darkmatter-sdk 0.4.0

Replay, fork, and verify any AI workflow. Execution record for AI agent pipelines.

darkmatter

Replay, fork, and verify any AI workflow.

The execution record for AI agent pipelines. Works across any model, framework, or provider.

pip install darkmatter
darkmatter demo        # try it now — no signup required

What it does

DarkMatter records every step of your AI pipeline as a cryptographically linked chain. You can then:

• Replay any workflow from root to tip, step by step
• Fork from any checkpoint to branch without losing the original
• Verify that a chain is intact and unmodified
• Diff two runs to see exactly what changed
• Export a portable proof artifact anyone can verify

Works with any model (Claude, GPT-4o, Gemini, local models) and any framework (LangGraph, LangChain, CrewAI, raw API calls).

Quick start

import darkmatter as dm
import os

os.environ["DARKMATTER_API_KEY"] = "dm_sk_..."  # get free key at darkmatterhub.ai/signup

# Commit agent output
ctx = dm.commit(
    to_agent_id="agent-writer-01",
    payload={
        "input":  "Analyze Q1 earnings",
        "output": {"summary": "APAC up 34%", "confidence": 0.94},
    },
    agent={"role": "researcher", "provider": "anthropic", "model": "claude-opus-4-6"},
)

# Chain commits by passing parent_id
ctx2 = dm.commit(
    to_agent_id="agent-reviewer-01",
    payload={"input": ctx["payload"]["output"], "output": "Approved."},
    parent_id=ctx["id"],
)

# Replay the full chain
replay = dm.replay(ctx2["id"])
print(f"{replay['totalSteps']} steps, chain intact: {replay['chainIntact']}")

# Verify integrity
result = dm.verify(ctx2["id"])
print(f"chain_intact: {result['chain_intact']}")

LangGraph integration (one line)

from langgraph.graph import StateGraph, END
from darkmatter.integrations.langgraph import DarkMatterTracer

# Your existing graph — unchanged
app = workflow.compile()

# Add DarkMatter — one line
app = DarkMatterTracer(app, agent_id="MY_AGENT_ID", to_agent_id="MY_AGENT_ID")

# Use exactly as before — every node auto-commits
result = app.invoke({"input": "Write a report"})

Anthropic SDK integration

import anthropic
from darkmatter.integrations.anthropic import dm_client

# Wrap your existing client — one line
client = dm_client(anthropic.Anthropic(), agent_id="agent-01", to_agent_id="agent-01")

# Use exactly as before — every call auto-commits
response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Analyze Q1 earnings"}]
)
print(client.last_ctx_id)  # ctx_1234567890_abc123

OpenAI SDK integration

import openai
from darkmatter.integrations.openai import dm_client

client = dm_client(openai.OpenAI(), agent_id="agent-01", to_agent_id="agent-01")
response = client.chat.completions.create(model="gpt-4o", messages=[...])
print(client.last_ctx_id)

Single agent / single developer

You do not need multiple agents. DarkMatter works for a single developer debugging their own pipeline:

import darkmatter as dm

# Run your pipeline step by step
for i, step in enumerate(pipeline_steps):
    result = run_step(step)
    ctx = dm.commit(
        to_agent_id=MY_AGENT_ID,
        payload={"input": step, "output": result},
        parent_id=ctx["id"] if i > 0 else None,
    )

# Replay it
replay = dm.replay(ctx["id"])
for step in replay["replay"]:
    print(step["step"], step["payload"])

Diff two runs

# Run the same pipeline twice (or compare original vs fork)
d = dm.diff(run1_ctx_id, run2_ctx_id)
print(f"{d['changedSteps']} steps changed")
for step in d["steps"]:
    if step["diff"]["modelChanged"]:
        print(f"Step {step['step']}: {step['a']['model']} → {step['b']['model']}")

Search your history

# Find all checkpoints from claude on a specific trace
results = dm.search(model="claude-opus-4-6", event="checkpoint", trace_id="trc_abc")
for ctx in results["results"]:
    print(ctx["id"], ctx["payload"]["output"])

API reference

Function	Description
dm.commit(to_agent_id, payload, ...)	Commit context, returns Context Passport
dm.pull()	Pull contexts addressed to this agent
dm.replay(ctx_id)	Replay full chain root → tip
dm.fork(ctx_id, ...)	Fork from a checkpoint
dm.verify(ctx_id)	Verify chain integrity
dm.export(ctx_id)	Export portable proof artifact
dm.search(q, model, ...)	Search execution history
dm.diff(ctx_id_a, ctx_id_b)	Diff two chains step-by-step
dm.me()	Agent identity

Links

• Documentation
• Live demo
• Get API key
• GitHub
• Context Passport spec

License

MIT