anjor 0.5.0

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 intercepts your agent's HTTP traffic at the protocol layer and gives you full visibility into every LLM call and tool use — 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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Install

pip install anjor

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Quickstart

1. Start the collector and dashboard (one command, one port):

anjor start
# Anjor collector  http://localhost:7843/health
# Anjor dashboard  http://localhost:7843/ui/
# Database         anjor.db

2. Add one line to your agent:

import anjor
anjor.patch()   # that's it — httpx is now instrumented

import anthropic
client = anthropic.Anthropic()
# make tool calls as normal — they're captured automatically

Open http://localhost:7843/ui/ in your browser to see the dashboard.

3. Query the API directly:

curl http://localhost:7843/health
curl http://localhost:7843/tools
curl http://localhost:7843/intelligence/failures
curl http://localhost:7843/intelligence/quality/tools

No API key? Use respx to replay a mock response — see the quickstart guide.

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What it captures

Signal	Phase	Details
Tool calls	1	Name, status (success/failure), failure type
Schema fingerprints	1	SHA-256 structural hash of tool input/output shape
Schema drift	1	Field-level diff against the baseline for each tool
Latency	1	Per-call and aggregated (p50/p95/p99)
LLM calls	2	Model, latency, finish reason — Anthropic, OpenAI, and Gemini
Token usage	2	Input + output + cache_read tokens per call
Context window	2	Tokens used vs model limit, utilisation %, per-trace growth rate
Context hogs	2	Per-tool average output size, % of context consumed
System prompt drift	2	SHA-256 per agent — alerts when prompt changes between calls
Trace context	1–2	Trace ID, session ID, agent ID — consistent across LLM + tool events
Failure patterns	3	Clustered failure analysis with natural-language descriptions and fix suggestions
Token optimization	3	Tools consuming >5% of context window, estimated token waste and cost savings
Quality scores	3	Per-tool reliability/schema-stability/latency-consistency grade (A–F)
Run quality	3	Per-trace context efficiency, failure recovery, tool diversity grade (A–F)
Multi-agent spans	4	W3C-compatible parent/child span linking across agent boundaries
Trace graphs	4	DAG reconstruction with topological order and cycle detection
Cross-agent attribution	4	Token usage and failure rate broken down per agent in a trace
Provider breakdown	5	LLM dashboard shows Anthropic / OpenAI / Google per model

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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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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 config required. Anjor reads the URL and routes to the right parser.

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What is NOT in v0.5

• requests library not intercepted (all three SDKs use httpx by default)
• No cloud sync, authentication, or team management
• Intelligence suggestions are heuristic — no LLM-powered explanations yet
• Streaming responses are not parsed (only non-streaming calls are captured)

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Releasing a new version

Tag the commit and push — the publish workflow runs CI first, then uploads to PyPI automatically:

git tag v0.5.0
git push origin v0.5.0

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Development

No Node/npm required — the dashboard is bundled static HTML served by the collector.

git clone https://github.com/anjor-labs/anjor.git
cd anjor
pip install -e ".[dev]"
pytest --cov=anjor --cov-fail-under=95 -q   # ≥95% coverage enforced
ruff check anjor/ tests/                     # zero lint errors
mypy anjor/                                  # strict type checking
anjor start                                  # collector + dashboard on :7843

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