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Framework Integrations

Agent Forensics provides one-line integrations for popular agent frameworks. Each integration auto-captures decisions, tool calls, LLM interactions, and errors — no manual recording needed.

Supported Frameworks

Framework Integration Auto-captures
LangChain / LangGraph Callback Handler LLM calls, tools, agent actions, prompt drift
OpenAI Agents SDK AgentHooks Agent lifecycle, tools, LLM calls with model config
CrewAI Step/Task callbacks Agent steps, task completion, errors
Any framework Manual API Whatever you record

How It Works

All integrations follow the same pattern:

  1. Create a Forensics instance
  2. Get a framework-specific handler (one line)
  3. Attach it to your agent
  4. Run your agent normally — forensics are captured automatically
from agent_forensics import Forensics

f = Forensics(session="my-session", agent="my-agent")

# Choose your framework:
handler = f.langchain()       # LangChain
hooks = f.openai_agents()     # OpenAI Agents SDK
callbacks = f.crewai()        # CrewAI

# After the agent runs:
print(f.report())
failures = f.classify()

Manual + Auto Hybrid

You can combine auto-capture with manual recording. This is useful for adding context that the framework doesn't capture:

f = Forensics(session="hybrid")

# Auto-capture via LangChain
agent.invoke({"input": "..."}, config={"callbacks": [f.langchain()]})

# Manually add context the framework missed
f.context_injection("internal_db", content={"policy": "max $500"})
f.guardrail(intent="buy item", action="purchase", allowed=True, reason="Within policy")