anjor 0.8.2

Observability and reliability platform for agentic AI systems

Anjor

AI agents fail silently. A tool times out, a schema drifts, the context window fills up — and you find out from a user complaint, not a dashboard.

Anjor fixes that. It gives you full visibility into every LLM call, tool use, and MCP server interaction — latency, token usage, context window growth, schema drift, prompt changes — without changing a single line of your agent code. Beyond passive logging, it surfaces actionable intelligence: failure pattern clustering, token optimization suggestions, and per-tool quality grades (A–F).

One-line install. No cloud. No account required.

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Dashboard

Platform-wide summary: tool health, LLM cost by model, MCP server status, top failure patterns, and drift alerts at a glance.

More screenshots

LLM Usage — token consumption and cost by model with daily trend and cache savings

MCP Servers — per-server call volume, success rates, and tool breakdown

Intelligence — failure clusters, token optimization opportunities, and quality scores

Tools — latency percentiles, drift detection, and per-tool drill-down

Traces — multi-agent span trees and cross-agent attribution

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Install

Recommended (macOS / Homebrew Python):

brew install pipx && pipx ensurepath
pipx install anjor           # collector + dashboard + CLI
pipx install "anjor[mcp]"    # add MCP server support (Claude Code / Gemini CLI)

Then open a new terminal tab so $PATH picks up the new command.

Or with pip (inside a virtualenv):

pip install anjor
pip install "anjor[mcp]"

Note: anjor watch-transcripts and anjor mcp require v0.8.0+.

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Quickstart

Tracking Claude Code (or Gemini CLI)

Best for users of Claude Code or Gemini CLI who want a visual dashboard of their sessions. No changes to your workflow needed.

Option A: Via MCP (recommended — auto-starts with Claude Code)

Add to .mcp.json in your project root (or ~/.claude/.mcp.json for global):

{
  "mcpServers": {
    "anjor": {
      "command": "anjor",
      "args": ["mcp", "--watch-transcripts"]
    }
  }
}

Anjor auto-starts the collector, ingests your Claude Code session transcripts, and exposes anjor_status as a tool Claude Code can call to check session health.

For Gemini CLI, add to .gemini/settings.json (or ~/.gemini/settings.json):

{
  "mcpServers": {
    "anjor": {
      "command": "anjor",
      "args": ["mcp", "--watch-transcripts", "--providers", "gemini"]
    }
  }
}

Option B: Standalone (no MCP required)

anjor start --watch-transcripts

One command starts the collector, dashboard, and transcript watcher together. Open http://localhost:7843/ui/ to see your sessions.

To watch specific agents or adjust the polling interval:

anjor start --watch-transcripts --providers claude,gemini --poll-interval 5.0

Run anjor watch-transcripts --list-providers to see which agents are detected on your machine.

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Tracking Your Own Agent (API Patching)

Best for developers building custom AI agents who want real-time telemetry.

1. Start the collector and dashboard:

anjor start

2. Patch your agent (one line):

import anjor
anjor.patch()  # instrument httpx automatically

import anthropic
client = anthropic.Anthropic()
# All calls are now captured — no other changes needed.

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What it captures

Signal	Details
Tool calls	Name, status (success/failure), failure type, latency
MCP servers	Per-server call volume, success rate, latency — parsed from mcp__server__tool naming
Schema fingerprints	SHA-256 structural hash of tool input/output shape
Schema drift	Field-level diff against the baseline for each tool
LLM calls	Model, latency, finish reason — Anthropic, OpenAI, and Gemini
Token usage	Input + output + cache_read + cache_write tokens per call
Context window	Tokens used vs model limit, utilisation %, per-trace growth
Cache savings	Prompt cache hit rate and estimated cost savings
Context hogs	Per-tool average output size, % of context consumed
System prompt drift	SHA-256 per agent — alerts when prompt changes between calls
Failure patterns	Clustered failure analysis with descriptions and fix suggestions
Token optimization	Tools consuming >5% of context window, cost savings estimates
Quality scores	Per-tool reliability/schema-stability/latency grade (A–F)
Run quality	Per-trace context efficiency, failure recovery, diversity grade
Multi-agent spans	Parent/child span linking across agent boundaries
Trace graphs	DAG reconstruction, topological order, cycle detection
Cross-agent attribution	Token usage and failure rate broken down per agent

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Supported providers

Provider	SDK	Intercepted endpoint
Anthropic	anthropic	api.anthropic.com/v1/messages
OpenAI	openai	api.openai.com/v1/chat/completions
Google Gemini	google-generativeai	generativelanguage.googleapis.com/.../generateContent

All three providers are auto-detected — no configuration required.

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AI Coding Agents (Transcript Watchers)

Anjor can ingest and visualize history from agents that write local session transcripts. It acts as a post-hoc observatory even for agents you didn't build.

Agent	Source tag	Discovery path	MCP support
Claude Code	claude_code	~/.claude/projects/**/*.jsonl	Yes — .mcp.json
Gemini CLI	gemini_cli	~/.gemini/tmp/**/*.json	Yes — .gemini/settings.json
OpenAI Codex	openai_codex	~/.codex/sessions/**/*.jsonl	Coming soon
AntiGravity	antigravity	~/.antigravity/**/*.jsonl	Coming soon

One-shot ingestion

anjor watch-transcripts --providers claude,gemini   # specific agents
anjor watch-transcripts                             # auto-detect all

Real-time watching (standalone)

Keep the watcher running in the background — it polls every 2 seconds:

anjor watch-transcripts --providers claude          # Claude Code sessions
anjor watch-transcripts --poll-interval 5.0         # custom interval

Or use anjor start --watch-transcripts to run the collector and watcher together in one process.

List detected agents

anjor watch-transcripts --list-providers

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MCP Server Support

MCP tools are automatically identified by their naming convention — no extra configuration needed. Any tool whose name follows mcp__<server>__<tool> is grouped by server in the MCP dashboard:

mcp__github__create_pull_request   →  server: github,     tool: create_pull_request
mcp__filesystem__read_file         →  server: filesystem, tool: read_file
mcp__brave_search__web_search      →  server: brave_search, tool: web_search

The /mcp endpoint returns per-server and per-tool aggregates and supports a ?days=N filter.

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API endpoints

Method	Path	Description
POST	/events	Ingest a tool/LLM/span event
POST	/flush	Force-flush pending batch writes; returns {"flushed": N}
GET	/tools	All tools with summary stats
GET	/tools/{name}	Tool detail (latency percentiles, drift)
GET	/mcp	MCP server and tool aggregates (?days=N)
GET	/llm	LLM call summary by model (?days=N)
GET	/llm/usage/daily	Daily token usage by model (?days=N)
GET	/calls	Paginated raw event log
GET	/traces	Trace list (newest first)
GET	/traces/{id}/graph	DAG graph for a single trace
GET	/health	Uptime, queue depth, db path
GET	/intelligence/failures	Failure clusters sorted by rate
GET	/intelligence/optimization	Token hog tools + savings estimates
GET	/intelligence/quality/tools	Per-tool quality scores + grade
GET	/intelligence/quality/runs	Per-trace run quality scores + grade
GET	/intelligence/attribution	Per-agent token and failure attribution

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Configuration

Via environment variables:

ANJOR_DB_PATH=./my_project.db python my_agent.py
ANJOR_BATCH_SIZE=1 ANJOR_BATCH_INTERVAL_MS=100 python my_agent.py
ANJOR_LOG_LEVEL=DEBUG python my_agent.py

Via .anjor.toml in your project root:

db_path = "my_project.db"
batch_size = 10
batch_interval_ms = 200
log_level = "DEBUG"

Via code:

import anjor
from anjor.core.config import AnjorConfig

anjor.patch(config=AnjorConfig(db_path="my_project.db", batch_size=1))

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Programmatic Access

Query your agent's history directly from Python — no running collector required:

import anjor
from anjor.models import ToolSummary, FailurePattern, ToolQualityScore

with anjor.Client("anjor.db") as client:
    # Per-tool summary stats
    for tool in client.tools():
        print(f"{tool.tool_name:30s}  calls={tool.call_count}  ok={tool.success_rate:.0%}")

    # Single tool detail (latency percentiles)
    t = client.tool("web_search")
    if t:
        print(f"p95={t.p95_latency_ms:.0f}ms  p99={t.p99_latency_ms:.0f}ms")

    # Raw call records (filterable)
    failures = client.calls(status="failure", limit=20)

    # Intelligence layer
    patterns  = client.intelligence.failures()      # list[FailurePattern]
    quality   = client.intelligence.quality()       # list[ToolQualityScore]
    runs      = client.intelligence.run_quality()   # list[RunQualityScore]
    opts      = client.intelligence.optimization()  # list[OptimizationSuggestion]

All return types are frozen Pydantic models importable from anjor.models:

from anjor.models import (
    ToolSummary,
    ToolCallRecord,
    FailurePattern,
    OptimizationSuggestion,
    ToolQualityScore,
    RunQualityScore,
)

The Client reads SQLite directly — no HTTP calls, no collector process needed. It opens the connection lazily on first query and is safe to use as a context manager.

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Limitations

• No cloud sync, authentication, or team features

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Development

git clone https://github.com/anjor-labs/anjor.git
cd anjor
pip install -e ".[dev]"
pytest --cov=anjor --cov-fail-under=95 -q
ruff check anjor/ tests/
mypy anjor/
anjor start

See CONTRIBUTING.md for full guidelines.

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Documentation

• Quickstart — see it in action
• Architecture — layer diagram and design decisions

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Contributing & Contact

• Bug reports / feature requests — open an issue
• Questions / ideas — start a discussion

License

MIT © Anjor Labs