Skip to main content

AgentDynamics

CI PyPI Python License: Apache-2.0

APM for AI agents. AgentDynamics does for agents what AppDynamics does for services. It shows how your agents work through tasks, what each step costs, where flows loop, stall or fail, and whether the agent's process is any good.

  • Works with what you already have: LangGraph, LangChain, the OpenAI Agents SDK, the Anthropic and OpenAI SDKs, CrewAI, LlamaIndex, anything that speaks OpenTelemetry, LangSmith, Langfuse, log pipelines and Claude Code.
  • Zero dependencies: one pip install, one process, runs on a laptop or in Kubernetes.
  • Self-hosted: your prompts and traces never leave your network.

Quickstart (2 minutes)

pip install agentdynamics
agentdynamics serve --open          # console at http://127.0.0.1:8787

Then connect your agent with one of these options. Pick the first one that matches how your agent is built.

1. Python: one line

import agentdynamics
agentdynamics.init()      # auto-instruments Anthropic, OpenAI, LangChain/LangGraph and OpenTelemetry

Every model call is now tracked, with tokens, cost, latency, truncations and errors. To group calls into tasks and see your agent's flow, add a decorator and name the stages:

@agentdynamics.trace                     # one call = one task in the console
def handle_ticket(question):
    with agentdynamics.span("plan"):     # stages become nodes in the workflow graph
        plan = client.messages.create(...)
    with agentdynamics.span("act"):
        lookup_order(order_id)           # decorate tools with @agentdynamics.tool
    ...

2. Python, zero code changes

agentdynamics run python app.py         # like ddtrace-run / opentelemetry-instrument

3. LangGraph / LangChain (Python or JS): environment variables only

export LANGSMITH_TRACING=true
export LANGSMITH_ENDPOINT=http://127.0.0.1:8787/langsmith

AgentDynamics speaks the LangSmith ingestion API, so graphs, nodes, threads and feedback arrive unchanged.

4. Any OpenTelemetry app (OpenAI Agents SDK, CrewAI, LlamaIndex, Vercel AI SDK, Java/Go/.NET)

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://127.0.0.1:8787/v1/traces

GenAI semantic conventions, OpenInference and OpenLLMetry are all understood, over protobuf or JSON.

5. Already using LangSmith or Langfuse? Don't change anything

agentdynamics connect langsmith         # prints the 4-line config to pull runs from your existing project

6. Anything else

agentdynamics connect http prints a single curl call. agentdynamics connect logs shows how to point Fluent Bit or Vector at AgentDynamics.

Check it works

agentdynamics doctor                    # checks URL + key, sends a test trace, confirms it shows up

The console's Get started page shows the same snippets and turns green when your first trace arrives.


What you get

Flow Map Live topology: users → agents (including agent-to-agent handoffs) → models and tools, with volume, latency and errors
Workflows The real execution graph mined from your traces: path variants and their success rates, loops, the critical node, handoffs
Task snapshots Span-tree waterfall for each request: context growth, cost against the baseline, what went wrong, what the user said next
Baselines & Apdex "Normal" cost and latency per workflow, learned automatically, plus an agent Apdex score
Health rules & alerts 28 rules (runaway cost, policy denials, boundary probing, revocations, retry loops, node loops, truncation, rate limits, ping-pong handoffs, unverified code changes, …) sent to Slack or webhooks
Tools & Models Error rate and p95 per tool; per model TTFT, tokens/s, truncation rate, cache hit rate and spend
SLOs Success rate, Apdex, latency and cost objectives with error budgets and burn rates
Governance Aegis policy enforcement per task: denials by rule, budget stops, kill-switch revocations, unused grants, policy export
Process Review Scores how well the agent works (efficiency, focus, reliability, verification, context, autonomy, compliance) and suggests fixes
Analytics & Compare Any metric by any dimension; compare models, prompts or releases side by side

The full metric catalog is in docs/METRICS.md.

Governance with Aegis

Aegis decides what an agent may do. AgentDynamics shows what it did. Connecting them takes one call:

from agentdynamics.integrations import aegis as governance
governance.instrument(kernel, root, watchdog=governance.Watchdog(max_repeated_denials=3))

That call sets up four flows:

  1. Every Aegis decision appears in its task. Denials carry the rule id, and the Aegis audit log carries the task's run id so the two logs join.
  2. Model calls are charged to the Aegis budget before they're sent. When the budget runs out, the call is refused and never made.
  3. A watchdog revokes the grant of an agent that keeps probing a boundary, for example after a prompt injection.
  4. agentdynamics policy export writes a tighter, least-privilege policy from observed behaviour, which aegis ratify and aegis drift then verify.

The console's Governance page shows denials, budget stops, revocations, unused grants and budget headroom per policy version. See docs/GOVERNANCE.md.

Python API

agentdynamics.init(url=None, api_key=None, project=None, environment=None, capture_content=None) Connect and auto-instrument. Every argument falls back to an environment variable (below). Safe to call twice.
@agentdynamics.trace / @trace("name") / with trace("name", prompt=..., thread_id=..., user_id=...) One task. A nested trace becomes a node. Works on sync and async functions.
with agentdynamics.span("node") A stage or graph node. Model and tool calls inside it are attributed to the node.
@agentdynamics.tool / @tool(name="search") Records a function as a tool call: arguments, output size and errors.
t.feedback("user_rating", 0.0..1.0) Attach a user or eval score to a task (with trace(...) as t).
with agentdynamics.llm_call(model, max_tokens=..., input=...) as c: ... c.usage(input_tokens=..., output_tokens=...) Records a call from any other model client (local models, raw HTTP), with the same gating as the patched SDKs.
agentdynamics.flush() Wait for queued telemetry. This also runs automatically at exit.

Environment variables:

Variable Default Purpose
AGENTDYNAMICS_URL http://127.0.0.1:8787 Where to send telemetry
AGENTDYNAMICS_API_KEY none Ingest key, if auth is on
AGENTDYNAMICS_PROJECT current folder name Project the telemetry is filed under
AGENTDYNAMICS_ENVIRONMENT development Environment label
AGENTDYNAMICS_CAPTURE_CONTENT 1 0 sends sizes only, never prompt text
AGENTDYNAMICS_DISABLED unset 1 turns everything off

Telemetry is sent from a background thread with a bounded queue. Your agent never waits on it and never fails because of it.

Running it for a team

agentdynamics keys create --role ingest     # for apps / collectors
agentdynamics keys create --role read       # for people and Grafana
agentdynamics keys create --role admin      # for rules, SLOs and config
docker compose -f deploy/docker-compose.yml up -d   # AgentDynamics + OpenTelemetry Collector gateway
  • Security: role-based API keys.
  • Privacy: redaction of emails, API keys and card numbers, or store_content = false to keep metadata only.
  • Operations: retention, Prometheus /metrics, /healthz, Slack and webhook alerts, and a status page for every source.

See deploy/ and docs/ARCHITECTURE.md.

Development

python -m unittest discover tests   # 28 tests: real LangSmith/Anthropic/OpenAI SDKs, Aegis kernel, OTLP protobuf, pull connectors
python -m agentdynamics --data .demo-data --claude-root "" serve --port 8790 &
python examples/langgraph_style_app.py 60          # LangGraph-style demo traffic
python examples/otel_multiagent.py 60 http://127.0.0.1:8790

License

Apache 2.0. See LICENSE.

Release files for agentdynamics 0.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for agentdynamics 0.4.0
File Size Uploaded
agentdynamics-0.4.0.tar.gz 150.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for agentdynamics 0.4.0
File Interpreter ABI Platform
agentdynamics-0.4.0-py3-none-any.whl Python 3 none any Details

Total release size: 292.2 kB

Release files / agentdynamics-0.4.0.tar.gz

Download URL agentdynamics-0.4.0.tar.gz
Size 150.2 kB
Tags Source
SHA-256 checksum
How to use checksums
c8943a152e79f69d2697ee38da583aaa4b2fc760177665c1ff23f9b08173085c
BLAKE2b-256 checksum
How to use checksums
e9c9b411a1605575338c2056bea3a804dbd9f168677ee123b530a89c4d8b6503
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.

Transparency log

Release files / agentdynamics-0.4.0-py3-none-any.whl

Download URL agentdynamics-0.4.0-py3-none-any.whl
Size 142.0 kB
Tags Python 3
SHA-256 checksum
How to use checksums
685d4a307607fd17324ce9adac8eee434e8d4f854b7c98aa6aa3fc968ee52e6b
BLAKE2b-256 checksum
How to use checksums
4bd878c314ad1c86f65cb95f76d43e6b2d32a001145ef97aea1740bcd3069497
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.

Transparency log

Release history Release notifications | RSS feed

0.4.1

2 release files

This release

0.4.0 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page