every call as a horizontal bar on a shared time axis. Parallel calls appear side-by-side at the same position — no instrumentation required. Works purely from timestamps. Click any bar to jump to the full call detail. Budget warnings show as an amber border on the bar without hiding the agent colour.
• Search — full-text search across all sessions by prompt, output, agent name, or user ID

---

Coding agents — Claude Code, Ralph loops & friends

Claude Code (and most coding agents) can be pointed at the proxy with a single environment variable — no headers, no code changes:

AGENTLEDGER_UPSTREAM_URL=https://api.anthropic.com uv run python -m agentledger.proxy

export ANTHROPIC_BASE_URL=http://localhost:8000
claude

Agentic Ledger fingerprints Claude Code traffic automatically: every call is tagged framework=claude-code, and instead of one undifferentiated bucket, each Claude Code session appears under its real session UUID (the same id claude --resume shows), with prompt-cache reads/writes captured and priced correctly — cache traffic is where most of a coding agent's real spend lives.

Running an overnight loop (Ralph-style while :; do cat PROMPT.md | claude -p; done)? Use the built-in loop runner — it re-executes your command each iteration, attributes every call to the run (via the base URL, no headers needed), and stops on a completion promise, a budget ceiling, or the iteration cap:

AGENTLEDGER_UPSTREAM_URL=https://api.anthropic.com \
AGENTLEDGER_COMPLETION_PROMISE="ALL TASKS COMPLETE" \
uv run python -m agentledger.proxy

agentledger run --max-iterations 50 --budget 25 -- \
  claude -p "$(cat PROMPT.md)" --dangerously-skip-permissions

Each iteration shows up as iteration N of the run in /api/runs; when the agent prints the completion promise in a response, run status flips to complete and the loop exits with a cost/token summary. Any existing loop script works too — poll GET /api/runs/{run_id} yourself, or let the proxy's budgets (AGENTLEDGER_BUDGET_DAILY=25.00) hard-stop a runaway loop.

The same recipe works for any client with a base-URL override (Codex CLI, opencode, OpenClaw, LiteLLM-based stacks) — set the OpenAI/Anthropic base URL to the proxy and traffic is captured; add x-agentledger-* headers when you want explicit attribution.

OTel-native tools (Gemini CLI, Codex [otel], AutoGen/AG2, Pydantic AI, Vercel AI SDK) don't need the proxy at all — point their OTLP exporter at the ledger and GenAI spans are ingested directly:

export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:8000
export OTEL_EXPORTER_OTLP_PROTOCOL=http/json

Framework guides: BMAD-METHOD — persona auto-detection (bmad:sm, bmad:dev, bmad:qa, …) and per-story budgets · OpenClaw — the hard spend cap OpenClaw doesn't have, plus per-agent attribution.

---

What gets captured

Every LLM call is stored with:

Field	What it contains
action_id	UUID assigned at interception time
session_id	Run grouping (from header)
timestamp	When the call was made
model_id	Model used
provider	openai or anthropic
messages	Full message history sent to the model
system_prompt	Extracted system prompt
tools	Tool definitions available to the model
tool_calls	Tools the model decided to call
tool_results	What the tools returned (from next call's messages)
content	Model's text output
stop_reason	Why the model stopped
tokens_in / tokens_out	Token usage
cache_read_tokens / cache_write_tokens	Prompt-cache usage — reads and writes are priced correctly per provider
thinking	Extended-thinking output (Anthropic), captured separately from content
cost_usd	Estimated cost based on model pricing
latency_ms	End-to-end response time
status_code	HTTP status from upstream — errors are captured too
error_detail	Upstream error message for non-200 responses
agent_name	From x-agentledger-agent-name header, or auto-detected (e.g. claude-code)
framework	From x-agentledger-framework header, or fingerprint-detected (e.g. claude-code, litellm)
user_id	From x-agentledger-user-id header
app_id	From x-agentledger-app-id header
environment	From x-agentledger-environment header
parent_action_id	Parent call in a nested agent graph
handoff_from / handoff_to	Agent handoff tracking for the Flow DAG

---

API reference

Method	Endpoint	Description
GET	/health	Liveness — {"status":"ok","version":"..."}. No auth, never touches the store.
GET	/readyz	Readiness — pings the store; 503 when unreachable. Also reports capture_dropped.
GET	/metrics	Prometheus metrics (captures persisted/dropped, queue depth).
GET	/api/audit	Audit trail of sensitive actions (admin).
DELETE	/api/users/{user_id}	Right-to-erasure: delete all of a user's captured calls (admin).
GET	/	Live dashboard
WS	/ws	WebSocket stream — powers live dashboard updates
GET	/api/sessions	List recent sessions with aggregated stats
GET	/api/runs	List loop runs (explicit or auto-inferred) with iterations, cost, status, and flagged-call counts
GET	/api/runs/{run_id}	One run's status (running / flagged / complete) — poll this from loop scripts
GET	/api/sessions/{session_id}/tools	Derived tool executions — each tool call paired with its result, latency, and error status
DELETE	/api/sessions/{session_id}	Delete a session and all its calls
GET	/api/search?q=...	Full-text search across all captured calls
GET	/session/{session_id}	All calls in a session, ordered by time
GET	/explain/{action_id}	Single call by action ID
GET	/export/{session_id}	JSON compliance export with SHA-256 integrity hash
GET	/export/{session_id}/report	Printable HTML audit report
POST	/mcp	MCP tool server — list_sessions, explain, get_session, search, list_runs, get_run_status
POST	/v1/traces	OTLP/HTTP JSON ingest — GenAI spans from OTel-native tools become ledger calls (/v1/logs, /v1/metrics acked)

Examples:

# All calls in a session
curl http://localhost:8000/session/run-1

# Search across all sessions
curl "http://localhost:8000/api/search?q=failed+to+connect"

# Download JSON audit trail (includes an integrity tag; keyed HMAC when configured)
curl http://localhost:8000/export/run-1 -o audit-run-1.json

# Printable HTML report — open in browser, print to PDF
open http://localhost:8000/export/run-1/report

---

MCP server

Agentic Ledger exposes its captured data as an MCP (Model Context Protocol) tool server at POST /mcp. Point Claude Desktop, Cursor, or any MCP-compatible client at it to query traces directly from your AI assistant.

Tools available:

Tool	Description
list_sessions	List recent sessions with cost, token, and call count summaries
explain(action_id)	Full trace for a single LLM call — prompt, tool calls, output, tokens, cost
get_session(session_id)	All calls in a session in chronological order
search(query)	Full-text search across all captured calls
list_runs	Loop runs with iterations, cost, and status
get_run_status(run_id)	One run's status — lets an agent inspect its own loop and decide whether to continue

Configure in claude_desktop_config.json:

{
  "mcpServers": {
    "agentledger": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

If AGENTLEDGER_API_KEY is set, pass it as a header:

{
  "mcpServers": {
    "agentledger": {
      "url": "http://localhost:8000/mcp",
      "headers": { "x-agentledger-api-key": "your-key" }
    }
  }
}

Once connected, you can ask your assistant things like:

• "What did the SearchAgent do in the last session?"
• "Show me all calls that mentioned rate limit errors"
• "What was the total cost of session run-abc123?"

---

Configuration

Environment variables

Core:

Variable	Required	Default	Description
AGENTLEDGER_UPSTREAM_URL	Yes	https://api.openai.com	LLM endpoint to forward requests to. Accepts OpenAI, Anthropic, LiteLLM, OpenRouter, or any OpenAI-compatible URL.
AGENTLEDGER_DSN	No	sqlite:///agentledger.db (Docker: sqlite:////data/agentledger.db)	Database. SQLite for local dev, Postgres URL for production.
AGENTLEDGER_HOST	No	0.0.0.0	Host to bind to. Use 127.0.0.1 to restrict to localhost only.
AGENTLEDGER_PORT	No	8000	Port to run on.
AGENTLEDGER_API_KEY	No	(none)	Master admin key. When set, the dashboard, read, and management endpoints require authentication; the key grants the admin role and bootstraps API tokens (below). Skip for local dev; set when the proxy is on a server — you choose the value.
AGENTLEDGER_INGEST_KEY	No	(none)	When set, the proxy forwards a request only if it carries a matching x-agentledger-ingest-key header — closing the open relay. Off by default; a loud startup warning fires when unset.
AGENTLEDGER_EXPORT_HMAC_KEY	No	(none)	When set, compliance exports carry a tamper-evident keyed hmac-sha256 integrity tag instead of a plain sha256 checksum.
AGENTLEDGER_EXTRA_PATHS	No	(none)	Comma-separated additional request paths to capture, e.g. v1/responses,v1/custom. Built-in paths (v1/chat/completions, v1/messages, v1/responses, plus v1/messages/count_tokens recorded as a free call) are always captured.
AGENTLEDGER_ASYNC_CAPTURE	No	off	Persist captures on a background worker so storage never adds latency to the agent's call. Trade-off: reads become eventually consistent (a just-captured call may not be queryable for a brief moment). Recommended for high throughput.
AGENTLEDGER_CAPTURE_QUEUE_MAX	No	10000	Max captures buffered in async mode before load is shed (drops are counted in /metrics).
AGENTLEDGER_CAPTURE_LEVEL	No	full	full stores everything; metadata stores only metrics/metadata (model, tokens, cost, latency, agent, status) and drops prompts, responses, and tools.
AGENTLEDGER_REDACT	No	(off)	Redact PII/secrets in stored data: all, or a comma list of email,ssn,credit_card,ip,api_key. Replaces matches with [REDACTED:<label>]. Only the stored copy is affected — the agent's response is untouched.
AGENTLEDGER_REDACT_PATTERNS	No	(none)	Extra redaction regexes as JSON: {"label": "regex", ...} or ["regex", ...].
AGENTLEDGER_RETENTION_DAYS	No	(keep forever)	Delete captured calls older than N days via a background purge worker.
AGENTLEDGER_AUDIT_LOG	No	on	Record an audit trail of who viewed/exported/deleted what plus token/erasure actions. Set 0 to disable.

Cost budgets — block calls that exceed a spend limit (returns HTTP 429):

Variable	Default	Description
AGENTLEDGER_BUDGET_SESSION	(none)	Max USD per session_id across its lifetime.
AGENTLEDGER_BUDGET_AGENT	(none)	Max USD per agent_name per calendar day (UTC).
AGENTLEDGER_BUDGET_DAILY	(none)	Max USD total across all calls per calendar day (UTC).
AGENTLEDGER_BUDGET_ACTION	block	What happens when a budget is exceeded: block returns HTTP 429 (call never reaches the LLM), warn lets the call through and fires a webhook alert, both blocks and fires the webhook.

Rate limits — block calls that exceed request frequency (returns HTTP 429, sliding 60-second window):

Variable	Default	Description
AGENTLEDGER_RATE_LIMIT_RPM	(none)	Max requests per minute globally.
AGENTLEDGER_RATE_LIMIT_SESSION_RPM	(none)	Max requests per minute per session_id.
AGENTLEDGER_RATE_LIMIT_AGENT_RPM	(none)	Max requests per minute per agent_name.
AGENTLEDGER_RATE_LIMIT_USER_RPM	(none)	Max requests per minute per user_id.

Loop engine — every call is stitched into ReAct threads (thread_id, step_index, prev_action_id) and fresh-context loop iterations are grouped into runs, with stuck-loop detection:

Variable	Default	Description
AGENTLEDGER_LOOP_ACTION	warn	warn records loop_flags and fires a loop_flag webhook alert; block additionally returns HTTP 429 (loop_detected) for a session that tripped a guard; off disables inference.
AGENTLEDGER_LOOP_REPEAT_THRESHOLD	3	Consecutive identical tool calls (same tool, same arguments) before a thread is flagged stuck.
AGENTLEDGER_LOOP_MAX_STEPS	(none)	Flag (and in block mode, stop) threads that exceed this many ReAct steps.
AGENTLEDGER_LOOP_RUN_GAP_SECONDS	900	Max gap between fresh-context spawns (same system prompt) that still count as iterations of one run.
AGENTLEDGER_COMPLETION_PROMISE	(none)	Regex matched against response text. On match the call is flagged completion_promise and the run's status becomes complete — loop runners poll GET /api/runs/{run_id} and stop.

Alerts — POST to your webhook when a threshold is breached (does not block calls — see Alerts):

Variable	Default	Description
AGENTLEDGER_ALERT_WEBHOOK_URL	(none)	URL to POST alert payloads to. Required for any alerts to fire.
AGENTLEDGER_ALERT_COST_PER_CALL	(none)	Alert when a single call costs more than $X.
AGENTLEDGER_ALERT_LATENCY_MS	(none)	Alert when a single call takes longer than Xms.
AGENTLEDGER_ALERT_ERROR_RATE	(none)	Alert when session error rate exceeds X (e.g. 0.5 = 50%).
AGENTLEDGER_ALERT_DAILY_SPEND	(none)	Alert when daily spend crosses $X. Unlike budgets, this does not block calls.

OpenTelemetry — emit spans to any OTLP-compatible collector (requires pip install "agentic-ledger[otel]" — see OpenTelemetry export):

Variable	Default	Description
AGENTLEDGER_OTEL_ENDPOINT	(none)	OTLP/HTTP base URL, e.g. http://localhost:4318. OTel export is disabled when not set.
AGENTLEDGER_OTEL_SERVICE_NAME	agentledger	Value of service.name reported to the collector.
AGENTLEDGER_OTEL_HEADERS	(none)	Comma-separated key=value auth headers, e.g. x-honeycomb-team=abc123.

Pricing overrides — override or extend the built-in per-token pricing table (merged at startup):

Variable	Default	Description
AGENTLEDGER_PRICING	(none)	Inline JSON map of model → [input_per_million, output_per_million] USD. E.g. '{"gpt-4o": [2.50, 10.00], "my-model": [1.00, 2.00]}'.
AGENTLEDGER_PRICING_FILE	(none)	Path to a JSON file with the same format. Applied after AGENTLEDGER_PRICING.

---

Common startup examples

# Local dev — OpenAI (default)
AGENTLEDGER_UPSTREAM_URL=https://api.openai.com uv run python -m agentledger.proxy

# Local dev — Anthropic
AGENTLEDGER_UPSTREAM_URL=https://api.anthropic.com uv run python -m agentledger.proxy

# Local dev — LiteLLM gateway (any model)
AGENTLEDGER_UPSTREAM_URL=http://localhost:4000 uv run python -m agentledger.proxy

# Production — Postgres + auth + budgets + rate limits + alerts
AGENTLEDGER_UPSTREAM_URL=https://api.openai.com \
AGENTLEDGER_DSN=postgresql://user:password@localhost/agentledger \
AGENTLEDGER_API_KEY=my-secret \
AGENTLEDGER_BUDGET_DAILY=20.00 \
AGENTLEDGER_BUDGET_SESSION=2.00 \
AGENTLEDGER_RATE_LIMIT_SESSION_RPM=20 \
AGENTLEDGER_RATE_LIMIT_USER_RPM=60 \
AGENTLEDGER_ALERT_WEBHOOK_URL=https://hooks.slack.com/services/xxx/yyy/zzz \
AGENTLEDGER_ALERT_COST_PER_CALL=0.50 \
AGENTLEDGER_ALERT_DAILY_SPEND=15.00 \
uv run python -m agentledger.proxy

When AGENTLEDGER_API_KEY is set, pass it to access protected endpoints:

# Header
curl -H "x-agentledger-api-key: my-secret" http://localhost:8000/session/run-1

# Query param (browser)
http://localhost:8000?api_key=my-secret

Scoped API tokens (RBAC)

The master key is convenient but coarse. For team access, mint scoped, revocable tokens with roles instead of sharing the master secret. Tokens are random secrets shown once at creation; only their SHA-256 hash is stored.

Roles are hierarchical:

Role	Can
viewer	read captured data — dashboard, API, export, MCP
editor	viewer + delete sessions
admin	editor + manage API tokens

# Mint a viewer token (admin only — use the master key to bootstrap)
curl -X POST http://localhost:8000/api/tokens \
  -H "x-agentledger-api-key: my-secret" \
  -H "content-type: application/json" \
  -d '{"name": "grafana-readonly", "role": "viewer", "expires_in_days": 90}'
# → {"token_id": "...", "token": "agl_…", "role": "viewer", ...}  (token shown once)

# Use it (Bearer header, x-agentledger-token, or ?token=)
curl -H "Authorization: Bearer agl_…" http://localhost:8000/api/sessions

# List and revoke
curl -H "x-agentledger-api-key: my-secret" http://localhost:8000/api/tokens
curl -X DELETE -H "x-agentledger-api-key: my-secret" http://localhost:8000/api/tokens/<token_id>

Auth is enforced only when AGENTLEDGER_API_KEY is set; the master key is the admin bootstrap for minting tokens. The live /ws feed accepts the same credentials (?api_key=, ?token=, Authorization: Bearer, or x-agentledger-token) and rejects unauthenticated connects with close code 1008 — the dashboard forwards its page credential to the socket automatically.

---

Request headers

Pass these from your agent on each LLM call. All optional. They enrich captured data, power the Flow tab, and enable per-dimension budgets and rate limits.

Header	Default	Description
x-agentledger-session-id	(none)	Groups all calls in a run. Use a consistent ID per agent execution (e.g. a UUID or "run-1"). Without this, calls are stored but not grouped in the dashboard.
x-agentledger-user-id	(none)	End user who triggered this run. Enables per-user rate limiting and auditing.
x-agentledger-agent-name	(none)	Name of the agent making this call (e.g. "orchestrator", "researcher"). Powers the Flow tab DAG and agent-level budgets and rate limits.
x-agentledger-app-id	(none)	Application name or ID. Useful when multiple apps share one proxy.
x-agentledger-parent-action-id	(none)	The action_id of the call that spawned this one. When set, the Trace tab draws explicit parent→child connectors. Without it, the Trace tab infers relationships from timestamps automatically.
x-agentledger-environment	development	production, staging, or development. Shown in the dashboard.
x-agentledger-handoff-from	(none)	Agent handing off control (e.g. "orchestrator"). Renders as a directed edge in the Flow DAG.
x-agentledger-handoff-to	(none)	Agent receiving control (e.g. "researcher"). Renders as a directed edge in the Flow DAG.
x-agentledger-framework	(auto-detected)	Framework/tool making the call (e.g. "langgraph", "bmad"). When absent, well-known clients are fingerprinted automatically (Claude Code, LiteLLM).
x-agentledger-run-id	(auto-inferred)	Groups sessions into a loop run (e.g. a Ralph overnight run). When absent, fresh-context sessions sharing a system prompt within AGENTLEDGER_LOOP_RUN_GAP_SECONDS are grouped automatically.
x-agentledger-iteration	(auto-inferred)	Iteration number within the run.

Single agent — fully annotated:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="your-openai-key",
    default_headers={
        "x-agentledger-session-id":  "run-abc123",
        "x-agentledger-user-id":     "user-42",
        "x-agentledger-agent-name":  "researcher",
        "x-agentledger-app-id":      "my-app",
        "x-agentledger-environment": "production",
    },
)

Multi-agent system — tracking handoffs:

from openai import OpenAI

# Orchestrator
orchestrator_client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="your-openai-key",
    default_headers={
        "x-agentledger-session-id":  "run-abc123",
        "x-agentledger-agent-name":  "orchestrator",
    },
)

# Researcher (receives handoff from orchestrator)
researcher_client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="your-openai-key",
    default_headers={
        "x-agentledger-session-id":   "run-abc123",
        "x-agentledger-agent-name":   "researcher",
        "x-agentledger-handoff-from": "orchestrator",
        "x-agentledger-handoff-to":   "researcher",
    },
)

The Flow tab renders orchestrator → researcher as a DAG with cost and latency on each node.

OpenAI Agents SDK (openai-agents) — per-agent clients:

The openai-agents SDK uses its own internal OpenAI client. To pass Agentic Ledger headers you need to create a client per agent using OpenAIResponsesModel and set it as the agent's model.

import uuid
import os
from openai import AsyncOpenAI
from agents import Agent
from agents.models.openai_responses import OpenAIResponsesModel

SESSION_ID = f"run-{uuid.uuid4().hex[:8]}"  # one per execution
BASE_URL = os.getenv("OPENAI_BASE_URL")      # e.g. http://localhost:8000/v1

def al_model(agent_name: str, model: str = "gpt-4o-mini",
             handoff_from: str | None = None, handoff_to: str | None = None):
    """Create a model instance that sends Agentic Ledger metadata headers."""
    if not BASE_URL:
        return model  # proxy not configured — use default client
    headers = {
        "x-agentledger-session-id": SESSION_ID,
        "x-agentledger-agent-name": agent_name,
    }
    if handoff_from:
        headers["x-agentledger-handoff-from"] = handoff_from
    if handoff_to:
        headers["x-agentledger-handoff-to"] = handoff_to
    client = AsyncOpenAI(base_url=BASE_URL, api_key=os.getenv("OPENAI_API_KEY", ""),
                         default_headers=headers)
    return OpenAIResponsesModel(model=model, openai_client=client)

planner = Agent(name="PlannerAgent", model=al_model("PlannerAgent", handoff_to="SearchAgent"), ...)
searcher = Agent(name="SearchAgent",  model=al_model("SearchAgent",  handoff_from="PlannerAgent", handoff_to="WriterAgent"), ...)
writer   = Agent(name="WriterAgent",  model=al_model("WriterAgent",  handoff_from="SearchAgent",  handoff_to="EmailAgent"), ...)
emailer  = Agent(name="EmailAgent",   model=al_model("EmailAgent",   handoff_from="WriterAgent"), ...)

Each agent's calls are tagged with its name and pipeline position. The Flow tab renders the full PlannerAgent → SearchAgent → WriterAgent → EmailAgent DAG automatically.

Why per-agent clients? set_default_openai_client() sets a single global client — fine for single-agent apps, but it can't carry different agent_name or handoff_* headers per agent in a multi-agent system. Per-agent OpenAIResponsesModel instances are the correct approach.

---

Alerts

Agentic Ledger fires a POST to your webhook URL when a threshold is breached. You connect it to whatever you already use — Slack, PagerDuty, Discord, email, or a custom endpoint. Agentic Ledger sends the payload; the integration is on your side.

Payload format:

{
  "type":       "high_cost",
  "message":    "Single call cost $0.1842 exceeded threshold $0.10",
  "value":      0.1842,
  "threshold":  0.10,
  "action_id":  "a1b2c3d4-...",
  "session_id": "run-1",
  "agent_name": "researcher",
  "timestamp":  "2026-04-03T12:00:00+00:00"
}

Alert types:

Type	Triggered when
high_cost	A single call exceeds AGENTLEDGER_ALERT_COST_PER_CALL
high_latency	A single call takes longer than AGENTLEDGER_ALERT_LATENCY_MS
high_error_rate	Session error rate exceeds AGENTLEDGER_ALERT_ERROR_RATE
daily_spend	Daily total spend crosses AGENTLEDGER_ALERT_DAILY_SPEND
budget_exceeded	A budget limit is hit and AGENTLEDGER_BUDGET_ACTION is warn or both

Budgets vs alerts:

• Budgets (AGENTLEDGER_BUDGET_*) — block the call before it reaches the LLM. Agent gets HTTP 429.
• Alerts (AGENTLEDGER_ALERT_*) — the call goes through, you get notified after.

Slack — create an Incoming Webhook and point AGENTLEDGER_ALERT_WEBHOOK_URL at it.

PagerDuty — use the Events API v2 URL or a thin adapter that maps type → PagerDuty severity.

Discord — use a Discord channel webhook URL directly.

Custom — any HTTP endpoint that accepts a JSON POST.

---

OpenTelemetry export

Agentic Ledger can emit every intercepted LLM call as an OTel span to any OTLP-compatible collector: Grafana Tempo, Jaeger, Honeycomb, Datadog, Dynatrace, or any vendor that supports OTLP/HTTP.

Install the extra (Docker image includes OTel — no extra step needed when using Docker):

pip install "agentic-ledger[otel]"
# or
uv add "agentic-ledger[otel]"

Configure:

Variable	Default	Description
AGENTLEDGER_OTEL_ENDPOINT	(none)	OTLP/HTTP base URL, e.g. http://localhost:4318. OTel export is disabled when not set.
AGENTLEDGER_OTEL_SERVICE_NAME	agentledger	Value of service.name in the emitted resource.
AGENTLEDGER_OTEL_HEADERS	(none)	Comma-separated key=value pairs for auth headers, e.g. x-honeycomb-team=abc123,x-honeycomb-dataset=llm.

Example — Grafana Tempo:

AGENTLEDGER_UPSTREAM_URL=https://api.openai.com \
AGENTLEDGER_OTEL_ENDPOINT=http://localhost:4318 \
AGENTLEDGER_OTEL_SERVICE_NAME=my-agent \
uv run python -m agentledger.proxy

Example — Honeycomb:

AGENTLEDGER_OTEL_ENDPOINT=https://api.honeycomb.io \
AGENTLEDGER_OTEL_HEADERS=x-honeycomb-team=YOUR_API_KEY,x-honeycomb-dataset=llm-traces \
uv run python -m agentledger.proxy

Span attributes emitted (GenAI semantic conventions):

Attribute	Source
gen_ai.system	Provider (openai / anthropic)
gen_ai.operation.name	Always chat
gen_ai.request.model	Model ID
gen_ai.request.temperature	If set
gen_ai.request.max_tokens	If set
gen_ai.usage.input_tokens	Tokens in
gen_ai.usage.output_tokens	Tokens out
gen_ai.response.finish_reasons	Stop reason
agentledger.action_id	Unique call ID
agentledger.session_id	Run grouping
agentledger.agent_name	From header
agentledger.user_id	From header
agentledger.cost_usd	Estimated cost
agentledger.latency_ms	End-to-end latency
agentledger.environment	From header
agentledger.handoff_from / agentledger.handoff_to	Agent handoffs
http.status_code	HTTP status from upstream

Spans are grouped into traces by session_id — all calls in a session appear as one trace in your backend. Parent-child relationships follow x-agentledger-parent-action-id. Error spans (status_code != 200) are marked with StatusCode.ERROR.

---

Compliance export

Every session can be exported as an integrity-tagged audit trail — useful for regulated industries, internal audits, or passing traces to external tools.

# Machine-readable JSON with an integrity tag over the calls array
curl http://localhost:8000/export/run-1 -o audit-run-1.json

# Printable HTML — open in browser and print to PDF
open http://localhost:8000/export/run-1/report

The JSON export carries an integrity tag over the calls array. By default this is a sha256 checksum — it catches accidental corruption but is not a signature (anyone who edits the calls can recompute it). Set AGENTLEDGER_EXPORT_HMAC_KEY to switch to a keyed hmac-sha256 tag, which is tamper-evident: a recipient holding the key can detect any modification, and the tag cannot be forged without the key.

---

Releasing

Tagging a version triggers the full release pipeline automatically:

git tag v0.2.0
git push origin v0.2.0

This runs three jobs:

1. Docker — builds and pushes ghcr.io/shekharbhardwaj/agentledger:{version} and :latest to GHCR
2. PyPI — builds and publishes agentic-ledger=={version} to PyPI using trusted publishing (no API token needed)
3. GitHub Release — creates a release with auto-generated changelog from commit messages

First-time PyPI setup (one time only):

1. Go to pypi.org/manage/account/publishing
2. Add a new pending publisher:

   PyPI project name:  agentic-ledger
   Owner:              ShekharBhardwaj
   Repository:         AgenticLedger
   Workflow name:      release.yml
   Environment name:   pypi
   

3. Create a pypi environment in GitHub: repo → Settings → Environments → New environment → name it pypi
4. That's it — no secrets needed

---

License

MIT