Langfuse
LLM engineering platform
LLM traces, prompt management, playgrounds, datasets, experiments, analytics, feedback, and evaluations.
// agentpm vs langfuse
LLM traces are not the same as coding-agent work history.
Langfuse watches LLM applications. AgentPM watches software get built by coding agents.
different problems / complementary layers
AgentPM
Coding-agent SDLC observability
Plans, commands, file edits, tests, retries, decisions, and the evidence trail behind software work.
// key differences
Langfuse captures LLM application telemetry. AgentPM captures coding-agent engineering evidence.
Langfuse is for improving LLM products. AgentPM is for improving how engineering teams use coding agents to ship software.
Langfuse
AgentPM
Observes LLM calls, traces, prompts, datasets, experiments, and user feedback.
Observes coding-agent plans, commands, file edits, tests, retries, and decisions.
Helps teams debug and iterate on LLM application behavior.
Helps teams review and coach coding-agent-assisted development.
Helps answer "What happened inside the LLM app trace?"
Helps answer "What did the coding agent do before the PR existed?"
Focuses on prompts, outputs, evals, and application quality.
Focuses on engineering execution, handoff quality, and work provenance.
Uses the LLM trace or observation as the unit of analysis.
Uses the software work session as the unit of analysis.
// what agentpm captures
The session is the artifact.
AgentPM captures the work trail that usually disappears between a developer's local coding-agent session and the final PR.
Plans
What the coding agent intended to do before editing.
Commands
The shell work, outputs, failures, and retries behind the result.
File Edits
Which files changed and how the implementation moved.
Tests
What was verified, skipped, retried, or left unproven.
Branches
The route from local session work toward PRs and commits.
Decisions
The why behind tradeoffs, reversals, and handoff context.
// compounded knowledge
The optimization loop is different for coding agents.
Observability is not just about catching failures. The long-term value is turning repeated behavior into better future performance.
For user-facing agents, knowledge compounds into better experiences.
Customer context, prior interactions, feedback, and evaluation data help AI products become more useful, personalized, and reliable for end users.
For coding agents, knowledge compounds into engineering efficiency.
Plans, commands, file edits, failed tests, reviewer feedback, and decisions become reusable workflow knowledge that reduces retries and improves future coding-agent sessions.
// summary breakdown
Langfuse watches AI applications run.AgentPM watches software get built.
Use Langfuse when you need LLM application observability.
It is the right layer for traces, prompt management, experiments, datasets, analytics, feedback, and evaluations for LLM applications.
Use AgentPM when you need agent-work observability.
It is the right layer for reconstructing how coding agents planned, edited, tested, retried, and decided while building software.
If your question is about model behavior in a deployed AI product, start with AI observability. If your question is about what coding agents did during engineering work, start with AgentPM.
// sources
Comparison source links
These pages summarize public positioning from the adjacent tool and compare it with AgentPM's coding-agent work layer.