Use both. Here's where each one wins.
Observability tools see failures. Pisama acts on them. They are precondition layers; we are the action layer above. The comparison below is honest about where each one is stronger, not a zero-sum claim.
Pisama vs Langfuse
Langfuse is the leading open-source LLM observability platform. It captures traces, manages prompts, and runs evals on outputs. Pisama is a process-level failure detector: it tells you when two agents looped on each other, when shared state was corrupted, or when an agent drifted from its persona, while the run is happening.
These are different layers. Langfuse answers "what happened in this trace?" Pisama answers "did something go wrong during execution, and which step caused it?". Most teams running multi-agent systems run both.
Langfuse was acquired by ClickHouse in January 2026; the open-source project remains MIT-licensed.
- Mature trace UI, session management, and prompt versioning
- Larger ecosystem of integrations (LangChain-native, OpenAI-native)
- Self-hostable with battle-tested ClickHouse backend
- LLM-judge eval framework with managed datasets
- 34 production structural detectors (Langfuse has none of these out of the box)
- Heuristic-first pipeline: 90%+ of failures detected at $0 / sub-10ms
- Process-level detection caught structurally, not via LLM judge: loops, recursion, persona drift, coordination
- 59.9% on TRAIL benchmark vs 11.6% best frontier; Langfuse evals depend on the LLM you wire in
At a glance
| Dimension | Langfuse | Pisama |
|---|---|---|
| Layer | Artifact-level (output scoring) | Process-level (execution forensics) |
| Detection mechanism | LLM-judge graders + manual rules | Heuristic detectors, embeddings, LLM judge, human (5 tiers) |
| Cost per trace | $0 (storage) + LLM cost for evals | Median <$0.01 (90%+ caught at T1–T3 for free) |
| Multi-agent coverage | Trace tree visualization | Coordination, loops, persona drift, withholding, by name |
| TRAIL benchmark | Depends on judge model wired in | 59.9% joint accuracy (best frontier: 11.6%) |
| License | MIT (acquired by ClickHouse) | MIT |
Recommendation
Run both. Langfuse for trace storage, prompt management, and the UI. Pisama for the structural detectors that catch the failures Langfuse cannot see. Pisama emits standard OTel spans that Langfuse ingests directly, so no double-instrumentation.
FAQ
- Can I use Langfuse and Pisama together?
- Yes, this is the recommended setup. Pisama emits OTel spans with `gen_ai.*` semantic conventions. Configure Langfuse as one OTel exporter and Pisama as another; the same traces flow to both.
- Why not just write detectors as Langfuse evals?
- Langfuse evals run post-hoc on stored traces and use LLM judges by default. Pisama detectors run synchronously during execution, are heuristic-first (free), and are calibrated on a labelled dataset of 7,212 traces. Different problem.